rafmacalaba/data-use-mentions-extended huggingface dataset:
43 at the 5% level. There is no discernible effect on _ETRL_ . The absence of an increase in the capital share is intriguing; we note that Chari, Henry, and Sasson (2012) find large wage effects, suggesting a proportionate (and high) growth of both factor incomes. Foreign inflow of capital, as well as any subsequent increase in capital goods import and aggregate investment, may positively impact _ETRK_ by contributing to the growth of large, complex firms with employees and thereby raising the tax-enforceable share of capital income (Section 7.3). Consistent with a role for tax capacity, we find that the capital liberalization events led to a decrease in the non-corporate sectors (Appendix Figure A15).

Qualitatively, these results are consistent with those from the previous sections, suggesting that the positive impact of globalization on effective capital taxation in low and middle income countries is robust to using capital instead of trade openness. However, given the inherent limitations with the measurement of capital flows, we consider that our results based on trade openness provide more meaningful and robust insights into globalization’s impacts on factor taxation.

# **9 Concluding remarks and perspectives**

In this paper, we combine a new global database with several empirical strategies to provide novel evidence on trends and causal effects of globalization on tax structures worldwide. Our starting point is the systematic harmonization of novel historical national accounts data and dis-aggregated government revenue statistics. This data collection permitted the construction of a new measure of effective tax rates on capital and labor in 156 countries between 1965 and 2018.

Using this database, we make two contributions. First, we establish new facts. Taking a global perspective, the average effective tax rates on labor and capital have converged, due to a increase in labor taxation and a fall in capital taxation. We find differences between developed and developing countries: while the effective tax rate on capital fell in OECD

> 43Consistent with our result, Quinn (1997) finds a positive correlation between de-jure capital account openness
Thanks to these data we can construct a relatively individualized measure of consumption, which is almost never available in household surveys. This is what we will use to assess individual economic welfare. The measure we use here is the amount of expenditures specific to the cell and not shared with any other cell plus the cell‘s imputed share of the household‘s joint expenditures. We will restrict our study to individuals who are heads of their cells. They are all adults with at least one dependent, and for consumption purposes, they are assumed to be the decision makers at the cell level. We are therefore left with 4401 observations, of which 56.8% are women. The average number of cells per household is 2.51. The range is from 1 to 12; 81% of the sampled households have more than one cell.

Importantly for the purpose of our paper, these data include information on parental characteristics and inheritance. If the parent has died, the survey asked whether he or she left any inheritance and then, for each person, whether they obtained any inheritance in the form of land, housing, money, durables or productive capital. No valuation of these inheritances was obtained. In particular, we do not know how much land was inherited, although the endogeneity concerns (which we return to) would clearly be even greater using amounts of inherited land rather than simply the incidence of inheritance.

# **4. Results**

We begin by describing inheritance patterns in our data, after which we study the associations with living standards, economic activity and mobility. (Appendix 1 gives summary statistics on the main variables used from the PSF.)

## **_Inheritance patterns_**

As can be seen in Table 1, many more fathers have any inheritance to leave to their heirs than do mothers. 72% of deceased fathers left land bequests, but only 22% of deceased mothers did so. Fathers bequeath their wealth to their sons more often than to their daughters
0.39|Island include multiple Villages|
||Island(Atoll)|23|31|50182|4480.78|0.2|443.42|37.44<sup>13</sup>|Island(Atoll)include multiple islands|
|_(continued_|_over)_|||||||||

> 13 Total area for Kiribati at EA and Village Level is 70.93 km2, at Island(Atoll) Level is 861.192 km2. Because the EA and Village Level map file does not cover the whole land area of the country.

29
# Introduction: The Myanmar Context

A well-functioning private sector is crucial to the growth and prosperity of all countries. The private sector is the engine of growth for a country by its contribution toward creating jobs and opportunities for people to move out of poverty and improve the quality of their lives (IFC, 2012).<sup>1</sup> The World Bank Enterprise Survey indicates that small firms (<50 employees) in developing countries have the highest job growth rates; thus, understanding how to improve employee retention, job satisfaction, and business growth, is crucial in this sector. In Myanmar, enterprises of all sizes face a range of challenges and barriers to their development, including lack of access to capital, a burdensome bureaucratic and administrative environment, a low-skilled workforce, infrastructure gaps, as well as conflict. As is the experience globally, women face similar challenges to men but often to a greater extent, thereby limiting their equal participation in the economy as leaders, entrepreneurs, employees, consumers, and community members. Given the importance of the private sector to the economic development of a country, it is fundamental that all members of society have equal access.

**Micro, small and medium-size enterprises (MSMEs) dominate Myanmar’s private sector** . According to the OECD Development Pathway Initial Assessment (2013), it was estimated that approximately 83 percent of businesses in Myanmar were in the informal sector. Of the approximately 17 percent in the formal sector, it was estimated that 99.4 percent were small and medium-size enterprises. As such, enhancing the opportunities for MSMEs to grow is a key policy area of interest for a country in transition such as Myanmar. The ongoing liberalization of the economy provides enormous opportunities for Myanmar to grow in terms of economic and social development.

**Across the world, women face numerous challenges to participate equally in the economy as leaders, entrepreneurs, employees, consumers, and community members** . Laws and regulations continue to prevent women from
education attendance for those aged 18 to 24 years is significantly lower in migrants compared to natives (11 percent vs. 39 percent); the most frequently reason given for not attending is looking for a job (Servicio Jesuita a Migrantes, 2019).

# **Transition from secondary school to higher education**

The transition between high school and higher education is a crucial step for every child. Decisions made at this stage can significantly impact children’s economic prospects and quality of life, as the returns to higher education are particularly high in Chile. In 2017, those aged between 25 and 64 years old with a tertiary degree earned 141 percent more than their counterparts with only a secondary education; the Organisation for Economic Development and Co-operation average is around 54 percent (OECD, 2020) In Grade 12, students in Chile must make two important decisions that directly affect their likelihood of enrolling in higher education. The first is whether to register for the college selection test, which determines placement as well as access to state benefits for higher education. The second decision is whether to apply for state financial aid in the form of free tuition, scholarships, and/or student loans; this aid is managed through a centralized system. Although a few tertiary institutions (mostly TVET) do not require the scores from the standardized college admission test, all students must complete the Ministry of Education’s socioeconomic form to apply for financial aid.

Table 5 presents our data from 2017–2018 regarding the differences between native, 15
# **3 Data**

This section describes the data used in the study. Appendix B provides details on outcome construction and Appendix C presents descriptive statistics for the main variables in the analysis. Our analysis period is 1992 to 2021, in line with available remote-sensing data.

## **3.1 Forced Displacement**

**The Venezuelan Refugee Panel Study (VenRePS), 2018.** VenRePS was collected in 2018 as a representative national sample of Venezuelans living in Colombia in 2018, at the peak of these forced migration flows. It includes approximately 3,000 Venezuelan households in Colombia. We use the sample to determine migrants’ municipality of origin in Venezuela before migration to create a proxy for outflows.

**Annual Venezuelan outflows to Colombia, 1992–2021.** We compile data on annual Venezuelan forced displacement outflows to Colombia from 1992 to 2021, combining two data sources. For 1992 through 2002, we use information from the 1993 and 2005 Colombian population censuses, which record the year when each Venezuelan forced migrant arrived in Colombia. These data represent the number of Venezuelan nationals living in Colombia each year, as reported by respondents in the retrospective censuses. From 2003 to 2021, our data are based on records from official Colombian migration checkpoints.

**Historic foreigner settlements, 1990.** To define the municipalities mainly affected by forced displacement, we use Venezuelan census data to quantify the presence of foreign residents in municipalities during 1990, the last census before Ch ́avez’s election. The location of foreigners at that time should be unaffected by the migration crisis, since it predates the onset of the crisis by more than two decades.<sup>10</sup> We construct the foreign settlement

> 10Although the census does not permit the direct identification of foreigners, it collects individual information on whether each person had lived outside Venezuela in the last five years. We use this measure as a

14
# **I. Introduction**

In 1980, China and India, the two most populous countries on earth, were both very poor. India had a nontrivial lead in per capita GDP; per capita GDP was $2.38 per calendar day, nearly one dollar more than in China.<sup>2</sup> In 1980, China was already more than two years into its gradual economic reforms, whereas India did not embark on economic liberalization until 1991. Economic liberalization in both countries was followed by accelerated economic development. By 2000, daily GDP per capita had risen to $7.30 in China and to $4.71 in India, giving China a lead of $2.59. By 2008, China’s per capita GDP (measured using the PPP exchange rate and 2005 international dollar) stood at $5,515 per year, or $15.11 per calendar day, and India’s per capita GDP at $2,721 per year or $7.45 per day. While both China and India are success stories in economic development, the performance gap between China and India deserves attention. With only two countries to compare, macro data are of little use since time-series variations alone may not offer sufficient degrees of freedom for statistical inference. Using firm-level data, however, we can transcend the constraint of macro data by examining cross sectional, as well as temporal, variations. We can link firm productivity—presumably the most important factor contributing to income per capita (Islam 1995; Hall and Jones 1999)—to city-level business environment, such as skills, infrastructure, labor regulation, regulation burden, and access to finance. In particular, we recognize that constraints to economic development tend to be location-specific. Following the diagnostic approach to growth (Hausman et al. 2005), we employ firm level data to identify key constraints to development (Lin and Monga 2010). Our presumption is that, even within a country, there are
measuring global poverty, it is important to assess the sensitivity of our understanding of global poverty to the assumption of zero household economies of scale.

To address this issue, this paper considers a commonly used, single parameter, constantelasticity scale adjustment that divides total household consumption of income not by household size but by the square root of household size. The analysis first examines how the global profile of poverty changes and also how many people have their poverty status reclassified when using the square-root adjustment. This analysis is based on national representative household surveys from 162 countries covering more than 3/4th of the world’s population, and 98 percent of the estimated population of people living in extreme poverty.

To focus on whether changing from per-capita to a square-root allocation of household resources changes the profile of who is poor, the analysis takes parametrically the level of extreme poverty as currently estimated and solves for the square-root allocation poverty line that maintains the same global poverty headcount. While the value of this line more than doubles, the net change to the regional profile of poverty is remarkably stable. By construction, the sum of the change in the estimated number of poor people in each region is zero. But in terms of the sum of the absolute value of regional net changes, 61 million people (or nine percent of the total population of poor people) moved in the regional profile of poverty.

The analysis shows though that net changes at the regional level mask significant variation at the country level. Figure 2 reveals that there are expected, systematic patterns from switching from per-capita to square-root measures of poverty. In particular, in low-income countries which tend to have larger households, country-level poverty rates drop when using the square-root measure. The opposite happens for high-income countries. In considering the sum of net changes 25
also covers Mode 2 (i.e. consumption abroad) and part of Mode 4 (i.e. temporary movement of labor), but does not cover Mode 3 (sales of foreign affiliates). Mode 3 trade however is covered in Figure 2.7 under domestic services value‐added in gross exports, directly or indirectly when used as inputs into downstream industries’ exports. The TiVA database does not (yet) make a distinction between foreign and domestic affiliates from FDI.

10
, the paper estimates the relationship between labor productivity and informal employment. For this purpose, a consistent measure of informality is used across countries which includes the self-employed and non-paid employees (which can account for more than 60 percent of total employment in low-income countries).<sup>8</sup> This measure is subsequently applied to a new standardized global micro dataset (I2D2) harmonized and assembled by the World Bank Group (see Appendix 1).<sup>9</sup> The empirical results show that an increase in labor productivity is associated with a reduction in informality, consistent with the theoretical model predictions.

> 8 This measure does not comprehensively capture the extent of informal employment in developing countries, as it does not take into consideration those workers who work for informal enterprises or formal enterprises while still maintaining informal employment status. In practice, informality measures vary across developing countries and are heavily influenced by the availability of relevant indicators in labor force surveys. The definition of informality used in this paper has the advantage of being consistent across countries and has been widely adopted in studies that employ cross-country data.

> 9 The I2D2 covers 147 countries and includes agriculture, manufacturing, and services as sectors of occupation. I2D2 distinguishes four employment categories: paid employee, non-paid employee, employer and self-employed.

4
Policy Research Working Paper 8888

# **Abstract**

Much micro-econometric evidence suggests that precipitation has wide ranging impacts on vital economic indicators such as agricultural yields, human capital, and even conflict. And yet paradoxically most macro-econometric evidence (especially in the climate economy literature) finds that precipitation has no robust and significant impact on various measures of aggregate economic output. This paper argues that spatial aggregation of weather at the country level explains this result. The paper uses annual subnational gross domestic product data to show a concave relationship between precipitation and local gross domestic product growth between 1990 and 2014. It then demonstrates that when the data are aggregated at larger spatial scales, the impact decreases and eventually vanishes. The impact of precipitation on aggregate economic activity is predominantly felt in developing countries; it is insignificant in developed countries. Agriculture is found to be the dominant pathway. The results have significant consequences for measuring the economic impacts of climate change.

This paper is a product of the Water 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 rdamania@worldbank.org, sdesbureaux@worldbank.org and ezaveri@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
# **I. The Developing Country Urban Productivity Puzzle**

There is overwhelming evidence of the economic benefits of agglomeration, arising from ‘sharing’ of factors of production, improved ‘matching’ of firms and workers, and externalities related to knowledge spillovers (see, for example, Duranton and Puga, 2004). The canonical measure of such agglomeration effects is the elasticity of wages with respect to density and the estimates are large: 0.043 in the United States (Chauvin et al., 2017); 0.03 in France (Combes et al., 2008); and 0.025 in Spain (de la Roca and Puga, 2012).

Recent findings suggest that these effects are even larger in developing countries: 0.1 to 0.19 in China (Combes et al., 2013; Chauvin et al., 2017); 0.12 in India (Chauvin et al., 2017); and 0.17 in Africa (Henderson et al., 2019)- that is, a given increase in density increases wages by four times more in Africa than in the US. This poses something of a puzzle given that other sources of information suggest that developing country cities are not so much extraordinarily productive, as just crowded and even dysfunctional. Satellite and geographic information system (GIS) data covering large cities across the Sub-Saharan Africa region suggest that they are sprawling and disconnected – a far cry from the dense packing of educated workers sharing ideas in advanced country cities such as Chicago historically (McMillen 2003), or today’s Silicon Valley. As an example, land markets often function poorly with overlapping property rights regimes, suboptimal and ineffective zoning regulations, and limited supporting infrastructure, including transport—all of which hinder efficient concentration and raise costs (Lall, Lebrand, and Soppelsa 2021). Further, if Glaeser and Resseger (2010) found that areas in the US with below average human capital showed no agglomeration benefits because of limited benefits of pooling
In this estimating equation,<sup>�</sup> _p_<sup>_Treatedip_0istheweightedsumoftreated</sup> women identified by woman _i_ in the control group at baseline, where weights correct for each woman’s position within the photobook. Weighting in this way allows us to interpret _η_ as the effect from each additional baseline connection with a treated woman.<sup>19�</sup> _gip_ 0 controls for the overall number of women in _p_ the study to whom a woman is connected at baseline, again using weighted sums of network connections. We control for location fixed effects, marital status, household size, number of children, age, network size, education, and the lagged outcome variable.

Our interest in estimating Equation 2 is twofold. First, we are interested in whether there are spillovers from the program and, if so, which types of network links are most likely to transmit spillovers. Second, we use estimated spillover effects to calculate adjusted average treatment effects. Doing so allows us to provide estimates and confidence intervals for the effect of the program accounting for network-based spillovers to give a sense of the magnitude of the bias in our main effects.<sup>20</sup> We use the high-frequency SMS data to qualitatively evaluate general equilibrium effects and to estimate the effect of the COVID-19 lockdown on sales revenues. However, response rates to the SMS surveys are lower than response rates for our in-person surveys.<sup>21</sup> We correct for this non-response bias using a two-step procedure. First, we use a LASSO procedure to select the set of baseline covariates, including baseline outcomes, that best predict responding to the SMS survey in the first month of the experiment. We then regress a binary indicator for responding during the first month of the SMS survey on

> 19We calculate weights as follows. Let _wp_ be -1 multiplied by the coefficient in Table A11 Column (1) that corresponds to the page on which an identified woman appears. Let
)|460|461|461|433|
|Total|10245|10260|10257|8311|
|R-squared|0.14|0.15|0.31|0.12|

Note: Variables used to estimate the kernel propensity scores are dropped are dropped. Note: The estimations account for state fixed effects to account for the clustering of IPV among women residing in the same state. The kernel density function is epanechnikov with a bandwidth of 0.06. Probit estimation is used for the propensity score in the first stage. Kernel-based DD estimates are on the common support. The results are robust to employing a logit estimation of the propensity scores in the first stage, a gaussian kernel density function as opposed to an epanechnikov kernel density function and varying the bandwidth of the kernel density function.***Significant at the 1% level, **Significant at the 5% level, *Significant at the 10% level. Standard errors in parentheses are robust.

# 5.4 Limitations of the study

It is likely that the study underestimates the effect of the BH insurgency, as areas with the largest number of conflict events are not captured in our analysis. Also, the BH insurgency is a regional crisis and we do not consider BH activities in the neighboring countries of Cameroun, Chad and Niger, which affect Nigerian communities located in areas bordering these countries. Therefore, our estimates represent a lower bound for the effect of the BH insurgency.

Another limitation is caused by the study reliance on GPS coordinates provided in the NDHS as well as the ACLED to identify the BH area. The geo-coordinates of the NDHS clusters are randomly displaced to protect the privacy of the respondents (Elkies et al. 2015). Urban clusters are displaced up to 2km while rural clusters are displaced up to 5km (Perez-Heydrichet al. 2013). The measurement error introduced might lead to attenuation bias in the regressions (Skiles et al. 2013), although robustness analysis does not suggest this is a problem