rafmacalaba/data-use-mentions-extended huggingface dataset:
decision-making. More generally, understanding the role of skills in agricultural decisions can be key to inform policy interventions that directly aim to improve skills, or the success of which could be conditional on the skills of the target population.

Almund et al. (2011) suggests that different skills and personality traits might help predict different outcomes, with cognitive ability being more important for complex tasks, while

> 3 Measures of risk aversion and time preferences, which have a longer history of use in developing country surveys, have received more scrutiny.

> 4 While there is a literature in psychology regarding the validity of scales across cultures (Benet-Martinez and John, 1998; John, Naumann, and Soto, 2008), these studies typically focus on highly-educated populations in different countries, and often are limited to high or middle income settings.

> 5 While explanatory factor analysis is used elsewhere in the economics literature on skills (Cunha and Heckman, 2010; Heckman, Stixrud and Urzua, 2006), we also build on insights from the psychometrics literature, such as for the corrections for acquiescence bias.

> 6 Following McKenzie (2012), in order to reduce the noise in the outcome variables, the measures of yield and practices are obtained from the average over the four seasons that followed the collection of skills data.

3
# (1) Introduction

A vast literature on intergenerational mobility and inequality of opportunity in economics and sociology focuses on the role of family and neighborhood background in shaping the life chances of a child.<sup>2</sup> This literature, however, primarily concentrates on developed countries, and the evidence base on developing countries remains relatively sparse. There has been a spurt in the interests among development economists in understanding the geography and evolution of intergenerational mobility in the recent years, partly spurred by the evidence of increasing inequality in the 1980s and 1990s in many developing countries (World Bank (2006), Chancel et al. (2022)).<sup>3</sup> Among the policymakers, there has been an increasing emphasis on inequality of opportunity as opposed to inequality of outcome.<sup>4</sup> The observed increase in cross-sectional inequality in socioeconomic outcomes is of serious concern when it reects increasing inequality of opportunity and declining intergenerational mobility.

It is, however, dicult to build a credible and comparable cross-country evidence base on intergenerational mobility in developing countries because of data limitations. Two essential building blocks are required for such an analysis: a measure of socioeconomic status of children and parents, and a measure of intergenerational mobility or inequality of economic opportunity.<sup>5</sup> Permanent income has been the preferred measure of socioeconomic status in the economic literature on developed countries, but reliable panel data for a long enough period to estimate permanent income remain scarce in developing countries.<sup>6</sup> In the absence of income data, it is feasible to use education data across a large number of developing countries,

> 2For recent surveys of the economic literature see, Black and Devereux (2011), Bjorklund and Salvanes (2011), Solon (1999), Heckman and Mosso (2014), Mogstad and Torsvik (2021). For surveys of the sociology literature see Breen (2010) and Torche (2015a).

>
PROSPERA brings children into school who might have otherwise stayed home or engaged in work. These children may be more likely to be in the lower part of the test score distribution. If selective school attendance is not taken into account, then estimated program impacts on test scores are likely to be downward biased.

A related caveat is that little is known about how impacts might vary by available school quality. Maintaining and/or improving the supply of schools and their quality are responsibilities of the Ministry of Public Education in Mexico. However, PROSPERA impacts in the area of education are intimately intertwined with school quality. First, schooling quality may, in fact, have deteriorated for schools where PROSPERA beneficiaries attend due to crowding induced by increased enrollment. Second, the extent of benefits accruing to children from attending school may depend on school quality. Third, changes in school quality may affect decisions about which schools to attend, which is more likely to be the case in urban areas and at higher school grade levels where households can choose among multiple schooling options.<sup>3</sup> This study analyzes nationally applied achievement test data that have recently been linked to administrative data on beneficiary receipt, socio-economic characteristics of children taking the tests and school quality characteristics to study how the PROSPERA program affects school enrollment, type of school attended and academic achievement. Nationwide standardized administrative test score data from schools (ENLACE National Evaluation of Academic Achievement in Schools “Evaluacion Nacional de Logro Academico en Centros Escolares”) were merged with information on which students come from PROSPERA households and with contextual information on family background obtained from a school survey. These data allow study of how students from beneficiary and non-beneficiary households differ in terms of their enrollment decisions, their grade progression and in terms of learning and academic achievement, as measured on standardized tests in math and Spanish.

As previously noted, a major challenge in assessing program effects on learning and academic achievement is the problem of selection. In primary school (up through grade 6), attendance at
# **3. Employment in South Asia**

# _3.1. Current employment structure_

For the overview of employment in South Asia, we rely on information only from sources that allow the construction of comparable data.<sup>4</sup> As shown in Figure 3, agriculture continues to employ a majority of the working-age population across the region. This is especially so in Nepal and Bhutan, but the share is high in Afghanistan and India as well. The second most important source of jobs is the services sector. Sri Lanka is the only country in the region where the services sector employs more people than agriculture, and in Bangladesh the two sectors are even. On the other hand, the share of the manufacturing sector is relatively low across all countries in the region, ranging from 3 percent in Bhutan to 9 percent in Sri Lanka. In many countries in the region, construction employs as many people as manufacturing.

**Figure 3.** Employment rate by sector of activity.

<!-- Start of picture text --> Percent<br>80<br>60<br>40<br>20<br>0<br>Nepal Afghanistan Bhutan Bangladesh India Sri Lanka Pakistan<br>Agriculture Manufacturing Service Construction<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset. **Note:** Period of reference specified in footnote 4.

Regarding the type of jobs, it is important to keep in mind that regular wage employment is not the same as formal employment. It only means that there is a stable and predictable employer-employee relationship, and that the worker can expect to still have the job the following month. But even with this definition, only around one-tenth of the working-age population has a regular job across South Asia, as shown in Figure 4. The share is higher in Bhutan, but it still reaches only 16 percent. Even casual wage work,
ment under the presence of bribery opportunities. For instance, the policy that increases tax rates for big enterprises will not have a large impact on the aggregate output nor the tax revenues collected, since enterprises can decrease the effective tax rates by increasing bribery payments.

Although this study uses the most detailed data about plant size distribution and bribery payments in Türkiye, there is still room for increasing the quality of the dataset by focusing on the size distribution of small enterprises and by having a larger sample by including small firms in bribery surveys. Hence, future studies can conduct more precise calibrations with more detailed datasets for both plant size and bribery payments. Future research can also focus on three different extensions of this model. The first extension is about the relationship between bribery and aggregate variables such as GDP per capita, mean size and managerial quality at the cross-country level. We know that bribery incidence is negatively correlated with these aggregate variables. Therefore, having a model which incorporates these cross-country facts can provide a better understanding of the relation between development and bribery. Second, the relationship between managerial skill accumulation and bribery is worth investigating. Managers exposed to bribery requests tend to spend more time and resources to ‘get things done’. Instead, they could use these resources to accumulate their managerial skills and increase their profits. For example, they could spend their time and income getting an MBA degree, instead of spending time on making connections with officers and bribing them. Thus, if the environment of Bhattacharya _et al._ (2013)-where managers can accumulate skills- and the setup of this paper were integrated, research could focus on the effects of bribery on managerial skill accumulation. Third, the level of distortions can be endogenized by allowing agents to choose whether or not to be (corrupt) government officials. With this extension, some of the high ability agents can choose to be a government official, depending on bribery returns. In addition, they can increase the distortion levels to enjoy more bribery returns. As
result to Damgaard et al. (2024), who separate _phantom FDI_ , investments into empty corporate shells with no link to the local real economy, and _real FDI_ . They find a positive impact of lower CIT rates on total FDI across countries, but zero impact on _real FDI_ .

The rest of the paper is organized as follows. The next section provides the institutional context including the background of the tax reform; sections 3 and 4 describe the data and the empirical methodology, respectively; section 5 presents the results at the sectoral- and firm-levels; and section 6 concludes and discusses the policy relevance of the findings.

# **2 Institutional Context and Policy Background**

Tunisia has long maintained a preferential regime aimed to attract and facilitate the activities of exporting firms, which are defined as those that export at least 70% of their output. This regime comprised generous tax and fiscal benefits since its creation in 1972, including duty-free imports and income tax exemptions. Additional benefits include "streamlined customs procedures, corresponding to significant costs savings" and the possibility of holding bank accounts in foreign currency, which protects against currency risks (Nucifora and Rijkers, 2014).<sup>4</sup> While the establishment of the offshore regime was associated with growth in export oriented investment, it also came at a high fiscal cost. A study by the IFC and ECOPA (2012) estimates that the offshore income tax exemption accounted for two-thirds of the total fiscal and financial incentives granted by Tunisia in 2009. Using administrative firmlevel data (described below), we estimate that the fiscal cost of this offshore exemption was worth 2.4 billion Tunisian dinars in 2013, equivalent to 6.8% of GDP or 14.7% of overall tax revenues.<sup>5</sup> While this is an upper-bound estimate, it illustrates the significant order of magnitude of the offshore tax holiday scheme.<sup>6</sup>

> 4The benefits of the offshore regime are such that
treatment effect over a period 9-15 months post-treatment is likely to be reasonable.<sup>10</sup> In contrast, if there is reason to believe the effects differ dramatically with time since treatment, one will not want to average over long periods. High seasonality may provide another reason to conduct multiple measures – it may be of more interest to get the average impact over high and low demand periods than to get the impact only at one particular point in time.

2. _Survey compliance and attrition._ Conducting multiple survey waves increases the burden on respondents, which might increase attrition. Using multiple pre-treatment waves can be beneficial in this respect, by eliminating the individuals most likely to attrit before the randomization is done, one can improve internal validity by randomizing only over individuals that are likely to be found in a follow-up survey. In contrast, if multiple rounds of post-treatment surveys cause significant attrition, this will limit the extent to which researchers will wish to conduct many rounds of the survey. If the survey is just collecting measurement of a few key outcomes, these additional rounds of post-treatment surveys can be short and less burdensome for respondents, which can minimize this dropout. Secondly, by conducting multiple post-treatment rounds, researchers stand a better chance of capturing at least once post-treatment individuals who are harder to track down. For example, a business owner who travels temporarily to other towns might get missed in a one-off survey, but may get found when going back at monthly or quarterly intervals. Finally, there is also the possibility that waiting for a long time to re-contact people may make it harder to find them, and make respondents think you have lost interest in them, whereas regular follow-ups may instead these risks.

3. _Will multiple measures change people’s reporting or people’s behaviors?_ Experience with measuring microenterprise profits suggests that respondents may report more accurately after one or two rounds of surveys, perhaps as they better understand the concepts being asked and can recall them better when asked for a second
(significant at the 10 percent level). This means that export activity in the HICs is associated with a larger increase in labor productivity than in the UMICs.

# **5. Robustness**

The regressions in Table 3 use information on whether a firm purchased fixed assets in the last fiscal year to proxy for physical capital, which is the only variable related to capital use that is available for the full sample of firms. However, such binary data do not provide any information on the value of the firm’s capital stock. The ES does collect data on the hypothetical repurchase value of machinery, which may explain part of the productivity gap if firms in UMICs face different constraints in purchasing capital than firms elsewhere.

The robustness check explores what happens to the productivity gap if we control for (log of) repurchase cost of the machinery ( _Capital_ ). Because this type of financial data is only available for a subset of manufacturing firms, this comes at the cost of a significant decline in sample size, from 14,944 in table 3 to 5,622 in table 8. For brevity, we present results only for the final specification with all the productivity determinants discussed above included in the specification. To separate the impact of the additional control variables from that of sample composition, table 8 column 1 re-estimates the base specification on the sub-sample of manufacturing firms with information on repurchase cost of machinery available. For this sample and without controlling for repurchase cost of machinery, labor productivity of a typical firm in UMICs is about 23.9 percent lower than in the HICs and it is significant at 5 percent level. The corresponding figure is somewhat lower at 19.6 percent (significant at the 5 percent level) when we include the control for the repurchase cost of machinery comparators (column 2). As expected, table 8 shows 25
allows a return compatible with a utility’s economic opportunity cost of capital (i.e., to be economically sustainable). The subsidies for each firm can then be computed as the difference between this costreflective full tariff and the effective tariff that a utility collects.<sup>13</sup> In order to obtain countrywide subsidy amounts for those countries with partial representation in the IBNET database, we extrapolate the results of those utilities that are listed in IBNET to the rest of the country based on 2015 WSS coverage rates estimated by the Joint Monitoring Programme<sup>14</sup> and population data from the World Bank. Average per capita subsidy figures were then obtained for each of the World Bank’s four country classifications—high income, upper middle income, lower middle income,

> 9 The Chilean method aims to maximize both allocative efficiency (by setting tariffs equal to marginal costs) as well as productive efficiency (by producing efficient quantities at the lowest cost possible, without passing on additional inefficiencies to the customers through pricing) and also allows each utility to generate enough revenue to cover the costs incurred in providing the service. The customer bases of the various Chilean water and sanitation utilities range widely in size, from a few thousand to even over a million, reflecting the heterogeneity present within the sector globally. The resulting data capture the nuances of various cost structures, improving the accuracy of the Chilean model firms when compared with other countries that have undertaken similar approaches.

10 In this context, greenfield capital investment refers to the capital investment required to construct all facilities necessary to provide a given level of WSS services to a utility’s customer base, independent of any preexisting infrastructure or site-related constraints.

> 11 - The World Bank’s International Benchmarking Network for Water and Sanitation Utilities (IBNET) database (https://www.ib <u>net.org/) is an initiative that supports WSS utilities in compiling a set of core cost and performance indicators that
to the 10<sup>th</sup> percentiles greater than 20 in the North Central and Central provinces. These numbers are likely an understatement of the actual firm-level dispersion in productivity, given that they are based on averages across firms within a given location, sector and size group. In principle, large dispersions in productivity for the same product highlight the enormous potential for catch-up productivity upgrading or exit by the less productive firms.

# **Methodology and empirical model**

The empirical specification to estimate the effects of business environment on productivity is given by: where ln(X/L)i,j,kis a generic productivity measure (in this case, output per worker) for an industrial cluster of firms in province _i,_ sector _j,_ of size class _k_ . _A_ is a vector of industry and location fixed effects to control for unobservable heterogeneity across clusters that varies by industry (due to, among others, different levels of technology and relative capital/labor intensity) or location (differential access to international markets, interaction density/information in cities, or location relative to conflict areas). εi,j,k is a randomly distributed disturbance term.<sup>11</sup>

> 10 The indicators listed in Table 3, except for foreign ownership and share of sales on credit, are used to construct the cluster averages that are then matched into the 2003 Census data.

> 11 We note that several existing studies argue for the exogeneity of cluster averages (as constructed) to firm-level outcomes such as employment growth or productivity. We expect this is also, if not more, the case when relating

14
# **1 Introduction**

Despite its potential to lift incomes and increase welfare, intra-regional trade in Sub-Saharan Africa remains low relative to other regions. Intra-African trade accounted for 17 percent of the region’s total exports in 2017, compared to 10 percent in 1995. By contrast, in Asia and Europe, the corresponding shares are 59 and 69 percent, respectively (Songwe, 2019).

Yet the lack of regional integration likely masks a deeper level of informal trade which is not typically captured with official customs data (Brenton and Isik, 2012). While measurement issues around the volume of cross-border trade flows abound (Mitaritonna and Traor ́e, 2017), concerted efforts to measure them hint at the scale of these informal flows. For example, a recent national survey at 171 unofficial border crossings in Benin found that informal trade made up a very large share of flows with its neighboring countries, with the ratio of informal to formal trade flows at 5.1 to 1 with Nigeria and 2 to 1 with Togo (Bensassi et al., 2018). Similarly, a comprehensive 2006 survey of Uganda’s informal cross-border trade flows into the Democratic Republic of Congo and four other neighboring countries found informal export levels that were 86 percent that of official export flows (Lesser and Mois ́e-Leeman, 2009).

Supporting informal cross-border trade is consequently an important element of the regional trade integration agenda. Cross-border trade can enhance welfare through increased income for producers and traders, reduced price dispersion, and cheaper food in areas where production is scarce (Brenton and Isik, 2012; Golub, 2015). Yet small-scale traders in the Great Lakes region of Africa, most of whom are women, face many obstacles to doing business, including harassment and requests for bribes at border crossings. The fragile institutional setting also constrains their ability to get their goods to market, as traders confront a maze of shifting formal and informal rules and procedures.

In this experimental study, we draw on the insights of Hirschman (1970
distinguish between the aggregates at domestic and world prices identified in equations (8) and (9). We then calculated the tariff aggregators for expenditure and tariff revenues using the MacMapHS6 v2.1 database (Boumellassa, Laborde and Mitaritonna, 2009) that provides detailed information on bilateral tariffs and trade flows at the HS6 level. Finally, we performed a series of simulation experiments.

# **3.1 The aggregation procedure**

For concreteness, we first present a simple illustration based on a one level aggregation problem where HS6 products are aggregated to the sectoral aggregation of the model. Then, we introduce real-world problems such as those arising from differences in the rates of protection applied on goods from different sources; where and where the model for an importing region is an aggregate of different countries. We focus first on the formation of aggregates for a single good, using data at the six-digit product level<sup>5</sup> .

## **3.1.1 The expenditure aggregator**

We first illustrate our approach in the simple case where one country imports goods from only one partner, the rest of the world. The Constant Elasticity of Substitution (CES) form is an appealing choice for several reasons: (i) its simplicity and parsimony, (ii) the availability of some relevant parameter estimates; and (iii) the ability to handle situations where the number of firms or commodities is endogenously determined (Feenstra 1994; Zhai 2008). Using a CES aggregator for products _j_ being aggregated in group _i_ , the price index for composite imported good _i_ at domestic prices is (omitting the _i_ index) given by: where _tj_ is the _ad valorem_ tariff at the six-digit level of the Harmonized System.

Since our focus in this paper is primarily on aggregation of existing products, the α _j_ coefficients can be inferred using standard calibration procedures (Mansur and Whalley 1984). Given these values, the value of τ<sup>_e_</sup> can be identified