rafmacalaba/datause-encoder-data huggingface dataset:
expenditures.<sup>4</sup> With soft money playing a bigger role in recent years, this proportion has increased on average. Among the models of special interest that have been advanced in political economy, the GH model yields the sharpest testable conclusions.

The Grossman-Helpman model is a small open economy model. A numeraire good is produced using only labor, which fixes wage. _n_ goods are produced using CRS technology with labor and sector-specific capital. Trade policy is quantified in the domestic price vector **p** . An import tariff or export subsidy on good _i_ raises _pi_ above the world market price π _i_ . Conversely, an import subsidy or export tax on good _i_ lowers _pi_ below the world market price π _i_ . Net government revenues are redistributed on a lump-sum basis. Individuals in the economy differ only in their ownership of sector-specific factors. Each individual owns specific capital in at most one sector, and the total supply of specific capital in any sector is inelastic. Hence, the reward to sector-specific input in good _i_ is increasing in _pi_ .

Government maximizes a weighted sum of welfare and lobbying contributions (free trade would be the efficient outcome if government maximized only welfare). Capital owners in some sectors _L_ organize into interest groups in order to attempt to influence government’s policy. Through lobbying contributions, the lobby representing sector _i_ aims to increase _pi_ , and/or decrease prices of other goods (since individual lobby members consume other goods, decreasing their price raises welfare of lobby members).<sup>5</sup> Lobby _i_ offers government contingent campaign contributions _Ci_ ( **p** ), conditional on the trade policy **p** that the government chooses. Grossman and Helpman model the lobbying game as a menu auction. After the lobbies submit their contingent contribution schedules, the government sets trade policy **p** . In a _pure_ menu auction, every lobby makes a menu of offers to the government (the menu may, and probably will, comprise zero contributions corresponding to subsets of the policy space), and government auctions policy to highest bidder. A pure menu auction would be the case if the government were interested purely in contributions and not on welfare. In the Grossman-Helpman model government maximizes a weighted sum of campaign contributions _C_ and gross welfare _W_ :

> 4  My own observation after perusing a number of congressional profiles between 1980 and 2000 comprehensively summarized by Congressional Quarterly in their “Politics in America” volumes.

> 5  Either that is the case or the output of other industries is used as intermediate inputs by industry _i_ , or both.

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# **Can a Market-Assisted Land Redistribution Program Improve the Lives of the Poor?** **_Evidence from Malawi_**<sup>**_1_**</sup>

Gayatri Datar Ximena Del Carpio Vivian Hoffman JEL Codes: D12, D24, O12, O18, O54, Q12, Q15 Keywords: Rural development, Productivity, Land consolidation, Impact evaluation, Malawi

> 1 We thank Julia Behrman, Brian Blankespoor, Peter Fleming, Irene Jillson, Niall Kelleher and Pamela-Velez-Vega for inputs and support.  We recognize great efforts from the World Bank country team who planned and implemented the project, and the field research team who collected the data.  We are indebted to World Bank reviewers and colleagues who commented on previous versions of the paper, especially Javier Baez, Kathleen Beegle, Norman Loayza, Elisabeth Sadoulet, Mark Sundberg, Hardwich Tchale and Andrew Warner.  Lastly, we thank the Swiss Agency for Development and Cooperation (SDC) and the World Bank Independent Evaluation Group for financial support.  All errors and omissions are our own.  Correspondence authors are  xdelcarpio@worldbank.org and <u>vhoffmann@arec.umd.edu</u>
4 in 2013. Since then, shortages of food and basic necessities became common, and looting began to occur systematically throughout the country (Revista Semana, 2017). Moreover, insecurity became endemic, repression of the opposition became common, and systematic human rights violations by public authorities were repeatedly reported by the international media (see El Nuevo Herald, 2014; BBC News, 2017; BBC, 2016). External factors such as plummeting oil prices since 2014 and international sanctions on Venezuela, have exacerbated the crisis.<sup>5</sup> This situation triggered large waves of out-migration by Venezuelans, who most often moved to neighboring Colombia. According to the official statistics, since that start of the humanitarian crisis caused by Chavez’ and Maduro’s regimes, about 7.7 million Venezuelans have left their country. Of these, about 2.5 million have settled in Colombia.

Initially, Venezuelan migrants consisted mainly of wealthy Venezuelans and entrepreneurs who came to invest in Colombia and fled to save their capital from expropriations and from high inflation (Revista Semana, 2017; Crasto and Alvarez<sup> ́</sup> , 2017). As the crisis intensified, however, the core of Venezuelan migration shifted to the less educated population, who report fleeing to escape violent crime, political repression, and to look for basic necessities for survival (NPR, 2018). Indeed, according to recent characterizations of Venezuelan migrants based on the Colombian household surveys of 2015 and 2016, over 80% of registered migrants have not completed a high school education, at least half are 25 years old or less, and they are balanced in terms of gender (see OLR, 2017).

> 5Following the repression of protests by the Maduro administration, several countries led by the U.S. and the European Union established in 2017 sanctions to individuals associated with Maduro, including politicians, military personnel and private citizens. The sanctions were soon extended to include private companies and, in 2019, entire industries associated with mining and banking activities. The United Nations High Commissioner for Human Rights has warned that sanctions could have worsened the precarious situation of Venezuelans.
Figure 7: Rolling Correlations of Soybean Oil Nominal Price Returns - 24-Month Window <!-- Start of picture text --> Crude oil Coal Natural gas<br>Barley Maize Sorghum<br>Rice Wheat Sugar<br>1970m1 1980m1 1990m1 2000m1 2010m1 1970m1 1980m1 1990m1 2000m1 2010m1<br>Coffee<br>1970m1 1980m1 1990m1 2000m1 2010m1<br>1<br>0<br>-1<br>1<br>0<br>-1<br>1<br>0<br>-1<br>1<br>0<br>-1<br><!-- End of picture text --> � Note. This figure reports a graphical representation of the evolution of<sup>�</sup> _ρxz,w,t_ – i.e., the rolling correlation between the nominal price returns of commodities _x_ (soybean oil) and _z_ , where _w_ is the length of the window used to compute the rolling correlations ( _w_ = 24 months, in this case). 95% confidence bands are also plotted.

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3 the classroom. The teacher’s decision to retain or promote a child could be based on factors other than academic performance, including policies on automatic promotion, student deportment, and parental preferences. This leads to a decomposition of promotions into two components – one based on observed academic merit and the other based on factors other than merit. We then examine how merit-based and non-merit-based promotions are related to continued enrollment versus student dropout.

Our analysis uses a unique data set collected in the NWFP in 1994. At that time, the NWFP Education Management Information System (NEMIS) was first exploring the use of a student assessment system. Prior to that time, there had not been any formal means by which parents or teachers could compare student performance across schools or even across children within a school. Despite this lack of prior knowledge of relative student learning, we find that teacher promotion decisions in primary schools are based primarily on student attendance and on student skills as measured by performance on tests of mathematics and language. The relationship to test scores is particularly interesting in that the teachers never saw the students' performance on the tests, and so they must know which children have mastered the material even without aid of formal assessments. Even more striking, largely illiterate parents appear to base their decisions of whether to send the child to school for another year largely on merit-based promotions. Promotions that are not correlated with measured student cognitive attainments have a much smaller positive impact on the probability of school continuation. This finding implies that parents make their decisions regarding a child’s continued schooling on the basis of perceived learning in the previous year, rather than on promotion or repetition _per se_ . It would also suggest that if a child's ability to learn in future years is reduced by being placed in a grade for which the child is unprepared, then promotion could lead to increased dropout.

Increasingly, researchers and development agencies have been exploring school-based management reforms and increased parental involvement as mechanisms for improving school
Setting time limitations on provision of specific taxpayer services help to ensure that the time to comply with the tax regime is kept within limits. It also helps keep tax corruption in check as delays in taxpayer services – such as, taxpayer registration, or issuance of tax refunds – can be monitored and corrupt practices identified and checked. An effective and efficient tax dispute resolution mechanism increases taxpayer confidence in the objectivity of the tax system and helps reduce the time to comply with the tax system by resolving disputes quickly.

Some of the measures described above need legislative amendments, changes in tax laws, but most of them are in the nature of improvements in the administration of the tax regime, and hence are easier to carry out. Experience shows that changes in tax laws, especially those aimed at reducing tax rates and getting rid of tax exemptions, are difficult and time consuming to make as they may negatively impact the economic interests of some taxpayers. On the contrary, several of the measures described above are in the nature of “win-win” propositions that improve the efficiency of the tax administration and impact positively all taxpayers. These measures do not need long drawn out legislative procedures. They are relatively simple ways to simplify tax regimes and reduce tax corruption.

# **5. Conclusion**

This study tries to construct an empirical link between tax simplification and tax corruption in tax administrations. The measure of tax corruption and the two alternative measures of tax complexity (the time to comply with tax requirements and the number of tax payments) are calculated using the World Bank’s databases. The study includes 104 countries from different regions and income groups and covers the period of 2002-2012. After identifying the statistically significant determinants of tax corruption, experiments are run to understand the economic significance of tax simplification in this process. The regression findings support the existence of a strong link between tax corruption and the indicators of tax complexity. The link is both statistically and economically significant. The tax complexity indicators are robust to the inclusion of a different set of country-level variables. It is predicted that a 10 percent drop in TAXTIME leads to an approximately 4 percent decline in tax corruption, while the same about 42
growing developing economies have experienced higher (or lower) rates of increase in the extent of polarization by this measure.

However, there is a strong positive correlation between the initial level of mean consumption and the subsequent change in the FW index; Figure 8 plots the annualized proportionate change in the index against the log of the initial mean.<sup>22</sup> The correlation is just as strong with the initial $2 a day poverty rate (r=-0.33), and the turning point is at a poverty rate of 24.83% (s.e.=8.78%). So there is evidence of a middle-income bulge relative to the median emerging in the poorer countries within the developing world.

Table 5 brings the above observations together in the form of regressions for the growth rates (annualized log differences) in the two measures used above; the regressors are the growth rate and the initial mean, using both survey means and household consumption per capita from the NAS. The growth effects on the bulge in the population shares in the ($2, $13) interval are evident for both surveys means and NAS data, though smaller for the latter. Note that the effect of the initial mean has opposite effects on the two dependent variables. Given that the regression includes the growth rate, the coefficient on the initial mean can be interpreted as a distributional effect. So the finding that the distributional effects in poorer countries tended to generate greater bulge around the median is not robust to the choice of that measure in that focusing on the income space above $2 a day gives the opposite conclusion. This sensitivity to the choice of measure is probably not too surprising given that the FW index is centered on the median while the bulk of the shift in density relative to the distribution-neutral counterfactual is to a region somewhat above the median. In fact, on controlling for the growth rate, I find no significant partial correlation between the two measures (a t-statistic of 0.78 using the growth rate in the survey mean and 1.34 using the growth rate for NAS consumption).

# **4. Does a larger middle class help assure pro-poor growth?**

Looking back over time, the countries with a larger middle-class tended to attain higher subsequent rates of poverty reduction from a given rate of growth. Regressing the rates of poverty reduction ( _gi_ (<sup>_F_</sup> _it_ (2)) ) on both the rate of growth (<sup>_g_</sup> _i_ (<sup>μ</sup> _it_ ) ) and its interaction with either the absolute share of the population in the middle class ( _Ait_ −τ ) or the log relative share ( ln _Rit_ −τ ) I

> 22  Figure 7 uses the survey means. The correlation is also found using private consumption per capita from the national accounts (r=0.31; prob.=0.006). The correlation is also robust to adding a regression control for the initial level of the polarization index.

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**Figure 9. Agriculture-dependent countries, share of wealth 2014 and TFP growth 19962014**

<!-- Start of picture text --> Guinea Agriculture share of total wealth 55.2% TFP growth with natural resource ‐0.8%<br>TFP growth excluding natural resource ‐2.1%<br>Guinea: Total Factor Productivity<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐2.00%<br>‐4.00%<br>‐6.00%<br>TFP, with natural resources TFP, excluding natural resources<br>.<br>Centre‐Afrique Agriculture share of total wealth 44.0% TFP growth with natural resource ‐0.8%<br>TFP growth excluding natural resource ‐0.8%<br><!-- End of picture text --> <!-- Start of picture text --> Central African Republic: Total Factor Productivity<br>10.00%<br>5.00%<br>0.00%<br>‐5.00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐10.00%<br>‐15.00%<br>‐20.00%<br>‐25.00%<br>‐30.00%<br>‐35.00%<br>‐40.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> 21
as a rapid assessment by the United Nations Office for the Coordination of Humanitarian Affairs. Our identifying assumption is that if we take these into account, we can identify locations that were equally likely to have received a deployment base. Moreover, identification depends on the fact that bases are not moved after they are set up initially. This stickiness is in fact evident in the deployment maps.<sup>12</sup> We implement this identification strategy using coarsened exact matching (Iacus et al, 2009) and then regression adjustment on the matched data to reduce residual bias (Rubin and Thomas, 2000). We matched communities on covariates understood to have been important in the UN’s decisions to locate bases along with additional covariates that help to account for, e.g., variation in ethnicity:<sup>13</sup>

- Proximity to the major road network, set as a binary covariate as “beside road” or not.

- Wartime conflict exposure, set as a three level covariate for low, medium, or high.

- Number of households, set as a three level covariate for small, medium, or large.

- Number of schools per household, set as a three level covariate for none, few, or many.

- Number of health posts per household, set as a two level covariate for none or some.

- Whether the community is in the coastal region, set as a binary covariate.

- Whether the community is in the Kru speaking region, set as a binary covariate.

> 12These were contained in the multi-annual United Nations Reports of the Secretary-General, available at http://www.un.org/en/peacekeeping/missions/unmil/reports.shtml.

13The ethnicity, region, and road network measures were taken from pre-deployment era public domain maps posted to the online University of Texas Perry-Casteneda Library Map Collection. The conflict exposure data were pulled from the Peace Research Institute of Oslo’s Armed Conflict and Event Location Date file (2009 release). The population and social infrastructure data were drawn from a data file produced as part of a rapid baseline assessment conducted by the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) in early 2004. This was early enough in the deployment period that population figures and infrastructure could not yet have been affected by deployments. These data were consulted by UNMIL itself in assessing baseline conditions, making them quite appropriate for the purposes of this study.

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areas, highlighting the limited degree of urbanization in the developing country sample analyzed.

**Table 5: Logistic Regression: Under-5 Child Mortality**

||**Odds Ratio**<br>**Water and Sanitation Infrastructu**|**Std. Err.**<br>**re**|**P>z**|
|---|---|---|---|
|Open/uncovered defecation|_Reference group_|||
|Improved sanitation|0.921|0.038|0.044|
|Private flush toilet|0.842|0.052|0.006|
|Open/unprotected water|_Reference group_|||
|Improved water|0.954|0.031|0.156|
|Private piped connection|0.842|0.047|0.002|
||**Child Characteristics**|||
|First born child|0.961|0.047|0.412|
|Birth spacing before|0.996|0.001|0.000|
|Birth spacing after|0.954|0.005|0.000|
|Female|0.897|0.024|0.000|
|Age 0-11 months|_Reference group_|||
|Age 12-23 months|0.467|0.016|0.000|
|Age 24-35 months|0.191|0.009|0.000|
|Age 36-47 months|0.070|0.005|0.000|
|Age 48-59 months|0.033|0.003|0.000|
||**Mother's Characteristics**|||
|Age mother|0.934|0.014|0.000|
|Age mother squared|1.002|0.000|0.000|
|Mother primary education|0.893|0.033|0.002|
|Mother secondary educ.|0.670|0.033|0.000|
|Mother tertiary education|0.410|0.053|0.000|
|Currently married|0.757|0.030|0.000|
||**Household Characteristics**|||
|Household size|0.907|0.006|0.000|
|Electricity|0.878|0.044|0.010|
|Radio|1.120|0.034|0.000|
|TV|0.946|0.043|0.228|
|Bike|1.050|0.033|0.123|
|Urban residence|0.973|0.038|0.487|

_Notes:_ Based on a sample of 306642 observations. Empirical model includes survey fixed effects as described in equation (1).

Table 5 shows the estimation results based on equation (1) – the estimated coefficients are presented as odds ratios. Relative to households with unimproved water and sanitation, improved sanitation reduces the odds of dying by 7.9%;<sup>15</sup> the effect of a private flush toilet is

> 15 The odds ratio of 0.921 corresponds to the odds of dying below the age of 5 in any given year with improved sanitation divided by the odds of dying without improved sanitation. Improved sanitation hence decreases the

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relationships with councilors – for instance, how willing and/or cooperative they feel chiefs are in sharing authority. Additionally, social network data documenting relationships among councilors, ruling families, and chiefs could illuminate the extent of influence each group has among these sets of actors. Finally, the evidence we have presented here has revealed a series of quite strong findings that all support the same general theory – but this research would also benefit from more extensive qualitative as well as potential experimental approaches to better test for specific causal mechanisms related to inter-elite power-sharing in newly decentralized contexts - both in Sierra Leone and elsewhere.

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