rafmacalaba/datause-encoder-data huggingface dataset:
with the highest random numbers were selected for the program. Considerable delays in program implementation led to a lengthy interval between the pre-assessment of applicants (March/April 2011) and the communication of treatment status to applicants (November/December 2011).

Training finally commenced in February 2012. We note that this was a politically unstable period in Nigeria, coming shortly after the resolution of a national crisis over the removal of fuel price subsidies and a Boko Haram bombing that killed over 150 people and led to the imposition of a dusk to dawn curfew in the program state of Kano. All training activities were completed by midApril 2012. Those who completed training could take the assessment again and receive a certificate. All applicants, regardless of their treatment status, could post their resumes on a webbased employment network and attend a job-fair in Lagos in late 2012, where prospective employers could meet with job candidates interested in working in the sector.

Figure 2 presents the full experimental design, including compliance rates and sample attrition across rounds using a CONSORT flow diagram. Given the lag between the initial assessment and the final allocation of program slots and the political instability immediately before the start of the program, compliance with treatment assignment was relatively high. According to training records, approximately 54 percent of the treatment group took the offer of training and attended at least one training session. There was no “contamination” or crossover attendance in training by the control group. However, administrative attendance data from training providers shows that average attendance at individual training sessions was far lower, and varied by region. In Lagos, Kano, and Kaduna, for example, training sessions were on average attended by only one-third of those who had been offered treatment, while in Abuja is was over 80 percent, according to training provider records.<sup>2</sup> While gender was not an explicit focus of the program, the study did include consultations with training providers prior to program implementation to determine whether female trainees and jobseekers in the ICT-BPO sectors faced any obstacles in seeking and maintaining employment. As in many countries, gender inequality is a feature of the Nigerian labor market and Nigerian society more generally, even among women with tertiary education. In 2013, for example, one third of women who had obtained a level of education higher than secondary school had not worked at all

> 2 We do not have aggregate attendance figures for training in Enugu.

7
28 Similarly, Table 7 shows that nontariff barriers were also structured along lines that would reinforce the trade distorting effects of the agreements preferential tariffs. According to the UNCTAD data, nontariff restrictions were applied to about 21 percent of all tariff line items, which is almost onehalf the corresponding ratio (41.9 percent) for Mercosur's first decile products. Similarly, the trade coverage ratio for products falling in the first three deciles averaged almost 60 percent (about 3 times the ratio for all imports) and reached 84 percent for the first decile group.

# V. Policy Implications

This paper identified dramatic changes in the Mercosur countries' trade patterns over the period 1988-94 and particularly over its latter half. It also argued that these are substantially due to the changes in trade policy and probably mainly those introduced under Mercosur transitional arrangements. Statements of this kind depend on comparing actual data with a counterfactural. Two natural candidates present themselves. The counterfactual implicit in this paper is that, but for these policy developments, Mercosur's trade patterns would not have changed much and that the shift towards apparently uncompetitive capital-intensive intra-block trade would not have occurred. The implication is that if the Mercosur countries had achieved an equivalent degree of liberalization on a non-discriminatory basis they would have maintained a more efficient import structure, paying less and/or obtaining better goods, and they would have purchased more from their trading partners outside the block.<sup>21</sup> Given the size of the trade effects identified, the evidence that preferences and the application of industrial policies within RTAs can be distortionary is both compelling and disturbing. It is true that one could imagine other explanations

> **21The** reader should specifically note that trade patterns in 1994 are being compared with those in 1988 and not with some "free-trade" counterfactual. The assumption is that 1994 would have been no more distorted than 1988. We believe that the latter is less distorted geographically because, in the earlier period, trade barriers were applied on a most-favored-nation basis with the exception of a few sectoral arrangements.
nificant. Personality traits - Big Five, Non-Cognitive, PSM and Integrity - are all strongly and significantly correlated with each other. The correlation of PSM/Big Five is 0.35 and PSM/Non-Cognitive is 0.39.<sup>19</sup> This suggests that these measures, captured in different questionnaire modules, do capture some underlying behavioral traits of the interviewees.<sup>20</sup>

# **2.2 Measure of productivity of district bureaucracies**

These survey data on bureaucrats and politicians are matched to government data on the productivity of districts in implementing national programs. The Ministry of Health (MoH) in Uganda compiles detailed information on the performance of districts in delivering nationally mandated public health programs. Using the Health Management and Information System (HMIS), the MoH compiles the data reported by local health facilities into Health District League Tables (DLT) that are intended to capture the relative performance of service delivery across districts (See Figure 12 for details). The DLTs are part of the Annual Health Sector Performance Report by the MoH. The closest year to our survey for which the DLT data are available is FY 2014/2015. The bureaucrats and politicians we interview in September-December 2015 were the presiding officials during FY 2014/2015 when the health performance indicators were compiled by MoH .

Table 4, top panel, presents descriptive statistics on the quality of health services across districts. Whereas there is little variation in some of the individual indicators (the median coverage of DPT3 vaccination is 98 percent), other indicators vary widely between Districts: the average share of women having 4 or more ante natal care visits is only 34 percent (s.d.: 10.9 ) and the average share of pregnant women receiving preventive malaria treatment in pregnancy (IPT2) is 53 percent (s.d.: 11.6). The DLT total score is a weighted average of 14 quality care and management practice indicators ranging from 0 to 100 - the average District scores 73 points. In the analysis below we will often use a normalized version of the total score, so that changes can be interpreted as standard deviation variation

> 19Dal Bo, Finan and Rossi (2013) report a 0.54 correlation for PSM/Big Five. Whereas we find a rather precisely estimated zero correlation between PSM and our Cognitive measure, Dal Bo et al find a 0.12 correlation between PSM and IQ.

> 20In Annex 9.2 we provide interitem average correlations and Cronbach’s alphas for both our aggregate indices and its components.

15
**7 Tables**

**Table 1A:** Rural Individual Characteristics: Summary Statistics

||||**Panel A:**<br>**Rural**<br>**Adults**|||
|---|---|---|---|---|---|
|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|LFP|0.68|0.47|0|1|710787|
|Literate|0.5|0.5|0|1|710787|
|Secondary +|0.28|0.45|0|1|710787|
|Age|34.15|13.74|15|65|710787|
|SC/ST|0.32|0.47|0|1|710787|
|Muslim|0.1|0.29|0|1|710787|
|Female|0.5|0.5|0|1|710787|
||||**Panel B:**<br>**Rural**<br>**Workers**|||
|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|Unemployed|0.02|0.12|0|1|453161|
|Non-Farm|0.23|0.42|0|1|453161|
|Non-Farm Self-Employed|0.08|0.28|0|1|453161|
|Non-Farm Casual Work|0.06|0.24|0|1|453161|
|Non-Farm Salaried Work|0.06|0.23|0|1|453161|
|Farm|0.75|0.43|0|1|453161|
|Farm Worker|0.32|0.47|0|1|453161|
|Manufacturing|0.07|0.26|0|1|453161|
|Trade|0.04|0.2|0|1|453161|
|Construction|0.03|0.18|0|1|453161|
|Services|0.08|0.27|0|1|453161|

_Notes_ : This table presents summary statistics for rural individuals using the four rounds of the NSS data. Panel A restricts the sample to all rural individuals aged between 15 and 65; panel B restricts the sample to rural individuals aged 15-65 who participate in the labour force.

40
obstacles have a negative and significant impact on firm growth when entered individually. When entered together, the effect of the corruption obstacle is subsumed by the other two. They also show that the smallest firms are consistently the most adversely affected by all obstacles. Financial and institutional development weakens the constraining effects of financial, legal and corruption obstacles, and again the small firms benefit the most from developments in financial and legal institutions.<sup>19</sup> Firms in developing countries complain about a laundry list of institutional obstacles that affects their day-to-day operations and growth. So in terms of policy prescriptions to governments on focusing reform priorities, it is not clear whether finance is a first order constraint to growth compared to other reported obstacles, thus deserving the most attention.

Ayyagari, Demirguc-Kunt, and Maksimovic (2008) attempt to answer this question using survey data on the different obstacles to growth that firms report - access to finance, inadequate security and enforcement of property rights, poor provision of infrastructure, inefficient regulation and taxation, corruption and macroeconomic instability. They find that not all of them are equally constraining. Some of the obstacles either affect firm growth only indirectly through their influence on other factors, or have no effect. Using regressions as well as Directed Acyclic Graph (DAG) methodology, they find that access to finance, emerges consistently as the most robust obstacle constraining firm growth. And of the many specific financing obstacles firms identify, only the cost of borrowing is directly associated with firm growth. But the cost of borrowing is itself affected by imperfections in financial markets such as difficulties with posting collateral, limited access to long-term financing, and firms that face high interest rates also perceive that the banks to which they have access are corrupt, underfunded, and require excessive paperwork.

Together, the studies reviewed in this section suggest that access to financing is one of the most constraining obstacles to growth for firms in developing countries.

> 19 Beck, Demirguc-Kunt, Laeven, and Maksimovic (2006) use the same data and find that the financing obstacles are a function of firm characteristics such as size, age, and ownership (domestic versus foreign).

37
# Table 12: Relationship of endline inputs and post-training outcomes

||Proportion of<br>plots with<br>phytosanitary|Proportion of<br>rubber plots with<br>phytosanitary|Proportion of<br>plots with org<br>fert.|Proportion of<br>rubber plots<br>with org fert.|Proportion of<br>plots with<br>chemical fert.|Proportion of<br>rubber plots with<br>chemical fert.|
|---|---|---|---|---|---|---|
|Panel A: Individual<br>|training observatio<br>|ns with spouse tasks a<br>|s a main explanator<br>|y variable (449 obs<br>|ervations)<br>||
|Spouse tasks with sole responsibility|0.009<br>(0.02)|0.024<br>(0.02)|-0.001<br>(0.01)|0.002<br>(0.01)|-0.002<br>(0.01)|0.001<br>(0.01)|
|Ind. Training Mean|0.299|0.282|0.070|0.061|0.103|0.099|
|<br>Panel B: Couples t|raining observation|s with spouse tasks as|a main explanatory|variable (478 obse|rvations)||
|Spouse tasks with sole responsibility|0.010*|0.011**|0.005**|0.003|0.007**|0.009**|
||(0.01)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Couples Training Mean|0.424|0.384|0.080|0.070|0.144|0.130|
|Ind. Training=Couples Training (p-val)|0.98|0.56|0.27|0.83|0.15|0.41|
|<br>Panel C: Individual train|ing observations wi|th planters’ opinion o|n spouse ability to|make decisions (26|7 observations)||
|Planter willing to delegate|-0.109*|-0.116*|-0.058|-0.033|-0.024|0.003|
||(0.06)|(0.07)|(0.04)|(0.05)|(0.05)|(0.05)|
|Ind. Training Mean|0.293|0.297|0.085|0.077|0.114|0.110|
|<br>Panel D: Couples traini|ng observations wit|h planters’ opinion on|spouse ability to m|ake decisions (311|observations)||
|Planter willing to delegate|0.018|0.007|0.089**|0.083**|0.011|0.015|
||(0.06)|(0.05)|(0.04)|(0.04)|(0.04)|(0.05)|
|Couples Training Mean|0.439|0.413|0.092|0.088|0.136|0.129|
|Ind. Training=Couples Training (p-val)|0.10|0.11|0.01|0.04|0.54|0.85|
|<br>Panel E: Individual train|ing observations wi|th planter opinion on|who should control|rubber income (28|0 observations)||
|Who should control rubber income|-0.009|-0.009|0.005|-0.007|0.017|0.001|
||(0.01)|(0.02)|(0.01)|(0.01)|(0.02)|(0.01)|
|Ind. Training Mean|0.289|0.291|0.081|0.073|0.119|0.116|
|<br>Panel F: Couples traini|ng observations wit|h planter opinion on w|ho should control r|ubber income (315|observations)||
|Who should control rubber income|0.012|-0.001|-0.011|-0.012|-0.009|-0.008|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|
|Couples Training Mean|0.433|0.404|0.091|0.087|0.134|0.127|
|<br>Ind. Training=Couples Training (p-val)|0.31|0.70|0.26|0.74|0.12|0.58|
|<br>Robust and clustered standard errors in parenthes<br>were recoded as zeros. Baseline control variables<br>Polygamous relationship, Matrilineal ethnicity, P<br>plot size.|es. * p<0.1 ** p<0.05<br>in all regressions: Age<br>lanter has IGA, Asset|*** p<0.01. Note: For a<br>of household head, Hou<br>index, Credit constraine|ll variables, individua<br>sehold size, Number<br>d, Share of plots owne|ls who did not take p<br>of adults, Number of<br>d, Average distance t|art in the training ha<br>children, Dependenc<br>o plots (minutes), N|d missing values, which<br>y ratio, Married planter,<br>umber of plots, Average|

2
23 agricultural output in Africa could still be argued for based on the gender gap in land productivity (estimated at 25 percent in Malawi, though less in other countries).<sup>32</sup> Nonetheless, caution remains counseled here as well. These gaps are largely calculated based on differences in land productivity between male- and female-managed plots. They are not based on differences in returns to male and female labor time spent on crops within the household (the metric revisited in this paper). Estimating the returns to agricultural labor inputs across household members would also be extremely hard to do. With female-managed plots seldom exceeding 25 percent of the plot population,<sup>33</sup> full elimination of the gender gap in land productivity (estimated at 25 percent at most) would increase aggregate crop output by no more than 6.25 percent (and often less). This highlights the importance of using consistent metrics.

Second, the findings underscore the continuing importance of household surveys to put the policy debate on solid empirical footing. An important data agenda for strengthening women’s empowerment through agriculture remains in particular. In addition to time modules to record their time allocation across activities, more systematic and nationally representative information on the locus of control over the returns to these activities is needed as well as methodological research on survey design effects on the information thus acquired. The new survey rounds supported under the LSMS-ISA initiative are making useful steps in this direction, creating promising opportunities for future research on gender and agriculture in Sub-Saharan Africa.

> 32 The estimated gender gap in land productivity (defined as the value of agricultural output per unit of cultivated area), based on the LSMS-ISA data, between male and female managed plots (or between male and female individual operators in the case of Ethiopia), stand at 23 percent for Ethiopia (Aguilar et al., 2015), 25 percent for Malawi (Kilic et al., 2015), 18 percent for Niger (Backiny-Yetna and McGee (2015), 4 percent for northern Nigeria and 24 percent for southern Nigeria (Oseni et al., 2015), and 8 percent for Tanzania (Slavchevska, 2015).

> 33 According to O’Sullivan et al. (2014), the proportion of plots that are under female management stand at 16 percent in Ethiopia, 27 percent in Malawi, 15 percent in Niger, 10 percent in Northern Nigeria, 42 percent in Southern Nigeria, and 21 percent in Tanzania.
selected poor households with rice, often combined with other in-kind transfers such as blankets.<sup>5</sup> They also provide work opportunities through constructing and repairing basic infrastructure for rural educational and public welfare institutions.

Selection criteria to qualify for support consider numerous factors, such as land ownership, assets, income, marital status, disability, and others. That is, households that qualify should have less than 0.5 acre of land, with no productive assets, and annual earnings of less than BDT 4,000 (approximately US$50) with the head of household working as a day laborer. However, it is unclear how these criteria are verified and enforced. Because a straightforward application process does not exist, a local committee of elected officials selects participating households that are deemed the poorest, supposedly using the abovementioned criteria.

In addition to these existing programs, a largel-scale, modern workfare program, EGPP, was introduced.<sup>6</sup> The addition was justified and supported mainly due to the modern features of the program, which set it apart from the MoDMR’s traditional safety net programs. First, it includes a clear and predictable work schedule with the objective of addressing seasonal poverty by providing short-term employment to agricultural laborers during the monga/lean periods (from October to December and from March to April). During the lean periods, a large segment of the rural population remains underemployed, mostly because of limited farm activities, and EGPP’s community projects were anticipated to be implemented during these periods. Second, along with the clearer objective, a better administrative process with the pre-determined rules on targeting, project schedule, and wage rate, are put into practice. Each inidividual is required to undergo an application process (either by paper or verbal expression of interest that leads to paper application). Pro-poor targeting is enforced with more rigor as the eligibility criteria are simplified and enforced<sup>7</sup> as well as the workfare’s self-targeting by low market rate.<sup>8</sup> Each beneficiary works for 40 days in each cycle up to two cycles for participation by the same indivdual in a given year and is paid BDT 200 (US$2.50) for each day’s work. Although the wage rate was considered to be lower than the

> 5 The price of rice fluctuates, but according to Food Price Monitoring and Analysis, 1kg of rice (Boro) costs about BDT 32; thus, the benefits of 10 to 20 kg of rice per month are equivalent to BDT 320 to 640 per month. 6 See Sharif and Ruthbah (2017) for a detailed discussion on the background and political economy of the introduction of EGPP.

> 7 The criteria state that a household cannot own more than 0.5 acre of land and the head of household must work as a day laborer. These two criteria were selected based on the finding of their close correlation with the poverty status of a household. See Sharif (2009).

> 8 The wage rate of EGPP (BDT 200 per day) is also lower than that of other traditional workfare programs (8 kg of rice, equivalent to BDT 256 per day).

5
effects of the minimum wage in the context of a developing economy, in which workers in are more likely to rely on informal employment to a greater degree.

Our work distinguishes between two sources of employment effects: employment destruction coming from firm exits (the extensive margin), and employment cuts in surviving firms (the intensive margin). The paper assesses their respective contributions to aggregate effects. Because the data allow us to track workers over time, we also study the labor-market trajectories of workers in firms that were eliminated after the minimum-wage increase, revealing potential reallocation effects. By complementing the administrative data with data from the nationally representative survey, we provide evidence on whether the destruction of formal firms and formal jobs was accompanied by a rise in informal employment and/or a rise in unemployment.

We find that raising the minimum wage substantially increases the destruction of formal firms and reduces the number of formal firms in the economy. The magnitude of the effect is sizable and robust to alternative specifications, sample restrictions, and treatment measures. Firm destruction, however, is primarily concentrated among firms that are small and had such low productivity levels that they were only marginally above subsistence before the minimum-wage increase. This suggests that minimum-wage increases can contribute to productivity gains in the formal economy by driving out less-productive firms. As expected from simple economic theory, the findings show that market conditions matter: the rise in firm destruction attributable to the minimum wage is stronger in industries with small profit margins, higher labor shares, and stronger market competition.

The paper also finds negative effects on formal employment overall and shows that firm destruction (the extensive margin) is the main driver, as opposed to employment cuts in continuing firms (intensive margin). In theory, the elimination of low-performing firms can help workers upgrade to higher-quality jobs in higher-performing firms; however, we find that, a year after the rises in the minimum wage, only a minority of workers from firms that exited had found in other, formal employment. Using complementary data from surveys, we find that the decline in formal employment was accompanied by a rise in inactivity and unemployment, as opposed to a rise in informal employment. This suggests that workers from these exiting firms mostly failed to find employment one year after the minimum wage hike.

3