rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_The role of income_**

The share of a country's population that resides in urban areas - that is, its level of urbanization - is highly correlated with its level of per capita income. The share of urban population increases rapidly at low levels of income (and of urbanization) to converge to an urbanization level of about 80% (Figure 3). This transformation is due to the structural changes that accompany development. The share of GDP derived from agriculture falls from 32% among low income countries to less than 3% among rich ones, and the share of employment accounted for by agriculture falls by even more: from about 66% to less than 6%.

Our basic model, therefore, is that urbanization is a function of income and income squared (to capture the non-linearity of the relationship),<sup>8</sup> and of the structure of the economy: U = E Y (YA/Y) (YM/Y) which in logs yields: In addition, we test the hypotheses mentioned above concerning rural-urban wage differentials, urban bias, rural "push" factors, civil disturbances and wars, and civil and political rights. Our data is organized as an unbalanced panel data set, with observations every five years from 1965 to 1995, for up to 100 developed and developing countries.<sup>9</sup> Regression 1 in Table 2 shows that income per capita, the share of GDP derived from agriculture and manufacturing, and a time trend ("year") explain 80% of cross country variations in levels of urbanization.<sup>10</sup> Because the variables are in logs, their coefficients are elasticities. Thus a 10% increase in the share of GDP derived from manufacturing occurs along with a 1.3% increase in the level of urbanization, while a 10% increase in the share derived from agriculture coincides with a -1.3% decrease in the level of urbanization.
incumbents.<sup>16</sup> This exercise supports our main findings and we com14We fix the electoral census at the year 2000 to keep the denominator constant in our estimates and to facilitate interpretation of the coefficients. Nevertheless, we also verify that our main results are not mechanical and remain consistent when using the contemporaneous electoral census that accounts for the population decline.

> 15The opposition formally boycotted the 2018 presidential election, which contaminated turnout and opposition support measures for that year. We address this by including results for the 2024 presidential election, which saw full opposition participation.

> 16The data are publicly available at Resultados con VZLA (2024).

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# **Can Information and Alternatives to Irregular Migration Reduce**

# **“Backway” Migration from The Gambia?**<sup>*****</sup>

Tijan L. Bah<sup>†</sup> , Catia Batista<sup>‡</sup> , Flore Gubert<sup>§</sup> and David McKenzie<sup>**</sup>

**Keywords:** Irregular migration, migration deterrence, information interventions, vocational training, cash transfer, randomized experiment.

**JEL codes:** O15, F22, J61

* We thank the editor and two anonymous referees for helpful comments on our stage 1 registered report, our formal discussant Giacomo Battiston and participants in various seminars for comments on the results, the migration experts and Gambian stakeholders who provided predictions of our treatment effects, Mam Jarra Njie and Vitor Cavalcanti for excellent research assistance and support of field activities, YAIM, and the Ministry of Youths and Sports, Gambian Embassy in Dakar, Senegal, The Gambian Migrant Associations in Senegal and Italy, and the Ministry of Foreign Affairs. This work was supported by the European Union grant number AMIF-2017-AG-INFO 824432. The content represents the views of the authors only and is their sole responsibility. The European Commission does not accept any responsibility for use that may be made of the information it contains. Ethical approval was granted by the Nova School of Business and Economics on March 1, 2019. The study was pre-registered in the AEA RCT registry as AEARCTR-0003998 on March, 2019. <u>http://www.socialscienceregistry.org/trials/3998</u>

† Navarra Center for International Development, University of Navarra, University of The Gambia and NOVAFRICA. Email: tbah@unav.es ‡ Nova School of Business and Economics and NOVAFRICA. Email: catia.batista@novasbe.pt § IRD, UMR LEDa-DIAL, PSL, Université Paris-Dauphine, CNRS. Email: gubert@dial.prd
Second, in relative terms, the excess supply is largest for workers with the least amount of education.<sup>15</sup>

**Table 11. Excess Supply of Workers by Schooling Category, 2006**

||_s_|_is_<br>_N_|(1<br>)<br>_s_<br>_is_<br>_N_<br><br>|_is_<br>_fs_<br>_N_<br>_N_<br>|(1<br>)<br>/ (<br>)<br>_s_<br>_is_<br>_is_<br>_fs_<br>_N_<br>_N_<br>_N_<br><br><br>|
|---|---|---|---|---|---|
|**Incomplete Primary**|0.180|227171|186260|413389|0.451|
|**Complete Primary**|0.288|469525|334510|1080167|0.310|
|**Incomplete Junior High**|0.243|137191|103819|289111|0.359|
|**Complete Junior High**|0.424|673273|387796|1968788|0.197|
|**Incomplete Senior High**|0.723|289234|80233|1235346|0.064|
|**Complete Senior High**|0.776|243601|54632|1102977|0.049|
|**University**|1.000|328109|0|1834499|0|
|**Total**||2368104|1147250|7924277|0.144|

_Source:_ Authors’ analysis based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note: Incomplete Primary_ refers
significant impacts. A second is what type of statistical inference is appropriate given the sample size. Third, the sample may be too small to be representative of large firms in developing countries. We discuss each concern in turn and the steps we took to address them.

<u>Significance of results:</u> Even though we have only 20 experimental plants across 17 firms, we obtain statistically significant results. There are five reasons for this. First, these are large plants with about 80 looms and about 130 employees each, so that idiosyncratic shocks – like machine breakdowns or worker illness – tend to average out. Second, the data were collected directly from the machine logs, so have very little (if any) measurement error. Third, the firms are homogenous in terms of size, product, region and technology, so that time dummies control for most external shocks. Fourth, we collected weekly data, which provides high-frequency observations over the course of the treatment and the use of these repeated measures can dramatically reduce the sample size needed to detect a given treatment effect (McKenzie, 2010b). Finally, the intervention was intensive, leading to large treatment effects – for example, the point estimate for the reduction in quality defects was over 50%.

<u>Statistical inference: A second concern is over using statistical tests which rely on</u> asymptotic arguments in the N dimension to justify the normal approximation. We use three alternatives to address this concern. First, we use firm-clustered bootstrap standard errors (Cameron et al, 2008). Second, we implement permutation procedures (for both the Intent to Treat (ITT) and Instrumental Variables estimators) that have exact finite sample size and so do not rely upon asymptotic approximations. Third, we exploit our large T sample to implement procedures that rely upon asymptotic approximations along the time dimension (with a fixed N).

_Permutation Tests:_ Permutation tests use the fact that order statistics are sufficient and complete statistics to derive critical
consider the difference of 54 yuan in village subsidies, aid and fund between cadre and non-cadre households to be the average compensation and subsidies for being a cadre from village coffers. Moreover, and again as we discussed above, the other part of compensation and subsidies for being a cadre is included among transfers from the government. Then, we also consider the difference of eight Yuan in the transfer from government between cadre and non-cadre households as the compensation and subsidies for being a cadre from government treasury. Taking these results together, we infer that, on a per capita basis, cadre households earn about 62 yuan of compensation and subsidies for being a cadre. Taking account of the average family size of five for cadre households in our sample, this means that the average cadre wage was about 310 Yuan (measured in 1986 Yuan) in the period of 1986 to 1991 (i.e., 62 * 5 =310). Our inference of the wage income earned by cadres appears to be reliable. To assess the reliability of our inference, ideally, we would draw a comparison with national/large-scale surveys on cadre wage income. Unfortunately, no such surveys are available. Instead, we put our inference in perspective by citing a number of studies, which survey cadre wage incomes in specific locations of China. Specifically, two studies show the cadre wage income to be 1510 Yuan in year 2003 in one county of Shannxi province and 4060 Yuan in 2006 in another county of the province while another study shows the average cadre wage income to be 2014 Yuan in 2003 in four counties of Hubei province (Peng and Zhang, 2003; Wang, 2004; Wang, Ning and Rae, 2008). When measured in 1986 Yuan, these cadre wage income numbers become 460, 1237 and 667 Yuan, respectively. Thus, taking into account the fact that per capita income in rural China during the period of 1986 to 2006 had increased by 150%, our inference of 310 Yuan does not seem to be at odds
lead to higher PM2 _._ 5 concentrations (McClure and Jaffe, 2018), are a particular source of concern. To limit the influence of surface PM2 _._ 5 related to wildfires, we follow Qiu et al. (2023) and omit observations plausibly affected by wildfires. Specifically, we use data from the Global Fire Emissions Database version 4 (GFED4s)<sup>6</sup> described in Van Der Werf et al. (2017). This dataset provides monthly information on carbon emissions from fires at a spatial resolution of 0.25<sup>_◦_</sup> x0.25<sup>_◦_</sup> and at a monthly frequency between 1997 and 2022. Using this information, we identify and exclude from the sample plant-month-year observations where fire emissions are observed within 50 km of a power plant.

To account for other confounding factors, we also include in our dataset several meteorological variables derived from the Weather for Energy Tracker database (IEA and CMCC, 2022).<sup>7</sup> These variables provide monthly frequency information and correspond to the mean value observed within 50 km of each power plant. The meteorological variables are: temperature (<sup>_◦_</sup> C at 2m), total precipita-

> 5https://sites.wustl.edu/acag/datasets/surface-pm2-5/

> 6https://www.geo.vu.nl/ gwerf/GFED/GFED4/

> 7https://www.iea.org/data-and-statistics/data-tools/weather-for-energy-tracker.

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1.198]|[1.150]|[0.006]|[0.000]|[0.000]|
|Obs.|18321|18321|18321|18321|18321|18321|16354|16367|16401|

_Notes:_ Estimates from reduced-form regressions relating time-invariant or pre-determined municipal attributes to theoretical transfers observed in the period 2000-2006. Data for 1991 come from the corresponding population census. All regressions include a three order population polynomial, state and year fixed effects. Robust standard errors clustered at the municipal level in brackets. *, **, *** represent significance at the 10%, 5% and 1% level, respectively.
).

# _B. Teenage pregnancy_

Another subject of specific relevance is pregnancy during adolescence. According to the ENSA and ENSANUT, the probability of teenage pregnancy increased from 9.3% to 10.7% between 2000 and 2012. The increase in teenage pregnancy occurred despite the fact that the prevalence of contraceptive use also increased and is thus attributed to the fact that an even greater proportion of teens reported having initiated sexual activity.

The analysis controlling for socioeconomic status showed that in 2000 the probability of pregnancy among adolescents who had initiated sexual activity was 20 percentage points higher for adolescents in the lowest income quintile compared to those in the highest income quintile. Moreover, this gap increased to 33 percentage points in 2012. Although the likelihood of having sex was higher among adolescents who live in higher income households, this group was also more likely to report the use of protection to prevent pregnancies (Gutiérrez et al., 2016a). Despite these disparities, the 2012 ENSANUT analysis suggests that the increase in the fertility rate for teens aged 15 to 19 occurred to a lesser extent among adolescents in PROSPERA households compared to adolescents in the two poorest income quantiles in households that did not receive aid from PROSPERA.

# **III. PROSPERA’s theory of change from the social mobility perspective**

The starting point for intergenerational mobility analysis in health and education attributable to PROSPERA is understanding the degree to which mobility is part of the program’s 7
official poverty in Kosovo is measured based on a consumption aggregate, and this can only be constructed with HBS data.

# **Another empirical challenge is to establish causality since identifying sources of exogenous variation**

**is not possible with available data.** Only short pooled cross-section time series data are available, with no significant variation over time, and there is no variation by region in nominal minimum wages. Another option would be to exploit the fact that the minimum wage in Kosovo is set differently for different age groups, so that individuals experience a significant hourly wage increase at age 35.<sup>14</sup> In this case, changes in employment for individuals who are a few months younger and older than 35 years provide an estimate of the employment effect of the legislated wage increase. However, given that the wage data are in brackets, we cannot use a regression discontinuity approach that compares employment outcomes for individuals around this age

> 12 The current minimum wage falls in the third and fourth brackets, for the young and the old, respectively.

> 13 Note that this method does not account for worker characteristics.

> 14 This approach has been used in the United Kingdom, where the legal minimum wage varies by age (Dickens, Riley, Wilkinson, 2011).

10
- _ 16

while the ni4'j are the numbers of audits of returns of type i at stage j that were performed during the same period. Then a regression of the type: Tk - Ei EJ tij nijk + e where e is an error term can be estimated so that the estimators of tij represents the mean time consumed by each activity.<sup>16</sup> Note that the direct method offers actually the 'normative' time required for each activity while the indirect method represents the 'actual' time spent on each activity.

# 4. An Illustration With Data From Israel

According to the Israeli income tax laws and by-laws, there are three major stages in which disagreement between the taxpayer and the tax administration can be settled. Because a typical decision tree is complicated we descriTe here only the basic components of the estimation process.

The data consist of tax returns for the fiscal year 1982 that were audited during fiscal year 1984 in one regional tax office. Since data were extracted from tax files,<sup>17</sup> only three types of decision trees were estimated: Corporations (COR hereafter), self-employed individuals who

- 16/ The variance of the dependent variable, which represents the number of working days in a given period of time is small relative to the variances of the independent variables. Hence, the regression should be run without a constant term. Therefore, it is impossible to estimate separately 'tea time' and other personal activities.

- 17/ Data needed for the estimation of the decision tree for several tax laws exists in the computer of tax administration. Unfortunately, we did not have access to this data set.
sector employment over the same period (World Bank, 1995). Private consumption per capita fell by 31 percent between 1987 and 1995, consistent with the 35 percent fall in household expenditure per capita over the same period as measured by the household surveys.

Due to four years of C6te d'Ivoire Living Standards Survey (CILSS) data between 1985 and 1988 and two Priority Household Surveys (PHS) conducted in 1993 and 1995, it is possible to trace the evolution of poverty during this period of severe economic recession in C6te d'lvoire, although the differences in the two types of surveys raise questions about comparability of the poverty estimates across surveys.<sup>4</sup> In 1985, the headcount index of poverty in C6te d'Ivoire was 11 percent based on the extreme poverty line of 75,000 CFAF per capita per year adopted by Grootaert (1996) shown in table 2. The 75,000 CFAF poverty line is close to the one dollar per day poverty line used in the 1990 World Development Report.<sup>5</sup> Between 1985 and 1993, poverty almost tripled, with almost three-fourths of the 26 percentage point increase occurring in the five year period between 1988 and 1993. Looking at the change in poverty by region and socioeconomic groups raises some questions about the validity of the data and about why certain groups fared better than others.

# Declining per **capita** expenditures **and** increasing poverty, 1985-1988

Between 1985 and 1988 the decline in household expenditure per capita was greater in the urban areas than in the rural areas, setting aside for the moment the West Forest region (table 3). Mean per capita expenditures fell by almost 30 percent in Abidjan and 40 percent in Other Cities, compared to the 15 percent decline in East Forest and the 21 percent cent decline in the Savannah. Despite the large fall in household expenditure in Abidjan, there was no increase in poverty, since even in 1988 mean household expenditure per capita
entrepreneurs and connect them with markets. In addition, DFIs can play a pivotal role in promoting gender-responsive procurement practices (e.g., "Sourcing2Equal" initiative of the IFC).

_Third_ , the findings highlight the potential for reducing capital misallocation by encouraging financial institutions to adopt fair lending practices and advocating for policy reforms that address the legal and regulatory barriers faced by women entrepreneurs. For example, in the Middle East, legal reforms aimed at enhancing women’s economic empowerment, such as amendments to inheritance laws and property rights, have led to several positive outcomes, including improved access to finance for women entrepreneurs (World Bank, 2017). Additionally, gender intelligence training for financial intermediaries, policies such as branch-level targets for lending to women-led firms without requiring a guarantor, and integrating the stories of successful women role models into banks’ internal communication and training programs can help mitigate implicit biases (Brock and Haas, 2023; Brock and de Haas, 2021; Alesina et al., 2013). Combining these policies with the collection of sex-disaggregated data (Moylan et al., 2023), which can be used to provide loan officers with factual information about gender discrimination within the loan officer population, can significantly reduce biases and produce better outcomes for women-owned businesses.

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costs increased by 13 percent due to greater spending on input materials to compensate for the growing shortages of electricity from 1999 onward, although this was partially offset by a 5 percent reduction in unit cost due to savings in the other inputs and small total factor productivity improvements.

In fact, we find an overall decrease in other non-electricity energy sources, suggesting that these primary energy sources are complementary inputs in producing the intermediate products that have been outsourced in response to electricity shortages. The overall effect of electricity shortages, which we proxy for with a measure of scarcity, was to increase production costs. From 1999 to 2004, firms’ costs rose by 8 percent, primarily due to input factor substitutions.

Grainger and Zhang (2019) go further by investigating the ultimate impact of unreliable power supply on manufacturing productivity, using firm survey data for Pakistan covering the period 2010 – 2011. They show that an additional average daily hour of unexpected power shortages decreases annual revenues by almost 10 percent, decreasing annual value-added at the firm level by approximately 20 percent, and increasing the labor share of output. Their findings underscore the importance of a reliable electricity supply to sustain firm productivity.

Mensah (2018) additionally explores the implications of unreliable power supply for employment. He finds that firms respond to the productivity losses arising from power outages by cutting jobs. To estimate the causal impact of electricity shortages on employment, Mensah (2018) employs household data from the Afrobarometer and Enterprise Surveys from more than 20 African countries. The results show that outages reduce the total factor productivity of African firms by more than 2.3 percent and reduce the probability that an individual is employed by between 35 and 41 percentage points. Significantly, the negative impact on employment is concentrated in high-skilled high-wage employment in non-agriculture sectors.

Finally, power outages also have negative impacts on development outcomes for households, which partially offset the benefits of electrification. Using household survey data from rural households for 2005 and 2010 in Bangladesh, Samad and Zhang (2017) find that a one
4. To Design Good Policies, Macro Outcomes Need to Be Understood “From the Ground Up” _<mark>Roberto Fattal, Hiau Looi Kee, and Sergio Schmukler</mark>_ Traditionally, macroeconomic outcomes such as growth, economic fluctuations, and reactions to policies and shocks have been studied using aggregate data at the country level. A flurry of new research over the past decade has centered on using micro data at the product, firm, and sector level to shed new light on aggregate outcomes.

# **Macro insights from firm dynamics**

One approach analyzes the macroeconomic implications of firm‐level dynamics and the misallocation of inputs and production across firms.<sup>38</sup> By studying macroeconomic performance as a result of the interaction of individual firms and the surrounding business environment, we have learned how policy distortions at the firm level affect firms’ behavior and lead to worse aggregate outcomes.

One example is the allocation of credit. Traditionally, the effect of financial conditions on economic growth was studied by comparing economic performance across countries with different credit market institutions. Developed financial markets foster prosperity by allocating capital efficiently across the various production possibilities in the economy. Even if the cross‐country comparison could uncover a positive relation between financial development and growth, it would be silent about the mechanism through which such a positive relation works.

The new firm‐level approach to the question proves to be very revealing. Recent research shows that financial markets benefit growth by directly providing credit to firms with good opportunities.<sup>39</sup> Although the overall functioning of financial markets is important, the allocation of credit across firms matters for economy‐wide growth. Small firms as well as young that create novel products and technologies but that lack the financing to scale up tend to be harmed the most by misallocation of credit. In countries where credit is expensive and hard to access, firms typically are small, leading to a misallocation of credit. Large firms that are relatively less