rafmacalaba/data-use-mentions-extended huggingface dataset:
# Changes in Female Employment in Mexico: Demographics, Economics, and Policies

Gladys Lopez-Acevedo<sup>1</sup> , Samuel Freije-Rodriguez<sup>2</sup> , Mexico Alberto Vergara Bahena<sup>3</sup> and Diego Cardozo Medeiros<sup>4</sup>

## **JEL classification** : J21, J22, O54

**Keywords:** Female labor force participation, Mexico, gender gap, female education, and childcare services.

**Resumen:** Las brechas de género en desempleo y participación laboral se redujeron en México después de la crisis económica de 2008, dado un aumento de la participación laboral femenil (PLF). Para identificar los principales determinantes que han permitido el crecimiento de la PLF, estimamos un modelo probit con datos de 2007 y 2017 de la Encuesta Nacional de Ocupación y Empleo (ENOE), año en que la tasa de desempleo regresa a niveles precrisis. Los resultados sugieren que: el aumento en la participación laboral de mujeres entre 36 y 65 años permitió el crecimiento general de la PLF, la educación de las mujeres contrarresta cualquier característica individual o del hogar negativa para su empleabilidad, y el acceso a estancias infantiles significativamente aumenta la probabilidad de que estén empleadas.

This paper is a product of the Poverty and Equity Global Practice of the World Bank Group. We thank Carlos Rodriguez-Castelan, Gabriela Inchauste, Deeksha Kokas, Stuti Manchanda, and Matias Morales for their comments. All remaining errors are the responsibility of the authors. To contact authors please write to <u>gacevedo@worldbank.org</u><sup>1</sup> , <u>sfreijerodriguez@worldbank.org</u><sup>2</sup> , <u>mvergarabahena@worldbank.org</u><sup>3</sup> or diego.cardozo@chicagobooth.edu<sup>4</sup> <u>. Gladys Lopez-Acevedo</u
and van Wincoop, 2003, 2004). Finally, ��� is a set of _undirected_ country‐pair fixed effects that captures all the time‐invariant determinants of trade costs and addresses the endogeneity in the formation of PTAs (Baier and Bergstrand, 2007). To account for the presence of zeroes in trade flows, we estimate equation (1) using the Poisson pseudo maximum‐likelihood (PPML) estimator proposed by Santos Silva and Tenreyro (2006).

We analyze the effect of deep agreements on different types of trade. First, using data from WIOD, we define �������� as the total exports of goods and services from _i_ to _j_ . Second, we estimate equation (1) for export of goods and services separately to allow for heterogeneous effects of depth. Finally, we use the decomposition of gross trade into value added components proposed by Wang et al. (2016) to measure the effect of deep PTAs on GVC‐related trade.

The �������� variable comes from the content of PTAs data set constructed by Hofmann et al. (2016) and is defined as the count of legally enforceable provisions included in each agreement. To identify the effect of depth on exports, we include country‐pair fixed effects and exploit the within country‐pair variation, while controlling for any country‐year shocks. The coefficient �� in equation (1) captures the effect of signing the deepest agreement in the sample.<sup>8</sup> While the coefficient of the interaction term �� tests if the UK’s exports or imports are more or less sensitive to deep agreements than other countries. A positive (negative) and significant coefficient implies that for the same level of depth the UK exported or imported relatively more (less) than the average country in the sample. Both coefficients capture the average effect of depth after the agreements enter into force and assume linear effects on trade, with a percentage
faced by nonagricultural producers bidding from the same national pool of inter-sectorally mobile resources. More than seventy years ago Lerner (1936) provided his Symmetry Theorem to prove that, in a two-sector economy, an import tax has the same effects on production, consumption, trade and national economic welfare as an export tax. This carries over to a model that has many sectors, and is unaffected if there is imperfect competition domestically or internationally or if some of those sectors produce only nontradables (Vousden, 1990, pp. 46-47).

Figure 1 plots the time-series evolution of the RRA for each of the 14 large Sub-Saharan African countries in our sample (which accounts for more than three-quarters of the population and even more of the GDP of Sub-Saharan Africa excluding South Africa).<sup>8</sup> It shows that, for all of those Sub-Saharan African countries except Nigeria, the RRA is negative almost all of the time. Hence on average there has been a strong policy bias against agriculture over the past half-century. However, there is also substantial RRA variation across time and countries. For example, in Ethiopia, Mozambique, and Tanzania there was a continuous reduction in policy biases against agriculture, while in countries such as Zambia and Zimbabwe the strong bias against agriculture was firmly maintained.

We note here that the RRA measure we use has several important advantages over other existing measures of policy distortions available for Sub-Saharan African countries. First, the estimated nominal rates of assistance to agriculture and non-agriculture reported in Anderson and Valenzuela (2008) provide by far the longest and most consistent annual time-series data on policy distortions in Sub-Saharan African countries. Second, measures of just trade policy distortions typically use trade weights to obtain sectoral averages, whereas the new World Bank study uses more-appropriate weights based on production valued at undistorted prices. Third, most other estimates of agricultural trade policy distortions focus on just import tariffs (see, e.g., WTO 2010), thereby missing export distortions (as well as occasional food import subsidies)
4|0.2|No||
|Health|153,325|4.4|2.2|Yes|b,h|
|Housing& Urban|9,135|0.3|0.1|No|b|
|**Subsidies(to corporations)****|18,309|0.5|0.3|No|c|
|**Economic affairs**|99,329|2.9|1.4|No|b|
|**Defense, public order and safety spending**|233,294|6.8|3.3|No|b|
|**Other**|1,381,290|40.0|19.7|No|b|

Sources: a. Brazil (2022b). b. Brazil (2020). c. IMF (2023). d. Open Data Portal Brazil Government. e. World Bank (2018). f. Fiscal transparency Portal Brazil Government. g. ECLAC (2023). h. Brazil (2022a). Notes: “n.a.” stands for not applicable. BPC stands for Benefício de Prestação Continuada.* Includes Food, School, Uniforms, etc. ** While Higgins and Pereira (2014) include indirect subsidies to electricity consumption in the analysis, we find that the subsidies directly targeting families (e.g., social tariff, universalization, rural) amounted to no more than 0.1 percent of the GDP in 2019 with a limited number of beneficiaries and effects over the welfare results.

# _Other in-kind income_

The estimation of households’ market income incorporates the value of goods produced for own consumption and the imputed value of owner-occupied dwellings and other income sources. None of these measures are readily available in the survey data. We now describe our approach to incorporate them in the analysis.

Value of own consumption was estimated in the following way.
|Observations|665|665|
|---|---|---|
|R-squared|0.797|0.413|
|Prov-Year FE|Y|Y|
|Lagged city characteristics|Y|Y|

Note: This table reports the effect of FDI inflow shock on city level school enrollment. FDI_shock is FDI inflow shock calculated as the interaction of initial industry composition across cities and industry FDI growth explained by policy changes. Lagged city characteristics include population, average age, share of Han ethnicity. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 4. Robustness checks

## 4.1 Additional control variables

The baseline regressions in Section 3 did not control for GDP per capita and the share of rural population because these two variables contain many missing values. GDP per capita and rural population share are gleaned from city statistical yearbooks. When rural population share is unavailable from city statistical yearbooks, we use population census data to calculate the share of population with rural Hukou. There is a difference between the share of population residing in rural areas and the share of population with rural Hukou, so we do not control rural population share in other robustness checks due to this caveat.

Table 4 presents the results on structural transformation and school enrollment with additional controls. We only report results on sectoral employment share as employment is the primary indicator to measure structural transformation, and we have many more observations on sectoral employment than sectoral GDP. We include initial city level GDP per capita in natural logarithm and initial rural population share as additional controls. As expected, cities with higher initial income level experience a faster structural transformation and college enrollment growth. Cities that have a higher share of rural population at the start of the period are slower to move into services and experience lower growth in college enrollment rate. The coefficients on FDI shock are very close to baseline results in
in South Africa. In Appendix Table 7, we compare sample characteristics between our study and the 2010 FinScope Small Business Survey, which utilizes a nationally representative sample of 5,676 small business owners in South Africa.

The comparisons in Panel A show that while race and origin of business owners are not different across the two samples, other differences are present. Businesses in our sample are more likely to be owned by men (45% female owned vs. 55% female owned in FinScope), run by slightly younger 29
) together with the 27 largest nonmembers. These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002). This report, published annually since 1973, was arguably the most comprehensive guide to international migration for many years and has been the basis for many studies (see, for example, Mayda 2007).

More recently, the OECD has developed a database that provides a comprehensive overview of migration to OECD countries in 2000 (OECD 2008). These data are disaggregated by a number of covariates including age, gender, educational attainment, and place of birth. Another series of papers, again concentrating on the OECD, examines the brain drain in 1990 and 2000 (see, for example, Docquier and Marfouk 2006); migrants‘ gender (Docquier, Lowell, and Marfouk 2009); age of entry (Beine, Docquier, and Rapoport 2007); and the medical brain drain (Bhargava and Docquier 2007). Parsons and others (2007) construct a matrix encompassing the entire world for the 2000 census round. Until now, this was the most comprehensive global overview of bilateral migrant movements. Ratha and Shaw (2007) use an earlier version of the dataset in a paper focusing on migration between developing countries (generally referred to as South – South migration in the literature) and bilateral remittance flows.

The data in the current article reveal several important patterns. Between 1960 and 2000, the global migrant stock rose from 92 million to 165 million, but fell as a share of world population, from 3.05 percent to 2.71 percent. A large share of the stock in 1960 3
ability to trade and transport goods over this period, thereby inducing variation in input prices at the supplier level. Second, we obtain new granular and high-frequency administrative data on the universe of firm-to-firm transactions for a state in India, with unique information on unit values and HS-product classifications. This previously unused data on prices and purchase quantities at the buyer-supplier level, combined with exogenous variation in prices induced by the lockdown, allows us to estimate new elasticities at the firm level and across different suppliers of a product.

We first document whether the lockdown indeed induced variation in prices we need to estimate these elasticities. We begin by presenting reduced-form evidence that leverages the nationwide, sudden, and unprecedented lockdown imposed by the Indian government in March 2020. Importantly, these lockdowns induced geographic variation: districts were categorized into _Green_ (mild lockdown), _Orange_ (medium lockdown), and _Red_ (severe lockdown). Since the lockdowns were sudden and unexpected, they were likely implemented independent of economic fundamentals and induced strong variation in transactions between firms across India.<sup>3</sup> We show, conditional on high-dimensional fixed effects, that these adverse supply shocks led to a sharp increase in unit values (prices), and dramatic fall in transactions of intermediate inputs, if either buyers or sellers were located in high lockdown zones.

We estimate the elasticity of substitution across suppliers leveraging the variation in prices we document in our reduced form. To do this, we construct a standard multi-sector firm-level model of input-output linkages with a production function augmented with substitution across suppliers within the same product category. Our model is intentionally parsimonious to fix ideas, but importantly, unlike standard trade models with Constant Elasticity of Substitution across products, it does not assume that suppliers of the same product are perfect substitutes. We derive analytical expressions that connect the relative values of quantities purchased of the same product from different suppliers to the equilibrium relative prices. A combination of this analytical framework, and the lockdown-induced variation in sellers’ marginal costs and transportation costs allows
formality is associated with a higher wage premium and higher returns to education for formal labor in 2006.

Table 4 – Percentage of Formal vs. Informal Labor

||1998|2006|
|---|---|---|
|Informal|88.66|64.12|
|Formal|11.34|35.88|

_Source_ : Authors’ calculations using ELMS 1998 and ELMPS 2006.

Table 5 - Formality and Firm Size - 2006

|Firm Size|informal Labor|Formal Labor|
|---|---|---|
|1 to 4|90.8%|9.2%|
|5 to 49|85.6%|14.4%|
|50+|35.8%|64.2%|
|dont-know/miss|15.8%|84.2%|

|Firm Size|informal|Formal|
|---|---|---|
|1to4|69.9%|12.7%|
|5to49|19.9%|6.0%|
|50+|2.5%|8.1%|
|dont-know/miss|7.7%|73.2%|

_Source_ : Authors’ calculations using ELMPS 2006.

Most of the informal labor (79.5 percent in 1998 and 70 percent in 2006) works in micro enterprises (1 to 4 employees), while most of the formal labor works in the government sector (Table 5). The majority of the informal workers are clustered in the private sector. Therefore, most of the informal labor is clustered in private micro enterprise, while most of the formal labor is in the government sector. The largest share of informal labor in 1998 and 2006 are illiterate, while the largest share of formal labor are 4-year university graduates.

Table 6 shows that: (a) informality decreases with education level; and (b
S X ESEARCH **WORRKING** PAPER 2265

# Sum;tMary **findings**

' Ilitucs and regulatory capture can play an important e in financial inst-itutions' distress. East Asia's financial .r sis featured many distressed and closed financial 'r,ermediaries in an environment with many links 'ween governmernt, politicians, supervisors, and ancial institutions. This makes the East Asian financial cis a good event for studying how such connections a:,_ect the resolution of financial institutions' distress.

Bouigini, Claessens, and Ferri investigate distress and _c_ 'osure decisions for 186 banks and 97 nonbank financial instutions in Indonesia, the Republic of Korea, Ialaysia, the Philippines, and Thailand. They find that a er July 1997, 42 percent of the institutions experienced distress (were closed, merged, or re.apitalized, or hac their operations temporarily suspended). By July 1999, 13 percent of all institutions in existence in July '1997 had been closed.

Using financial dara for 1996, the authors find that: Traditional (AM\4EL-type variables - returns on assets, loan growth, and the ratio of loan loss reserves to capital, of net interest income to total income, and of loans to borrowings -- help predict subsequent distress and closure.

* None of the foreign-controlled institutions was closed, and foreign portfolio ownership lowered an institution's probability of distress.

"Connections" -with industrial groups or influential families - increased the probability of distress, suggesting that supervisors had granted forbearance from regulations. Connections also made - closure more, nor less, likely suggesting that the closure processes themselves were transparent.

* But larger institutions, although more likely to be distressed, were less likely to be closed, while (smaller) nonhank financial institutions were more likely to be closed. This suggests a "too big to fail" policy.

T'hese policies, together with the fact that resolution processes were late and not necessarily comprehensive, may have added to the overall uncertainty and
; living arrangements) and parental labor market information (labor force participation, hours of work, and hourly wages). It contains detailed information on child care modes (parental care, relative care, non-relative care, center-based care), hours of use for each one, and hourly child care expenses. Further, it provides an assessment of the quality of center-based care using an instrument designed by developmental psychologists, the ITERS (Infant/Toddler Environment Rating Scale).<sup>13</sup> Scores range from 1 to 7 (inadequate and excellent quality, respectively). ITERS scores are only available for the centers that agreed to be observed (45 percent of centers, enrolling about half of the children.) Starting from the second wave’s full dataset, we arrive at our estimation sample by imposing three main restrictions: we focus on two-parent and one-parent households with only one child, who is two years old; we restrict one-parent household to cases where the parent is the mother; and we focus on households in which the father, if present, works full time. The first restriction greatly reduces the problem’s dimensionality as child care is chosen for only one child. It also avoids modeling fertility or scale economies in child care. The second restriction reflects that virtually all one-parent households in our sample are led by women. The

> 13 The ITERS scale contains 39 items in seven sub-scales: space and furnishings, personal care routines, listening and talking, activities, interaction, program structure, and parents and staff. We focus on the overall score (mean over the seven sub-scales.)

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change over time. For example, for the Transportation bundle, the value of expenditures ( _vcTR_ ), the price _CE_ index ( _pTR_ ) and implied quantity is: The demand for gasoline, item 15, is then: The consumption items listed in Table 1 are those used in the consumption survey and must be linked to the factory gate values in the Input-Output Accounts. For example, Food & tobacco in the Consumption accounts consist of commodities from Agriculture, Food Manufacturing, Trade (Commerce) and Transportation in the IO categories. The CE superscript denotes that these are prices for the consumption expenditure items. Table 2 gives the bridge that links these two accounts in the benchmark year 2010 for urban consumption. Column _i_ of the bridge **H** _u_ gives the shares to allocate consumption item _i_ to the 33 IO commodities. A similar table is constructed for rural consumption. Let be _VCtu CE_ , the vector of consumption values for the urban sector, then the vector of consumption in IO terms is given by: (A57) _VCtu IO_ ,  **H** _uVCtu CE_ , The prices of the consumption commodities are linked to the prices of the IO commodities via the same share matrix: The total value of consumption of commodity _i_ is the sum of the urban and rural components: The value of national consumption in equation (A48) is the sum over all the commodities: ‐56‐
satisfaction. Improvements in service provision may be misattributed to other actors, such as higher levels of government or foreign donors (Cruz and Schneider, 2017; Guiteras and Mobarak, 2015), or trust may adjust only slowly to new information (Khan et al., 2021). In addition, our broad measures of trust and satisfaction may not fully capture welfare gains from improvements in specific services. While administrative data and citizen surveys point to meaningful gains in service access and quality (Table 7, columns 2–3), attitudes toward the village government may reflect additional considerations, including fairness, expectations, and experiences beyond local service delivery. This disconnect is also consistent with rising expectations following turnover, which may offset the effects of improved service provision on reported satisfaction.

# **6 Alternative Explanations**

Our results show that turnover in village elections shakes up village bureaucracies, fosters increased engagement and policy alignment between bureaucrats and citizens, and improves service provision. In this section, we discuss potential explanations for the shifts in bureaucratic organization and improvements in service quality that we observe. We focus on four possible explanations: positive selection of new leaders, lame-duck village heads driving down bureaucratic effort, patronage appointments by newly elected leaders, and social desirability bias in survey data collected from bureaucrats.

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# Croatia

Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1994 and source l1l, Code 1: _Employment offce statistics._ Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to 1993.

Central government employment includes providers of Social Services and culture, arts and information. It is taken from IMF Report SM/95/199 of August 15, 1995 and relates to 1995. Local Government employment is taken from IMF Report No. SM/95/199 of August 15,1995 and relates to 1995. Health and Education employment are taken from IMF Report SM/951199 of August 15, 1995 and relate to 1995. State-owned enterprise employment is taken from IMF Report SM/941251 Background Notes and Statistical Appendix of September 29, 1994. Military employment data include 65,000 conscripts, but do not include personnel in paramilitary organizations, e.g., the Police (40,000). GDP data are for 1995 and come from Marcelo Bisogno, Research Assistant for the Croatia country office. Average Government wage estimate and average manufacturing wages are taken from IMF Report No. SM/95/199 of August 15, 1995 and relate to 1994.

# Czech Republic

Unemployment rate was taken from the Economist Intelligence Unit 1995-96 (p. 11), and reflects unemployment as of February 1995. Employment data are from the draft paper for the year 1993. Data on paid employment in nonagricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Data on military employment include conscripts (40,400), but exclude personnel of paramilitary units, i.e., the Border Guards (4,000) and the Internal Security Forces.

# Federal Republic of Yugoslavia (Serbia and Montenegro)

Data on military employment include conscripts (60,000).

Hungary Unemployment rate was taken from Economist Intelligence Unit 1995-96 and reflects unemployment as of November 1994