rafmacalaba/data-use-mentions-extended huggingface dataset:
202|0.076|***|1.538|0.035|***|
|Sample size||1750|||7786||

_Source:_ Author's calculations based on VLSS and VHLSS data.

# _Returns to skill and gender_

Another interesting method for extending our model is to consider the differences in the labor market earnings between the male and female workers. Confirming the previous findings by Liu (2004), we have seen that in the 1990s, women were paid more favorably in the public than in the private sector. After controlling for the workers' characteristics, the gender gap was not significant in the public sector in the 2000s (table 4). We use the model described by equation (3), replacing Di with the gender dummy Gi = 0 for men and Gi = 1 for women. As before, the estimated returns to the comparative advantage in the public sector are about half of those in the private sector in the 1990s and are not significantly different from each other in the 2000s (see table 8). We find that in the 2000s, the returns to the comparative advantage are much higher for women, with γ being a positive and significant coefficient, whereas the public premium 32
<br>linked<br>to<br>outcomes<br>and<br>emphasising<br>equipment<br>purchases that benefit specific<br>individuals).|**Very few solutions have been**<br>**implemented.**Even standard<br>interventions like the QISS have<br>only just begun implementation.<br>Proposed interventions, such as<br>the<br>installment<br>of<br>a<br>data<br>monitoring chart in all the health<br>facilities, show some promise<br>and an appetite for innovation<br>though whether such solutions<br>will<br>achieve<br>implementation<br>remains to be seen.|In<br>the<br>absence<br>of<br>implementation, no evidence<br>of specific problems being<br>fixed.|**Regressed.**<br>Results<br>according<br>to<br>the<br>5<br>indicators<br>that<br>were<br>captured during the 2016<br>MICS survey show Abia as<br>having regressed in all<br>except<br>one<br>indicator<br>(SBA). Their sum total<br>across<br>the<br>indicators<br>showed a regression of ‐<br>47.6%.|

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sector. From the perspective of our paper, what made a vital difference to emerging market countries were the steps they took after 2001 to self-insure, of which the most visible manifestation was the building up of IR—although, as we shall see, other equally important measures were taken.

# **DEVELOPING COUNTRY RESPONSE TO MACROECONOMIC VULNERABILITY**

Putting the crises of the 1980s and the 1990s together, emerging markets have been on a steep learning curve. In this section we link financial stability to the broader context of the macro tradeoffs facing emerging markets and developing countries. In the 1980s, most emerging markets operated with low financial integration and rampant capital controls, strong preferences for exchange rate stability, relatively low levels of international reserves-to-GDP ratios, and active monetary policy. These patterns were modified substantially during the 1990s and 2000s.

# **Navigating the Trilemma**

A compact way of capturing the response of developing countries is to base it on the macroeconomic policy trilemma. This trilemma, or the ability to accomplish at most two out of following three policy objectives – financial integration, exchange rate stability and monetary autonomy- is a key implication of the Mundell-Fleming macroeconomic framework. A lingering challenge in applying it is that, in practice, most countries rarely face the binary choices articulated by the trilemma. Instead, countries choose the degree of financial integration and exchange rate flexibility. Aizenman, Chinn and Ito (2010) allow for this by constructing three indexes measuring the trilemma dimensions of each country. Applying these indexes, Aizenman et al. (2011) validated that the weighted sum of the three trilemma policy variables adds up to a constant, where all the weights are positive.<sup>12</sup> This result confirms the notion that a rise in one trilemma variable is traded off against a linear weighted drop in the sum of the other two.

12 The analysis was applied to a panel of the trilemma indices of 50 countries (32 of which are developing countries) during 1970-2006 period for which there
This example illustrated the application of models of yield risk to a simple problem of rating an all-peril yield insurance contract. Essential to constructing accurate insurance contract parameters is an adequate measure of the risks underlying random yields. Conceptually, these risks are represented by yield density estimates. As discussed previously, a number of different parametric and nonparametric techniques are available to estimate yield densities. Alternative approaches each have relative merits and shortcomings. Our application to groundnut yield data demonstrates that insurance parameters such as premium rates are likely to be quite sensitive to the assumptions made in modeling yield distributions.

# **5.  Crop Insurance Ratemaking Procedures**

Two ratemaking procedures can be considered, depending on the previous existence of the insurance product. If the insurance scheme has been offered for several years (at least 15-20 years), as it is the case many crops covered by the U.S. Multi-peril crop insurance (MPCI) program, then historical indemnity and liability data can be used to assess the premium rate through the loss cost ratio. The main advantage of this procedure is that changes in farming practice once the farmer is insured (for each level of coverage), i.e., moral hazard, is captured in the expected value of future costs. When a new insurance product is launched, such historical data are not available and thus the ratemaking procedure relies on the yield data available and on simulated losses.

# **_5.1.  Rating an Existing Insurance Scheme_**

It is first noteworthy that there is no single ratemaking approach that will apply to all insurance contracts. Insurance companies have derived their own ratemaking procedures. The case of U.S. MPCI scheme is detailed here to illustrate how public-private multiperil crop insurance contracts are rated. The involvement of the Federal government in the MPCI program creates a social insurance program which operates on different principles than a privately underwritten insurance market. For example, insurers are required to accept all applicants. The U.S. Department of Agriculture (USDA) is mandated
FE|Yes|Yes|Yes|Yes|
||Importer-Time FE|Yes|Yes|Yes|Yes|
||Exporter-Partner-Sector FE|Yes|Yes|Yes|Yes|
||Sector-Time FE|Yes|Yes|Yes|Yes|
||Cluster|Exporter-Partner|Exporter-Partner|Exporter-Partner|Exporter-Partner|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Note: Sector-time fixed effects control for unobserved effects affecting aggregated sectors over time (see footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

# **5. Conclusions**

Using monthly trade data for 28 exporting countries and multiple importers over the January-June 2020 period, this paper investigated how the Covid-19 pandemic impacted international trade. We find strong evidence that sectoral characteristics such as the feasibility of remote work, durability of goods, and integration into global value chains played a large role in mitigating or augmenting the trade effects of Covid-19 shocks. While more work is needed, particularly to include sectoral trade data from a larger set of developing countries, these findings provide relevant policy insights. First, they help identify the sectoral attributes that create vulnerability during a pandemic, providing guidance for sectoral policy 26
designation. Though agreements of this type only began to proliferate from around the mid2000s onward, the number of PTAs possessing these standards continues to increase steadily. Notably (and as seen explicitly in Appendix Table A1), these BTRIPS agreements have increasingly been undertaken by countries besides the United States and EU/EFTA, such as Japan, Korea, and Australia, a fact that points to the increasing centrality of IPRs in these countries’ policy emphases.

A second important point is that IPAs, while increasing the scope of protection over time, do not treat all forms of intellectual property equally. They also vary in depth because they incorporate different numbers of specific IPRs provisions and their enforceability. Countries entering PTAs may have different socioeconomic interests in intellectual property protection and related regulatory policies (Maskus, 2012a). For example, the United States attaches considerable importance to assuring copyright protection for its own nationals’ rights held in foreign markets and negotiates its international agreements accordingly. The EU and EFTA do so also but prioritize strong systems of registering and enforcing GIs especially. GIs protect the rights to use place names in wines, spirits, and other consumer products, where production locality matters for the quality and character of such goods. Japan and Korea have focused on extending patent rights, while Australia traditionally has preferred weaker copyright rules. Developing countries often seek to sustain access to international technologies and information, perhaps through more limited patent standards or weaker trade secrets protection. Indeed, it may seem surprising that emerging and developing economies increasingly have agreed to strong IPRs chapters in PTAs. Here, we simply note that different countries seek to emphasize specific aspects of IPRs in negotiating and joining IPAs.

Rather than describe fully the many differences in strategies and provisions that can be found in the World Bank database, we summarize them in Table 1. The database identifies 130 detailed IPRs provisions, broken into 16 broad categories. These provisions are simply listed as either 0 (absent in the PTA) or 1 (present). In Panel A we list
different from what was used inAzevedo 2020).<br>Includes recent results for PASEC 2019, TIMSS 2019,<br>LLECE 2019, and from new assessments, such as SEA-<br>PLM 2019 and AMPL-b 2021 (Zambia), as well as NLA<br>from Lesotho that has undergone policy linking. This<br>applies only to Learning Poverty simulations.<br>Global HCI database from 2021 (used inAzevedo,<br>Hasan et al. 2021).|Global Learning Poverty Database from October 2019<br>(used inAzevedo 2020).<br>Global HCI database from 2021 (used inAzevedo,<br>Hasan et al. 2021).|
|Learning Poverty reporting<br>window|The new Learning Poverty window for aggregate<br>estimates is ±4 assessment years around 2019. There are<br>some exceptions: Afghanistan, Kyrgyzstan, Lesotho,<br>Pakistan, Tunisia, Uganda, and Yemen. Population<br>estimates for 2019 are used for aggregate calculations.|The previous Learning Poverty window for aggregate<br>estimates was ±4 assessment years around 2015.<br>Population estimates for 2015 were used for aggregate<br>calculations.|
|Data on actual school closures|UNESCO school closures tracker for February 2020 –<br>February2022.|Not available at the time. The length of school closures<br>was scenario-based.|
|Length of period in school<br>years|2, minus country-specific breaks.|1.|
|Share of the school system<br>closed|Schools can be considered partially closed.|Education system was assumed to be fully open or fully<br>closed.|
|Country-specific school<br>closure information|Yes. UNESCO school closure tracker has country-<br>specific information.<sup>15</sup>|No. All countries were assumed to have the same length<br>of
that the proportion of women in total workers for a typical firm increases by as much as 6.8 percentage points (against a mean of 32 percent women workers) when we move from a country that does not mandate paternity leave to a country that does.

# **2. Data and Main Variables**

Our main data source is the World Bank’s Women, Business and Law (for year 2010) and a stratified random sample of 33,302 firms in 53 developing countries (Enterprise Surveys, World Bank). Enterprise Surveys were conducted in various countries between 2006 and 2013 by the World Bank using a common questionnaire and sampling methodology, and are representative of the non-agricultural and non-financial registered private sector of the economies. Note that each firm has only one observation in the sample.

Our dependent variable is the proportion of all permanent full-time workers at the firm that are women ( _Women workers_ ). The mean value of the variable is 0.32 and the standard deviation equals 0.27. Across countries, the variable ranges between 0.03 (Pakistan) and 0.51 (Belarus). Note that _Women workers_ is a relative measure and hence our results are immune to spurious correlations due to factors that affect men and women employment equally. However, our results are qualitatively similar even if we use the absolute number of women workers at the firm.

Our main explanatory variable, _Paternity Leave_ , equals 1 if the country has laws mandating paid or unpaid paternity leave and 0 otherwise. The data source for the variable is WBL (year 2010). There are 22 countries in our sample that mandate paternity leave while the remaining 31 do not.

Since _Women workers_ varies at the firm level and _Paternity Leave_ at the country level, the reverse causality problem is unlikely to affect our results much, although it cannot be ruled out 3
labor markets, this finding illustrates that socio-emotional skills are transversal. In the case of job portals, it may stem from the fact that socio-emotional skills are simply specified more explicitly in job descriptions than other types of skills. However, this finding is consistent with findings based on data from other sources (such as STEP data) showing that socio-emotional skills are equally needed in lower, medium and high-skilled occupations (World Bank, 2019a).

Apart from technical and socio-emotional skills, there is substantial heterogeneity in terms of required skills across industries and occupations when looking at a higher disaggregation level. Figure 10 highlights that demand for cognitive skills is relatively high for jobs in IT, Science and Research, Law, and the Public Sector (for the universe of sectors, see Appendix C, Figure 42). In contrast, these industries show little demand for language skills.<sup>40</sup>

> 40 In appendix C (Table 9) we also explore pairwise skills correlations across industries and occupations, however finding no particular pattern.

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# **3 Electricity Access and Structural Transformation in Brazil**

We employ the model to study the relationship between structural transformation and electricity infrastructure formation guided by the experience of one of the largest developing nations, Brazil. Unlike many developing countries, Brazil offers a good quality, regionally and sectorally disaggregated historical data on both public infrastructure and structural transformation variables that can be used to calibrate our model. The data we use comes from the Brazilian Institute of Geography and Statistics ( _Instituto Brasileiro de Geografia e Estatística_ , IBGE), the Institute of Applied Economic Research ( _Instituto de Pesquisa Econômica Aplicada_ , IPEA), and Geographic Information System of the Electric Sector ( _Sistema de Informações Geográficas do Setor Elétrico_ , SIGEL) of the Brazil National Electricity Regulatory Authority ( _Agência Nacional de Energia Elétrica_ , ANEEL). In this section, we first describe the process of electrification and the main features of the structural transformation in this Latin-American country. Later, we present results from a reduced-form econometric analysis to illustrate the causal effects of electricity access on the Brazilian structural transformation process.

## **3.1 The Brazilian Experience**

Over the last century the country underwent massive electrification accompanied by the rapid structural transformation process. Advances in the electrification process in Brazil took place in the early 1960s, when high voltage transmission lines connected the Southeast states of São Paulo, Rio de Janeiro and Minas Gerais. The process accompanied the rapid pace of industrialization observed during the 1960s and 1970s, which required significant infrastructure investments. From 1963 to 1980, total electricity consumption increased from 22,618 gigawatt hours (GWh) to 112,055 GWh, with industrial users representing approximately 55% of total consumption. Residential electrification rates also rose sharply, with household access increasing from approximately 40% to 70% during the same period.

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22 have more access to radio signals and to government education services: the most recent census in Benin, from 2002, has information on village population for nearly all of the villages in the sample.

Poorer villages might have unobserved characteristics that affect both radio access and education outcomes. Though there is no census or statistically representative data of incomes and economic conditions at the village-level in Benin, the surveys offer several proxies for village incomes. Most are based on village averages of responses to the household survey: the percentage of households that report their income as belonging to the lowest of the income brackets; the percentages of sampled households that report owning a mobile phone and a television; the percentage of households surveyed with brick walls; and the percentage with houses with more than five rooms. With 20 observations per village, these averages are not precise enough to reject small income differences across villages, but they are sufficient to test whether larger income differences separate villages with more and less radio access. From the village surveys, it is also possible to compare villages according to the number of private schools serving the village.

Finally, village linguistic characteristics could be associated both with their inclusion into specific radio markets and independently with cultural practices that influence education. Based on the household survey, it is possible to compare villages according to, first, the probability that any two of these households would speak the same language at home; and, second, an indicator variable for whether the most common language among the sample of 20 households in a village is different from the most common language among all the households surveyed in the commune in which that village resides.

Table 2 reports results from a multivariate regression that yields no statistically significant association between any of these variables and either the number of community radio stations or the share of community stations as a fraction of all stations to which a village has access. No measures of income, wealth, remoteness or linguistic diversity are significant. In the first column,
duration (average migration duration is 3.6 years with a standard deviation of 3.7), while undocumented migrants who returned voluntarily have on average 5.8 years of migration duration with a standard deviation of 6.6. In line with our results in Table 12, these differential effects might be associated with the length of the migration spell abroad. These results confirm once again that the longer undocumented migrants stay abroad, the more their human capital is depleted.

# **7 Concluding Remarks**

This paper examines the impact of the legal status of temporary overseas migration on migrants’ wages upon return to the home country. Using unique data from Egypt, we distinguish between return migrants according to their type of international migration—documented versus undocumented—and examine the impact of temporary migration on wages upon return. Relying on a conditional mixed process model, which takes into account the triple selection into emigration, illegality, and return migration, we examine the effect of illegality on wages upon return.

Our findings show that controlling for the various layers of selection, return migrants who had an illegal status while abroad have significantly lower earnings upon 28
beyond a certain employment threshold, yet any of these measures tends to explain only a small fraction of misallocation.

Conversely, regulatory restrictions to movements across space appear to play a more important role. Tombe and Zhu (2015) study the impact of goods- and labor-market frictions using a general equilibrium model calibrated with Chinese data. Overall the reduction in trade and migration frictions explain almost 50% of productivity growth in 2000-2005, and the fall in internal trade and migration costs contributed about 20%.

Other studies of China examine also state-owned enterprises, their preferential access to resources, and the related productivity losses (e.g., Song, Storesletten and Zilibotti (2011)). Brandt, Tombe, and Zhu (2013) study the period 1985-2007 and find that within-province misallocation of capital between state and nonstate sectors reduced the nonagricultural TFP growth rate by 0.5% per year. The costs of misallocation appear to have sharply increased during the latest period analyzed, 1997-2007.

**Property rights.** Property rights affect resource allocation and therefore productivity, by limiting expropriation and facilitating market transactions (Besley and Ghatak (2010)). Adamopoulos and Restuccia (2014) assess the impact of the Comprehensive Agrarian Reform Program (CARP) on agricultural productivity by calibrating a model with data from the Philippines.<sup>4</sup> In 1988, the reform introduced ceilings on existing land holdings, promoted transfer of above-ceiling land to landless and smallholders, and restricted the transferability of the redistributed land. Between 1989 and 1993, agricultural productivity fell by more than 10%. According to the model, this decline is the result of distortions in farm size and occupational choices, as land is transferred from more to less productive holders.

**Trade and competition.** Trade policy influences the allocation of resources across heterogeneous producers and consequently impacts aggregate productivity. Two approaches are used to quantitatively assess the impact of tariffs and/or other distortionary forms of protection. On the one hand, evidence comes from
estimates the GDP-employment elasticities following alternative methodological approaches and shows that elasticities estimated as the average ratio of annual percentage changes in employment and GDP based on historical data have superior performance in terms of predictive power among all the methods evaluated. The estimated elasticities using this approach imply that employment in manufacturing and services grows less than GDP growth in these sectors across developing economies, given consistent complementary growth in labor productivity. Employment growth in agriculture follows similar dynamics. Given the dominance of smallholder farming across many developing economies, large gains in productivity may imply decreasing employment as economic activity in the sector grows, and thus, a higher number of countries have negative GDP-employment elasticities for this sector. In addition, the paper shows that labor productivity growth is associated with declining informality, implying further welfare effects for households through access to better jobs.

The results of this paper have important implications for estimations of job creation and improvements in job quality resulting from policy or investment interventions to grow economic activity in different sectors across developing economies characterized by high informality. Conditional on data availability, further research in this area could explore the variation of GDP-employment elasticities for more disaggregated sectors and different kinds of occupations or worker profiles; as well as by documenting value-added employment elasticities at the firm level, for companies operating in different sectors across developing economies.

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