rafmacalaba/datause-encoder-data huggingface dataset:
WPS3452

# Import Demand Elasticities and Trade Distortions<sup>∗</sup>

# Hiau Looi Kee<sup>†</sup> Alessandro Nicita<sup>‡</sup> Marcelo Olarreaga<sup>§</sup>

## Abstract

To study the effects of tariffs on gross domestic product (GDP) one needs import demand elasticities at the tariff line level that are consistent with GDP maximization. These do not exist. We modify Kohli’s (1991) GDP function approach to estimate demand elasticities for 4,625 imported goods in 117 countries. Following Anderson and Neary (1992, 1994) and Feenstra (1995), we use these estimates to construct theoretically sound trade restrictiveness indices (TRIs) and GDP losses associated with existing tariff structures. Countries are revealed to be 30 percent more restrictive than their simple or import-weighted average tariffs would suggest. Thus, distortion is nontrivial. GDP losses are largest in the United States, China, India, Mexico and Germany.

JEL classi fi cation numbers: F1, F10, F13 Keywords: Import demand elasticities, GDP function, trade restrictiveness, deadweight loss

> ∗We are grateful to James Anderson, Paul Brenton, Hadi Esfahani, Rob Feenstra, Joe Francois, Kishore Gawande, Catherine Mann, William Martin, Jaime de Melo, Christine McDaniel, Peter Neary, Guido Porto, Claudio Sfreddo, Clinton Shiells, David Tarr, Dominique Van Der Mensbrugghe, L. Alan Winters, and participants at seminars at the Center of Global Development, the Econometric Society Meetings at Brown University, the Empirical Trade Analysis Conference at the Woodrow Wilson International Center, PREM week, University of West Virginia, and the World Bank for helpful comments and discussions. The views expressed here are those of the authors and do not necessarily re fl ect those of the institutions to which they are affiliated.

> †Development Research Group, The World Bank, Washington, DC 20433, USA; Tel. (202)473-4155; Fax: (202)522-1159; e-mail: hlkee@worldbank.org

> ‡Development Research Group, The World Bank, Washington DC, 20433, USA; Tel. (202)473-4066; Fax: (202)5221159; e-mail: anicita@worldbank.org

> §Development Research Group, The World Bank, Washington, DC 20433, USA, and CEPR, London, UK; Tel. (202)458-8021; Fax: (202)522-1159; e-mail: molarreaga@worldbank.org
suggestive of at least some reverse causation from income to morality. These tests each throw away some of the WVS data, however, and (as mentioned above) we agree with Bjornskov (forthcoming) and others that the within-country variation over time is a highly noisy measure of actual change. In Table 5 therefore we adopt an alternative (and more conventional) approach by instrumenting for the potentially endogenous morality variables.<sup>19</sup> Results for the moralityability correlation from these tests are strikingly similar to those presented in Table 4, while results for the morality proxy are more favorable (i.e. more positive and consistently significant).

For instruments we rely primarily on religious composition of the population, using data from McCleary and Barro (2006). There are several precedents in the literature. La Porta et al. (1997, 1999) find that interpersonal trust and tax compliance are lower, and corruption higher, in countries with more adherents of “hierarchical” religions (Catholic, Eastern Orthodox, or Muslim). Zak and Knack (2001) instrument for interpersonal trust with these religious composition variables in cross-country growth regressions. Knack (2002) uses a broader set of religious composition variables in instrumenting for several “social capital” indicators in analyzing determinants of government performance across the American states. Building on Weber (1905), McCleary (2007) and McCleary and Barro (2006) describe how some of the major religions foster traits such as honesty and charity, and attitudes toward work. Religious composition potentially provides a relevant and valid set of instruments for our morality variables. They are relevant instruments if “differences in motivational mechanisms” across the major religions explain much of the cross-country variation in the morality variables, and they may be valid as “it is not

> 19 Rule of law and trade openness have been treated as endogenous in some studies, including RST (2004). We treat them as exogenous for three reasons. First, with more endogenous regressors it is more difficult to find a set of instruments that successfully discriminates among them. Second, a test of “weak instruments” telling us whether they discriminate among them sufficiently is available only for the case of three or fewer endogenous variables. Third, in general fitted values from first-stage regressions are less effective as “control” variables in testing more central hypotheses (Dollar and Kraay, 2003; Angrist and Pischke, 2010), in this case for the morality variables.

25
# **References**

Aedo, Cristian, and Ian Walker. 2011. “Education and Skills for the 21<sup>st</sup> Century in LCR.” Mimeo, LCR Regional Study, Office of the Chief Economist for LCR, World Bank, Washington, DC.

Aghion, Philipe, Nicholas Bloom, Richard Blundell, Rachel Griffith, and Peter Howitt. 2005. “Competition and Innovation: An Inverted-U Relationship.” _Quarterly Journal of Economics_ 120(2): 701-28.

Amiti, Mary, and Jozef Konings. 2007. “Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia.” _American Economic Review_ 97(5): 1611-38.

Artuc, Erhan, Shubham Chaudhuri and John McLaren. 2010. "Trade Shocks and Labor Adjustment: A Structural Empirical Approach." _American Economic Review_ 100(3): 1008-1045.

Artuc, Erhan, and John McLaren. 2010. "A Structural Empirical Approach to Trade Shocks and Labor Adjustment: An Application to Turkey." Chapter 2 in _Trade Adjustment Costs in Developing Countries: Impacts, Determinants and Policy Responses_ , edited by G. Porto and B. Hoekman. Washington, DC: CEPR for the World Bank.

Brambilla, Irene, Rafael Dix-Carneiro, Daniel Lederman, and Guido Porto. 2011 (forthcoming). “Exports, Skills and the Wages of Seven Million Latin American Workers.” _World Bank Economic Review._ Brambilla, Irene, Daniel Lederman and Guido Porto. 2010. “Exports, Export Destinations, and Skills.” NBER Working Paper #15995, Cambridge, Massachusetts.

Broda, Christian, and John Romalis. 2009. “The Welfare Implications of Rising Price Dispersion.” Mimeo, Booth School of Business, University of Chicago, Illinois.

http://faculty.chicagobooth.edu/john.romalis/Research/Draft_v7.pdf Deaton, Angus. 1997. _The Analysis of Household Surveys: A Microeconometric Approach to Development Policy_ . The Johns Hopkins University Press for the World Bank: Washington, DC.

Deaton, Angus. 1989. “Rice Prices and Income Distribution in Thailand: A Non-Parametric Analysis.” _The Economic Journal_ 99(395): 1-37.

Deaton, Angus. 1988. “Quality, Quantity, and Spatial Variation of Prices.” _The American Economic Review_ 78: 418-430.

Dix-Carneiro, Rafael. 2011. “Trade Liberalization and Dynamics.” Mimeo, Princeton University, Princeton, NJ.

Durán Lima, Jose, Alfonso Finot, and Marcelo Lafleur. 2011. “Analysis of the Effects of Trade Opening on Household Welfare: An Application to Chile, 1999-2006.” Mimeo, United Nations Economic Commission for Latin America and the Caribbean (ECLAC), Santiago, Chile.

Fereira, Francisco H.G., Anna Fruttero, Phillipe Leite, and Leonardo Lucchetti. 2011. “Rising Food Prices and Household Welfare.” World Bank Policy Research Working Paper No. 5652, Washington, DC.

14
<!-- Start of picture text --> .<br>oa<br>4 oF = ‘ ws<br>I\oeee ts<br>; An—ay*0 en<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>= BRA = ESP = KOR TM World<br>= CHN = IND = USA<br><!-- End of picture text --> <!-- Start of picture text --> 25<br>Wy a A, =<br>wmes a<br>. *<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>= BRA = ESP TM KOR TM World<br>= CHN = IND = USA<br><!-- End of picture text --> <!-- Start of picture text --> mnt SF!<br>0 awe nf Ns PNT; pbA re wetwe<br>-2 eaWrWy VaZz Ue<br>4 (]<br>4<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>= BRA = ESP = KOR TM World<br>= CHN = IND = USA<br><!-- End of picture text --> <!-- Start of picture text --> 150<br>100 fl<br>“| ff\ } ~<br>0 aA 2d os a :<br>“0 wt<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>= BRA = ESP = KOR TM@ World<br>= CHN = IND = USA<br><!-- End of picture text -->

<!-- Start of picture text --> 4<br>1 ns<br>2 ‘y *<br>w ate<br>\ AAS:<br>0 ARspf Waa Je < | bo een<br>NY a OA ¥ pokerLA<br>( N/<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>= BRA = ESP = KOR TM World<br>= CHN = IND = USA<br><!-- End of picture text -->

<!-- Start of picture text --> 80<br>40<br>Jv N<br>0 oe nc a Ahan<br>oe PIASAA s .<br>-40 \ NEY=p SeaSS fet:<br>r\ *<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>= BRA = ESP TM KOR TM World<br>= CHN = IND = USA<br><!-- End of picture text -->
years or so that have provided at least some answers to the policy questons that one is grappling with in regard to the private capital flows. Some of these are discussed below, although, much work still remains to be done.

Studies that have looked into the determinants of the large private capital inflows to developing countries include the seminal work by Calvo, Leidermen and Reinhart (1993) which examined the capital inflow episode in 1988-1991 in 10 Latin American countries. Using monthly data on international reserve changes as a proxy for capital inflows they concluded that external "push" factors (beyond the control of the recipient country), such as the decline in interest rates and the recessionary environment in industrialized countries (particularly in the United States), were the primary determinants of the observed surge in private capital flows, particularly portfolio equity inflows, to developing countries in the early 1990s. Lower interest rates make it less attactive at the margin for investors to keep their investible funds at home (the "asset substitution channel" _a la_ Goldstein (I 995)) and improves the creditworthiness of a debtor country by raising the discounted present value of resources available for meeting external debt service obligations and by lowering the discounted present value of future debt service payments on its floating rate debt (the "creditworthiness channel" _a la_ Goldstein (1995)). This result was supported by Dooley, Fernandez-Arias, and Kletzer (1994), who examined the behavior of the secondary-market price of a country's sovereign debt since 1989 as a sensitive proxy for capital inflows. Their rationale being that changes in the demand for claims on a developing country, whether emanating from changes in domestic or external factors should be reflected in the prices of that country' sovereign debt. Fernandez-Arias (1994) used quarterly estimates of portfolio inflows (through bonds and equities) for thirteen countries, rather than proxies for capital inflows that were used in the aforementioned studies, to show that over 85 percent of the inflows to these countries since 1989 was explained by the decline in international interest rates.

Domestic "pull" factors, such as improved domestic policies and sustained high growth performence in the recipient countries, have also contributed to the surge in 15
13 countries pass through the different stages of development<sup>the size of</sup> government--measured as the ratio of real government<sup>expenditure to GDP--first</sup> rapidly expands, then it starts to stabilize and<sup>finally as the countries</sup> industrialize it starts shrinking.

The reason for this pattern, obviously, lies in<sup>the movement of relative</sup> prices. As we saw earlier the public sector price<sup>level relative to the price</sup> level in the overall economy follows a U-shaped<sup>evolutionary path. In early</sup> stages of development the relative prices in the public<sup>sector decrease but at</sup> later stages of development they start to rise. Consequently, even though the nominal government expenditure to **GDP** ratio may<sup>increase, as documented in</sup> some studies'9, in real terms it would show a quadratic movement.

It is important to point out that the results on government<sup>expenditure</sup> growth reported here are broadly consistent with those<sup>presented in other</sup> studies. As was noted at the beginning of this paper,<sup>analyses of time-series</sup> data often favor the hypothesis of rising real government<sup>expenditures ..</sup> response to rising income with a somewhat similar<sup>dichotomy between the</sup> developing and developed countries in terms of the<sup>behavior of government</sup> expenditure to GDP ratio (Beck 1976; Pluta 1979; and<sup>Ram 1987)'. However,</sup> none of these st.dies exDlict:y, exDore ehe<sup>imoact</sup><sup>_3-_:han-nz</sup> - 'sc.e prices on real government size or the likely form of the<sup>relationship</sup> between real government expenditure and real gross national<sup>product as countries move</sup> from one development stage to another.2 ' 'a See, for instance, Peacock 1985.

20 Actually, the results of the present study<sup>show a slight improvement</sup> over Ram's 1987 analysis--probably one of the more<sup>comprehensive studies on</sup> the subject. On analyzing real time-series data for the<sup>period 1960-80, Ram</sup> found that 64 percent of his 115 countries--65<sup>percent of the developing</sup> countries and 62 percent of the developed countries--had<sup>a larger-than-one</sup> government expenditure elasticity of GDP. In<sup>comparison, the present study</sup> finds that 70 percent of the 71 country sample--77<sup>percent of the developing</sup> countries and 53 percent of the developed countries--show<sup>a larger than one</sup> value of the elasticity.

**21** There is, of course, a whole group of studie'<sup>that examine the</sup> relation ship on the basis of cross-sectional data but since one cannot incorporate changes in relative prices in cross-sectional analyses results from these studies cannot be considered comparable to<sup>what is presented here.</sup> Further none of these studies allows for a quadratic relationship<sup>between the</sup>
# **Table 4: Cross-Country Regressions with**<sup>**LegalOrisins and Leual Efficiency**</sup>

The dependent variable is the ratio of the<sup>numberof bankruptciesto the numberof firms.The</sup> regressions are estimated using ordinary<sup>leastsquareswithrobuststandarderrors.</sup><sup>_LN(GDPPC)_</sup> is the log of GDP Per Capita. _GGDP_ is the 1-year<sup>growthrateof realGDP.</sup><sup>_D_SYSTCRISIS_is a</sup> dummy equal to 1 in the case of a systemic<sup>bankcrisis(CaprioandKlingebiel,2000).</sup> _RINTEREST_ is real interest rates. _FRENCH,_<sup>_GERMAN,_and</sup><sup>_SCANDINAVIAN_aredummies</sup> indicating legal origin (La Porta, et al., 1998).<sup>_RULE OF L4 W_is anassessmentof the"efficiency</sup> and integrity" of the legal environment as<sup>measure by BusinesInternationalCorp (La Porta,et al.</sup> 1998 and Berkowitz, et al., 2000.) _CREDITOR_<sup>_RIGHTS_isthesumof dummiesidentifying</sup> Restrictive Reorganizations, No Automatic<sup>Stayon Assets,SecuredCreditorsPaidFirst,and</sup> Management Does Not stay in Reorganization<sup>(La Porta,et al.,1998 and Pistor,2000.) t-statistics</sup> are in parentheses, *, *, and *** indicate significance<sup>at 10%,5%, and1%respectively.</sup>

||(1)|**(2)**|**(3)**|**(4)**|**_(5)**|
|---|---|---|---|---|---|
||Le<br>**Ogalin**<br>**Ori**<br>_<br>_<br>_ _<br>_|gal<br>**RuleofLaw**<br>**gin**<br> <br>_<br>_<br>_ _<br>_<br>_ _<br>_<br>_ _|**~~~~~~Legal**<br>**Originand**<br>**~~~Ruleof**<br>**L aw**|**Creditor**<br>**Rights**|**Creditor**<br>**R**<br>**and**<br>**RuleofLaw**|
|_Constant_|3.1452***<br>(6.11)|-0.4398<br>(-0.55)|2.8068***<br>(4.53)|0.9798*<br>(1.76)<br>|-0.3225<br>(-0.43)<br>|
|_LN(GDPPC,,J_|0.0022***<br>(2.32)|0.0020<br>(1.57)<br>|0.0009<br>_(0.65)_<br>|0.0052***<br>(5.33)<br>|0.0018<br>(1.43)<br>|
|_GGDPI-I_<br>_GG_<br>,|-0.0990***<br>(4.32)|-0.0560**<br>(-1.93)|-0.0986***<br>(4.26)<br>|-0.0705***<br>(-2.59)<br>|-0.0558**<br>(-1.92)<br>|
|_D_SYSTCRlSlS,,_Z(0.19)|0.0666|0.0464<br>(0.11)|0.0657<br>(0.19)|0.0792<br>(0.18)|0.0620<br>(0.15)<br>|
|_RINTEREST,,_|-0.0108*<br>_(-1.61)_|0.0075<br>(0.74)|-0.0077<br>(-1.08)|-0.0060<br>(-0.72)|0.0064<br>(0.65)|
|_FRENCH_<br>_FRENVCH_<br>r|-~~2.352***<br>-.<br>5**-.33*<br>~~~~~(-12.64)||-2.3323***<br>(-11.95)|||
|_GERMAeN_|-2.3996***<br>(-12.48)||-2.2680***<br>(-10.16)|||
|_SCANDINAVUN_<br>_SCA N DIN_<br>_AVIAN _|_0._(01600***<br>~~(0.34)|<br>_ _<br>_<br>_<br>_<br>_|0.2032<br>(0.44)<br>|_<br>_<br>_<br>_<br>_||
|_TRANSIT_|-2.4683***<br>_(-5.62)_||-2.5908***<br>(-5.61)|||
|_CREDITORRIGHTS_<br>________|_____**________**<br>_<br>__||____________|-0.0414<br>(-0.54)|-0.0667<br>_(-0.85)_|
|_RULEOFLAW_|_RULE_|0.2533***<br>_OF LA W_<br>(2.89)|0.0725**<br>(2.16)||0.2584***<br>(2.81)|
|_YearDummies_|Yes|Yes|Yes|Yes|Yes|
|_Adj.R-Squared_|0.50|0.17|0.50|0.14|0.17|
|_Observations_|273|273|273|273|273|

30
measure IPV, though measures in relevant surveys, such as the Conflict Tactic Scale, do allow for the exploration of multiple levels of severity such as physical and psychological violence (Papageorge et al. 2019; Heath 2014; Heath, Hidrobo, and Roy 2020; Hidrobo and Fernald 2013). It appears that only one other paper explores the various sources of data on violence: Amaral’s study (2017) confirming that improved inheritance rights among women in India reduce self-reports of IPV in surveys, police reports of violence against women, and female mortality.

This study focuses on the most populous municipalities in Brazil because of data limitations and confirms that, in these populations, an increase in the gender wage ratio in favor of women is negatively associated with the most severe form of violence against women—female homicide. The impacts are more prominent among younger women (ages 15–29) and in municipalities with lower a human development index.

However, for lesser forms of violence captured in medical professionals’ reports of nonlethal abuse, context matters: the analysis finds that a reduction in the gender wage ratio triggers a decrease in reports only in municipalities in which women’s police stations are located, that is, police stations that specialize in crime against women and that are predominantly staffed by women police officers. The effect is opposite in municipalities that lack women’s police stations. It appears that, for less severe forms of violence, economic empowerment may trigger a backlash unless it is complemented by other programs with the direct objective of ensuring women’s safety.

# **Background: Measurement of IPV**

Empirically testing whether IPV increases or decreases with economic opportunity is not a trivial exercise. One of the limitations of collecting data on IPV through survey data is the likelihood of underreporting, which often happens because women fear stigma or retaliation or because they may normalize incidents of domestic violence in cultures where IPV is “acceptable.” Surveys that only include a module on violence result in a lower incidence of reported violence relative to surveys dedicated to violence, such as Demographic and Health Surveys, which allow more opportunities for women to reveal instances of victimization (UNSD 2014). These discrepancies suggest that underreporting leads to measurement error in many of the outcome sources that aim to capture the prevalence of IPV. Self-response technology through questionnaires can improve on this because they allow literate individuals to report violent incidents without fear of stigma from the enumerator. This is the case of the British Crime Survey (Anderberg et al. 2016). Nonetheless, Cullen (2020) finds that, in Nigeria and Rwanda, self-response technology continues to underreport by up to 50 percent compared with list experiments, which provide greater anonymity. Agüero and Frisancho (2017) find that, in Peru, underreporting is more likely to occur among specific groups of women, especially women with more 3
**Table 2: Demand for cement.** The instrument for price is the cement exports of the neighboring country in columns 1-4, and is the cement exports of all countries weighted by their inverse distance in column 5. The neighboring country is the one with the nearest geographic distance. All variables are in natural logs except for the indicator for whether the neighboring country is an importer, the indicator for whether the year is 2017, and the constant. Robust standard errors in parenthesis. * for _p < ._ 05, ** for _p < ._ 01, and *** for _p < ._ 001

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES|Quantity|Quantity|Quantity|Quantity|Quantity|
|Price of cement (USD)|-1.96**<br>(0.80)|-1.38***<br>(0.26)|-1.41***<br>(0.36)|-1.40***<br>(0.27)|-1.22***<br>(0.31)|
|Population||0.87***|0.88***|0.84***|0.88***|
|||(0.04)|(0.04)|(0.03)|(0.04)|
|GDP per capita||0.37***|0.33***|0.32***|0.38***|
|||(0.03)|(0.05)|(0.04)|(0.04)|
|Construction share of GDP||0.38***|0.45***|0.30***|0.39***|
|||(0.10)|(0.11)|(0.09)|(0.09)|
|Exchange rate (USD/LCU)|||0.02<br>(0.02)|||
|Price of diesel|||-0.10|||
||||(0.11)|||
|Price of aggregate|||-0.03<br>(0.12)|||
|GDP per capita of neighbor||||0.04||
|||||(0.04)||
|Year (=2017)|-0.34|-0.15|-0.17*|-0.15|-0.11|
||(0.31)|(0.09)|(0.10)|(0.09)|(0.10)|
|Constant|24.86***|5.44***|6.05**|5.85***|4.27*|
||(4.10)|(1.98)|(2.37)|(1.99)|(2.27)|
|Observations|166|166|158|155|166|
|R-squared|0.30|0.93|0.93|0.92|0.93|
|First Stage F-stat|29.23|30.65|21.10|30.24|11.63|
|Elasticity|1.957|1.384|1.415|1.395|1.219|

37
services are more expensive in low income countries 47. This is important because services compose a large share of government consumption in low income countries. Finally, to sharpen further the point estimate of price elasticity, we revisited the Summers-Heston data and excluded countries that Summers and Heston had given a low quality rating (quality D) and re-estimated the size regression of _Table 2_ using the remaining data. As expected, this reduced the estimate of elasticity to -0.578 (OLS) and - 0.697 (IV).

Turning to the growth estimations _(Table 12),_ we can again largely replicate results. The growth regression that uses World Bank data has a lower R-squared (0.5602 versus 0.6161) than the regression with constant 1985 international prices . For the IV regression, we again plug in the prediction from the size regression of _Table 11._ Lagged five-year averages are used as instruments for the other endogenous variables. The state variables generally yield similar signs and point estimates. In the case of the initial income or convergence term, the coefficient when using the World Bank values is smaller in absolute value, indicating a slower conditional convergence rate of 0.3 percentage points per year as opposed to 1.8 percentage points . The government size variable is negative and significant in both estimations. The quality of bureaucracy variable

> **47The** correlation coefficient between relative prices of government consumption and real per capita GDP is 0.62 when using Summers Heston data and -0.1 when using World Bank data; both are significant at I **percent level.**

> The dependent variable is different, but it is known that per capita growth from the two data sets are closer to each other than the level of per capita GDP (see _Table 5_ and Barro and Sala-i-Martin (1995) p. 444). Hence, the comparison of the R-squared is valid and is in fact equivalent to a non-nested J- type hypothesis test carried out by Barro and Sala-i-Martin (1995).

**49** See also Barro and Sala-i-Martin (1995).

52