rafmacalaba/datause-encoder-data huggingface dataset:
Table S.25: Impacts on children’s educational achievement - Islam studies: complementarity <u>(OLS)</u>

|Dependent variable:||I|slam studies: G|PA z-score|s||
|---|---|---|---|---|---|---|
|_X_:|No. of||School|-level ratio|of||
||students<br>in each<br>school-<br>grade|no. of<br>teachers<br>to<br>no. of<br>students|no. of<br>blackboards<br>to<br>no. of<br>students|no. of<br>benches<br>to<br>no. of<br>students|no. of<br>fans<br>to<br>no. of<br>students|no. of<br>toilets<br>to<br>no. of<br>students|
||(a)|(b)|(c)|(d)|(e)|(f)|
|**December in 2013**|||||||
|All products|0.020|-0.244|0.034|-0.112|0.021|0.016|
||(0.203)|(0.342)|(0.330)|(0.361)|(0.087)|(0.111)|
|One product|0.419**|0.264|0.201|0.315|0.051|-0.143|
||(0.166)|(0.338)|(0.225)|(0.335)|(0.078)|(0.130)|
|All products _× X_|0.001|12.312|1.021|0.381|7.859**|9.782|
||(0.003)|(12.422)|(14.292)|(0.750)|(3.196)|(28.159)|
|One product _× X_|-0.008**|-10.056|-9.442|-0.670|-6.260|43.198|
||(0.003)|(14.348)|(11.508)|(0.735)|(9.018)|(26.084)|
|No. of students|-0.008|-|-|-|-|-|
|in each school-grade|(0.006)||||||
|R-squared|0.267|0.260|0.258|0.259|0.261|0.260|
|No. of obs.|849|850|850|850|850|850|
||(g)|(h)|(i)|(j)|(k)|(l)|
|**December in 2014**|||||||
|All products|-0.289|0.282|0.084|-0.252|-0.033|-0.176|
||(0.210)|(0.346)|(0.249)|(0.291)|(0.095)|(0.162)|
|One product|-0.207|0.577|0.366|0.012|0.078|-0.231|
||(0.290)|(0.374)|(0.294)|(0.359)|(0.104)|(0.177)|
|All products _× X_|0.005|-14.241|-7.577|0.440|-6.024|30.325|
||(0.003)|(13.749)|(12.760)|(0.609)|(5.255)|(31.724)|
|One product _× X_|0.005|-22.343|-17.042|0.067|-8.751|71.661**|
||(0.005)|(14.165)|(15.323)|(0.816)|(9.306)|(34.321)|
|No. of students|0.001|-|-|-|-|-|
|in each school-grade|(0.004)||||||
|R-squared|0.110|0.111|0.109|0.108|0.109|0.114|
|No. of obs.|845|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

70
# **References:**

- Agarwala, Rina. 2013. _Informal labor, formal politics, and dignified discontent in India._ New York: Cambridge University Press.

- Alesina, Alberto, Paola Giuliano, and Nathan Nunn. 2013. “On the origins of gender roles: Women and the plough.” _The Quarterly Journal of Economics_ 128(2): 469-530.

- Barron, Patrick, Michael Woolcock, and Rachel Diprose. 2011. _Contesting Development: Participatory Projects and Local Conflict Dynamics in Indonesia_ . New Haven, Yale University Press

- Bourdieu, Pierre. 1977. _Outline of a Theory of Practice_ . Vol. 16. Cambridge University Press.

Butler, Judith. 2004. _Undoing Gender_ . Psychology Press.

- Corbridge, Stuart, Williams, Glyn, Srivastava, Manoj and Véron, René. 2005. _Seeing the State: Governance and Governmentality in India_ . Cambridge: Cambridge University Press.

- Datta, Upamanyu. 2015. “Socio-Economic Impacts of Jeevika: A Large-Scale Self-Help Group Project in Bihar, India.” _World Development_ 68: 1-18.

- Dworkin, Shari L., and Kim Blankenship. 2009. "Microfinance and HIV/AIDS prevention: assessing its promise and limitations." _AIDS and Behavior_ 13(3): 462-469.

- Fine, Gary A. 2001. “Enacting Norms: Mushrooming and the Culture of Expectations and Explanations.” Pp. 139-64 in Michael Hechter and Karl-Dieter Opp (editors), _Social Norms_ . New York: Russell Sage Foundation.

- Geertz, Clifford. 1963. _Peddlers and Princes: Social Development and Economic Change in Two Indonesian Towns._ University of Chicago Press.

- Giddens, Anthony. 1984. _The constitution of society: Outline of the theory of structuration_ . University of California Press.

Hechter, Michael, and Karl-Dieter Opp (editors). 2001. _Social norms_ . Russell Sage Foundation.

- Imbens, Guido W. and Rubin, Donald B. 2015. _Causal Inference for Statistics, Social and Biomedical Sciences: An Introduction_ . Cambridge University Press.

- Inglehart, Ronald, and Christian Welzel. 2005. _Modernization, Cultural Change, and Democracy: The Human Development Sequence._ New York: Cambridge University Press.
# Firm Heterogeneity and the Impact of Payroll Taxes<sup>_∗_</sup>

Anik ́o B ́ır ́o Originally published in the Policy Research Working Paper Series on December 2022. This version is updated on October 2025.

To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** payroll tax; tax incidence; firm heterogeneity **JEL codes:** H24; H32; J23; J31

> _∗_ B ́ır ́o: Centre for Economic and Regional Studies (biro.aniko@krtk.hu). Branyiczki: Central European University and T ́arki (branyiczki ~~r~~ eka@phd.ceu.edu). Lindner: University College London (a.lindner@ucl.ac.uk). M ́ark: Central European University (mark ~~l~~ ili@phd.ceu.edu). Prinz: World Bank (dprinz@worldbank.org). We thank Gallina Andronova Vincelette, Reena Badiani-Magnusson, Jesper Bagger, Arin Dube, Jon Gruber, David Card, Ana Rute Cardoso, Sandra Gain, Pat Kline, J ́anos K ̈oll ̋o, David Leite, Claire Leroy, David Phillips, Bal ́azs Reizer, Jesse Rothstein, Fabien Postel-Vinay, Raffaele Saggio, Johannes Spinnewijn, Lajos Szab ́o, Dario Tortarolo, Emmanuel Saez, Benjamin Schoefer, David Seim, Roula Yazigi, and seminar participants at Georgetown University, Ben-Gurion University, IFS, MKE, CERS, ParisLondon Public Economics Conference, ZEW, University of Chile, Mannheim-Bonn Workshop, Federal Reserve Bank of Dallas, KTI, Annual Mannheim Taxation Conference, EALE, University of Barcelona, IZA, Nova School of Business and Economics, Munich Center for the Economics of Aging, University of Oxford, CEU, Corvinus University, NTA, University of Copenhagen, LSE, ASSA, Stockholm School of Economics, RES, Ghent University, Tel Aviv University, Bocconi University, RIDGE, NBER Conference on Labor Market for Older Workers, NBER Summer Institute, Stockholm University, Bank of Portugal, CEPR, OFS, the IZA Workshop on Labor Market Institutions, and SSE Riga for helpful comments. Agoston Reguly provided<sup> ́</sup> excellent research assistance. B ́ır ́o, Branyiczki and M ́ark were supported by the “Lend ̈ulet” program of the Hungarian Academy of Sciences (grant number: LP2018-2/2018). Lindner and Prinz were supported by the ESRC Centre for the Microeconomic Analysis of Public Policy (grant number: ES/T014334/1). Lindner also acknowledges financial support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant number: 949995) and from the Economic and Social Research Council (grant number: ES/T008474/1). The administrative database used in this paper is a property of the National Health Insurance Fund Administration, the Central Administration of National Pension Insurance, the National Tax and Customs Administration, the National Employment Service, and the Educational Authority of Hungary. The data was processed by the Databank of the Centre for Economic and Regional Studies. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
data that captures information not only on all outcomes of interest, but also on beneficiary status at baseline. In order to have clear treatment and control groups, the aggregate shock must not affect the entire country in equal measure but have considerable variation. This variation, in turn, must not be correlated with unobservable household characteristics- as may be the case with economic crises. Natural disasters are thus a good candidate given that they usually show considerable variation in the degree to which households are exposed to them. Moreover, the precise geographical area of their impact can be considered quasi-random. This, unfortunately, raises the requirements on the data, which now need to have enough density to yield a sample with statistical power in a geographically limited area. This is a requirement that longitudinal surveys with their smaller sample sizes are unlikely to meet. In addition, the data need to be observable within a relatively short time window before and after the shock. Lastly, the data need to allow for the observation of either beneficiary status or eligibility to the program under study.

While this perfect data may not exist , there are several second-best options to address this question. Causal inference is still possible under the imposition of some plausible assumptions on the data generating process. The principal data sources used here are the 2012 and 2015 rounds of the Family Income and Expenditure Survey (FIES). The effects of the typhoon are thus evaluated roughly two years after the event. The FIES constitutes a representative stratified random sample of Filipino households (with the ex12
**Table 1:  Unweighted and weighted summary statistics for variables of interest**

||Unweigh<br>|ted||Weighted<br>|by popul|ation<br>|
|---|---|---|---|---|---|---|
|Variable|Number<br>of Obs.|Mean|Std.<br>Dev|Number<br>of Obs.|Mean|Std.<br>Dev|
|nr4ha|1,552|169.68|365.47|901,727|153.24|352.05|
|Precipitation_1<sup>st</sup>Quarter (mm)|1,552|-62.70|47.59|901,727|-67.04|45.31|
|Precipitation_2nd Quarter (mm)|1,552|60.47|58.88|901,727|60.42|57.95|
|Precipitation_3<sup>rd</sup>Quarter (mm)|1,552|29.13|76.81|901,727|32.23|76.17|
|Precipitation_4<sup>th</sup>Quarter (mm)|1,552|104.49|27.69|901,727|106.28|25.32|
|Average Annual Temperature (C)|1,552|26.03|2.41|901,727|26.05|2.28|
|Size of Household|1,552|4.66|1.66|901,727|4.59|1.67|
|Male head of household|1,552|0.88|0.33|901,727|0.88|0.33|
|Age of the Household head (years)|1,552|50.76|13.46|901,727|50.46|13.81|
|Years Education of the Household<br>head|1,395|7.07|3.27|822,028|7.25|3.26|
|Farms<br>practicing<br>rainfed<br>agriculture|1,552|0.60|0.49|901,727|0.57|0.50|
|Farms<br>practicing<br>rainfed<br>and<br>irrigated agriculture|1,552|0.06|0.23|901,727|0.05|0.22|
|Household has land tenure|1,552|0.64|0.48|901,727|0.64|0.48|
|Size of cropping area (HA)|1,552|1.54|3.29|901,727|1.41|2.11|
|Farm cultivating single crop|1,552|0.67|0.47|901,727|0.71|0.45|
|Sell produce at Local market|1,552|0.38|0.48|901,727|0.33|0.47|
|Sell produce at Urban market|1,552|0.08|0.27|901,727|0.08|0.27|
|Sell produce to Middleman|1,552|0.33|0.47|901,727|0.35|0.48|
|Receive Subsidy|1,552|0.02|0.13|901,727|0.01|0.12|
|Receive income from renting crop<br>land|1,552|0.03|0.18|901,727|0.04|0.19|
|Born in current location|1,552|0.71|0.45|901,727|0.71|0.46|

Notes: Authors’ estimation using SLIS 1999-2000 25
Let a<sup>c</sup> ht<sup>bethefractionoflandallocatedtocotton.A generalmodelofthesesharesis</sup> where z is a vector of regressors which includes district effects that affect selection into cotton production. For instance, we use the district dummies to capture access to market and local infrastructure that facilitates farmer participation in market cash agriculture. The function m allows regressors z and unobservables φ to affect the shares a non-linearly.

We begin by considering the simplest model with a linear functional form Estimation of (5) is straightforward, except for the fact that the share of land devoted to cotton is censored at zero. This means that OLS may be inconsistent. A simple solution is to implement a Tobit procedure. More generally, we explore a more semi-parametric estimation of (5) by using a CLAD (censored least absolute deviation) model. Notice that, provided the right specification for the model is used, consistency follows because the regressors z are exogenous to φ. This requires that fertility, family composition, or farm size do not depend on unobservables such as cotton-specific ability or land quality. Importantly, since we use data on all households to estimate (5), this equation does not suffer from a selection problem like the one we are attempting to control for in the productivity model.

The allocation of land to cotton depends on several factors that we need to account for. In particular, the selection into cotton depends on the reform. This means that we should include F<sup>1</sup> and F<sup>2</sup> in (5). Cotton choices depend on output and input prices, too. Unfortunately, we do not have information on prices at the farm level. To the extent that prices vary by time, or by region, however, we can account for them with year or regional dummies. In practice, we estimate a different model like (5) in each of the six years from 1997 to 2002 (notice that γt is indexed by t in (5)). This means that we will not be able to separate the effects of the reforms from the effects of changes in international prices on land 17
xciv DATA AND METHODS

# **REFERENCES**

- Arnold, Fred. 1989. Revised estimates and projections of international migration. Policy, Planning, and Research Working Papers, No. 275. Washington, D.C.: World Bank.

- Bourgeois-Pichat, Jean. 1978. Future outlook for mortality decline in the world. _Populatfon Bulletin of the United Nations._ 11:12-41.

- Bos, Eduard, and Rodolfo A. Bulatao. 1989. Projecting fertility for all countries. Policy, Planning, and Research Working Papers, WPS 500. Washington, D.C.: World Bank.

- Bos, Eduard, My T. Vu, and Patience W. Stephens. 1992. Sources of World Bank estimates of current mortality rates. Policy Research Working Papers, WPS 851. Wash;ngton, D.C.: World Bank.

- Bulatao, Rodolfo A., Eduard Bos, Patience W. Stephens and My T. Vu 1989. Projecting mortality for all countries. Policy, Planning, and Research Working Papers. Washington, D.C.: World Bank.

- Bulatao, Rodolfo A., Eduard Bos, Patience W. Stephens and My T. Vu 1990. _World Populaton Projections. 1989-90 EditionL_ Johns Hopkins University Press. Baltimore. Maryland.

- Bulatao, Rodolfo A., and Ann Elwan. 1985. Fertility and mortality transition: Patterns, projections, and interdependence. World Bank Staff Working Paper No. 681. Washington, D.C.

- Coale, Ansley J., and Guang Guo. 1989. Revised regional model life tables at very low levels of mortality. Unpublished paper. Office of Population Research, Princeton University.

- Eurostat. 1991. _Demographic Statistics._ Luxembourg: Statistical Offioe of the European Communities.

- Hill, Kenneth. 1990. PROJ3S - A Computer Program for Population Projections. Washington, D.C.: World Bank.

Manton, Kenneth G. 1986. Past and future life expectancy increases at later ages: Their implications for the linkage of chronic morbidity, disability and mortality. _Jounal of Gerontolfgy_ 41:672-681.

- Monnier, Alain and Catherine de Guibert-Lantoine. 1991. "La cornjoncture demographique: L'Europe et les pays d6veloppes d'outre-mer," _Population_ 46(4):943-961.

- United Nations. Department of Intemational Economic and Social Affairs. 1990. _World Population Prospects 1990._ New York: United Nations.

- United Nations. Department of Intemational Economic and Social Affairs. Various years. _Population and Vital Statitics Report._ New York: United Nations.
# **Introduction**

During the last decade, violence against women and girls, also referred to as “gender-based violence,” has gained international recognition as a grave social and human rights concern affecting virtually all societies.<sup>1</sup> Epidemiological research has demonstrated that genderbased violence is a major cause of ill health among women and girls, whose impact can be seen directly through death and disability due to injuries, as well as indirectly through increased vulnerability to a host of physical and mental health problems. Violence and the fear of violence severely limit women’s contribution to social and economic development, thereby hindering the achievement of important national and international development goals such as the Millennium Development Goals of eradicating extreme poverty and hunger and improving maternal and child health.

This working paper presents an overview of gender-based violence (GBV) in Latin America, with special emphasis on good practice interventions to prevent GBV or offer services to its survivors or perpetrators. Section 1 begins with definitions and magnitudes, a theoretical framework for understanding the risk factors that increase the likelihood of GBV, and the health consequences and socioeconomic costs generated by GBV. Section 2 identifies the challenges in identifying good practice responses to GBV and presents good practice interventions in the justice, health, education sectors, respectively. It also includes a description of multi-sectoral interventions that involve economic and social empowerment of women, improved access to services for survivors of GBV, and the transformation of community norms about GBV. Section 3 presents overarching conclusions and recommendations.

# **1. Gender-Based Violence:  Definitions, Prevalence, Risk Factors and Consequences**

## **1.1 Definitions of GBV**

The United National Declaration on the Elimination of Violence against women (1993) defines violence against women as “any act of GBV that results in, or is likely to result in, physical sexual or psychological harm or suffering to women, including threats of such acts, coercion or arbitrary deprivations of liberty, whether occurring in public or private life” (United Nations General Assembly 1993—see Box 1.1 for the complete definition). GBV takes place in the home as well as in schools, workplaces, and in the community at large. Although most violence is perpetrated by individuals or groups of individuals, the UN definition also encompasses violence perpetrated by the state, either through direct actions or through failure to protect its citizens from harm.

> 1 The term GBV is commonly used synonymously with violence against women, although, in reality, virtually all violence is “gendered,” in that men and women have different risks of participating in or being victimized by violent behavior.
**Table 6. Sensitivity analysis with respect to selected parameters. Germany**

|Root**(St)**|_a g_/**_da,_**|_ag /at4_|**_a@t_**<br>I_Aa_5|0p/_aC_6|_a8 /aca_<br>_8_|
|---|---|---|---|---|---|
|-0.4819|-1.0||-0.1444|||
|-0.5765|||-0.8575||-0.2124|
|-1.3315||||-1.0394||
|-1.59||-1.0415||||
|Root(gL|)_a_**_1t/_**_a_<br>5|_a_<br>_I_**_/_**_ap1_<br>_0_|_Oap/ a3_4|||
|-0.4819|0.2149|0.4089|0.011|||
|-0.5765|-0.1792|-0.3915||||
|-1.3315||||||
|-1.59|0.143|0.2883||||

|**Table7. Se**<br>|**nsitivity an**|**alysis with respect to se**<br> <br>|**lected parameters. France**|
|---|---|---|---|
|Root (t)|_ag/act4_|_apg/at_<br>_aLaa_**6**|_a/aa,_|
|0.189||||
|-0.2049||-0.9395||
|-0.5923|-1.2790|-0.3694||
|-0.7909|0.6524|-0.4185||
|+0.2965i||||
|Root (p)|_a_g/_a3,_|_a@p/ap9 ag/ap,3_|_/ap,_|
|0.189|1.2072|-0.1959<br>0.1541|0.1078|
|-0.2049||||
|-0.5923|0.459|||
|-0.7909|2.2156||0.198|
|±0.2965i||||

**29**
the inflation environment is likely to be benign, and we assume that the implicit GDP deflator is 2 percent every year.<sup>61</sup> The debt/GDP trajectories under the two scenarios are presented in Figure 4. In the base case, the general government debt/GDP ratio reaches 110 percent by the end of the Tenth Plan period. In the reforms scenario, it reaches 92.5 percent after peaking at 93 percent in 2005/06. These results are being driven by the general government primary deficit, SEB losses and the calling of guarantees, and underline the need for implementing fiscal and structural reforms. The broken lines are debt/GDP excluding contingent liabilities, (CL=0), which would lower the debt/GDP ratio to 106 percent by 2006/07 in the base case and 89 percent in the reforms case.

**Figure 4: Government Debt/GDP Ratio  Projected to 2006/07**

<!-- Start of picture text --> 115.0<br>110.0<br>105.0<br>100.0<br>95.0<br>90.0<br>85.0<br>2002/03 2003/04 2004/05 2005/06 2006/07<br>Base Case Base Case (CL=0)<br>Reform Case Reform Case (CL=0)<br><!-- End of picture text --> Annex 3 summarizes the methodology used to project the general government gross fiscal deficit. The results are shown in Figure 5. For this exercise, the calling of guarantees is ignored in projecting debt and hence interest payments (CL=0). While the uncovered portion of SEB losses (the part not explicitly included in state government budgets, see Box 2) is factored into the projection of debt levels and hence interest payments, they are not included in Figure 5 for comparability with present deficit reporting practices; thus, the “true” picture especially in the base case is likely to be worse than depicted in Figure 5.

> 61 The scenarios presented in World Bank (2003) assumed that the convergence of real interest rates to the growth rate observed after 1997/98 would hold over the rest of the Tenth Plan period, and further, focused on CPI inflation rather than that measured by the implicit GDP deflator.  The net effect is that projected nominal interest rates and therefore fiscal deficits are much higher than those reported here, especially under reforms.

22
**Table 11. Guatemala: Decomposition of GDP Growth in Basic Framework, 1951-2002** (in percent)<sup>a/</sup>

|Time<br>|GDP Growth<br>||Contribution of||
|---|---|---|---|---|
|Period|Rates||||
|||Capital|Labor|TFP|
|1951-55|2.3|0.6|-2.3|4.0|
|1956-60|5.4|1.7|4.0|-0.3|
|1961-65|5.3|1.1|3.8|0.3|
|1966-70|5.8|1.7|3.6|0.5|
|1971-75|5.6|1.6|2.2|1.9|
|1976-80|5.7|2.3|5.4|-2.0|
|1981-85|-1.1|0.6|-2.0|0.2|
|1986-90|2.9|0.4|3.1|-0.5|
|1991-95|4.3|0.9|1.2|2.2|
|1996-00|4.0|1.5|0.8|1.6|
|2001-02|2.3|1.3|1.6|-0.7|
|Averae|39|1.2|2.0|0.7|
|g|.|32%|50%|18%|

_Source:_ Author’s calculations. a/ Discrepancies are due to rounding.

**Adjusted for Quality of Inputs, 1971-2002** (in percent)<sup>a/</sup>

|Time|GDP Growth||Contribution of||
|---|---|---|---|---|
|Period|Rates||||
|||Capital|Labor|TFP|
|1971-75|5.6|1.5|3.6|0.5|
|1976-80|5.7|1.5|6.4|-2.1|
|1981-85|-1.1|0.2|-0.7|-0.5|
|1986-90|2.9|-0.1|3.5|-0.4|
|1991-95|4.3|1.1|0.6|2.6|
|1996-00|4.0|1.8|2.6|-0.4|
|2001-02|2.3|1.1|3.4|-2.3|
|Averae|35|1.0|2.7|-0.2|
|g|.|28%|78%|-6%|

_Source:_ Author’s calculations. a/ Discrepancies are due to rounding.

-55-
Our results, in an important sense, restore the SST findings about the importance of heteroscedasticity in the gravity model and in favor of the PPML model over traditional estimators for the gravity model of international trade. However, they also highlight the potential risks of limited-dependent variable bias. They also highlight the importance for Monte-Carlo testing of taking into account the characteristics of the distribution of the driving variables as well as of the residuals. Using a driving variable that is distributed as a log standard normal variable resulted in a relatively qualified recommendation for the PPML estimator as a least-bad option. When we moved to Monte-Carlo testing with a driving variable based on the empirical distribution of the key explanatory variables for trade, we were able to easily explain the large shares of zeros observed in real-world trade data and to validate the recommendation of the PPML as a good estimator for the gravity model, at least for models of aggregate trade flows.

28