rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 11060

# **Abstract**

This paper investigates gender disparities in residential property ownership and tax compliance in a large Argentine municipality using detailed tax administrative data. While ownership is evenly distributed between women, men, and co-owned properties up to the 40th percentile of the value distribution, higher-value properties exhibit significant gender disparities, with women’s share dropping to less than 20\% in the top 1\%. Tax compliance increases with property value, with an average evasion rate of 46\%, and men and women are equally likely to meet their tax obligations across the distribution. However, women face slightly higher effective tax rates due to owning lower-value properties, which are disproportionately affected by a mildly regressive tax schedule. Gender responses to enforcement measures are also comparable. A soft randomized communication campaign significantly increased timely payments equally for both men and women, with men responding more quickly. Similarly, the findings show no gender-based differences in responses to macroeconomic shocks such as COVID-19. The study underscores the role of property tax in promoting equitable revenue mobilization and highlights the importance of gender-disaggregated data for informing tax policy and enforcement strategies.

This paper is a product of the Development Research Group, Development Economic. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dtortarolo@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 10381

# **Abstract**

The paper presents a first global investigation of the longer-term inequality implications of COVID-19 by examining the effect of school closures on the ability of children from different countries and backgrounds to engage in continued learning throughout the pandemic, and their implications for intergenerational mobility in education. The analysis builds on the data from the Global Database of Intergenerational Mobility, country-specific results of the learning loss simulation model using weekly school closure information from February 2020 to February 2022, and high-frequency phone survey data collected by the World Bank during the pandemic to assess the incidence and quality of continued learning during periods of school closures across children from different backgrounds. Based on this information, the paper simulates counterfactual levels of educational attainment and corresponding absolute and relative intergenerational educational mobility measures with and without COVID-19 impacts, to arrive at estimates of COVID-19 impacts. The simulations suggest that the extensive school closures and associated learning losses are likely to have a significant impact on both absolute and relative intergenerational educational mobility in the absence of remedial measures. In upper-middle-income countries, the share of children with more years of education than their parents (absolute mobility) could decline by 8 percentage points, with the largest impacts observed in the Latin America region. Furthermore, unequal access to continued learning during school closures across children from households of different socioeconomic backgrounds (proxied by parental education levels) leads to a significant decline in relative educational mobility.

This paper is a product of the Poverty and Equity Global Practice and the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at acojocaru@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Female Labor Force Participation Trends|1|
|3.|The Determinants<br>of Female Participation|6|
|4.|Female-Male<br>Pay Differentials|10|
|5.|Decomposing<br>the Pay Differential|12|
|6.|Contribution of Specific Variables to the Decomposition|18|
|7.|Summary of Findings and Policy Options|21|
|8.|Concluding Remarks|27|
|Refe|rences|28|
<!-- Start of picture text --> 70%<br>2 60% ies<br>3<br>i 50%<br>= 0%<br>£<br>‘5 320%<br>5 20%<br>20% 16%<br>10%<br>0%<br>@ Agricultural products = Food, Tobacco, Beverages<br>= Raw materials = Manufactures<br><!-- End of picture text -->
performance of East Forest farm households is mirrored in the outcomes for food crop farmer households. Food crop farmers in the East Forest had a 19 percent decrease in farm income compared to the 52 percent drop in the West Forest. While the household expenditure data are more or less in line with the farm income data, they nonetheless beg the question of why West Forest farmers fared so much worse than East Forest farmers during this period.

Table 4 Farm Income of Farmers Across Rural Regions _(in 1985 CFAFperyear)_

||_Percentagze charge, 1985-1988_|
|---|---|
|_Region_|_Export crop farmer_<br>_Food crop farmer_|
|East Forest|-17.9<br>-3.5|
|West Forest|-33.6<br>-51.6|
|Savannah|-42.1<br>-18.6|

Source: Grootaert (1995). Redeflated with the INS deflators.

Cocoa production data from other sources are at some variance with the household survey results for West Forest.8 The cocoa production data, put together by Agricultural Price Stabilization Fund (CSSPA), are broken down by administrative department and reflect the estimation of cocoa production based on cocoa sales (table 5). The survey data show that sales increased by roughly 20 percent in the East Forest if we compare 1985 with 1987 or 1988 and decreased by roughly 40 percent in the West Forest. Trends in the survey data are roughly consistent with the CSSPA data for East Forest in that both show an increase in the 1986/87, 1987/88 and 1988/89 campaigns compared to 1984/85. The strong increase in the sales per household evident in the 1988 East Forest survey data is 1985 and 1988, and by 28 percent in the West Forest. Coffee sales declined by 7 percent in the East Forest and by 47 percent in the West Forest over the same period.

The CILSS surveys were fielded over the following periods: 1985: February 1985 to Januaiy 1986 1986: February 1986 to January 1987

- 1987: March 1987 to February 1988

1988: May 1988 to April 1989.

Most of the cocoa and coffee crop is purchased between November and March. Thus it is likely that the survey years of 1985 and 1986 will correspond to the cocoa and coffee marketing campaign years of 1984/85 and 1985/86. For the 1987 survey, some farmers will be reporting on the 1986/87 harvest, while others will be reporting on the 1987/88 harvest. And for the 1988 survey, some households will be reporting on the 1987/88 campaign year, while others will be reporting on the 1988/89 campaign year.

6
the country’s population. However, this principle does not always hold in practice and consequently the lower houses in many countries are malapportioned.

The paper first provides a political economy rationale for the emergence and persistence of legislative malapportionment. We base this rationale on the argument that, at the time of transition to democracy, groups that held political power during the preceding dictatorship<sup>2</sup> may have strong incentives to manipulate the newly established political institutions in order to protect their political and economic interests.<sup>3</sup> We claim that legislative malapportionment provides these groups with a way of enhancing their _de jure_ power in democracies by overrepresenting certain geographic areas and by favoring certain political parties versus others. This skewed political representation survives in equilibrium as long as it makes democratic consolidation more likely. At the same time, it is associated with lower political competition and distorts public policies, which also helps to preserve the power of the pre-democratic elite.

We then test this theoretical argument using data from Latin America<sup>4</sup> . In contrast to other features of political institutions, such as patronage, corruption or lobbying, malapportionment is clearly defined and measurable, allowing us to test the predictions of our argument empirically. We first rely on within country data to examine the political tendencies of electoral districts that are overrepresented in the sense that they have a higher share of representatives in the lower house than their population share. Consistent with our theoretical argument, we show that in the first election after transition to democracy, overrepresented districts are more likely to vote for parties that are close to pre-democracy ruling groups. As an additional check, we also provide evidence showing that overrepresented districts were more likely to support dictatorships in elections held in pre-democratic times.

We then use panel data for eleven Latin American countries, covering the late XIX century to the present, to show that higher legislative malapportionment makes democratic

> 2 In this paper we use the term dictatorship to refer to any non-democratic regime.

> 3 This argument is closely related to the models developed in Acemoglu and Johnson (2006, 2008) where the elite manipulates institutions to increase their political power after transition to democracy.

> 4 We chose to limit the analysis to Latin America for several reasons. First, a long time series of data on legislative malapportionment is available for these countries. Second, the history of Latin American countries is characterized by many episodes of transition from dictatorship to democracy (and vice versa), providing variation for our empirical analysis. Finally, the relatively high degree of historical and institutional homogeneity in this sample of countries allows us to better isolate the relationship between legislative malapportionment and other political and economic outcomes.

2
15 The bi-variate and multi-variate analyses of voucher eligibility have given a good description of who is more likely to receive a voucher and the extent to which this is correlated with the targeting criteria either derived from economic theory or those given by the guidelines. To enable a more explicit analysis of targeting performance, an explicit targeting metric is introduced in the next section, the targeting differential, which is subsequently used to explore the different factors that affect the targeting performance across and within villages.

# **3 Targeting Performance**

_Methodological Considerations_ Following Galasso and Ravallion (2005), three measures of targeting are defined: where _H_ is the proportion of all households in the targeted group, _s11_ is the proportion of all households in the targeted group that receive the program, _s12_ is the proportion of all households who are not in the targeted group that receive the program. _G_<sup>_p_</sup> and _G_<sup>_n_</sup> therefore measure the proportions of households in the targeted group that receive the program and the proportion of households not in the targeted group that receive the program respectively. The difference between the two measures is the targeting differential, T.

If the program is perfectly targeted to the ones in the targeted group, the untargeted group receives none, and the targeted group is completely covered, then _T=1_ ; if the program only, but fully, reaches the non-targeted group, then _T=-1_ ; a uniform allocation (no targeting) implies _T=0_ . If proportionately more of the targeted than the untargeted group is reached, then 1>T>0; if the program reaches proportionately more of the untargeted than the targeted group, then 0> _T>-1_ .
∆FDI_export_L is calculated similarly for all low export intensity industries. Table 11 reports the heterogenous effects by export intensity of FDI. Quite intuitively, only export oriented FDI inflows have a significant impact on drawing labor out of agriculture into industry and services. While export oriented FDI plays a dominant role in moving people from farms to factories, domestic market-seeking FDI has a greater positive effect on college enrollment. This is also perfectly reasonable as export-oriented FDI is concentrated in labor-intensive industries including textile, apparel, leather, furniture and electronics. The primary goal of export-oriented FDI in China is to reduce cost and demand for skills is relatively low. In fact, the average share of employees with at least a college degree is 9.9 percent in domestic market-seeking sectors, significantly higher than the 6.6 percent in export-oriented sectors. Therefore, it is not surprising that domestic market-seeking FDI is more conducive to college enrollment.

Table 11. Heterogeneity: Export intensity of FDI shock

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||Panel A<br>||Panel<br>|B<br>|
|Dependent var|∆agri_emps|∆indu_emps|∆serv_emps|∆Highschool|∆College|
|∆FDI_export_H|-3.538***|2.038***|1.215*|1.342***|0.536**|
||(0.993)|(0.737)|(0.647)|(0.405)|(0.254)|
|∆FDI_export_L|-1.172|-0.180|0.685|1.147**|0.570**|
||(1.153)|(0.819)|(0.548)|(0.544)|(0.229)|
|Observations|609|609|609|609|609|
|R-squared|0.558|0.581|0.494|0.818|0.735|
|Prov-Year FE|Y|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|Y|
|Excludingmigrants|Y|Y|Y|Y|Y|

Note: This table shows the heterogenous effects of FDI on city level structural transformation and school enrollment by export intensity of FDI. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 6.2 FDI shock by skill intensity

Li (2018) shows that high-skill export shocks raise both high school and college enrollments, while low-skill export shocks depress both. By the same token, the effect of FDI shock could also vary by the skill intensity associated with FDI. We construct high-skill FDI shock and low-skill FDI shock in a similar way as high export intensity and low export intensity shocks. We first calculate the share of employees with at least a college degree in each 3-digit manufacturing industry based on the 2000 population census. We then divide industries into high-skill and low-skill based on whether the college employee share is above the median of all manufacturing industries. Next, we calculate high-skill and low-skill FDI shocks as the interaction between each city’s initial industry employment share and predicted national FDI employment share changes at the industry level for their respective group of industries: 28
capitalization of improved water supply, we show that total benefits that include direct effects and neighborhood multipliers are considerably higher than estimates from non-spatial estimation. For Bangalore and Bhopal, the spatial estimates exceed standard MWTP estimates by 23 percent and 16 percent, respectively. Although we cannot isolate the specific underlying process by which neighbors’ quality of access affects each other, there are a number of possibilities. Generally, real estate markets tend to factor in neighborhood quality, so upgrading of a dwelling unit or maintenance of yards on a block has an effect on the value of all houses on the block. High quality water supply and sanitation also has considerable health benefits. One could thus interpret these neighborhood effects as specific health externalities as neighbors’ improved living conditions reduce the risk of communicable diseases. Beyond these more speculative conclusions, the policy implications of these results are clear. By looking at individual or private benefits only, we may underestimate the overall social welfare from investing in service supply especially among the poorest residents in developing country cities. In decision making under strict efficiency rules, this may lead to an underinvestment in critical infrastructure.

Besides presenting estimates of MWTP, we also report on a number of policy simulations that show how benefit estimates can be derived for each household on the basis of its actual (rather than mean) characteristics. The resulting information can be mapped geographically which informs prioritization and sequencing of investment decisions. This approach results in a flexible framework in which urban investment options can be evaluated. Using efficiency criteria, investments could be prioritized in areas where returns in the form of housing value increases (and thus user fee or tax increases) are highest. Introducing equity concerns would possibly alter the investment schedule to first target the poorest households where returns may be lower, but welfare benefits and positive health spillovers may be highest.

# **IX. References**

Aitchinson, J., and Brown, A.A. (1957). _The Lognormal Distribution_ . Cambridge: Cambridge University Press.

Anselin, L. (1988). _Spatial Econometrics: Methods and Models_ . Kluwer Academic Publishers, Dordrecht, The Netherlands.

Anselin, L. (2001). Rao’s score test in spatial econometrics. _Journal of Statistical Planning and_
Table 6: Impact of anticipated vs. unanticipated price shocks (first-stage regression on price expectation)

||(1)|
|---|---|
|VARIABLES|d_price|
|d_price_t-1|-1.137***|
||(0.257)|
|TR_t-1|-0.0106***|
||(0.00206)|
|AR_t-1|0.642*|
||(0.374)|
|SD_t-1|-0.000631***|
||(0.000170)|
|Share of rice in 07|0.249**|
||(0.101)|
|Share of maize in 07|0.777***|
||(0.247)|
|Share of estate crop in 07|-0.492***|
||(0.0713)|
|Share of horticulture crop in 07|-0.465***|
||(0.0970)|
|Average age|-0.00116|
||(0.000849)|
|Average years of schooling|0.00275|
||(0.00196)|
|Househouse size|-0.00378*|
||(0.00194)|
|Yrs of schooling x Share of maize|-0.0297|
||(0.0214)|
|Yrs of schooling x Share of cassava|-0.00923|
||(0.00955)|
|Yrs of schooling x Share of estate crop|-0.00956**|
||(0.00381)|
|Yrs of schooling x Share of horticulture crop|0.00224|
||(0.00444)|
|Constant|0.632**|
||(0.296)|
|Observations|737|
|Provincial dummies included but not reported|YES|
|R-squared|0.843|
|*** p<0.01, ** p<0.05, * p<0.1||

Standard errors are corrected by two-step bootstrap estimator with 2,000 replications, and are reported in the parentheses.

Farmers with fixed price arrangement ( _Tebasan_ and _Ijon_ ) with traders are excluded from the sample.

33
# **Migration of Dirty Sectors Within China?**

In the previous section, we established that trade reforms have not had a 'dirty-sector' bias during the past decade. However, it is at least possible that internal differences in regulatory strictness could have induced relocation of dirty industry within China. Despite the evident trend toward equalization of the effective levy in the eastern provinces, Figure 8 includes differences as great as 3:1 in levy rates. Lucas (1996) has recently investigated whether such differences have induced faster growth of pollutionintensive sectors in provinces with laxer regulation. His results suggest that differential levies are not significant as location factors, so there has apparently been no regulationinduced migration of dirty sectors within China. 12

# **Economic Reform and The Cost of Abatement**

<!-- Start of picture text --> (20a)<br>Pariculate Marginal Abatement Costs. Large Plants<br>70 .<br>s0o/<br>I i<br>20 -<br>10<br>o , O : :<br>R e of AbY ...<br><!-- End of picture text --> In recent econometric research, we have used plant-level data from NEPA to develop detailed estimates of abatement costs for air and water emissions by Chinese industry (Appendix; Dasgupta, Huq, Wheeler and Zhang, 1996). Our results show that marginal abatement costs (MAC) vary greatly by sector, scale, abatement rate, ownership, and pollutant. The impact of these variables on marginal cost is dramatically illustrated by a few comparisons. Holding other factors constant, MAC ratios can vary as much as 20:1 between large and small facilities; 45:1 across sectors; 13:1 between 10% and 90% abatement; 5:1 between state-owned plants (SOE's) and non-SOE's; and 4:1 across pollutants. The range of potential variation is revealed by the comparative MAC schedules in Figure 20. In the case of particulates (20(a)-(b)), MAC for large, nonSOE plants is barely above $10/ton at 90% abatement. By contrast, large SOE facilities reach the same MAC at 40% abatement and

> **Figure 20: Marginal** Abatement Costs for **Chinese Industry**

<!-- Start of picture text --> (20b)<br>Partculate Marginal Abatement Costs Small Plants<br>1400 .<br>12D0-<br>XsINSOE<br>/<br>400<br>200<br>0 . t. -___4>- _ _*-- ___ __<br>MaOe. t 0 0 a 0<br><!-- End of picture text --> increase to $70/ton at 90%. For small plants, MAC's for particulates increase by an order of magnitude in both cases: to over $200/ton for non-SOE's and $1,400/ton for SOE's.

> 12 See Lucas (1996).

16