rafmacalaba/datause-encoder-data huggingface dataset:
A Road to Trust We now turn our attention to broader determinants of the decision to construct a road. We regress a dummy, equal to one for road-villages, on a set of village characteristics<sup>7</sup> measured in 2003 (to deal with concerns over endogeneity). Regressions include municipal dummies. In addition, as standard errors might be correlated within a municipality, we compute standard errors robust to arbitrary covariance structure within municipalities. Results are presented in Table 2. The regressors do not jointly predict whether a road will be built in the village over the period (joint p-value from the OLS regression is 0.40).

These results suggest few systematic differences between road and non-road villages in 2003. This increases our confidence that non-road villages in our sample will provide unbiased estimates of the counterfactual. However, as a further robustness test we will also allow for selection on observables.

# **_4. Does Reducing Transaction Costs Enhance Generalized_**

# **_Trust?_**

In this section, we explore the relationship between road construction and changes in generalized trust. Specifically, we compare the changes in trust levels between road and non-road villages over the period 2003-2006. Our estimation strategy relies on panel data, which allows us to control for community fixed-effects.

> 7 Share of households who are engaged in agricultural activities, whose head attended college or secondary school, whose head did not attend school, average wealth in the village and dummies for distance to municipal center.

10
# **Table 3:  Weights Used to Aggregate Individual Data Sources in 2008**

|**VA**<br>**Commercial Business Information**|**PV**<br>**Providers**|**GE**|**RQ**|**RL**|**CC**|**Average**|
|---|---|---|---|---|---|---|
|**bri**<br>..|0.065|0.082|..|0.064|0.041|0.063|
|**dri**<br>..|0.108|0.040|0.020|0.030|0.022|0.044|
|**eiu**<br>0.109|0.103|0.094|0.061|0.118|0.059|0.091|
|**gad**<br>0.030|0.074|..|0.065|0.008|0.019|0.039|
|**ijt**<br>..|0.088|..|..|..|..|0.088|
|**prs**<br>0.048|0.061|0.062|0.039|0.023|0.022|0.042|
|**wmo**<br>0.031|0.181|0.131|0.086|0.100|0.067|0.099|
|**Surveys of Firms or Households**|||||||
|**afr**<br>0.026|..|0.016|..|0.035|0.025|0.025|
|**bps**<br>..|..|0.002|0.000|0.000|0.001|0.001|
|**gcb**<br>..|..|..|..|..|0.010|0.010|
|**gcs**<br>0.006|0.040|0.079|0.031|0.059|0.056|0.045|
|**gwp**<br>0.002|..|0.007|..|0.003|0.006|0.004|
|**lbo**<br>0.003|..|0.000|..|0.010|0.000|0.003|
|**prc**<br>..|..|..|..|..|0.069|0.069|
|**vab**<br>0.019|..|..|..|0.011|0.007|0.012|
|**wcy**<br>0.005|0.036|0.051|0.052|0.071|0.088|0.051|
|**Non-Governmental Organization Da**|**ta Provider**|**s**|||||
|**bti**<br>0.120|..|0.046|0.085|0.014|0.052|0.063|
|**ccr**<br>0.141|..|..|..|0.006|0.002|0.049|
|**egv**<br>..|..|0.008|..|..|..|0.008|
|**frh**<br>0.190|..|..|..|0.137|0.241|0.189|
|**gii**<br>0.043|..|..|..|0.014|0.004|0.020|
|**her**<br>..|..|..|0.053|0.050|..|0.051|
|**msi**<br>0.044|..|..|..|..|..|0.044|
|**obi**<br>0.024|..|..|..|..|..|0.024|
|**rsf**<br>0.023|..|..|..|..|..|0.023|
|**Public Sector Data Providers**|||||||
|**adb**<br>..|..|0.087|0.152|0.039|0.027|0.076|
|**aeo**<br>..|0.043|..|..|..|..|0.043|
|**asd**<br>..|..|0.114|0.070|0.008|0.005|0.050|
|**ebr**<br>..|..|..|0.084|..|..|0.084|
|**hum**<br>0.043|0.072|..|..|0.011|..|0.042|
|**ifd**<br>0.002|..|0.037|0.048|0.021|0.022|0.026|
|**ipd**<br>0.080|0.092|0.065|0.029|0.066|0.098|0.072|
|**pia**<br>..|..|0.060|0.108|0.087|0.046|0.075|
|**tpr**<br>..|..|..|..|0.004|..|0.004|
|**Commercial Business Information**|**Providers**||||||
|Share of Sources<br>0.20|0.58|0.28|0.31|0.24|0.25|0.31|
|Share of Weights<br>0.22|0.71|0.42|0.27|0.35|0.23|0.37|
|<br>**Surveys of Firms or Households**|||||||
|Share of Sources<br>0.30|0.17|0.33|0.19|0.28|0.38|0.27|
|Share of Weights<br>0.06|0.08|0.16|0.08|0.19|0.26|0.14|
|**Non-Governmental Organization Da**|**ta Provider**|**s**|||||
|Share of Sources<br>0.35|0.00|0.11|0.13|0.20|0.17|0.16|
|Share of Weights<br>0.59|0.00|0.06|0.14|0.22|0.30|0.22|
|<br>**Public Sector Data Providers**|||||||
|Share of Sources<br>0.15|0.25|0.28|0.38|0.28|0.21|0.26|
|Share of Weights<br>0.13|0.21|0.37|0.50|0.24|0.20|0.28|

31
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23 the analysis to a single measure of dispersion.<sup>28</sup> While microsimulation-based disaggregated decompositions have been conducted for Brazil in other periods<sup>29</sup> , a more detailed analysis of the 1993-2004 inequality decline must be left for future research. In that context, the contribution of the present paper is to describe the poverty and inequality dynamics and – on the basis of this exploratory decomposition analysis – to identify the main candidate explanations. Table 5 suggests that these explanations include: (i) a continuing decline in inequality across educational sub-groups, presumably driven by falling average returns to schooling; (ii) continuing convergence in incomes between the country’s urban and rural areas; and (iii) a potential decline in racial inequalities. When one looks at the entire 1981-2004 period, one must add (iv) regional convergence, to the list.

What can be said about the temporal evolution of poverty, which was described in Table 2 and Figure 2, over the same period? Table 6 below reports on a now standard decomposition of poverty changes, into its growth and redistribution components. Originally suggested by Datt and Ravallion (1992), the decomposition is given by: Where _z_ denotes a poverty line that is constant in real terms, _Lt_ denotes the Lorenz curve at time t, and _μt_ denotes the distribution’s mean at time t. The first term (in squared brackets) on the righthand side is therefore the growth component, which is calculated by holding the Lorenz curve constant. The second term (in squared brackets) is the redistribution component, which holds the mean constant and allows for the Lorenz curve to change.<sup>30</sup> The decomposition is path-dependent, and the terms would be somewhat different if the reference year was t+n, rather than t. Because of this path-dependence, there is a residual term when the decomposition is calculated for a single reference year t, denoted _Rt_ .<sup>31</sup>

> 28 Bourguignon, Ferreira and Lustig (2005) discuss the advantages of a disaggregated counterfactual analysis of distributional dynamics, and propose a general methodological framework. 29 See Ferreira and Paes de Barros (1999).

> 30 Datt and Ravallion (1992) rely on grouped data, and must therefore use parameterized estimates of the Lorenz curve in order to simulate the counterfactual distributions. Because we use household level data, we compute the counterfactual poverty terms P(z/μx , Ly), x≠y, by rescaling the distribution in year y with the ratio μx/μy.

> 31 As Datt and Ravallion suggest, this residual term can be interpreted as capturing the interaction between growth
Table 8: EFFECTS ON RECIPROCITY OF ULTRA-POOR’S TRANSFERS

||Reciprocity<br>of transfer<br>links|Re<br>ultra<br>poor|ciprocity<br>other<br>poor|of transfers<br>middle<br>class|with<br>upper<br>class|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Treatment|0.076***|0.044*|0.094***|0.086***|0.117***|
||(0.018)|(0.024)|(0.020)|(0.020)|(0.026)|
|Mean in control|0.510|0.627|0.560|0.449|0.267|
|Adjusted R-squared|0.217|0.189|0.208|0.235|0.192|
|Observations|12774|2792|4823|9474|3135|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is the fraction of transfer sources within the village who are also reported as recipients of transfers (of last 12 months’ or food transfers ever) from the respondent’s household. The dependent variables in columns 2 - 5 are the fraction of ultra-poor, other poor, middle class or upper class households (respectivey) who are reported as sources of transfers (food, cash or other) and also reported as recipients of transfers given by the respondent’s household.

43
# **Does Child Labor Always Decrease with Income?** **_An evaluation in the context of a development program in Nicaragua_**

## **I. Introduction**

Prior theoretical work on child labor has attempted to establish a link between poverty and child labor by modeling the relationship and obtaining interesting, but disparate results. Parallel to this, a whole body of empirical work designed to study this seemingly important relationship emerged; a few notable papers mix both, theory and empirics, in order to improve our knowledge of the causes and consequences of child labor and provide us with the groundwork necessary to evaluate this complex relationship. Unfortunately, good panel data on child labor (e.g. children’s time use, activity types) and income data in the same survey are rare; as a result, we have been limited in obtaining conclusive evidence on why child work is present in households at various levels of the income distribution.

The research question in this paper is evaluated in the context of a conditional cash transfer (CCT) program targeted at improving human capital outcomes of school aged children and the productive possibilities of a sub-set of households in a poor rural region in Nicaragua (see Macours and Vakis, 2005 for detailed program information). In this paper we ask if child labor always increases with income. Moreover, we expand our research question to incorporate the CCT program into the analysis and pose a secondary question, how does a cash transfer program, with three distinct interventions and meant to improve the economic welfare of the families, affect child labor? The empirical section makes use of a rich panel data-set collected for a randomized experiment.

The paper contributes to the child labor literature through a simple yet comprehensive model and an empirical application by revisiting the relationship between income and child labor; more specifically the paper evaluates what happens to child labor in households that are distinctly placed at various levels of the income distribution. The model illustrates the relationship of different forms of child labor with each other and with household production and assets; it allows for the inclusion of a transfer program through a tax on child labor or a subsidy to alternative activities (such as education or leisure). Given that the CCT program offers standard benefits<sup>1</sup> plus income to promote productive activities in non-agricultural work, the expected effect of the program on child labor ex-ante is ambiguous.

For the purposes of this paper we use several definitions that influence our theoretical and empirical approach. We follow the definition of human capital used by Bardhan and Udry (1999)

> 1 Traditional CCT benefits are transfers for nutrition, education fees, materials, teachers etc. and cash for basic health visits.  In this program the health component was not implemented.

1
# **5.3 Ownership**

Another interesting question is whether the ownership, private or public, tends to influence the compliance of firms to environmental regulations. Table 5 presents the results keeping also the air category variable as a control. One finds that in general, privately-owned firms seem to be more in compliance than publicly owned firms when firms are inspected. Selfreported data, on the other hand, suggest that public firms tend to report to be more in compliance. This analysis requires considering that in some industries most of the firms are either privately or publicly owned (as the descriptive statistics showed). For this reason, it is important to carry out the same study at the industry level.

||(1)<br>Self mon.|(2)<br>Self mon.|(3)<br>Inspection|(4)<br>Inspection|
|---|---|---|---|---|
|Air category|0.829<sup>_∗∗∗_</sup>|0.829<sup>_∗∗∗_</sup>|-0.551<sup>_∗∗∗_</sup>|-0.551<sup>_∗∗∗_</sup>|
||(0.087)|(0.087)|(0.068)|(0.068)|
|Private||-0.534<sup>_∗_</sup>||1.535<sup>_∗∗∗_</sup>|
|||(0.305)||(0.513)|
|Observations|5624|5624|3638|3638|
|Pseudo _R_<sup>2</sup>|0.165|0.165|0.117|0.117|

Standard errors in parentheses.<sup>_∗_</sup> _p < ._ 1,<sup>_∗∗_</sup> _p < ._ 05,<sup>_∗∗∗_</sup> _p < ._ 01 Table 5: Ownership Carrying out the analysis at the industry level allows us to get more insight into these results. The annex tables present these results industry by industry. Industry-level regressions show that for some industries it is not possible to conclude if there exists a significant difference because there are not enough observations of the two types of firms. One also finds that in the case of the steel and sugar industries, with enough data of both types, there is a significant difference, with private firms being more in compliance. While one needs to be cautious in interpreting these results, this calls for the attention of public firm managers and regulators in better understanding of how the interaction between industry and ownership affects compliance. In some sectors with enough observations of both types of firms like aluminum and thermal, there is no statistical difference. The case of mines 16
The calibration is based on the initial stationary equilibrium. We arbitrarily truncate productivity at the 80th percentile as discussed above. We truncate capital so that the average optimal labor demand of entrants is 25.63 percent of the average labor demand of incumbents.<sup>24</sup> We set the exit rate _ρx_ to 0.098 based on the annualized average exit rate in Dunne et al (1988). We compute the expected value at entry, _V_ 0 , from equation (22).<sup>25</sup> In a stationary equilibrium the number of entrants is equal to the number of exiting rms. With the number of entrants and the expected value at entry we pin down the distribution of entry costs from equation (13). The shape parameter is _h_ = 1 _._ 5961 . We then proceed with the simulations of the trade policy shock, which are discussed in the main text.

# B.2 Cobb-Douglas production function

In our model we assume that the technology of production is Leontief in materials and a CobbDouglas index of labor and capital. This is a convenient assumption so that productivity and price shocks enter multiplicatively into the prot function and can be treated as a single prot shock. In this section we re-estimate the adjustment cost parameters and simulation results assuming a Cobb-Douglas production function in the three inputs, given by The variables are dened as before, with aggregate and idiosyncratic productivity shocks _b_<sup>0</sup> _jt_<sup>and</sup> _Aijt_ , and the new parameter _αM_ which is the output elasticity of materials. The objective of this exercise is to check whether the qualitative implications of the model remain unchanged under a dierent functional form assumption for the technology of production.

Under the new technology assumptions instantaneous prots are where the aggregate shocks _b_<sup>0</sup> _jt_<sup>and</sup><sup>_pjt_donotentermultiplicatively.</sup> As before, we dene the combined protability shock as _bjt_ = _b_<sup>0</sup> _jt_<sup>_pjt_andtreatitasasinglestatevariablethatfollowsthe</sup> AR(1) process in equation (15). We further make the following two assumptions to pin down the

> 24Dunne et al report that on average the size of entrants is 25.63 percent of the size of incumbents.

> 25In a stationary equilibrium aggregate variables _b_ and _w_ are xed over time and consequently the expectation operator _Et_ is dropped from the equation.

54
# **5 Conclusion**

Though ICT is firmly integrated into business operations and personal use, in LAC it is not yet as optimized as in high-income countries. When competitive forces materialize, ICT can stimulate growth spurts by improving productivity. Competitive forces create strong incentives for leaders to escape the effects of competition, mainly by heightening productivity and upgrading quality. The direct and spillover effects of ICT productivity increases have meaningful implications for aggregate productivity. Poorly designed regulations and poorly enforced competition policy may not create a truly competitive environment or make a sector more productive.

This paper demonstrates that while countries in LAC in have been targeting pro-competitive regulations, distortions still exist. Because the business environment has not yet noticeably reduced these distortions, there is room for more productivity gains.

To explore the channels and consequences of competition regulation and enforcement we used a heterogeneous firm model that distinguishes between ICT and non-ICT firms, and within those groups productive from less productive firms. The model accounts for a dispersion in markups across ICT firms. Competitive forces (e.g., lower barriers to entry) reduce the dispersion of markups within the ICT sector and ICT sub-sectors and lead to a general increase in the market share of ICT firms. Ultimately, an increase in competition raises productivity within both ICT and non-ICT groups, thus improving aggregate productivity.

The theoretical findings are supported by firm-level empirical analysis. Data for firms are combined with data on ICT competition regulations and enforcement. The data are split further into ICT subsectors—infrastructure, hardware, software, and telecommunications. Ultimately, de jure and de facto competition does improve productivity, but primarily in firms that were already the most productive in support of the escape-competition hypothesis. This result is consistent across firm age groupings. Interestingly, the impact on markup dispersion is not significant across age and productivity groups. Older productive firms have both higher productivity and higher markups when regulations are made more complex; younger productive firms benefit from regulations that simplify processes. In several cases, low-productivity firms confronted by competitive pressures have even lower productivity and markups. This suggests that competition occurs leads to churning at the lower end of the productivity distribution, while market leaders, defined as the top 25 percent in that distribution, are able to escape competition and maintain or increase their market shares.

The subsector results reveal that the types of regulation and enforcement data collected relate to the regulator’s implicit objectives. The objections for infrastructure and telecommunications, as defined by the laws and enforcement rulings, are not necessarily 23
were considered least susceptible to manipulation by schools. The government possessed two data sources that fulfilled these criteria: the Annual School Census (ASC) surveys and the Punjab Examination Commission (PEC) exams.

Initiated in 2003, the ASC is an annual field survey of public schools which captures basic school and teacher information, including student enrollment by grade and gender, with a reference date of October 31. Initiated in 2006, the PEC exam is an annual, standardized academic test which is mandatory for public school students in fifth and eighth grades. The test uses selected and constructed response questions based on standard learning objectives of Pakistan’s official curriculum. Core subjects are English, Urdu, Islamic studies, mathematics, science, and social studies, all assessed through pencil-and-paper tests.

The provincial education department is responsible for managing all stages of the ASC survey and PEC exam activities, and is solely responsible for the design of the data collection instruments. At the field level, Monitoring and Evaluation Assistants (MEAs) visit assigned public schools in November to collect ASC survey data. The MEAs are mostly retired members of the armed services and report directly to the provincial education department. The fifth-grade PEC exam is administered in January and/or February by district education department staff and enlisted public middle and high school teachers. Public middle and high schools serve as exam centers. Filled-in multiple choice scoring sheets are dispatched to PEC headquarters to be read by optical mark readers. Answers to open-ended questions are scored by recruited public middle and high school teachers at grading centers in the district; the scores are transmitted to PEC headquarters.

The provincial education department selected three indicators for measuring school performance using the ASC survey and PEC exam data:

- the gain in mean exam scores ( _GTS_ ),

- the gain in enrollment in first through fifth grade ( _GSE_ ), and

- the exam participation rate ( _TPR_ ).

_GTS_ is defined as the year-over-year gain in the school’s percent mean score in the fifth-grade PEC exam. The indicator is measured in percentage point terms. The exam scores used by the government for the indicator were raw scores—the number of exam questions answered 8
**Figure 3: Location of management of the surveyed firms**

Location of management: <!-- Start of picture text --> Small city or<br>Capital city F countryside Capital city<br>Large city D 22% 49%<br>Small city or countryside D<br>Large city<br><!-- End of picture text --> Capital city firms constitute about one half of the surveyed firms. It is remarkable for a survey such as this one that was organized in capital cities, that one half of the respondents are not located there. In particular, it is encouraging that almost one quarter of the firms had their management located in a small city or on the countryside. The aggregate results of Figure 3, however, hide strong variations within individual countries. The share of firms located in the capital city varies between 100% and 0% for individual countries. Such a bias and variation can be explained by the distribution of private businesses over country territory. In some former republics of the Soviet Union, more than 50% of registered businesses are situated in the capital city. Still in other surveyed countries the socio-economic and political situation limited access to the remote parts. Or in some countries the mail system was simply unreliable, making it infeasible to distribute questionnaires in such remote places and have them returned in a timely manner.

Figure 4 shows the break down of surveyed firms according to their line of businessmanufacturing, services or agriculture.

8