Personal income tax mimicry

Int Tax Public Finance

DOI10.1007/s10797-012-9261-9

Personal income tax mimicry:evidence from

international panel data

Denvil Duncan·Ed Gerrish

?Springer Science+Business Media New York2013

Abstract This paper investigates personal income tax(PIT)mimicry at the interna-tional level.It is the?rst to empirically investigate the extent to which PIT mimicry varies along the tax schedule and the?rst to include nations which are not part of the OECD.We use data on international personal income tax schedules from the world tax indicators to estimate marginal and average tax rates at various multiples of per capita gross domestic product(GDP).These tax rates are then used to estimate the extent to which countries respond to their neighbors’PIT policy.We?nd evidence of PIT mimicry using a balanced panel of53countries over24years.This?nding is strongest for tax rates at lower multiples of per capita GDP and survives several robustness checks.

Keywords Personal income tax·Average tax rate·Marginal tax rate·Tax mimicry·Tax competition

JEL Classi?cation H24·H25·H87

1Introduction

It is often argued in political circles that corporate income tax(CIT)rates ought to be lowered to increase competitiveness;lower rates keep domestic?rms in the host D.Duncan()·E.Gerrish

School of Public&Environmental Affairs,Indiana University,1315E.10th Street,Bloomington 47403,USA

e-mail:duncande@https://www.wendangku.net/doc/d215293783.html,

D.Duncan,

E.Gerrish country while attracting capital from abroad.1This line of reasoning,which implies strategic behavior in the setting of CIT policy both between and within countries,has been explored extensively in the public?nance literature.The general?nding is that governments do respond to their neighbors’CIT policy in an effort to maintain their CIT base(Brueckner2003).Because the personal income tax(PIT)base is not as mobile as that of the CIT,arguments for strategic behavior in PIT policy seem less salient,especially at the international level.

Consequently,while PIT competition has received some attention within coun-tries,less is known about the extent to which countries mimic each other’s PIT poli-cies.We are aware of only two studies that estimate the extent of international PIT mimicry.Both studies focus on OECD countries and?nd mixed results.Altshuler and Goodspeed(2002)?nd no evidence that countries behave strategically in setting PIT rates;estimates are negative and are not statistically signi?cant.On the other hand, Egger et al.(2007)?nd evidence of strategic complements;countries tend to increase their PIT rates if their neighbors’tax rate increase.In addition to providing con?icting results,both studies focus on developed nations,which reduces the generalizability of their?ndings.The responsiveness of OECD and EU countries to their neighbors’tax rates may differ systematically from the responsiveness of a more global sample of countries.

Moreover,existing studies generally use a single tax rate to measure the tax burden in a given country.Although informative,this approach ignores the possibility that policy makers are more sensitive to changes in different parts of the tax schedule.For example,a country with a large share of very mobile high skilled workers might be relatively more sensitive to changes in top rates.

We address these issues and provide additional insights on the extent to which PIT mimicry exists at the international level using a new data set on international PIT schedules.Following the literature,we use the weighted control variables of neigh-boring countries as instruments to correct for endogeneity of the weighted neighbors’tax rates.Our results indicate that countries appear to mimic neighbors in setting per-sonal income tax rates.In particular,countries increase their own tax rate when their neighbors’rate increases.This result holds for both average and marginal tax rates and survive several robustness tests including different weighting functions,sample of countries,and model speci?cations.

Although our results con?rm that of Egger et al.(2007),we extend the sparse literature on international PIT mimicry in several important ways.First,we expand the country coverage signi?cantly,making our results more general than the existing literature.This is achieved primarily through the use of a new data set on interna-tional PIT schedules;world tax indicators(WTI).The WTI provides information on personal income tax schedules for over180countries for the period1980to2005. Consequently,we are able to estimate Nash reaction functions for a panel of coun-tries much larger than any of the existing studies.Our preferred speci?cation includes 1For example,the Jamaican government is now reexamining its corporate income tax rate following recent rate reductions by its Caribbean neighbors(Collister2011).Additionally,Canada lowered its Federal CIT rate to15%on January1,2012,which means they have the lowest combined(federal plus local)CIT rate among the G-7;the combined rate is25%.The United Kingdom is set to join Canada on April1,2012, with a25%CIT rate of their own.

Personal income tax mimicry:evidence from international panel data

a balanced panel of OECD and non-OECD countries as well as countries from every World Bank income group.This country coverage makes our results more general-izable;results reported in Altshuler and Goodspeed(2002)and Egger et al.(2007) cannot be readily generalized to countries outside the OECD.Second,we estimate marginal and average tax rates that control for basic deductions,credits,and other country speci?c tax rules(Sabirianova Peter et al.2010).These rates are preferable to other measures commonly used in the literature,such as the top statutory PIT rate, which does not accurately re?ect the tax burden on a typical taxpayer.

Third,we estimate both average and marginal tax rates at four multiples of per capita gross domestic product(GDP),which roughly represent four income classes. This allows us to explore the possibility that strategic interaction is more binding at different points in the tax schedule.For example,high skilled workers tend to be more mobile and are more likely to respond to between country differences in tax rates(Kleven et al.2012;Egger and Radulescu2009).This implies that policymak-ers might be more sensitive to changes in tax rates at the top of the tax schedule.On the other hand,the median voter is likely to be in the mid to low skill group,which then implies greater strategic pressure on lower tax rates via yardstick competition under the assumption that low and median income voters have access to informa-tion on their neighbor’s tax policies.Greater strategic pressure on high tax rates is possible if high income voters are more informed(Goodspeed2000).We?nd that distinguishing among the various tax rates is important.In particular,mimicry ap-pears to be strongest at lower tax rates(lower multiples of per capita GDP).In other words,the change in low PIT rates in response to a change in neighbors’low PIT rates is larger in magnitude than the change in high rates in response to a change in neighbors’high rates.

Fourth,our study provides support for empirical studies that rely on the idea of international PIT mimicry to create instrumental variables.For example,Lee and Gordon(2005)and Ferede and Dahlby(2012)uses the weighted average tax rate of neighboring countries as an instrument for top statutory PIT rate in identifying the effect of tax rates on economic growth,while Duncan and Sabirianova Peter(2012) uses the same strategy to identify the effect of tax progressivity on income inequality.2 Finally,lowering tax rates is often viewed as a policy option to stimulate growth, expand the tax base,and hence increase tax revenues in times of rising de?cits and debt.It follows from the CIT competition literature that such efforts might prove futile if countries respond strategically to their neighbor’s tax policy.Our?ndings on PIT competition indicate that similar conclusions extend to the PIT.In other words,it is not clear that lowering PIT rates will stimulate growth via increased international competitiveness if neighbors respond by lowering their PIT rates.This?nding is timely given the?scal crisis,which continues to engulf countries around the world.

A key question at this point is,what are the channels through which PIT mimicry occur at the international level?There are at least four competing explanations.First, competition might arise via yardstick competition if voters,in comparing their tax

2At the very least,our results con?rm that a country’s PIT rate is strongly correlated with its neighbors’tax rates thus implying that neighbors’tax rate satis?es one of the conditions of a good instrument.

D.Duncan,

E.Gerrish burden with that of their neighbors,make electoral decisions on the basis of tax pol-icy changes.This pathway does not require voter mobility as long as the incumbent and entrant cannot collude.Second,emigration,especially among very high skilled workers,has grown signi?cantly in recent years(OECD2011),and thus provides an additional pathway for international PIT competition.Third,PIT competition may ?ow indirectly through CIT competition as nations attempt to reduce the total tax burden on?rms in order to attract capital.Finally,it is possible for strategic inter-action to arise from noncompetitive means such as policy diffusion.In other words, countries that appear to be competing with each other might simply be adopting the current optimal tax policy.

The policy implications of our?ndings depend largely on which of these is the source of mimicry.For example,mimicry is most likely welfare enhancing if driven by countries adopting what is believed to be optimal tax policy.On the other hand, mimicry that results from competitive forces such as mobility or yardstick competi-tion may increase or decrease social welfare(Kiss2012;Olsen and Osmundsen2011; Slemrod and Wilson2009;Schindler and Schjelderup2009;Conconi et al.2008; Wilson and Wildasin2004).Still,the importance of distinguishing among these com-peting pathways is conditional on there being mimicry in the?rst place.Therefore, we focus exclusively on identifying the extent to which mimicry exists and leave identi?cation of the channels for future work.

The remainder of the paper is organized as follows.Section2provides a brief overview of theoretical arguments for why we might observe international PIT mimicry.This is followed by a description of the data in Sect.3,the methodology and results in Sect.4,robustness checks in Sect.6,and the conclusion in Sect.7.

2Arguments for international PIT mimicry

The concept of tax competition was originally applied to subnational units of gov-ernment where the logic of strategic tax competition is strongest due to the absence of legal and natural barriers(Brueckner2003;Wilson1985;Oates1972).Wilson (1986),for example,motivates a model of tax competition between governments un-der the assumption of perfect capital mobility.Taxes on capital reduces its rate of return and sends owners seeking better returns in other taxing jurisdictions.This out-?ow of capital to neighboring jurisdictions produces revenue windfalls.Neighbors can respond by doing nothing,lowering taxes(to maintain current revenue),or raise taxes knowing that their capital stock would be https://www.wendangku.net/doc/d215293783.html,ing the assumption of mobility,numerous studies have looked at CIT competition both within(Chirinko and Wilson2010;Feld and Reulier2009;Hernandez-Murillo2003;Rork2003;Hayashi and Boadway2001;Heyndels and Vuchelen1998)and between nations(Devereux et al.2008;Winner2005;Quinn2003;Altshuler and Goodspeed2002).Although theoretical results suggest reaction functions can be either negative or positive,em-pirical?ndings seem to suggest the latter is more common(e.g.,see Brueckner2003; Fuest et al.2005;Zodrow2010for excellent reviews).

While the lack of international mobility was a credible foundation on which to dis-miss strategic interaction of the international PIT base in the1970s and1980s,there

Personal income tax mimicry:evidence from international panel data

are a number of reasons to believe this argument is losing its potency.First,a country may face direct competition from its neighbors through the mobility of the tax base. According to the OECD’s most recent outlook on international migration(OECD 2011),barriers to the free movement of labor have eased considerably with a no-ticeable increase in the movement of skilled labor and foreign-born entrepreneurs in recent years.In particular,the outlook identi?es the1980s and1990s as periods of in-tensi?ed migration after approximately two decades of relative stability(see Fig.1). More importantly,there is evidence that people factor PIT rates into their migra-tory decisions;this is especially true for high income workers(Kleven et al.2012; Egger and Radulescu2009;Ross and Dunn2007).Therefore,it is possible that pol-icy makers use the PIT as a means of attracting highly skilled labor from neighboring countries.

A related competitive pathway for PIT competition involves an indirect effect through the corporate structure of?rms.Since sole proprietorships,partnerships,and other small businesses are generally subjected to the PIT in most countries,it is pos-sible for policy makers to attract such businesses with PIT policy.In other words, policymakers can use the PIT to create a more favorable business environment for potential investors thus attracting potential tax base from their neighbors.Egger et al. (2007)develops a theoretical model on the basis of this relationship and?nds empir-ical evidence that countries treat their neighbors’PIT as strategic complements.

The third pathway for strategic interaction over the PIT base is yardstick compe-tition(Besley and Case1995;Case1993).This occurs if individuals compare their tax rates with that of their neighbors when making voting decisions.The compar-ison forces policy makers to adopt tax rates similar to that of their neighbors in an effort to increase their probability of remaining in of?ce.The convenient fea-ture of this pathway is that it reduces the need for competition to be driven by mobility;elections involving tax policy are suf?cient.Yardstick competition is be-lieved to be the most plausible explanation of horizontal tax mimicking within coun-tries(Allers and Elhorst2005;Bosch and Solé-Ollé2007;Bordignon et al.2003; Hall and Ross2010).However,yardstick competition as a source of international tax mimicry remains unexplored.Devereux et al.(2008),who?nds evidence of mo-bility rather than yardstick competition as the source of CIT mimicry,is a notable exception.

Finally,what appears to be strategic competition may simply be each country adopting what is believed to be the current optimal tax policy.For example,low rates and broad base was a dominant theme in tax policy of the1980s when countries around the world began adopting widespread reductions in their statutory PIT and CIT rates(Sabirianova Peter et al.2010;Mooij and Nicodéme2008).Additionally, pressure to adopt The Washington Consensus as part of IMF relief packages may have created spill-over effects into other nations.This suggests that correlations between national tax rates may re?ect a diffusion of policy ideas between neighbors,unrelated to mobility or voting behavior.Such policy diffusion has been suggested as an expla-nation for the rapid decline in tax rates in the1980s(Whalley1990)and remains a possible explanation for the adoption of linear PIT structures in Eastern Europe.

The various channels through which international PIT mimicry might arise further implies that the strength of mimicry likely varies across tax rates in the income dis-tribution.In other words,mimicry might be stronger for high rates than for low rates

D.Duncan,

E.Gerrish or vice-versa.For example,yardstick competition in the form of Besley and Case (1995)suggests that competition ought to be most salient at the lower or median in-come levels under the assumption that low and median income voters have access to information on their neighbor’s tax policies.This follows from the fact that middle and low income individuals outnumber the rich,which implies that policy makers seeking(re)election should be more sensitive to the demands of voters in the former group.Therefore,if yardstick competition contributes to international PIT mimicry, we would expect to see relatively stronger mimicry in lower tax rates.

On the other hand,because high skilled and educated workers are more mobile than low skill workers,policy makers who are concerned about the depletion of their PIT base via mobility ought to be more sensitive to changes in tax rates applicable to workers at the top of the income distribution,i.e.,top tax rates.A similar trend is likely to be observed if PIT mimicry is being driven by competition for the CIT base.Policy diffusion does not yield any clear prediction on how mimicry might vary across tax rates.

Two questions follow from this discussion.Is there evidence of international PIT competition?If yes,what is the source of the competition?This paper addresses the ?rst question,which has received very little attention in the empirical literature.While the second question is also interesting,it is only relevant if the?rst is answered in the af?rmative.Furthermore,distinguishing among the various channels is not an easy task,since they yield identical econometric models(Brueckner2003).Regardless of the dominant pathway,knowing if PIT mimicry exists at the international level is an important policy question in and of itself.This is especially true in the current economic climate of budget de?cits and growing debt.Therefore,we focus on the ?rst question here and leave the second question for future research.

Because we are unable to de?nitively distinguish between the four tax pathways, we use the phrase tax“mimicry”from here onward to describe the range of possible pathways,rather than“competition,”which connotes active participation.

3Measuring international PIT rates

Estimating tax mimicry at the international level requires data on tax rates that are consistent over time and across countries.We estimate average and marginal tax rates that satisfy these conditions by combining raw data on personal income tax schedules from the world tax indicator with data on per capita GDP(Sabirianova Peter et al. 2010).3Our tax rate estimation procedure starts with the derivation of100data points that ranges from0.1to10times per capita GDP.4Each data point represents an income level equivalent to its respective multiple of per capita GDP.These income data,along with the tax schedule information,are used to estimate tax liability for each of the multiples of per capita GDP by country-year.The estimated tax liability is then used to calculate average and marginal tax rates at four points in the hypothetical 3The world tax indicators database can be downloaded free of cost at https://www.wendangku.net/doc/d215293783.html,/isp/wti.html.

4The data points are generated with the following formula:y

j=j?Y

10

?j=1,2,3,...,100,where Y is

per capita GDP and y j is the income for tax unit j.

Personal income tax mimicry:evidence from international panel data

income scale.In particular,we select average and marginal tax rates at income levels equivalent to one,two-and-a-half,?ve,and ten times per capita GDP.One times per capita GDP arguably re?ects the tax rates of moderately skilled workers,while ten times per capita GDP re?ects tax rates facing highly-skilled professionals who are potentially more mobile than their lower-skilled countrymen.

Because the WTI has very detailed information on PIT schedules for each country in our sample,we are able to estimate tax rates that re?ect standard tax deductions, standard credits,and country speci?c tax formulas.This provides an advantage over simpler calculations of PIT rates in that we are able to estimate a fairly accurate measure of the tax burden.By comparison,the top statutory PIT rate is known to be a poor measure of tax burden(CTPA2009;Slemrod2004).The ratio of income tax revenue to gross national product is also problematic since the composition of revenue and gross national product,as well as the accuracy with which these mea-sures are collected may vary across countries and time(see Jedrzejowicz et al.2009; CTPA2009,for a discussion of other problems with this measure).We avoid many of these concerns by estimating tax rates using the actual tax schedule for each country year.5

Additionally,the WTI has very broad country year coverage.This allows us to test for mimicry using data on a much larger sample of countries than the exist-ing literature,which focuses almost exclusively on OECD or EU countries(Egger and Radulescu2009;Goodspeed2002;Altshuler and Goodspeed2002).6Our com-plete dataset features an unbalanced panel of189countries for the period1982–2005. OECD countries account for16%of the sample,while,high,upper middle,lower middle,and low income countries account for30,18,30,22,respectively,for a to-tal of2,432country-year observations.Because of econometric issues motivated and described in Sects.4.2and6.3,we create subsamples from our larger panel.We sum-marize tax rates and other covariates after describing the samples in Sect.4.2.

As indicated in Sect.2,the magnitude of the mimicry coef?cient might depend on the source of mimicry.Having a data set with tax rates at various points in the income scale allows to check if this is indeed the case.This is an important advantage of the WTI since it brings us closer to identifying the source of mimicry.

Despite these advantages,the WTI dataset does not convey the full picture of country-level personal income tax burden.The estimated tax rates exclude payroll taxes and individual-speci?c tax deductions and credits.Some countries rely less heavily on personal income taxes and more on employee and employer social security contributions and levy these taxes using a progressive structure.As a result,the PIT rates used in the world tax indicators only conveys part of the picture of personal income tax competition;we are unable to detect income tax mimicry which results 5Egger and Radulescu(2009)uses an estimate of the average effective tax rates to model the migration of skilled labor in the OECD.Their approach allows them to incorporate business’share of labor taxes as well as state and local taxes.However,the level of detail required to do the calculations limits the number of countries for which it can be applied.Another technique developed by Easterly and Rebelo(1993)faces a similar limitation.

6A large panel is also advantageous for estimating models with clustered standard errors,which requires N→∞,asymptotically.Simulations of clustered standard errors show that a panel size less than30will often in?ate rejection rates of the null hypotheses(Cameron et al.2008).

D.Duncan,

E.Gerrish from social security contributions and other individual speci?c components of the PIT schedule.7Therefore,our estimates are expected to capture mimicry that arise primarily from rate changes.

4Methodology

4.1Econometric model

We employ a spatial autoregressive model to estimate the magnitude of tax mimicry. This technique is widely used to estimate strategic interactions(Devereux et al.2008; Brueckner2003;Altshuler and Goodspeed2002)and is implemented by including neighbors’dependent variables(PIT rates)on the right-hand side of the regression equation.Because adding tax rates for each neighboring country quickly uses up degrees of freedom,we aggregate neighbors’tax rates into a single variable using a spatial weight matrix.

Following the literature,we write down our baseline speci?cation as follows:

τit=ρWτj=i,t+X itβ+μi+λt+ it,(1) whereτis the tax rate(average PIT rates,marginal PIT rates,or top statutory marginal PIT rate)indexed by country and year,W is the weight matrix(see Sect.4.2),andρis the coef?cient of tax mimicry.The vector X is a standard set of controls used in the literature;top statutory CIT rate,logged per capita GDP,in-?ation rate,trade openness in constant prices(imports plus exports divided by GDP), government expenditures as a percent of GDP,the Freedom House index of political freedom,8the proportion of the population both over65and under15,and the size of the rural population.9μi andλt are country and year?xed effects,respectively,and it is the error term.

The parameter of interest in Eq.(1),ρ,measures the extent to which countries respond to their neighbors’tax rates.While our a priori expectation is a positiveρ, we are aware thatρcan take on any value between?1and1,which indicates strategic substitutes and complements,respectively.10A common problem in estimatingρis endogeneity that results from simultaneity.That is,the Nash equilibrium framework suggests that countries simultaneously set their tax rates,which implies that OLS estimates are inconsistent.We address this problem by using neighbors’weighted control variables as instruments for the spatial lag term(Brueckner2003;Devereux et al.2008).We exclude the corporate income tax rate from the vector of instruments 7In addition,tax schedules are for individual tax payers only.This is unlikely to be problematic since very few countries allow tax payers to?le joint tax returns.

8https://www.wendangku.net/doc/d215293783.html,/.

9See Devereux et al.(2008),Egger et al.(2007),Altshuler and Goodspeed(2002)for comparable controls. De?nitions and data sources for the control variables included in X are presented in Tables1and2.

10Theoretical models have shown thatρcan take any value between?1and1,while the empirical?ndings in the tax literature seem to converge on values∈(0,1].Brueckner(2003),Altshuler and Goodspeed (2002)?nd negative responses.

Personal income tax mimicry:evidence from international panel data

since it is possible that neighbors’CIT has a direct effect on a country’s PIT rate.This implies the following two-stage model:

τit =ρ W τj =i,t +X it β+μi +λt + it ,

W τj =i,t =αW X j =i,t +X it γ+μi +λt +υit (2)

where αis a vector of parameters on the instruments,γis a vector of parameters on the exogenous regressors,and υit is the ?rst stage error term.All other variables and parameters are as de?ned in Eq.(1). W τj =i,t is the predicted weighted tax rates derived from the ?rst stage regression.

4.2Choice of weight matrix

The spatial weight is an important consideration in de?ning a country’s neighbor-hood,and the empirical literature suggests a number of potentially viable weights.The most common weights tend to rely on distance or contiguous borders (Brueckner 2003).However,other papers have de?ned the “distance”between two jurisdictions using per capita GDP,average temperature,pre-de?ned economic regions,and border contiguity (Hernandez-Murillo 2003).

We weight countries by the inverse of distance (in kilometers),then population.First,we select the ten closest neighbors using the distance between the home coun-try’s most populous city and the most populous city in neighboring countries.The inverse of this distance is the ?rst component of our weight.We then reweight the ten closest neighbors by population under the assumption that countries compare them-selves to larger neighbors.This approach reduces the relative in?uence of countries with small populations.For example,the second step in our procedure reduces the weight assigned to Malta in the Italian neighborhood while simultaneously increasing the in?uence of Austria.Finally,as per the literature,each column in the weighting matrix is normalized to one (Brueckner 2003).

Distance is the primary weighting mechanism because we are reasonably assured that distance is not determined by the variable of interest.Correlation between the spatial weighting variable and the right-hand side variable of interest can lead to biased results (LeSage and Pace 2009).It is possible that population is correlated with tax rates based on the theoretical discussion above,but as it is both a secondary weight and we use average population over the period examined,we are reasonably comfortable that the potential for correlation with PIT rates is low.

Another potential dif?culty for the weight matrix is missing observations.The presence of missing observations implies neighbors may move in and out of the weight matrix over time.As a result,observed changes in the weighted tax rates will re?ect changes in both tax policy and country composition.Since we are interested in tax rate changes due to policy decisions only,our primary results are obtained by estimating Eq.(2)using a balanced panel.A balanced panel keeps the composition of countries in the weight matrix ?xed over time,thus eliminating any error due to

D.Duncan,

E.Gerrish compositional effects.11While the size of our dataset allows us to select panels of various sizes,we chose the panel that maximizes the number of observations.Our balanced panel includes every country for which we have at least24years of com-plete data on all baseline variables.12This leaves us with a sample of51countries covering the24-year period from1982to2005.While a balanced panel keeps each country’s neighbors?xed over time,it introduces a potentially serious selection prob-lem.We?nd that this selection issue has no qualitative effect on our main?ndings (see Appendix B).

More information on the tax rates and panel composition can be found in Tables1 and3and Figs.2,3,4,5.Approximately53%,34%,and12%of the observations in our primary balanced panel are from high,middle,and low income nations,respec-tively,and51%come from OECD countries.As expected,summary statistics on PIT rates demonstrate that both average and marginal tax rates rise with income(pro-gressive)and that marginal rates are greater than their respective average tax rates. Average tax rates range from approximately10%at income equivalent to one times per capita GDP to27%at ten times per capita GDP.Similarly,marginal rates at in-come equivalent to ten times per capita GDP,are twice the marginal rates at one times per capita GDP.

5Results

In this section,we describe the main results obtained from estimating Eq.(2)with an instrumental variables approach(Schaffer2010).Robustness checks using alternative weights and panels are discussed in Sect.6.Results from our baseline speci?cation are presented in Table4.The columns represent estimates of PIT rates at four multi-ples of per capita GDP for both average and marginal tax rates.

First stage results show that the instruments perform well.The F-statistics for?rst stage regression models are reasonably large and statistically signi?cant,indicating that the instruments are suf?ciently identi?ed.This conclusion is reinforced by a Kleibergen and Paap Lagrange multiplier test also suggesting that the instrumental variables are identi?ed(Kleibergen and Paap2006).Additionally,the Hansen J tests of over-identifying restrictions do not reject the null hypothesis of a well-identi?ed model.We conclude that the instruments seem reasonably well suited for this pur-pose.Standard errors are corrected for arbitrary within-group and across-group au-tocorrelation(which we abbreviate as“HAC robust standard errors”in table notes) (Thompson2011).

The results from Table4demonstrate that the coef?cients for tax mimicry are pos-itive,economically meaningful,and generally statistically signi?cant.We also?nd

11Egger et al.(2005)derive a method to estimate an unbalanced panel in spatial models.We roughly recreate that method here,but note that the instability of the weight matrix makes this method less than ideal.

12An alternative approach to selecting the balanced panel is to select on the tax rate only(Klemm and Parys 2012).However,we?nd that in order to maintain a balanced panel in the?rst stage of the instrumental variable model,all independent variables must also be nonmissing.

Personal income tax mimicry:evidence from international panel data

relatively stronger evidence of mimicry when we examine average tax rates;estimates are larger in magnitude and have greater statistical signi?cance.The exceptions are the mimicry coef?cients for marginal PIT at two-and-a-half times per capita GDP and the top marginal tax rate.Although the coef?cient for marginal PIT at two-and-a-half times is positive and economically meaningful,we cannot reject that the coef?cient is different from zero.

Positive coef?cients in Table4suggests that countries treat their neighbors’as strategic complements;if the trend is for reduction in PIT rates,neighbors will do likewise.In particular,our estimates range from0.39to0.71for average tax rates and0.24to0.66for marginal tax rates.The coef?cient on the spatial term may be interpreted as the effect of a marginal change in neighbors’tax rate as in Rork(2003) and Devereux et al.(2008).Using this interpretation,we?nd that a one-percentage point change in neighbors’weighted average(marginal)tax rate induces a country to reduce its average(marginal)tax rate by0.39to0.7(0.25to0.66)percentage points for one times and ten times per capita GDP,respectively.

These results are consistent with Egger et al.(2007),who estimates mimicry at 0.42,but runs counter to Altshuler and Goodspeed(2002)who?nds no evidence of PIT mimicry.Note,however,that results from these papers are not directly com-parable given differences in model speci?cation,sample(years and countries),and measures of tax rates.

The major?nding presented in this paper is that tax mimicry appears to be strongest at lower tax rates.Coef?cient estimates for both marginal and average tax rates at one times per capita GDP are approximately two percentage points larger than estimates for ten times per capita GDP.Mimicry seems to disappear at the top statutory marginal PIT rate.This?nding seems to suggest that PIT mimicry is not driven solely by the mobility of higher income individuals.

Although the results in Table4indicate that the strength of mimicry varies along the tax schedule,we are not able to say which of a neighbors’tax rate is most in?u-ential or which of a country’s tax rate is most responsive to changes in neighbors’tax rates.Answering these questions is important since the answers provide a hint, even if not conclusive,of the possible source of mimicry.For example,if countries are more responsive to their neighbors’high rates than low rates,this might be an indication that mobility is a key consideration for policy makers.Alternatively,if the response is stronger when neighbors’low rates changes,then maximizing reelection probability might be the main concern of policy makers.13

To address these questions,we extend the analysis by regressing each of the eight tax rates on each of the neighboring countries weighted tax rates.For example,the average tax rate at one times per capita GDP is regressed on the weighted average rate at one,two and a half,?ve,and ten times per capita GDP,separately(as well as marginal rates).The estimates from these regressions are used to create a matrix of mimicry coef?cients,which we present in Table5.Because each model has the same set of covariates as in Table4,the results presented in Table4are repeated on 13Because the median voter is more likely to be affected by low rates than high rates,responding more strongly to the low rate may indicate pandering to the median voter in an effort to increase the probability of reelection.

D.Duncan,

E.Gerrish the main diagonal of Table5.The results demonstrate that countries respond more strongly when their neighbors’low rate changes and that a country’s high rate is most responsive to tax rate changes in neighboring countries.While it is dif?cult to square these?ndings with mobility-based tax competition,they are consistent with yardstick competition under the assumption that low and median income voters have access to information on their neighbor’s tax policies.14Of course,it is also possible that information on tax policies in neighboring countries is skewed toward the high income group(Goodspeed2000).Our results would not necessarily be consistent with yardstick competition under this alternative assumption.

Given these?ndings,we refrain from making any stronger statements on this ques-tion here.15However,given a choice between future work in the direction of mobility and wealth drain—the type demonstrated in the recent Eduardo Saverin/Facebook IPO incident—versus yardstick competition binding through elections and median voters,our?ndings suggests that the latter might be more fruitful.

We also consider the possibility that international PIT mimicry is best modeled as a Stackelberg equilibrium rather than a Nash equilibrium(Altshuler and Good-speed2002).Similar to Altshuler and Goodspeed(2002),we specify a Stackelberg model with the United States of America(US)as the Stackelberg leader and present the results in Table6.We?nd a positive and statistically signi?cant coef?cient on the leader(US)as well as on theρcoef?cient in the Nash weight matrix.While these results are consistent with Altshuler and Goodspeed(2002),we are unable to make any de?nitive statement about the superiority of the Stackelberg speci?cation vis-a-vis the more common Nash speci?cation.This problem arises from the fact that Nash competitors may appear to be signi?cant even if isolated as a Stackelberg leader.16

Finally,we note that most of the control variables in Table4are of the expected sign,though not all are statistically signi?cant.One exception is that we?nd in-creases in CIT rates to be associated with increases in PIT rates.We hypothesize that tax reform comes in waves,and that both PIT and CIT may be increased(or de-creased)in one tax reform.Additionally,the impact of an increase in the percent of the population under15is negative in all but one model,which runs counter to our expectation.

14Yardstick competition in the form of Besley and Case(1995)suggests that competition ought to be most salient at the lower or median income levels.We also reestimate the baseline model with an interaction term between the mimicry coef?cient and the Freedom House index of political rights.While we continue to observe stronger mimicry at lower tax rates,which we argue is consistent with yardstick competition, there is no evidence that mimicry varies signi?cantly with political rights.These results are available upon request.

15Furthermore,it is possible that the effect in Table5is due to the mechanics behind the calculation of tax rates.In other words,changes in lower marginal tax rates necessarily impact higher average tax rates through a reduction in the rate paid at lower income levels.Therefore,an observed change in high rates—in response to a rate change in neighboring countries—possibly re?ects an accumulation of smaller changes made at lower points in the tax schedule.

16Distinguishing between these competing equilibrium concepts is outside the scope of this paper.

Personal income tax mimicry:evidence from international panel data

6Robustness checks

This section of the paper discusses robustness checks for the choice of neighbors,the use of contemporaneous tax rates,a sample of OECD countries,and the inclusion of additional control variables.We also brie?y discuss the importance of using a balanced panel in spatial models.

6.1Identifying neighbors

As there is no de?nitive rule on the de?nition of neighbors,this section of the paper presents alternative de?nitions of neighbors.Our baseline model de?nes neighbors as the10closest countries where distance is based on physical distance between two countries’most populous cities.We vary this de?nition in several ways and?nd that our results are mostly invariant to the weight matrix.In addition to the10closest neighbors used in the baseline model,we use the5closest countries and the square root of distance to the10closest countries.These modi?cations have no qualitative effects on the results(see Table7).

Additionally,we examine the ten closest neighbors weighting by distance only and ?nd that the results are stronger than the baseline.Second,we use distance and pop-ulation within World Bank income classi?cation.That is,we assume countries only compete with countries of similar economic standing.This produces similar results to the baseline with coef?cients generally declining with multiples of per capita GDP and with more ef?cient standard errors.Third,we use all51countries as neighbors in the spatial weight matrix and?nd that while the coef?cients are of similar mag-nitudes to the baseline(except average rates at1×per capita GDP,which is poorly identi?ed),they are generally not statistically signi?cant.This result is likely due to an increase in the noise to signal ratio as evidenced by the signi?cant increase in the standard errors,which are now1.5to2times larger than standard errors in the baseline model.In other words,each country’s relevant neighborhood for tax compe-tition is much smaller than the full sample of countries.Finally,we de?ne neighbors based on contiguity.Again,our results indicate the presence of mimicry.However, the pattern of greater strategic response to lower tax rates does not generally hold for average rates.17These results are presented in Table9in Appendix A.

The general trend,in all but the contiguous neighbor model,is a general down-ward mimicry coef?cient by per capita GDP and our baseline model falls somewhere in between the stronger effects found with distance only and the weaker effects of all51neighbors or contiguous neighbors.However,we prefer the weight matrix in our baseline speci?cation because it does a good job balancing distance and size as measured by population.We agree that GDP might be a more appropriate measure of 17There are likely two reasons for this result:(1)this represents a smaller sample of840because some countries had no contiguous neighbors in or sample and(2)the number of countries in the spatial weight drops precipitously to an average of2.1countries.This is not because the average number of contiguous countries is2.1,but that2.1is the average number of contiguous countries we have in our sample;384 countries have only one country in their spatial weight.Unlike a sample containing only EU or OECD countries,contiguous neighbors in a larger(e.g.,Asian countries)sample tend to be less similar to one another.This sample also eliminates island nations.

D.Duncan,

E.Gerrish

size,but GDP is problematic because it is correlated with tax https://www.wendangku.net/doc/d215293783.html,ing contiguous neighbors presents some challenge because we do not have data on all contiguous neighbors.

6.2Lags,OECD,and other controls

This section of the paper describes additional robustness checks.First,we are con-cerned that our results are based on the assumption that countries adjust their tax rates instantaneously whenever their neighbors’tax rates changes.The fact that?scal policy is usually implemented with a lag implies this assumption is not likely to hold. We address this concern by reestimating our baseline model with the lagged value of neighbors’weighted tax rate and report the results in Panel B of Table7.18The results in panel B of Table7are not qualitatively different from the baseline model.19 Second,we reestimate Eq.(2)on a balanced sample of25OECD countries,each with24years of data,and report the results in Table8.20Results from this speci?ca-tion should be more comparable to Altshuler and Goodspeed(2002)and Egger et al. (2007),who estimate PIT mimicry using OECD data.Still,we expect some differ-ences since we use a different time period,model,and most importantly,different measures of tax rates.The results presented in panel A shows evidence of mimicry for both average and marginal tax rates.These estimates are positive,economically meaningful,statistically signi?cant,and are consistent with the?ndings in Sect.5 that mimicry is stronger for lower tax rates.While these?ndings are consistent with Egger et al.(2007),they run counter to Altshuler and Goodspeed(2002)who?nds no evidence of PIT mimicry among OECD countries.Our estimated coef?cients for the OECD sample are larger than the other samples,which implies greater mimicry among OECD countries.While this?nding is not implausible,we urge the reader to exercise caution when interpreting these results for two reasons.21First,the size of the mimicry coef?cient for one times per capita GDP is outside the theoretical boundary forρ.This is a possible indication that while the sign ofρis robust,its size must be interpreted with caution.Second,the model is estimated on a sample with N=25,T=24,and standard errors clustered on N,which implies standard errors are likely to be biased.

Finally,we explore the possibility that our results are sensitive to the inclusion of additional controls.The results presented in Table10of Appendix A shows that

18Using the lag of neighbors’tax rates is also advantageous because it reduces endogeneity resulting from reverse causality.That is,a country’s future tax policy should have a smaller effect on its neighbors’current tax policy.Neighbors weighted tax rates are instrumented by the lag of the neighbors weighted X vector in this model.

19We also show that the results continue to hold if we lag tax rates two periods.These results are available upon request.

20The OECD model is estimated with time trend instead of year?xed effects.Estimation with year?xed effects causes the covariance matrix to be of less than full rank when standard errors are clustered at the country level.Note that we essentially have25clusters with24observations each.

21The higher level of homogeneity among OECD countries implies that voters,high skill mobile workers, and entrepreneurs can easily?nd a comparable reference country if they feel their own tax rate is too burdensome.In other words,mimicry is likely higher among homogenous countries such as the OECD.

Personal income tax mimicry:evidence from international panel data

our?ndings are invariant to the inclusion of country-speci?c time trend,a linear time trend,the size of the shadow economy,and?uctuations in the business cycle (measured by GDP growth).22

6.3Compositional effect

While we strongly believe that using a balanced panel is econometrically sound(see discussion in Sect.4.2),its use could introduce bias in our context.Because our bal-anced panel does not include the complete universe of countries,some close neigh-bors are eliminated from the weight matrix in exchange for more distant neighbors that have complete data.This is problematic if close neighbors and far neighbors dif-fer systematically in their tax rate policy.For example,suppose country A has a close neighbor B and far neighbor C,and suppose we observe data for A and C only.Now suppose countries A and C increase their tax rates,but country B reduces its tax rate. Our balanced panel would include countries A and C,and our methodology would ?ndρto be positive when it is in fact negative(if only B is used as neighbor)or close to zero(if both B and C are used as neighbors).23

We address this issue in the Appendix and show that our main results are not affected(see Appendix B and Table11).

7Conclusion

This paper investigates PIT mimicry at the international level.It is the?rst to em-pirically investigate the extent to which PIT mimicry varies along the tax schedule and the?rst to include nations which are not part of the OECD.We use data on in-ternational personal income tax schedules from the world tax indicators to estimate marginal and average tax rates at various multiples of per capita gross domestic prod-uct(GDP).These tax rates are then used to estimate the extent to which countries respond to their neighbors’PIT policy.

Our baseline results indicate that countries mimic each other in setting personal income tax rates.In particular,countries increase their own tax rate when their neigh-bors’rate increases.This result is consistent across our measures of average and marginal tax rates,and is robust to various de?nitions of neighboring countries and panel construction.Additionally,our?ndings are strongest for tax rates at lower mul-tiples of per capita GDP.Since tax rates generally increase with skill level,we in-terpret the variation in mimicry along the tax schedule as evidence that mimicry is relatively more important for low skilled workers.It is important to note that our mea-sure of personal income tax rates from WTI cannot account for individual deductions nor social security contributions through payroll taxes.As a result,this analysis only tells one part of the story of labor tax competition.

22We also?nd no evidence that reform periods substantively alter the mimicry term as in Altshuler and Goodspeed(2011),although we acknowledge that this might be an artifact of our particular sample or reform periods.These results are available upon request.

23We realize that a country may be in?uenced by far neighbors rather than close neighbors.Therefore,the problem highlighted here implicitly assumes that tax mimicry is strongest for close neighbors.

D.Duncan,

E.Gerrish

The policy implications of our?ndings depend largely on the source of mimicry. For example,mimicry is most likely welfare enhancing if driven by countries adopt-ing what is believed to be optimal tax policy.In fact,this type of response to neighbors’tax policy would be encouraged.On the other hand,mimicry that re-sults from competitive forces such as mobility or yardstick competition may in-crease or decrease social welfare(Wilson and Wildasin2004).An increase in welfare may occur if competition forces policy makers to spend scarce resources more ef?ciently,reduce the tax burden on taxpayers,and reduce the economic distortions of taxes.Alternatively,competitive forces may lead to a race-to-the-bottom,which can seriously constrain policymakers as they attempt to provide public goods.This is especially important in debates regarding the stimulative ef-fect of tax rate reductions.Reductions in tax rates aimed at stimulating economic activity via international PIT competition might lose some of its effect if neigh-bors respond by reducing their own tax rates.We argue that policy makers ought to adjust their expectations accordingly,especially in the current economic cli-mate.

Because our results are consistent with each of the channels discussed in Sect.2, we are unable to determine the dominant international PIT mimicry pathway in this paper.Instead,we leave this question for future research.However,our?nd-ings do indicate where one might begin searching for the source of PIT mimicry. In particular,empirical evidence shows that the probability of re-election is high-est when of?cials focus on the tax rates of lower and middle income earners (Besley and Case1995;Case1993;Bordignon et al.2003).This implies that our?ndings of stronger mimicry both in a country’s lower PIT rates and in re-sponse to changes in their neighbors’lower PIT rates is suggestive of,though not evidence for,yardstick competition,especially if low and median income voters have access to information on their neighbor’s tax policies.The possibility that yardstick competition is the main source of international PIT mimicry is not en-tirely surprising given its role in explaining intranational PIT competition(Bosch and Solé-Ollé2007).As a result,we argue that yardstick competition is possibly the most fruitful place to start in any future attempt to disentangle these chan-nels.

Of course,we cannot ignore the fact that our?ndings are also consistent with mo-bility and policy diffusion.However,because countries sometimes tax global income and/or implement tax rules that require taxpayers to pay tax in their home country regardless of where income is earned,the labor mobility pathway for PIT mimicry is likely weaker than implied by recent labor force migratory trends.A more likely mobility related channel is the movement of small businesses that are part of the PIT net.An initial attempt to identify the role of this channel has been made by Egger et al.(2007),however,more work is needed before any conclusive statement can be made.On the other hand,the work of international agencies such as the Interna-tional Monetary Fund and the World Bank imply that policy diffusion also deserves serious consideration.Future work might consider estimating models with interac-tion terms that control for presence of the IMF,World Bank,or other in?uential international agencies in a country as a way of identifying the role of policy dif-fusion.

Personal income tax mimicry:evidence from international panel data

8Tables and?gures

Fig.1Net migration in selected OECD countries,1959–2009.

Notes:Immigration countries include Australia,Austria,Belgium,Canada,France,Germany,Luxem-bourg,the Netherlands,New-Zealand,Sweden,Switzerland,the United Kingdom,and the United States. Emigration countries include Czech Republic,Denmark,Finland,Iceland,Italy,Norway,Slovak Republic, Japan,Greece,Hungary,Ireland,Poland,Portugal,and Spain.However,Korea,Mexico,and Turkey are out of the scope of the study due to unavailability of data.

Source:Adapted from the OECD’s International Migration Outlook2011

Fig.2Countries included in Full Balanced Panel(and Baseline Model)

D.Duncan,

E.Gerrish

Fig.3Countries included in OECD Balanced Panel

Fig.4Countries included in Unbalanced Unstable Panel

Personal income tax mimicry:evidence from international panel

data Fig.5Countries included in Unbalanced Stable Panel

Table1Summary statistics for tax rates by panel structure Notes:Rows report mean tax rates for income equivalent to the stated multiple of per capita GDP with standard deviations in parentheses.Descriptions of the panel structures can be found in Sect.4,Appendix B,and Table3.

Source:Authors’calculations with data from the World Tax Indicators(WTI)

Balanced Unbalanced

Full OECD Stable Unstable

Average Tax Rate

1×Per Capita GDP10.28

(11.44)

19.20

(9.44)

10.49

(10.32)

7.96

(10.29) 2.5×Per Capita GDP17.31

(16.24)

30.60

(11.13)

17.48

(14.59)

14.11

(14.03) 5×Per Capita GDP22.67

(18.66)

38.05

(11.10)

22.75

(16.36)

19.41

(16.02) 10×Per Capita GDP27.34

(19.82)

43.32

(10.80)

27.04

(16.69)

24.44

(16.95) Marginal Tax Rate

1×Per Capita GDP16.51

(16.99)

29.86

(12.89)

17.16

(15.60)

12.87

(14.97) 2.5×Per Capita GDP25.06

(21.25)

42.47

(12.43)

25.15

(18.97)

21.37

(18.39) 5×Per Capita GDP29.83

(21.79)

47.05

(11.75)

29.70

(18.01)

27.00

(18.81) 10×Per Capita GDP33.22

(21.63)

49.16

(11.55)

32.37

(17.00)

31.11

(18.60) Top Marginal Rate35.15

(20.62)

44.25

(15.02)

33.35

(15.50)

37.68

(17.94) N12246004822709

D.Duncan,

E.Gerrish

Table2Summary statistics for model covariates by panel structure

Notes:Rows report means with standard deviations in parentheses.Panel structures are described in Sect.4, Appendix B,and Table3. Openness is measured in constant1996prices and CIT is statutory corporate income tax. Sources:World Bank’s World Development Indicators(WDI) and the Penn World Tables (PWT)

Balanced Unbalanced

Full OECD Stable Unstable Log of Per Capita GDP 3.60

(0.97)

4.21

(0.42)

3.46

(0.92)

3.06

(1.05)

In?ation Rate19.22

(129.58)

7.58

(15.65)

12.01

(95.67)

67.12

(665.68) Openness71.56

(45.15)

62.76

(45.10)

83.44

(44.90)

78.90

(48.85) CIT Rate33.89

(11.88)

35.20

(9.31)

30.15

(8.95)

34.40

(11.57) Pop.over65(%)8.49

(5.39)

12.97

(3.59)

10.26

(4.96)

7.16

(4.77) Pop.under15(%)28.20

(9.73)

20.91

(5.42)

26.18

(9.09)

31.92

(10.25) Pop.in Rural Areas(%)34.26

(22.33)

25.39

(11.30)

31.34

(17.10)

43.45

(23.03) Freedom House Index 2.57

(1.99)

1.26

(0.76)

2.09

(1.58)

3.17

(2.09) Govt Expenditures(%GDP)17.44

(6.27)

18.46

(4.57)

17.27

(5.50)

16.63

(6.46)

N12726004822709

Table3Composition of panels used in analysis

Details Balanced Panels Unbalanced Panels Correlation

Panel

Full OECD Stable Unstable

N.of countries512578135169

N.of OECD2525233030

N.of Non-OECD26055105139

Avg.N.of Years242462017

N.Observations122460048227092916 Years included1982–20051982–20051982–20051982–20051982–2005

Share of Observations by World Bank Income Classi?cation

High income53.1087.3343.2427.1332.13 Upper middle17.818.1722.5418.6818.66 Lower middle16.67 4.5025.6132.7827.30

Low income12.4208.6121.4121.91

Notes:The stable panel has a consistent weight matrix with the same10of the20closest geographic neighbors every year for a given country.The weight matrix varies by country-year in the unstable panel. The correlation panel is also unstable,but includes every country for which we have nonmissing tax rate. The other four panels excludes a country if it has missing information on tax rates and/or any of the covariates.Income classi?cations de?ned by the World Bank

Acknowledgements We would like to thank Bradley Heim,Deborah Carroll,Hannes Winner,Justin Ross,and two anonymous referees for providing useful comments.

网络与新媒体专业毕业设计若干规定

文学与新闻传播学院网络与新媒体专业 关于毕业作品设计的规定（初稿） 为培养网络与新媒体专业学生实践操作技能和应用能力，本专业拟改变以往全部毕业生撰写毕业论文的局面，允许毕业生根据自身学习情况，自主选择进行毕业论文写作，或毕业作品设计。这有助于引导学生掌握网络与新媒体专业的专业实践能力，为做好相关工作，现将网络与新媒体专业毕业作品设计实施方案公布如下： 一、毕业作品设计选题来源 （一）指定：根据专业教师给出的题目进行毕业作品制作。 （二）自选： 1. 已有就业意向的学生，可策划制作就业意向单位（媒体）指定的新媒体作品（提供就业意向单位的证明）。 2. 选定自己感兴趣的网络与新媒体选题进行毕业作品设计。 二、毕业作品设计形式 方向一：交互产品设计： 选择领域包括网站设计、网络应用设计、基于Android、IOS等平台的移动终端产品设计等。 （1）网站设计要求页面中文字内容、PNG、JPEG、GIF图片或Flash动画链接完整，至少包含三级完整页面设计，且至少完成两个完整网站设计。 （2）网络应用及移动终端产品设计需充分考虑所选平台的交互和操作便利性，如选择游戏创作，作品必须完整的游戏策略，游戏人物、角色需原创。 方向二：新媒体动漫设计： 可选择Flash动画、三维动画、定格动画、数字插画、漫画等各种形式，制作叙事类、宣传广告类、互动娱乐类等各种作品。 （1）情节动画作品不低于2分钟； （2）广告动画要求完成45秒、30秒、10秒一式三份； （3）数字插画、漫画作品不低于A4格式20张。 方向三：影视媒体创作： 选择领域包括微电影、电视专题片、影视广告、广播电视节目等

（1）微电影：片长标准为20—30分钟； （2）电视专题：片长标准为15—30分钟； （3）影视广告：制作片长为15秒、30秒、60秒和90秒的广告作品各1部； （4）电视节目：时长30分钟左右； （5）广播节目：时长30分钟左右； 方向四：新媒体传播策划案 三、毕业作品设计工作流程 新媒体作品设计制作（可独立制作，也可以2至3人合作，合作项目要注明人员分工） 1. 设计开题报告，阐述选题的价值、初步思路与预期效果，经过专业指导教师认可后进行制作。 2. 中期向指导教师阐述项目进展情况，教师提出指导性意见。 3. 进行作品后期设计制作，完成毕业作品。 4．根据完成的作品撰写网络与新媒体作品综述报告书或策划案（见附录） 5．开题报告及毕业作品设计最终完成时间与毕业论文写作保持同步，设计期间要保持与指导教师的联系。 四、毕业作品完成形式 毕业作品的完成形式为：作品策划书（或作品说明书）+ 音像制品。 1．作品策划书（或作品说明书）（见《广播电视节目策划书（作品说明书）基本格式》，A4纸打印），并将电子版刻录在影视作品之后。 2．音像制品：将作品刻录成DVD光盘。注明（学生姓名）作品，﹡﹡﹡专业，年级学号；新闻专题名称、作品时间长度、指导教师、创作时间。 五、毕业设计成绩评定 毕业作品设计是展示网络与新媒体专业学生四年学习成绩的重要环节。毕业生在专业指导教师指导下完成作品创作。由网络与新媒体专业教师评定学生毕业作品设计的成绩。毕业作品按照百分制计分。严禁抄袭，一旦发现将取消该毕业设计的成绩，并按学院有关规定给予处理。 六、参加重要学科竞赛获奖作品 （一）网络与新媒体专业学生在参加“挑战杯”大学生课外学术科技作品竞赛、“挑战杯”大学生创业计划竞赛、全国大学生广告艺术设计大赛等重要学科竞赛获得国家二等以上奖励，项目参与前三名可以该成果代替毕业论文；获得省级二等以上奖励，第一作者可以

2016网络与新媒体专业人才培养方案1013

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四.通?素质重要度评估 调查结果显?，在对?个维度的通?素质重要度进?评估时，“沟通与合作能?（4.23分）”、“表达与表现能?（4.22分）”与“?作责任?（4.16分）”三项?于平均分值3.91分，被认为是最为重要的三项专业能?，具体分值*如下图所?： 分值*：满分5分，取所有受访者评分的平均值为各维度参考分数

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本文部分内容来自网络整理，本司不为其真实性负责，如有异议或侵权请及时联系，本司将立即删除！ == 本文为word格式，下载后可方便编辑和修改！ == 网络与新媒体专业与新闻学的区别 你们知道关于网络与新媒体专业与新闻学的区别是什么吗?下面是小编为大家搜集整理出来的有关于网络与新媒体专业与新闻学的区别，欢迎阅读! 1. 定义的不同 新闻学是研究新闻事业和新闻工作规律的科学。 新闻学是以人类社会客观存在的新闻现象作为自己的研究对象，研究的重 点是新闻事业和人类社会的关系，探索新闻事业的产生、发展的特殊规律和新 闻工作的基本要求的一门科学。它研究的内容是新闻理论、新闻史和新闻业务。 网络新媒体，顾名思义，则是以网络为介质的新闻媒体(网络媒体)或信息 载体(商业网站)刊载的新闻报道它突破了传统的新闻传播概念，在视、听等方 面给受众全新的体验，并将无序化的新闻进行有序的整合，大大压缩了信息的 厚度，让人们在最短的时问内获得最有效的新闻信息。 可以说，网络新媒体是新闻学在新时代的发展和延伸，网络新媒体本质上 是属于新闻学范畴内的。 2. 学习的侧重点不同 新闻学主要学习关于报纸、杂志等传统媒体的理论知识，掌握采访、编辑、写作、摄影等传统新闻业务与技能，了解新闻工作的方针、政策法规和中国新 闻工作现状与发展趋势; 而网络新媒体除了要掌握基本的采写编技能外，最好还要会新媒体运营、 视频编辑、文案策划等技能，可以说对人才的要求更高更全面。 3. 发展历史不同 中国近代报刊的产生晚于欧洲资本主义国家 200多年。在19世纪中国逐 步沦为半封建半殖民地社会的过程中，西方传教士来华创办了最初的报刊。之 后中国新闻业的发展随着时代的改变而不断发展，期间有过繁荣时期也有过黑 暗的过渡期。

网络与新媒体专业人才培养方案

网络与新媒体专业人才 培养方案 内部编号：（YUUT-TBBY-MMUT-URRUY-UOOY-DBUYI-0128）

网络与新媒体专业人才培养方案 学科门类：文学代码：05 类别：新闻传播代码：0503 专业名称：网络与新媒体专业代码：050306T 一、培养目标 本专业培养熟悉新闻法规与政策，具有扎实语言与文化功底和良好的科学人文素养，具备新闻与传播基础理论知识，掌握新闻采写编评、网络与传播技术、数字媒体与素材加工等技能的复合型应用型网络新闻信息传播类高级专门人才。 毕业生适应岗位：媒体策划与宣传、新闻信息采编、数字媒体处理、网页设计与制作、网站维护与开发、平面设计与制作、办公自动化。 二、培养要求 本专业学生学习新闻传播、汉语言文学和网络技术的基础知识、基本理论和基本技能，接受新媒体传播在内容和技术方面的基本训练，具备良好的人文修养和深厚的文字功底，具备新闻采写和编辑、评论的基本业务能力，能够熟练掌握文本、图片、音频、视频的编辑与发布，能够操作大型数据库及信息发布系统，能够熟练制作网页，能够使用程序设计语言及工具设计、开发网络信息发布系统。毕业生应具备以下知识和能力： （一）知识结构 1．掌握新闻传播学基础理论和基本知识； 2．掌握汉语言文学基础理论和基本知识； 3．掌握网络基础知识及INTERNET应用技能; 4．掌握网络信息搜索、文献检索、资料查询的基本方法；

5．掌握一门计算机编程语言及web应用开发技术； 6．掌握平面设计基础知识和网络编辑基本技能； 7．熟悉知识产权法规和新闻法规； 8．学习并掌握一门外语； （二）能力结构 1．具有新闻采写编评等实际业务能力； 2．具有扎实的文学功底和良好的人文素养与艺术修养； 3．具有操作大型数据库,使用数据库进行信息发布的能力； 4．具备应用平面设计和动画设计软件制作网页的能力与技巧； 5、具有使用计算机编程语言及web开发技术进行网络信息发布系统开发、使用的能力； 6．了解传媒行业现状及未来发展趋势的能力； 7．具备阅读浅易外文资料的能力； 8．具备良好的心理素质和健康的体魄。 三、主干学科：新闻传播学、汉语言文学 四、核心课程：新媒体概论、传播学、新闻学、网络编辑学、基础写作、语法与修辞、中国文学名着选讲、外国文学名着选讲、网络基础与应用、数据库基础、面向对象程序设计、视听语言艺术、网页设计、网络数据库技术、Web应用开发。 五、主要实践教学环节：数据库基础实验、网络数据库技术实验、web应用开发实验、面向对象程序设计实验、流媒体采编实验、摄影技术实验、网页设计实验等。 六、学制与学分

网络与新媒体专业人才培养方案

网络与新媒体专业人才培养方案 学科门类:文学代码:05 类别:新闻传播代码:0503 专业名称:网络与新媒体专业代码:0５０306T 一、培养目标 本专业培养熟悉新闻法规与政策,具有扎实语言与文化功底与良好得科学人文素养,具备新闻与传播基础理论知识，掌握新闻采写编评、网络与传播技术、数字媒体与素材加工等技能得复合型应用型网络新闻信息传播类高级专门人才。 毕业生适应岗位:媒体策划与宣传、新闻信息采编、数字媒体处理、网页设计与制作、网站维护与开发、平面设计与制作、办公自动化。 二、培养要求 本专业学生学习新闻传播、汉语言文学与网络技术得基础知识、基本理论与基本技能，接受新媒体传播在内容与技术方面得基本训练,具备良好得人文修养与深厚得文字功底,具备新闻采写与编辑、评论得基本业务能力,能够熟练掌握文本、图片、音频、视频得编辑与发布，能够操作大型数据库及信息发布系统,能够熟练制作网页,能够使用程序设计语言及工具设计、开发网络信息发布系统。毕业生应具备以下知识与能力: （一)知识结构 1.掌握新闻传播学基础理论与基本知识; 2.掌握汉语言文学基础理论与基本知识； 3.掌握网络基础知识及INTERＮＥT应用技能； ４.掌握网络信息搜索、文献检索、资料查询得基本方法; 5.掌握一门计算机编程语言及weｂ应用开发技术； ６.掌握平面设计基础知识与网络编辑基本技能； 7.熟悉知识产权法规与新闻法规; ８．学习并掌握一门外语; (二)能力结构 1.具有新闻采写编评等实际业务能力; 2．具有扎实得文学功底与良好得人文素养与艺术修养; 3．具有操作大型数据库,使用数据库进行信息发布得能力;

4.具备应用平面设计与动画设计软件制作网页得能力与技巧； 5、具有使用计算机编程语言及ｗｅb开发技术进行网络信息发布系统开发、使用得能力; 6.了解传媒行业现状及未来发展趋势得能力； ７.具备阅读浅易外文资料得能力; ８.具备良好得心理素质与健康得体魄。 三、主干学科:新闻传播学、汉语言文学 四、核心课程：新媒体概论、传播学、新闻学、网络编辑学、基础写作、语法与修辞、中国文学名著选讲、外国文学名著选讲、网络基础与应用、数据库基础、面向对象程序设计、视听语言艺术、网页设计、网络数据库技术、Ｗeｂ应用开发。 五、主要实践教学环节:数据库基础实验、网络数据库技术实验、ｗeb应用开发实验、面向对象程序设计实验、流媒体采编实验、摄影技术实验、网页设计实验等。 六、学制与学分 (一)修业年限:基本学制4年,实行弹性学制,修业年限原则上为3-6年。 (二）毕业学分说明:修完本专业必修课程与选修课程，达到最低毕业学分,符合学院有关毕业规定即可申请毕业。毕业最低总学分为180。 (三）学分结构 1．理论课周学时为1,原则上行课满１个学期计为1学分,即１8学时计为1学分； ２．实践性教学环节2周计为1学分,每周约按26学时计算;实验课程1８-２7学时计为1学分； 3.创新教育活动学分按照学院相关规定认定。 (四)授予学位:文学学士 （五)修读说明 七、教学进度表 网络与新媒体本科专业教学进度表