rafmacalaba/data-use-mentions-extended huggingface dataset:
We also provide new evidence on gender differences in tax compliance. Prior research indicates that women are generally more compliant with tax filing and reporting obligations compared to men (Kleven, Knudsen, Kreiner, Pedersen, and Saez, 2011; Asmare and Yimam, 2020; Cabral, Gemmell, and Alinaghi, 2021). Several key factors have been proposed to explain this disparity, including women’s higher risk aversion (Croson and Gneezy, 2009), stronger tax morale, disparities in tax declaration and enforcement processes (Ambel and Woldeyes, 2024), and genderspecific social norms that interact with gaps in literacy, employment type, and access to information, among others (Komatsu, Shaukat, and Ozer, 2024). Our granular administrative data enables us to offer gender-disaggregated compliance insights on property taxation, in a low-enforcement setting, finding no meaningful gender differentials. Our finding contrasts with earlier studies, which suggested that women experienced either lower effective tax rates due to exemptions for lower-valued properties—more commonly owned by women in São Paulo, Brazil (Scot et al., 2023)—or, as Komatsu et al. (2021) found, a heavier tax incidence for female-headed households based on survey data from rural Ethiopia.

Lastly, while there is ample evidence on the effect of nudges—such as deterrence letters—on tax compliance and collection (Antinyan and Asatryan, 2024), research linking compliance and gender remains limited (see, for example, López-Luzuriaga and Scartascini, 2023; Awasthi et al., 2023). Hence, the results from this project exhibit great promise of informing policy debates and the conduct of tax policy that contribute toward the achievement of gender equality goals.

The remainder of the paper is organized as follows. In section 2, we describe the institutional context. Section 3 details the sources and composition of our tax data. Section 4 characterizes residential property ownership in _Tres de Febrero_ by gender. Section 5 brings three stylized facts on property tax compliance and also leverages experimental evidence on differences in
unskilled self-employed, or zero-income family workers (Perry, et al., 2007). The specific definitions used in this paper and corresponding data sources are detailed next.

# **Sources of data for measuring informal employment in Russia**

Several different data sources are available for the purpose of studying the impact of informal employment on well-being in Russia. There are four sources of microdata that include information on informal employment: the Russia Longitudinal Monitoring Survey (RLMS), the Russian Labor Force Survey (LFS), the Life in Transition Survey (LiTS) and the European Social Survey (ESS). For the estimation of levels and trends of informal employment, we utilize all four surveys to see how the trends compare across data sources and by definition. We then focus on the RLMS which has rich and detailed information on employment, and complement the analysis with other surveys when appropriate. For all data sources, we maintain a definition of informal employment that is consistent with the legalistic view described above (mainly whether the worker has a contract). This definition is in line with the majority of Russian literature and, as mentioned before, deemed more appropriate for transition economies.<sup>6</sup> Below follows a description of each data source used and details on how informal employment is derived from each survey. In all surveys, we focus on the main job only, excluding secondary jobs and other irregular activities.

The Russia Longitudinal Monitoring Survey (RLMS) is a series of nationally representative surveys designed to monitor the effects of Russian reforms on the health and economic welfare of Russians. The project is run jointly by the National Research University Higher School of Economics and ZAO Demoscope, together with the Carolina Population Center at the University of North Carolina, Chapel Hill. The RLMS is a panel survey with annual data available starting in 1994 and is representative at the national level. Data are available through 2016 which maintained a sample size of 7,000 households. The survey includes very detailed information on health
estimates with a few countries where establishment censuses were available. Our methodology and estimates aim to address the limited information available on firm demographics in African countries. The results should be interpreted as a first attempt for these estimates that should be improved over time by adding a larger number of harmonized establishment censuses and representative surveys for providing more accurate and rich description of firms in the continent.

Our results suggest that as of 2020, there were about 12.7 million firms with more than one worker and about 232 million own-account businesses in which the proprietor constitutes the sole employee. Ninety-four percent of own-account businesses are informal, totaling 218 million informal own-account businesses. Conversely, 73 percent of firms with more than one worker are informal, amounting to 9.3 million informal firms of this type. The informality rate decreases as firm size increases: from 82 percent among micro-firms (excluding own-account businesses) to 66 percent among small firms, and 36 percent among medium and large firms with twenty or more workers. Distinguishing by formality status and size is instrumental in identifying businesses with growth potential, unlike those driven by necessity.

Our analysis is closely related to various studies emphasizing the relevance of firm characteristics to the economy. One important effort worldwide is the dataset assembled by Bento and Restuccia (2021). This dataset captures the average employment size of establishments in the manufacturing and service sectors, regardless of their formality status. Their findings suggest that manufacturing firms tend to be larger than those in the services sector, and each sector is larger in more advanced economies. They provide estimates of employment size for 42 of the 54 African countries. Another relevant study (Eslava et al., 2024) combines information from harmonized household and labor surveys with firm-level data to analyze the relationship between firms and inequality in Latin America. Informality and self-employment are widespread across Latin America, but even more prevalent in Africa. They are important in driving income inequality and slowing down technology diffusion (Levy, 2024).

This paper also contributes to the broad literature on
_Alternative framing of climate action questions_

So far, the willingness to pay for climate action results were based on a question that implies that the money would go to “combating climate change” without specifying what combating climate change means. The LiTS survey includes three questions on willingness to pay for specific climaterelated outcomes: _reducing or preventing pollution by improving the quality of air or water or dealing with waste/sewage_ , fighting _global warming or the greenhouse effect_ , and preventing _the loss of plant or animal species or biodiversity_ . These questions allow us to test the sensitivity of the age gradient to the framing of the question related to willingness to pay for climate change action.

> 14 We cannot perform overidentification tests as we only have one instrument per endogenous variable.

20
- 35 -

**Impavido G., A. R. Musalem and T. Tressel** (2000) Contractual Savings, Capital Markets, and Firms' Financing Decisions, forthcoming.

**La Porta R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny** (1997) Legal Deterninants of External Finance, Journal of Finance 52 (3).

**Levine R.** (1997) Financial Development and Economic Growth: Views and Agenda., Journal of Economic Literature, 35(2): 688-726.

**Levine R. and S. Zervos** (1997) Stock Market Development and Long-Run Growth., The World Bank Economic Review, 10(2): 323-39.

**Mackenzie G. A., P. Gerson, and A. Cuevas** (1997) Pension Regimes and Saving., IMF Occasional Papers 153.

**Monks R. A. G. and** N. **Minow** (1995) Corporate Governance. Cambridge, MA: Basil Blackwell Inc.

**Munnell** A. H. (1976) Private Pensions and Saving: New Evidence. Journal of Political Economy, 84(5): 1013-32.

**OECD** (1998) Institutional Investors Statistical Yearbook.

**Pesando J. E.** (1992) The Economic Effects of Private Pensions. in OECD, Private pensions and public policy, OECD, Paris.

**Pomerleano M.** (1998) The East Asia Crisis and Corporate Finances: The Untold Story, Emerging Markets Quarterly, 2 (4).

**Samwick** A. (2000) Is Pension Reform Conducive to Higher Saving?, Review of Economic and Statistics 82 (2).

**Schmidt-Hebbel K.** (1998) Does Pension Reform Really Spur Productivity, Saving and Growth?, Central Bank of Chile Working Paper No 33.

**Schmidt-Hebbel K. and L.
The VLSS and VHLSS are representative of Vietnam for a given year provided that we use the appropriate sample weights. A multiple-stage sampling design was used, and within each geographic sample unit, households were randomly drawn from the official population registers. For every second round, additional households were sampled to compensate for attrition from the panel. Selection into the survey sample was a concern; workers who had migrated to urban areas without a residency permit were not sampled. These workers were presumably employed in the private sector. Thus, the sample that we used may provide a biased view of the earnings distribution in that sector. To test for the representativeness of our sample, we compared the repartition of the workforce in the VLSS 1998 with the statistics published by the General Statistical Office based on the Labor and Job Survey of 1997. The repartition across economic sectors was exactly the same: 65 percent in agriculture, 13 percent in industry, and 22 percent in services. The proportion of public workers was smaller in the VLSS, which conflicts with the hypothesis of a sampling bias for private sector jobs.<sup>2</sup> The issue of selective attrition from the panel sample is also important. Workers who were surveyed on two successive dates and who were working for wages on both dates were likely to differ from those who were not. If we compare the individual characteristics of the workers in the panel sample to those of the workers in the crosssection, the only significant difference is the years of experience, as expected. In contrast, the repartition of the workforce by sector in the panel sample is clearly biased toward the more stable forms of employment, and public employees and white-collar workers are overrepresented. This overrepresentation implies that the attrition bias attenuates the differences between the public and the private sectors and, in general, works against our 6
being relevant outcomes like consumption is almost systematically missing.<sup>11</sup> As a result, expected longevity cannot be estimated by consumption categories.

Last but not least, the data contains very limited information on the preference u over non-monetary outcomes. The problem is that there are no well-defined prices on nonmarket dimensions. More generally, the non-monetary outcomes cannot reveal preference u when individuals cannot (or do not) optimally select these outcomes. As a result, it is ˆ harder to estimate a preference u over J<sup>non−market</sup> . It is therefore difficult for practitioners ˆ ˆ to select appropriate weights between variables xij and xij′. This contributes to the popularity of ad-hoc methods for the selection of these weights, like the “equal weights” procedure.

# **2.5 Multidimensional indices under data constraints**

The stringent data constraints described just above help categorize the types of multidimensional indices used in practice to aggregate well-being across dimensions. This is in particular the case for the latter two constraints, namely the missing joint distributions and the scarce information on preference u. We illustrate this in Table 1, in which the two rows differ in the availability of joint distributions and the two columns differ in the availability of data on preference u.

**Table 1:** Multidimensional indices classified by data constraints.

<!-- Start of picture text --> Data on preference u<br>None Available<br><!-- End of picture text -->

|||Data o<br>None<br>(Mash-up “weights” )|n preference u<br>Available<br>(Calibrated “weights”)|
|---|---|---|---|
|Data|None|HDI|Jones and Klenow (2016),|
|on|(Synthetic index)||Boarini et al. (2016),|
|joint
, we keep women who had their first birth within a calendar year from their survey data, so that their preferences at the survey date reflect their preferences around the birth of their first child. We then compare mother’s reported son preference between firstborn-girl and firstborn-boy families and across the 3 survey rounds, that span about 15 years. In Panel A, we compare rounds 2 and 3 with round 1; the omitted group is the first NFHS round. In Panel B, we compare the two rounds (NFHS-2,3) in the late diffusion period of ultrasound availability with one round (NFHS-1) in the early diffusion period. Standard errors are clustered by state. *** 1%, ** 5%, * 10%.

58
the Philippines. For all participating countries in EAP, the SEA-PLM assessment appears more difficult at specific levels of competency than some of the other assessments used in the learning poverty estimates, even though in theory this is accounted for in the equating exercise; (iii) due to limited participation in international assessments, there are limited data; SEA-PLM is a new assessment and for Lao PDR (with the largest gap in our data between estimated and actual learning poverty at 73 percent) it is the only assessment that provides internationally comparable data showing how many of their children are in “learning poverty”; (iv) estimations of specific data by officials are frequently inexact based on other surveys, including data collected for the Global Education Policy Dashboard (World Bank, 2023), where public officials in Jordan, Peru, Niger, and Rwanda for example often misestimated average classroom size and level of teacher absenteeism by 10 to 20

> 5 - EFW, 2022 (https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416 <u>0200022022/related/EFW-2022-Jul1.pdf ).</u>

5
Has Children**|.492|.486|{.820}|-.007|
|**If in relationship: experienced any**<br>**form of intimate partner violence**|.464|.486|{.491}|.032|
|**_B. Empowerment and Aspirations_**|||||
|**Gender Empowerment Index [0-100]**|17.4|16.6|{.506}|-.030|
||[21.8]|[20.2]|||
|**Sexually active**|.747|.712|{.103}|-.056|
|**If active: Age at sexual debut**|14.7|14.6|{.348}|-.026|
||[2.26]|[2.09]|||
|**If active: Uses contraceptive**|.440|.422|{.390}|-.026|
|**(any, excluding condoms)**|[.497]|[.494]|||
|**If active: Ever used condoms**|.104|.095|{.375}|-.021|
|**Leisure activity: Engaged in sexual**|5.08|5.14|{.705}|.009|
|**activities with men (weekly hours)**|[5.33]|[5.46]|||
|**Time use: Learning activities (weekly**|28.6|28.2|{.805}|-.009|
|**hours)**|[34.1]|[33.6]|||
|**Unwanted sex over past year**|.106
# Autonomy and Specificity in Agricultural Technology Adoption: Evidence from Mexico<sup>_∗_</sup>

Carolina Corral<sup>1</sup> , Xavier Giné<sup>2</sup> , Aprajit Mahajan<sup>3</sup> , and Enrique Seira<sup>4</sup>

> 1Feed the Future

> 2World Bank

> 3UC Berkeley 4ITAM

> _∗_ We are grateful to Qué Funciona para el Desarrollo (QFD) for managing the entire project, to Fertilab for the soil analyses, to Agropecuaria Amozoc for providing and mixing fertilizers and to Ipampa S.C. for the provision of agricultural extension services. We would also like to thank Pranab Bardhan, Chico Ferreira, Andy Foster, and Berk Özler for very helpful comments and suggestions. We also thank seminar participants at various workshops for their comments and suggestions. Michelle Infanzón, Victor Manuel Pérez, Bernardo Ribeiro, Mónica Vargas and Victor Vergara provided outstanding research assistance. We also gratefully acknowledge funding from 3ie, BASIS (UC Davis), the World Bank and ISPC-SPIA (under the grant “Strengthening Impact Assessment in the CGIAR System (SIAC)"). Views expressed in this paper are those of the authors, and do not necessarily reflect the opinions of the World Bank, its executive directors, or the countries they represent.This project was reviewed and approved by the ITAM IRB and UCLA IRB # 12–001756. This study is registered in the AEA RCT Registry with the unique identifying number AEARCTR-0006264.
We focus on the most important ones here. It becomes clear that the signs and significance of the coefficients on institutional quality variables are robust with respect to the estimation procedure: all coefficients are significant at least on a 5 percent confidence level with the exception of coefficient on corruption, in which case the t statistic is only slightly below the 5 percent critical value. Coefficients on Tertiary Education appear significant and imply somewhat larger returns on education than in previous regressions. The above specification predicts a larger rate of conversion to a common cross-country steady state of per capita industrial output than that suggested by previous specifications. Another set of findings is worth paying special attention to: the results above suggest that developed legal and regulatory framework positively influences industrial growth both through investment and total factor productivity. To put it differently, the fact that after controlling for an estimated rate of investment, coefficients on PRS indicators remain strong (see Table 3), suggests that institutional quality affects both the amount of investment in the economy as well as the efficiency of resource allocation (at least in the industrial sector).

It can be inferred from the regression results that _ceteris paribus_ a one percent increase in per capita industrial value added can be achieved by either a 7.7 percent increase in the level of Tertiary Enrollment or a 2.2 percent increase in the rate of investmentl'. Our findings are not significantly different from those of Mankiw _et al._ (1992) who report the elasticity of per capita output growth with respect to the investment stage regression without including PRS indicators. The outcome of the regression is summarized below: Dep. Var. = -1.6 - 0.16*Log(Initial) + 0.8*Log(TerEdu) + 0.092*Est. Log(I/GDP); R<sup>2</sup> _=0.45_ (-2.31) (-2.32) (0.86) (3.08) The results
Policy Research Working Paper 9383

# **Abstract**

This paper uses data from Sri Lanka and Tanzania to evaluate the benefits of combining household surveys with geographically comprehensive geospatial indicators to generate small area estimates of non-monetary poverty. The preferred estimates are generated by utilizing subarea-level geospatial indicators in a household-level empirical best predictor mixed model with a normalized welfare measure. Mean squared errors are estimated using a parametric bootstrap procedure. The resulting estimates are highly correlated with non-monetary poverty calculated from the full census in both countries, and the gain in precision is comparable to increasing the size of the sample by a factor of three in Sri Lanka and five in Tanzania. The empirical best predictor model moderately underestimates uncertainty, but coverage rates are similar to standard survey-based estimates that assume independent outcomes across clusters. A variety of checks, including adding noise to the welfare measure and model-based and design-based simulations, confirm that the main results are robust. The results demonstrate that combining household survey data with subarea-level geospatial indicators can greatly increase the precision of survey estimates of non-monetary poverty at comparatively low cost.

This paper is a product of the Poverty and Equity 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 dnewhouse@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
sufficiently high quality (e.g., meat, eggs, dairy). For a very poor family, however, “food” may just mean enough cereals or other staple foods. Hence, it is possible that the food insecurity measure is biased upward by education or income or downward by overly low standards of food intake. There is some indication of such biases in the data, although formal tests of the presence of biases proved to be inconclusive (Headey 2011a). For example, there is surprisingly high selfassessed food insecurity in developing countries with relatively high levels of education/literacy, such as the former Soviet Bloc countries and Sri Lanka (see the online supplemental appendix S2 for individual country-year observations). At the other extreme, food insecurity appears too low in several countries where we know that undernutrition is quite prevalent. In Ethiopia, for example, where diets are very monotonous and undernutrition is very high, self-assessed food insecurity was just 14 percent in 2006 (although it subsequently rose rapidly). However, in crosscountry regressions, we did not find an impact of education on food insecurity after controlling for income (see Headey 2011a). There are no indications that large numbers of poor countries systematically underreport food insecurity.

To illustrate this issue, table 1 reports regional means (the full Gallup data are presented in appendix S2). At the bottom of table 1, we observe that the mean “global” prevalence of households reporting problems with affording food is almost 32 percent. As expected, however, there are large variations around the world, with some countries reporting almost no food insecurity and others reporting that 80 percent of households had problems affording food. For the most part, the pattern across continents is plausible. Food insecurity is highest in sub-Saharan Africa, which is by far the poorest region in the world in monetary terms. Food insecurity in South Asia is higher than in East Asia, as expected, but only when two large outliers, Nepal and 9
in 2018 euros). <mark>In Section V, we compare the potential effect of alternative minimum wage policies (Scenarios 1-5).</mark>

**Table 1. Current minimum wage and scenarios for increases**

||Gross Nomin<br>Wage, EU|al Minimum<br>R 2018|Net Nomin<br>Wage, E|al Minimum<br>UR 2018|Percentag<br>Nominal Mi|e Change in<br>nimum Wage|
|---|---|---|---|---|---|---|
|Age|15-34|35-64|15-34|35-64|15-34|35-64|
|Current|130|170|124.8|163.2|||
|Scenario 1|250|250|240|240|92.3%|47.1%|
|Scenario 2|230|230|220.8|220.8|76.9%|35.3%|
|Scenario 3|212|212|203.5|203.5|63.1%|24.7%|
|Scenario 4|137.6|180|132.1|172.8|5.9%|5.9%|
|Scenario 5|193.6|193.6|185.9|185.9|48.9%|13.9%|

_Source: Statutory minimum wages and official CPI estimates from Kosovo Statistics Agency (Harmonized Indices of Consumer Prices; April 2018)._ 5 A debate over a potential increase in the minimum wage in Kosovo began in 2016/2017. The same analyses used in this paper were conducted using the 2016 Labor Force Survey (the latest available data at that time) and were intended to inform the debate in 2018 when the government was considering an increase between 230 and 250 euros for all employed. The