rafmacalaba/datause-encoder-data huggingface dataset:
of changing gender barriers. In the model, individuals differ by gender and ability, while occupation-sector pairs differ in wage rates per unit of human capital, returns to ability, and costs or barriers that men and women face when working in them. Individuals receive idiosyncratic preference shocks across occupation-sector pairs and then choose their occupation and sector. We model the option of not participating in the labor force as an additional choice, which we call the “home sector” (Hsieh et al., 2019; Ngai and Petrongolo, 2017). On the demand side, consumers have non-homothetic preferences, allowing them to shift consumption away from agriculture and toward services as their – income rises a key driver of structural transformation emphasized in the literature.<sup>4</sup> Wage rates per human capital unit in each occupation-sector and prices in each sector are endogenously determined in equilibrium.

Following Hsieh et al. (2019), we define two types of gender barriers: first, “gender norms” which capture the excess utility costs that women incur relative to men when working in a given occupation-sector pair; and second, female “wage discrimination”, which captures differences in remuneration that women receive _per human capital unit_ relative to men. In the absence of distortions, an efficient allocation of talent ensures that high-ability workers sort into sectors and occupations that offer the highest returns to their ability. However, gender barriers distort these choices, shifting workers away from the jobs where they have a comparative advantage. For instance, high-ability women may opt out of the labor market or work in clerical jobs with low returns to their ability rather than pursuing professional or managerial jobs with higher returns. Such distortions therefore lead to a misallocation of talent, reducing aggregate productivity and output.

– We estimate the model to a core sample of six large economies India, Indonesia, Brazil, Mexico, Canada and the United States – for which we have employment and hourly wage data spanning four to five decades. Gender barriers in each occupation-sector and country-year are quantified as “wedges” which ensure that our model equations precisely match the data on gender employment and wage gaps after accounting for economic fundamentals and individuals’ employment and consumption choices in equilibrium. For example, the model expresses the observed gender wage gap in each occupation-sector as a function of a model-implied gender gap in human capital and the wage discrimination women face relative to men. To measure gender barriers, we assume that men and women have the same productivity within each occupation-sector, conditional on their model-implied human capital levels.<sup>5</sup>

> 4See Boppart (2014); Herrendorf et al. (2014); Comin et al. (2021).

> 5In robustness checks, we relax this assumption and allow for gender-specific comparative advantage

4
||0<br>0.1|0.2<br>0.3<br>0.4|
|---|---|---|
|Councillors spend revenue effectively|||
|Highly satisfied with education|||
|Highly satisfied with health care|||
|Health clinic run by government|||
|Registered death of a family member|||
|Health clinic has a working fridge|||
|Primary school run by government|||
|School provides free textbooks|||
|(a)|Year 2007||
||0<br>0.1|0.2<br>0.3<br>0.4|
|Local councillors spend revenue effectively|||
|Health clinic run by government|||
|Highly satisfied with health care|||
|Highly satisfied with education|||
|Registered death of a family member|||
|Health clinic/hospital has a working fridge|||
|Primary school run by government|||
|School provides free textbooks|||
|(b)|Year 2008||

Figure 9: This figure shows the first differences that are represented by dark circles and the horizontal lines represent the simulated 95% confidence intervals. We calculate the effect of a change in quantities of interest from their minimum value to their maximum values (while holding all other variables at the central tendencies including year) on beliefs about the trustworthiness of local councillors.

37
11 useful to turn to a discussion of theories that may rationalize a meaningful interaction between the real and financial sectora.

# IX!. Financial Factors in Growth and Developments Theory

A major challenge for any theory of finance and growth ls to explain the joint evolution of the financial and real sectors. It is insufficient, for example, to simply posit that financial markets work less well in poorer countries, and then proceed to explore the consequences. Financial systems are endogenous, after all. And they change over time. Required is an understanding of what determines the relative efficiency of a country's financial system, and how this efficiency may evolve.

A useful way to organize thinking is to first identify the primitive factors that might explain why the Miller-ModiglLani theorem doesn't apply in practice. The most natural candidates are limited information and limited ability to enforce contracts.<sup>10</sup> Either factor is, to varying degrees, characteristic of real-world financial markets, particularly financial markets in developing countries. And incorporating either factor in a model is conceptually the most basic way to step outside the confines of -..he Miller-Modigliani.

In this section we first present a general description of how informational and enforcement problems introduce frictions in the relationship between individual borrowers and lenders. As we argue, these factors effectively force borrowers to pay an additional premium for uncollateralized loans and for insurance. We refer to this added cost generically as "the p-mium for external finance." We then illustrate the implications for financial structure, including the nature of both financial contracts and institutions. Next is a disc-4sion of what we view as an important general ;diction of these kinds of tneories: an inverse relation between borrower net worth and the premium for external finance. We conclud' by describing the

**l°See** Hart and Hotmstrom (1986) for an excetlent survey of the economics of information, which contains applications to finarcfaL markets.
Policy Research Working Paper 6416

# **Abstract**

This paper documents and analyzes gender differences in the use of financial services using individual-level data from 98 developing countries. The data, drawn from the Global Financial Inclusion (Global Findex) database, highlight the existence of significant gender gaps in ownership of accounts and usage of savings and credit products. Even after controlling for a host of individual characteristics including income, education, employment status, rural residency and age, gender remains significantly related to usage of financial services. This study also finds that legal discrimination against women and gender norms may explain some of the crosscountry variation in access to finance for women. The analysis finds that in countries where women face legal restrictions in their ability to work, head a household, choose where to live, and receive inheritance, women are less likely to own an account, relative to men, as well as to save and borrow. The results also confirm that manifestations of gender norms, such as the level of violence against women and the incidence of early marriage for women, contribute to explaining the variation in the use of financial services between men and women, after controlling for other individual and country characteristics.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at lklapper@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
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# **Table  13b:       Piecemeal Sensitivity Analysis: Impact on Tanzania of EACU Deep Integration and  Multilateral Liberalization**

|in Equivalent Variation (E|V) as a percentage of consum|ption||**Equiv**|**alent V**|**ariatio**|**n**||
|---|---|---|---|---|---|---|---|---|
||**Parameter Valu**|**e**|**EACU D**|**eep Inte**|**gration**|**EACU**|**Liberal**|**ization**|
|Parameter|Lower<br>Central|Upper|Lower|Central|Upper|Lower|Central|Upper|
|σ(qi, qj) – services sectors|2<br>3|4|0.95|0.95|0.95|7.13|7.11|7.11|
|σ(qi, qj) – goods sectors|see below||0.85|0.95|1.06|6.76|7.11|7.64|
|σ(va, bs)|0.625<br>1.25|1.875|0.95|0.95|0.95|7.08|7.11|7.14|
|σ(D, M)|see below||0.94|0.95|0.95|7.10|7.11|7.13|
|σ(M, M)|GTAP values<br>30||0.98|0.95|NA|7.04|7.11|NA|
|σ(L, K)|0.5<br>1|1.5|0.95|0.95|0.95|7.12|7.11|7.11|
|σ(A1,...An)|0<br>0|0.25|NA|0.95|0.95|NA|7.11|7.11|
|εEACU,εCOMESA,εSADC|Lower (upper) values are 0.5 (1.|5) central|0.97|0.95|0.93|7.10|7.11|7.09|
|εEU ,εROW,εUSA,εCHINA|values. See table 7 for central va|lues.|||||||
|θr|0<br>0|1|NA|0.95|0.68|NA|7.11|1.05|
|θm|0.025<br>0.05|0.075|0.95|0.95|0.95|7.11|7.11|7.11|
|**σ(qi, qj) – IRTS goods**|**Parameter Valu**|**e**|**σ(D, M)-**|**-CRTS s**<br>|**ectors**<br>|**Par**|**ameter V**|**alue**|
|chemicals and metals|3.4<br>6.8|10.2||||Lower|Central|Upper|
|energy and minerals|5.8<br>11.6|17.4|agricultur|e and for|estry|1.3|2.5|3.8|
|food products|2.6<br>5.1|7.7|other serv|ices||0.8|1.5|2.3|
|petroleum and coal prod.|2.1<br>4.2|6.3|trade|||0.8|1.5|2.3|
|other manufacturing|3.9<br>7.7|11.6|utilities|||1.4|2.8|4.2|
|textiles, apparel and leather|3.8<br>7.6|11.4|||||||
|wood andpaperproducts|3.2<br>6.3|9.5|||||||

## **Key:**

σ(qi, qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors σ(va, bs): Elasticity of substitution between value-added and business services σ(D, M): Elasticity of substitution between domestic goods and imports in CRTS sectors σ(M, M): Elasticity of substitution between imports from different regions in CRTS sectors σ(L, K): Elasticity of substitution between primary factors of production in value added σ(A1,...An): Elasticity of substitution in intermediate production between composite Armington aggregate goods εROW, εEU, εCHINA, εUSA εEACU, εCOMESA, εSADC: Vectors of elasticities of imperfectly competitive firms' supply in the Rest World, EU, China. USA, EACU, COMESA and SADC with respect to the price of their outputs.

θr: Share of rents in services sectors captured by domestic agents θm: Shares of value added in multinational firms due to specialized primary factor imports Source: Authors’ estimates.

80
perverse equilibria in which voters do not reward politicians who deliver public services, so politicians in turn see no incentive to deliver these services. Voters are thus conditioned to not expect such services and do not vote for politicians who promise them.

I study the reaction of citizens to a programmatic and well-organized delivery of a social good in Tanzania; the mass distribution of insecticide-treated bed nets (ITNs) in 2008 and 2010-2011.<sup>1</sup> Distribution of mosquito nets is a particularly interesting policy to study in this context because it involves direct, highly visible and clearly attributable transfer of a good from the government to individuals for free. This is quite different from other government actions such as management of the economy or stewardship of a national health or education system, where the link from a given politician’s effort to individual benefit is more diffuse (Harding and Stasavage, 2014). Furthermore, it is a universal program, in that all individuals (technically, all sleeping spaces) in the country were targeted.<sup>2</sup> Finally, it should be noted that these are potentially life saving products: a meta-analysis of 6 RCTs estimates that distribution of ITNs results in an average reduction in all-cause mortality in children under age 5 of 18% (Lengeler, 2004).

Tanzania conducted two mass distributions of insecticide-treated bet nets, in 2008-2009 and 2010-2011. Fortuitously, these two bed net campaigns occurred at roughly the same time as the first two rounds of the Tanzania National Panel Survey (NPS)<sup>3</sup> . The timing of National Panel Survey interviews was randomized at the strata level in two groups (an early “cycle 1” and a later “cycle 2” group), in order to accurately measure agricultural production and consumption, without the distortion of seasonality effects. The result of this unusual cross-cutting of national surveys and mass national health campaigns means that it is possible to exploit a regression discontinuity 1For detail on the relatively smooth implementation of Tanzania’s mass ITN distribution campaigns see Bonner et al. (2011) and Renggli et al. (2013).

> 2Although there is good evidence that individuals in these settings are not willing to pay for even highly subsidized ITNs (Cohen and Dupas, 2010), they clearly value them, as they have been shown to use them once they have been given for free.

> 3The 2008-09 ITN campaign only partially overlapped with the National Panel Survey (13% of interviews took place after the ITN campaign), compared to a much more substantial overlap in 2010-11 (53% of interviews took place after the ITN campaign.

3
Table 8: Largest <u>gains</u> from TFA implementation and share of world total

||**Impor**|**t gains**||
|---|---|---|---|
||Reduction in days|Welfare gain|Share of global total|
|Country|(|billions of USD)||
|China|2.12|24.60|0.24|
|Mexico|2.70|7.16|0.07|
|Russian Federation|3.35|7.01|0.07|
|Italy|1.93|5.56|0.06|
|Indonesia|3.63|4.63|0.05|

||**Expor**|**t gains**||
|---|---|---|---|
||Reduction in days|Welfare gain|Share of global total|
|Country|(|billions of USD)||
|China|2.48|32.76|0.30|
|Russian Federation|3.98|14.03|0.13|
|Saudi Arabia|1.96|4.56|0.04|
|Indonesia|3.21|4.01|0.04|
|Mexico|1.54|3.60|0.03|

Notes: 1) Estimates for countries that account for the largest share of total welfare gains. 2) Authors’ calculations using model counterfactuals, and data from the World Development Indicators.

days in import customs: _Good governance and impartiality_ and _Formalities regarding automation_ . Coefficients on both variables have the expected sign. We also find that the Good governance and impartiality variable significantly affects reported time in export customs, again with the expected sign. The significance of the good governance and impartiality variable across several specifications with multiple control variables included suggests that the policies contained in this indicator are important predictors of private sector perceptions of customs performance. Absent direct evidence from impact evaluation of a specific policy change, cross-country estimates like this one may be useful for guiding policy choices.

Our estimates suggest that cross-country variation in policy is quite important for explaining cross-country variation in time to clear customs. When policy for all WTO members is set to best practice in all policy areas, cross-country variation in time in import customs falls. Our estimates suggest that variation in reported time in export customs would be virtually eliminated by full implementation of the TFA.

In order to illustrate the approximate magnitude of the TFA reforms we calculate implied tariff equivalent reductions, applying an outside estimate that a 1-day reduction in days in customs is roughly equivalent to a 0.6 percent _ad valorem_ tariff cut. Using these figures we calculate that full Bali implementation would imply the equivalent of a 0.9 percentage point average _ad valorem_ 36
commencement. The trial began in early 2008 and lasted one year, with STI testing at baseline, 4, 8, and 12 months.

# _Participants_

Inclusion criteria consisted of males and females, aged 18-30 (and spouses ages 16 or over), residing in one of 10 study villages within the Kilombero/Ulanga districts of the Ifakara Health and Demographic Surveillance System (IHDSS) in south-west Tanzania (Schellenberg et al., 2002). The villages consisted of 8 rural villages and 2 semi-urban neighborhoods in Ifakara town, with participants evenly distributed across the villages. On average across the 10 villages approximately 20% of the 18-30 old residents were enrolled in the study. There were three exclusion criteria: being pregnant at the time of registration, having the intention to permanently migrate out of the IHDSS area within the next year, and unwillingness to participate if assigned to the control arm. Participants testing HIV-positive at baseline were eligible for enrollment.

# _Interventions_

The intervention arm was divided into two sub-arms – a low-value cash award arm eligible for up to $30 over the course of the study (Tsh 10,000 or approximately $10 at each of the three 4-month testing intervals testing round), and a high-value CCT arm eligible for up to $60 (Tsh 20,000 or approximately $20 per testing round). Those amounts were determined based on focus-group discussions in neighboring villages conducted before the intervention started, balancing sufficient incentive levels against concerns about scalability and potential coercion. All participants were tested for STIs at baseline and then every 4 months for one year. Participants in the two intervention arms were eligible to receive cash award incentive payments if they tested negative for curable STIs (excluding mycoplasma genitalium) at the 4, 8, and 12-month testing rounds. STIs tested at all of these incentivized rounds were Chlamydia trachomatis, Neisseria gonorrhea, Trichomonas vaginalis, and Mycoplasma genitalium; at the 12-month round syphilis and HSV2 (among baseline negatives) were also tested. Individuals in the CCT arms were 7
problem as one might fear. Moreover, the present study is not based on panel households but rather panel communes. Within a commune, the set of households is random. Hence, individual household attrition is not a concern, but changes through time in the types of households within a community would be a concern. A similar concern is that a household survey in general does not observe homeless individuals and street children. Thus, the data will miss any effect of rice price movements on homelessness or the incidence of street children.

Third, many forms of child labor are not captured by the VLSS. In the extreme, some of the most exploitative forms of child labor, such as child prostitution, are likely to be hidden because they are illegal. One could imagine that improvements in household income may increase demand for some illegal activities but decrease the household's willingness to supply children for these activities. Thus, there is scope for a link between rice price increases and the worst forms of child labor although it is not clear what direction the link goes. Even within activities that are captured in the VLSS, there is no information on working conditions. This may be substantive if, for example, the declines in child labor that come with rice price increases result from households substituting mechanical or chemical inputs for child time. A child may be better off working 20 hours a week hand-weeding than 5 hours a week spreading pesticides and fertilizers without protection. Thus, the conditions of working children could deteriorate as a result of rice price increases in tandem with declines in child labor. There is simply no available data to evaluate whether this happens.

Fourth, endogenous household composition may substantively affect the conclusions of this study. As discussed in the previous section, if one could assume that household composition was fixed and exogenous to price changes, then it would be possible to separately identify income and substitution effects with the available data. However, this is a strong assumption.

25
income risk. The analysis proceeds in three steps. First, longitudinal data are used to estimate individual income risk of manufacturing workers in various human capital categories. Second, the variation in income risk and trade barriers − both over time and across sectors − is used to arrive at estimates of the relationship between trade policy and individual income risk for these different workers. Finally, using the estimates of this relationship between trade policy and income risk, a simple dynamic general equilibrium model with incomplete markets is used to obtain estimates of the welfare costs of the effects of trade policy on income risk.

Our findings can be summarized as follows. The level of openness of an economy is not found to be related to income risk for workers of any type. Furthermore, changes in trade policy (i.e., trade policy reforms) are not found to have any affect on the risk to income faced by workers with either low or high levels of human capital. However, workers with intermediate levels of human capital are found to experience a statistically and economically significant increase in income risk immediately following liberalization of trade. Our findings thus point to an interesting nonmonotonicity in the interaction between human capital, income risk and trade policy changes. Finally, the welfare costs associated with the estimated increases in income risk, for workers with intermediate levels of human capital are substantial. However, it is worth pointing out that our welfare analysis here focuses _exclusively_ on the link between trade policy and individual income risk, and other possible channels through which trade policy may affect the economy are not studied here. More specifically, we would expect trade reform to have positive effects on the efficiency of resource allocation and economic growth, and such effects are important factors that ought to be taken into account when evaluating the total costs and benefits of trade reform. Additionally, our welfare calculations are based on a simple theoretical model whose limitations include its neglect of the effect of income risk on labor supply and capital accumulation. Moreover, our calculations do not take into account that the welfare cost of an increase in income risk might be partially offset by a rise in transfer payments from the government or firms.<sup>14</sup> Finally, while our estimates of income shocks were obtained using observations on individuals over a limited time period, our welfare analysis assumes that shocks that are highly persistent through our sample

> 14Being that such transfers are provided by entities within the economy, they should perhaps

19