rafmacalaba/data-use-mentions-extended huggingface dataset:
the world.

Then, the definition of the middle class with the vulnerability-to-poverty approach is applied to obtain the lower PPP income threshold to cross-sectional data in order to analyze the evolution of the middle class over a longer period of time. For Chile (1992-2009), the data is taken from the cross-sectional CASEN, carried out every two years since 1985 by MIDEPLAN. This data source is a nationally and regionally representative household survey used to measure the incidence of poverty and the level of inequality in the country. For Mexico (1992-2008), we use the _National Household Consumption and Expenditure Survey_ (ENIGH), undertaken by the Institute of Statistics and Geography (INEGI). The ENIGH is a nationally representative survey, covering urban and rural areas, and it contains detailed information on income and different categories of expenditures. This survey is available for 1984 and 1989, and for every two years since 1992. Finally, in the case of Peru (1997-2009) we use data from the cross-sectional ENAHO, collected on a quarterly basis by INEI since 1997. The fourth wave of the survey is nationally representative, and it is used for poverty measurement as it collects information on income and expenditure. All three surveys collect information of dwelling characteristics, and indicators related to age, sex, employment, and education of the household members.

# **5. A vulnerability approach to middle class analysis**

We propose an empirical framework to define the middle class in terms of households‘ vulnerability to poverty following a regression-based approach to estimate an income lower threshold associated to a low probability of falling into poverty, which will be used for cross-

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property goods and low-intellectual property goods, raising questions about whether the effects of the trade aspects of these PTAs were sufficiently well controlled. Maskus and Ridley (2021) studied this question in an identification context, defining treatment trade agreements as those with elevated IP standards of various forms versus those without such rules. They found significantly positive impacts on the trade of members with third parties (those outside the IPAs), controlling for prior TRIPS membership, in biopharmaceuticals, medical devices, and other sectors that are highly reliant on intellectual property protection.

Unstudied to date is whether and how IPAs influence international knowledge flows, as measured by bilateral patent applications among member countries and between non-member and member countries. This is a remarkable omission in light of the evident intent of intellectualproperty chapters to reduce application costs and strengthen protection for patent holders. Our aim is to address this shortcoming by estimating the effects of IPA membership on bilateral applications, both at the country level and using various high-tech sectoral clusters. We make use of the World Bank’s extensive database covering regulatory provisions in comprehensive PTAs (Mattoo et al., 2020), employing various definitions of what constitutes TRIPS-Plus agreements.

Prior literature has emphasized the interrelationships between patent filings or grants and detailed trade flows. For example, countries that engage in unilateral patent reforms tend to find rapid increases in foreign patenting in their economies, relative to domestic patenting (Lerner, 2002). The essential reason is that foreign firms seek to sell products, whether exported or produced through local affiliates, in markets where their goods may be patented and those patents enforced. This correlation between patenting and trade has attracted significant attention in recent years, given the widespread availability of micro data on patents and trade. Brunel and Zylkin (2022), in an extensive panel-data study between 1974 and 2006, find that international patent grants have a positive impact on bilateral exports to the patent-destination country, though these effects varied across sectors. De Rassenfosse, et al. (2020) studied transactions data among French exporting
efforts to encourage households to take advantage of government health products seem to lead to the diversion of those products from potentially free into paid markets.

An additional contribution of this study is to increase our understanding of the mechanisms through which media access influences policy outcomes. All theories agree that media access matters because it increases the transmission of relevant information to households, better enabling them to hold government accountable or to make appropriate decisions regarding health, education or simply the take-up of government policies. With the exception of research by Snyder and Strömberg (2011) using US data, earlier researchers could not confirm both that media actually broadcast relevant information and that media affected respondent knowledge. The data from Benin fill both of these gaps.

They confirm that radios do broadcast information on health and, specifically, malaria. Moreover, these broadcasts are relevant both for accountability (the existence and timing of free bed net distribution) and household behavior (the importance of using bed nets). Nevertheless, consistent with the reduced-form estimations that find no effect of radio access on government accountability and a significant effect on household behavior, radio access has similarly little effect on knowledge relevant for government accountability, namely, government health policies. Instead, access has significant effects on knowledge relevant for private household health practices.

The data do not allow for an evaluation of the welfare consequences of the diversion of bed nets to the paid market. First, they do not reveal whether local officials use bed net payments to subsidize other health interventions provided by health centers, or for private purposes. Second, they do not indicate whether households that pay for bed nets are more or less vulnerable to malaria compared to households that would have received the bed nets for free. Third, we cannot measure the effects of media on how households utilize the nets – better utilization may offset any welfare losses due to the effects of pricing on how bed nets are distributed across households.

The literature reviewed earlier yields mixed evidence on whether the shift from free to paid bed nets and other health
and the costs and use of health facilities.

# **Health**

The health situation in Congo is dire. For example, in control communities, 15% of respondents mention that, within the household, a child younger than five years old passed away during the previous year (a number similarly found in the DHS 2013). Panel (a) of **Figure 2** shows that there is no evidence that Tuungane improved health outcomes. The program had no impact on under-

> 11 That is, enumerators calculated the number of antibiotics, anti-malaria and anti-inflammatory tablets present in the hospital’s stockroom.

8
5 BDI SOM 5 BDI SOM<br>4 4<br>3 4 4 5 5 6 6 -8 -6 -4 -2 0 2 4<br>Log of Av. Trade (% GDP) (1970-2020) Log of Av. Trade (% GDP) (1970-2020)<br>Log of Av. GDP per capita (1970-2020) Log of Av. GDP per capita (1970-2020)<br><!-- End of picture text -->

**Source:** Authors’ calculation using data from the World Bank World Development Indicators (WDI). Av. denotes the average. The red line represents fitted values.

# **4.2 The causal relationship between trade and economic growth**

The study follows a two-step estimation procedure, where the first step estimates a simple bivariate model of trade intensity and GDP per capita, and the second step adds control variables to the model. Table 2

> 31 Panel data estimates can help to provide a more credible inference of parameters by accounting for both inter-individual differences and intraindividual dynamics. They contain more degrees of freedom and more sample variability than pure cross-sectional data or pure time series data, and thereby improve the efficiency of the estimates (Hsiao 2007). The structure of the panel has important implications for the exogeneity assumption. For example, if the cross-sectional dimension N is small, then the estimation of time-specific effects becomes harder. Similarly, a small T makes the estimation of the individual country-specific effects harder. Thus, the benefits of asymptotics can be realized if both N and T are large. 32 The sample contains 56 low-income developing countries, 88 emerging market economies and 38 advanced economies (IMF classification). 33 The results are also similar when relying on other measures of international trade (trade value in US$).

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# **3.  The Data and Its Description**

This paper uses NSS data to study the relationship between mothers’ employment, presence of young children and household structure. The NSS surveys are the largest and most frequently used data sets on employment for India. We use six “thick” rounds of the Employment and Unemployment schedules of the NSS: 1983, 1993–94, 2004–05, 2007–08, 2009–10 and 2011–12. While the NSS include an extensive set of questions on employment and individual characteristics, they do not have questions on child care or norms of motherhood and caregiving.

Our analytic sample comprises wives of household heads aged 25 to 55 years in urban areas.<sup>6</sup> The age range excludes women who may be in college or may have exited the labor market due to early retirement. It is worth noting here that marriage in India is early and near universal for women, with mean age at effective marriage in urban areas standing at 22.7 according to the Census.<sup>7</sup> Other analysis shows that marriage by itself is an important predictor of employment, in that it depresses women’s labor force participation in several countries, and certainly in India (Das and Desai, 2003; Das, 2006).

We have 25,000 to 29,000 observations for each year in our sample, and the pooled sample has nearly 162,000 observations. The dependent variable is women’s employment, which is estimated based on their activity in the last 365 days (i.e. whether a woman worked for the major part of the year preceding the survey, also called usual principal status). The main variable of interest is the presence of at least one young child under age 6 in the household, because by age 6 most children start going to school and require less intensive care. Other explanatory variables are woman’s education, age, a squared term for age
Despite the relevance and strong interest in the issue, analyses of the impacts of wealthier middle classes face constraints in measuring income directly, and most use GDP per capita as a proxy for income. Only a handful of cross country datasets report headcount indexes for income thresholds other than poverty, and the ones that exist span too short time periods to exploit both cross country and time series variations. As a result, current analyses fail to investigate directly whether, as postulated by the literature, “critical masses” of people overcoming certain income thresholds can affect institutional outcomes.

This paper makes two contributions. First, it presents a new panel dataset that contains information about households’ mean income and expenditures, inequality, and headcount indexes for several income and expenditures thresholds, which complements existing large cross-country datasets reporting poverty and inequality measures. The dataset spans 672 yearly observations across 128 countries. To build the dataset, we draw both from a collection of nationally representative household surveys, and from parameterized distributions of income and expenditures using parameters from the World Bank’s _PovCal_ database. Second, we use this new dataset to test the extent to which policy and institutional outcomes are affected by an expansion of the middle class, which in the paper is measured as the proportion of the population who have achieved income above 10 US dollars a day.

The joint endogeneity of the size of the middle class (and of the income distribution in general) is a concern. So is the likely presence of unobserved country-specific effects. We use an econometric methodology that, by taking advantage of the panel nature of the data, attempts to gauge the causal effect of expansions in the middle class while controlling for country-specific effects. Specifically, the estimation draws from the generalized method of moments (GMM) estimator for panel data developed by Arellano and Bond (1991) and Arellano and Bover (1995). We estimate the impact of the middle class on policies and institutions in three broad categories: social policy regarding public expenditures on health and education, market-oriented economic
continuing having access to it increase by about 0.5 (column 1) and 0.4 percentage points (column 2) for both women and men in a governorate with average per capita expenditure in communications in 2010 (235 JOD in 2010 prices). In all cases, the F statistic surpasses 10, the rule for rejection of the hypothesis of weak instruments with one endogenous variable.

The assumption of similar pre-treatment trends is crucial for our identification strategy. We provide additional evidence on the validity of distance to 3G broadband towers as an instrument in Figure 4 and Table 3. We use data from the Jordan Population and Family Health Survey (JPFHS) 2002, 2007 and 2009 which provide us with information on employment previous to the deployment of 3G technology in the country. We calculate the average employment rate of women aged 15-49 in each subdistrict and we group them according to whether their distance to the nearest 3G cell tower in 2016 is above or below the average distance across subdistricts.<sup>4</sup> Figure 4 shows that the average women’s employment rate followed the same trend in both groups of subdistricts. The same conclusion is obtained when estimating OLS models at the subdistrict level where the female employment rate is regressed on year dummies, an indicator for whether the distance to the nearest 3G tower in 2016 is below or equal to the average distance across subdistricts, and the interaction between these two variables. Results appear in Table 3 and show that the indicator of distance and the interaction terms are not statistically significant.

# **5 Effect of internet adoption on labor participation**

Our main results are presented in Table 4. They show an increase in women’s labor force participation associated with internet access under OLS and IV estimations, and no statistically significant effects for men. IV results indicate that a 1 percentage point increase in internet adoption or continuation between 2010 and 2016 led to an increase in female labor force participation of around 0.7 (column 5) and 0.
7 graduated from IDA borrowing and 0 for eligible borrowers. We therefore used a logit estimation model. We run pooled logit regressions and correct standard errors for clustering by country. We also test the model with a probit estimator and find similar results. All data come from the World Development Indicators (WDI) database, unless stated otherwise.

As the graduation status of a country for the coming year cannot be determined by data for that same year, the IDA decisions are based on historical data. This means that the model has to reflect a two-year lag between the IDA graduate status in a certain fiscal year and the datapoints. For example, it is assumed that the FY2018 graduation decisions are based on calendar year 2016 data, as they are the most recent data available at the time the determination is made.

This analysis treats graduation and reverse graduation symmetrically. In practice, graduation and reverse graduation are not fully symmetric. Reverse graduations usually occur because of a crisis resulting in a major fall in income per capita and/or loss of IBRD creditworthiness, whereas decisions on graduation are affected by a broader set of factors. Notwithstanding this limitation, our analysis helps reveal whether IDA graduates differ systematically from IDA borrowers in ways consistent with the IDA graduation policy and, as demonstrated in the following paragraphs, explains graduation decisions reasonably well.

# _Explanatory variables_

In this model we are using the data that proxy for the issues analyzed when graduation decisions are made. In some instances, the data used in the model were not available when the decision was made. There are also variables that have been used for recent graduation decisions but are not available for most years used in this study and are therefore excluded.

Regressors in the model include two income-related indicators: the ratio of GNI per capita to the operational cutoff and (the log of 1 plus) the number of consecutive years in which GNI per capita has exceeded the operational cutoff. If income falls below the cutoff in any given year, this indicator resets at 0. The number
average of annual elasticities. In Costa Rica, North Macedonia, the Philippines, and Romania, it is the average elasticity without outliers. In Bulgaria, Colombia, and <mark>Türkiye,</mark> the best method is the median regression. In Indonesia, it is the mean regression controlling for the logarithm of total GDP. Finally, in Ecuador and Peru, the method with the best performance is the mean regression controlling for the logarithm of total GDP interacted with the informality rate. Table A2 in the Appendix shows the percentage difference between our employment projection for each country and method and the actual employment level, highlighting the method with the best performance. In general, the mean regression, median regression, and the average without outliers methods perform well in all cases. In many cases, there is little difference between the performances of the different methods.

In Figure A1 in the Appendix, we present the comparison between the employment projections for 2020 and the observed employment levels disaggregating by country, estimation method and economic sector.<sup>9</sup> We find that the magnitude of the bias is larger in the agriculture sector in 8 of 14 countries regardless of the estimation method and direction of the bias. In 4 countries, the bias is larger in the service sector, while in the remaining two countries, the largest biases appear in the industry sector. Another important finding to highlight is that in 8 of the 14 countries included in the analysis, the direction of the bias differs between sectors. In four of these 8 countries, the sector where the elasticity estimations lead to a bias of a different sign is the agriculture sector. In Costa Rica, Peru, Philippines, and South Africa, the bias for the agriculture sector is negative, meaning

> 9 Sectoral employment data for 2020 is not available for Albania.

8
We observe that the relationship between self-employment and migration is similar across all three levels, suggesting that our findings do not depend on the specific definition of migration adopted.

This tendency of the self-employed to be less likely to migrate than wage workers and those without jobs captures the joint effect of migrant selection and of any causal effect of self-employment on migration. We next attempt to isolate how much of this effect could be causal.

# **4 Causal analysis using randomized experiments**

As section 2.2 shows, theoretically the causal impact of becoming self-employed will depend on the extent to which it increases job opportunities at home (and hence the opportunity cost of migrating) and to which it involves a lock-in effect, versus the extent to which it alleviates credit constraints and changes human capital and hence job opportunities abroad. The result is that ”the” effect of becoming self-employed will likely depend on what causes an increase in the likelihood of self-employment. For example, becoming self-employed in order to take-over a family business when a parent or spouse dies is likely to have a different effect on the likelihood of migration from becoming self-employed because you have developed new entrepreneurial skills or had access to new forms of capital or credit. Our focus is on the impact of deliberate policy actions that increase self-employment. To examine this, we bring together data from seven randomized experiments, none of which had focused on migration as a key outcome, and examine the extent to which interventions to promote self-employment also change migration propensities.

## _Selection of studies_

We to collate as randomized as that attempted together many experiments possible were focused on or and stimulating supporting self-employment entrepreneurship, and for which we could measure migration as an outcome. We used our own knowledge of the literature, systematic reviews of business support programs, and web searches to identify studies which met the following criteria: (i) the intervention was aimed at self-employment or job creation and was successful in either stimulating new or in to be more to sur- self-employment, getting existing self-employed firms
then allocating taxes, social contributions, subsidies and public social spending to individuals included in the household survey in a consistent and methodologically sound way, so that it is possible to compare incomes and incomebased measures of wellbeing before and after taxes and public transfers.

The methodology comprises two standard scenarios depending on how contributory pensions are treated: as deferred income or as government transfers. In the analysis for Chile, each of both scenarios can be constructed by using two definitions of income that are employed in the estimation of official figures of income inequality and income-based poverty. The measurement of inequality in Chile uses 6
and Duflo 2007; La Porta and Shleifer 2014; McCraig and Pavcnik 2015). Finally, by offering a new perspective on the barriers to growth-enhancing structural transformation, this study fits into the emerging class of studies that aims to understand why structural transformation has failed to produce significant productivity growth in many developing countries (Caselli and Tenreyro 2005; Restuccia et al. 2008; McMillan et al. 2014).

We use data from Ghana to examine the role of subnational intersectoral productivity gaps. Ghana is a representative case among developing countries of marked structural transformation in recent decades accompanied by limited national aggregate productivity growth. The country also has the needed spatially disaggregated data for our analysis.

Using nonfarm enterprise census data for the country collected between 2014 and 2016, we find that average productivity in the informal sector is lower than in the formal sector in all 4