rafmacalaba/datause-encoder-data huggingface dataset:
to seek work throughout the year, though employment opportunities in rural areas remained scarce for women during many months of the year. Some women used what they learned in agriculture as a stepping stone to better quality employment, moving to urban sector jobs in commerce and manufacturing rather than as maids—the traditional employment of most young women who made the rural-urban migration. Others managed reasonable employment through a series of jobs throughout the year. Still others worked only several months a year and others were frequently unemployed.

This paper uses a longitudinal Chilean data set collected in 1992 and an economic analytical framework to link the fruit sector, as a provider of female seasonal employment, to changes in household economic welfare and the transformation of gender relations in regions of rural Chile. Using results from prior studies, we examine women’s decisions regarding labor force participation, earnings and employment, and report new information on women’s attitudes toward fruit sector employment, how they utilize the income earned, and how their income contributes to household income. Thus, the analysis provides additional evidence on how new employment opportunities are changing women, their households, and the rural sector.

This paper adds to the existing literature in a number of ways. First, it uses formal statistical approaches to update and extend previous sociological/historical studies, including several more recent ones, e.g., Barrientos et al.; Bee, Bee and Vogel; and Matear, seeking to determine how fruit sector development has affected women and their households in rural Chile. Second, the availability of a data set collected with unique characteristics allows such a quantitative study. Detailed day-by-day information, collected for one full year regarding participation, earnings, and employment, as well as

- 4 -
# Box 1A4: **Balaning Investment and Recurrent Spending:** _R,cure,nt/Capital_ Cost **Ratios in MalawNi and Rwanda**

Throughout thle developing world the productivity of investment programs has been jeopardized by the failure of governrments to provide adequately for their operation and maintenance over time. "R" coeffic-ents, equal to the ratio of incremental recurrent expenditures required by investment expenditures to total investment expenditures, can be a useful tool in predicting recurrent cost needs and evaluating the adequacy of recurrent expend,tures historically.

The Rwanda PER estimates "r" coefficients for education projects in order to predict recurrent costs of the planned investment in various levels of education. The following conclusions emerge: (i) the primary education "r" coefficient compares favorably with those for similar countries;tJ (ii) when recurrent costs are estimated in the requests, they are noticeably underestimated; and (iii) the future demand on budgetary resources by each level of education will far exceed resources, as well as the financial capacity of the sector.

Malawian authorities estimated "r" coefficients for education subsectors, based on projected recurrent costs of individual investment projects: Primary schools 0.18 Teachers' Training College 0.06 Secondary schools 0.13 Distance Education 0.06 The weighted average of the coefficients (0.12) is much greater than the sector's "r" coefficient derived from actual spending during the 1983/84 to 1987/88 period (0.03). The discrepancy between projected and actual "r" coefficients indicates acute underfunding of recurrent costs necessary to service education sector investments (4 MK million spent versus 15 MK million required).

The magnitude of recurrent costs associated with projects tends to differ greatly across sectors as well as subsectors. Nevertheless, "r" coefficients can provide essential information on the recurrent cost implications of proposed investment spending.

a;'Education sector "r" coefficients for a restricted sample of developing countries are reported in Peter Heller, "The Underfinancing of Recurrent Development Costs," in Finance and Development, March 1979.

Slrcg.s: "'alawi: Public Expenditure Review," Report No. 7281-MAI (February 10, 1989); and "Rwanda: Public Expenditure Program: An Instrument of 1<sup>j,</sup> oomc Strategv,' Report No. 7717-RW (October 19, 1989).

34
||1|2|3|4|5|6|7|8|9|10|11|12|13|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||New<br>product<br>line|Upgraded<br>existing<br>product<br>line|New<br>Techno<br>logy|Discontinued<br>at least 1<br>product|Opened<br>a new<br>plant|Closed<br>at least<br>one<br>existing<br>plant|New JV<br>with<br>foreign<br>partner|New<br>Licensing<br>Agreement|Outsourced<br>a major<br>activity|Brought<br>in-house a<br>previously<br>out-<br>sourced<br>activity|CORE|INDEX8|INDEX2|
|Does the firm have a<br>state competitor?|-0.048|-0.035|-0.03|-0.407<sup>b</sup>|0.157|0.233|-0.248|-0.145|-0.255|0.127|-0.017|-0.043|.|
||[0.149]|[0.168]|[0.152]|[0.166]|[0.225]|[0.234]|[0.308]|[0.271]|[0.210]|[0.197]|[0.133]|[0.123]||
|Foreign Competition||||||||||||||
|<br>had greatest influence||||||||||||||
|to reduce production<br>costs|0.183<sup>b</sup>|0.102|0.097|0.135|0.302<sup>b</sup>|0.147|0.707<sup>a</sup>|0.445<sup>a</sup>|0.063|-0.024|0.168<sup>b</sup>|0.268<sup>a</sup>|0.142<sup>c</sup>|
||[0.088]|[0.122]|[0.088]|[0.088]|[0.123]|[0.139]|[0.139]|[0.142]|[0.108]|[0.115]|[0.079]|[0.075]|[0.084]|
|Foreign Competition||||||||||||||
|<br>had greatest influence||||||||||||||
|<br>to develop new<br>products|0.142|0.084|0.166<sup>c</sup>|0.166<sup>c</sup>|0.089|0.002|0.634<sup>a</sup>|0.113|0.212<sup>c</sup>|0.291<sup>b</sup>|0.167<sup>b</sup>|0.272<sup>a</sup>|0.128|
||[0.092]|[0.128]|[0.093]|[0.092]|[0.137]|[0.152]|[0.146]|[0.159]|[0.112]|[0.116]|[0.083]|[0.078]|[0.089]|

a b c , , and represent significance at 1%, 5% and 10% respectively 43
“the sensitivity variables” rather than the robust-check variables that cause the original interaction term to be statistically significant.

Given the cross-sectional nature of the investigations, and questionable validity of exclusion restrictions needed to construct instrumental variables, most research using the investment climate data cannot establish causality convincingly. Invariably, the estimations suffer from reverse causality, omitted variable bias, and other issues. The results should, therefore, be interpreted as a collection of correlations. To the extent that the results are robust across similar contexts, or can be coherently interpreted by plausible theories, the conclusions from the results are more credible.

Since the conclusions related to the effects of the business environment for each individual study have to be tentative, it is even more imperative to summarize existing evidence and seek a coherent storyline to tie them together. As the reader shall see, the body of correlations gathered from the studies does point to a plausible story: the effects of the business environment vary across industry, complementary institutions, and initial conditions. Some elements of the business environment turn out to loom large in most economies, such as a basic protection of property rights against government expropriation, labor flexibility, and low entry and exit barriers, which are found invariably to be important in explaining economic performance in various economic contexts. Other elements, such as infrastructure and contracting institutions, hinge critically on initial conditions. Infrastructure, for instance, is found to matter much more in countries with a low initial stock of infrastructure, while the quality of courts and access to finance are more important in richer countries. Country-specific development bottlenecks may also be important for understanding why some countries are under-developed.

The rest of the paper will first offer a simple framework of the effects of business environment reforms in section 2. The paper then summarizes evidence from specific areas of reforms: physical infrastructure (section 3), property rights (section 4), labor regulation (section 5), and entry regulation (section 6). The final section concludes and offers policy implications.

# 2. **A Simple Framework of Heterogeneous Effects of Business Environment Reforms**

The effects of the business environment often differ by specific contexts and there are often country-specific bottlenecks (Kremer 1993; Shleifer 2005). For this paper’s purpose, it is useful to consider three types of fundamentally different government–business relationships and associated business environments to attain desirable social outcomes: market discipline, private litigation, and 5
The result is quantitatively robust as the estimates remain in a stable range: a one logunit increase in population density increases the change in infant mortality associated with moving from no neighbors defecating in the open to all neighbors defecating in the open by about 2 deaths per 1,000 live births, and increases the corresponding decline in height-for-age by about 0.04 of a height-for-age standard deviation.

Results are similar if we use fixed effects for countries (64 for height and 69 for infant mortality) or if we instead use over 1,800 disaggregated fixed effects by region within each survey year, with or without a long vector of controls. Indeed, the regional fixed effects may represent over-controlling if part of what is important for child health in differences across region-years is differences in the density of open defecation across space and time. Two of the twelve coefficient estimates are not statistically significantly different from zero; we include them for completeness and note that their coefficients are of important magnitude and not statistically distinguishable from the other coefficient estimates. Moreover, this lack of statistical significance is only because we have conservatively clustered standard errors at country or country-year levels; if standard errors were clustered by sub-national region or survey PSU (as is common in use of DHS surveys) then both of these coefficients would be highly statistically significant in our very large dataset.

# **3.4 Falsification: Other measures of SES do not interact to predict infant mortality**

In this section, we conduct falsification tests: we interact open defecation with other “placebo” measures of community socioeconomic status. If the interaction documented in table 3 merely reflects some unobserved spurious correlation between population density and health, rather than an effect of population density on the consequences of open defecation, then we would expect many other measures of community socioeconomic status to similarly apparently interact with population density.

14
entire sample period 1970-2004, for 172 countries, graphed against the log-level of GDP per capita in 2000. From this figure it is apparent that countries with higher income are somewhat less likely to engage in “sovereign theft”. However, this relationship is not particularly strong. In fact, a somewhat surprising feature of the data is that both types of sovereign theft have at one time or another occurred in countries of nearly all income levels. And similarly, there are countries at nearly all income levels that have not chosen to engage in either form of sovereign theft.

**Table 1** provides a basic description of the joint distribution of the two types of sovereign theft events across countries.  The first panel distinguishes between countries that have never defaulted or expropriated over the entire sample period 1970-2004 and those that have defaulted or expropriated at least once over this period.  On the one hand, most countries have either never committed sovereign theft of any kind, or they have both defaulted and expropriated at least once (a total of 137 out of 191 countries fall in these two categories).  On the other hand, only a few countries have either only defaulted but never expropriated (20 of 191 countries), and somewhat more have expropriated but never defaulted (34 of 191 countries).  A simple chi-squared test of the null hypothesis that the two events (ever expropriating, or ever defaulting) are independent strongly rejects the null with a p-value of 0.00.  This basic feature of the data was first noted in Tomz and Wright (2010), Table 3.1, over the longer period since 1929 covered in their full dataset.

At higher frequencies, however, the coincidence between the two types of events diminishes. The second panel of **Table 1** reports the same information, but focusing on a pooled sample of 1050 5-year periods for the same set of countries. At this quinquennial frequency, there are only 20 cases of countries that have engaged in both forms of sovereign theft within the same 5-year period. In this case, the p-value for the null hypothesis of independence of the two types of events jumps to 0.243. And at annual frequencies, the third panel, the lack of coincidence of the two types of events is even starker. There are only 5 cases of countries that 8
Table 2: Balancing Test of Means Around the Months of Exposure to the NHIS

||RD test|Sample of|analysis<br>Te|st of equal mea|ns Standard di|f.<br>C.I.|
|---|---|---|---|---|---|---|
||(1)|_d ∈_[_−_6; 0)<br>(2)|_d ∈_[0; 6]<br>(3)|p-value<br>(4)|(5)|(95%)<br>(6)|
|Rural household|0_._039<br>(0_._060)|0_._698<br>(0_._460)|0_._712<br>(0_._453)|0_._536|0_._101|[ 0.012; 0.189]|
|Poor|0_._046<br>|0_._361<br>|0_._399<br>|0_._113|0_._124|[ 0.035; 0.213]|
||(0_._047)|(0_._481)|(0_._490)||||
|Farmers|0_._033<br>|0_._590<br>|0_._599<br>|0_._690|0_._070|[-0.019; 0.159]|
||(0_._046)|(0_._492)|(0_._490)||||
|Traditional household<br>|_−_0_._047<br>(0_._044)|0_._205<br>(0_._404)|0_._193<br>(0_._395)|0_._546|_−_0_._100|[-0.188; -0.011]|
|Household size<br>|_−_0_._343<br>|6_._293<br>|5_._987<br>|0_._021|_−_0_._176|[-0.265; -0.087]|
||(0_._380)|(2_._907)|(2_._585)||||
|Father in household<br>|_−_0_._007<br>|0_._725<br>|0_._716<br>|0_._688|_−_0_._096|[-0.185; -0.007]|
||(0_._039)|(0_._447)|(0_._451)||||
|Married or cohabiting<br>|_−_0_._041<br>(0_._032)|0_._853<br>(0_._354)|0_._829<br>(0_._377)|0_._178|_−_0_._095|[-0.184; -0.006]|
|Age head|1_._208<br>(0_._903)|47_._579<br>(12_._714)|48_._133<br>(13_._183)|0_._394|_<_0_._001|[-0.089; 0.088]|
|Number children in household|_−_0_._092<br>(0_._181)|2_._330<br>(1_._476)|2_._243<br>(1_._312)|0_._200|_−_0_._092|[-0.181; -0.003]|
|Formal sector (paid worker)<br>|_−_0_._001<br>(0_._009)|0_._040<br>(0_._197)|0_._037<br>(0_._189)|0_._747|_−_0_._025|[-0.114; 0.063]|
|Informal sector (paid worker)|_−_0_._022<br>(0_._019)|0_._044<br>(0_._205)|0_._049<br>(0_._217)|0_._607|_−_0_._003|[-0.092; 0.086]|
|Own business<br>|_−_0_._020<br>(0_._035)|0_._231<br>(0_._422)|0_._252<br>(0_._434)|0_._317|0_._055|[-0.033; 0.144]|
|Not working<br>|_−_0_._002<br>(0_._007)|0_._013<br>(0_._113)|0_._015<br>(0_._123)|0_._666|_−_0_._035|[-0.124; 0.054]|
|Born in village<br>|_−_0_._017<br>(0_._054)|0_._659<br>(0_._474)|0_._611<br>(0_._488)|0_._046|0_._042|[-0.047; 0.131]|
|No education (father)<br>|_−_0_._039<br>(0_._041)|0_._504<br>(0_._500)|0_._524<br>(0_._500)|0_._404|0_._075|[-0.013; 0.164]|
|Some school (father)|0_._010<br>(0_._068)|0_._207<br>(0_._405)|0_._193<br>(0_._395)|0_._487|_−_0_._057|[-0.146; 0.032]|
|No education (mother)<br>|_−_0_._067**<br>(0_._030)|0_._275<br>(0_._447)|0_._304<br>(0_._460)|0_._194|0_._057|[-0.031; 0.146]|
|Some school (mother)|0_._022<br>(0_._066)|0_._183<br>(0_._387)|0_._177<br>(0_._381)|0_._727|_−_0_._053|[-0.142; 0.036]|
|_Households_|2,235|717|1,518||||

_Notes:_ The first column reports the _θ_ coefficients estimation of specification (1) with demographic characteristics as the dependent variable. Standard errors are in parenthesis. Columns (2) and (3) report the sample averages, and standard deviation in parentheses, 6 months before and 6 months after the NHIS implementation, respectively. Column (4) reports the p-value of a test of equal means.

18
Our municipal budget execution dataset is organized into three tables: _commitment_ , _verification_ , and _payment_ .

In the _commitment_ table, one observation is a commitment by one agency in a given municipality. Commitments vary from very large expenses, such as the commitment for the entire wage bill of the mayor’s office in a month, to very specific commitments such as the acquisition of replacement parts for a car. Each commitment is often linked to four levels of “functional programming” that map the nature of a commitment (such as Transportation > Road Transportation > Road Recovery > Recovery of a specific road in a given street), as well as a text that explicitly describes the nature of the commitment.<sup>16</sup> After an initial commitment, officials can increase or decrease the amounts committed as well as annul them. Across municipalities in our dataset, the quality of tracking these actions after initial commitment varies, but we include initial, increases, annulments, adjustments, and net amounts for each commitment when these are available. Each commitment is also linked to a unique identifier (id_empenho_bd) that allows users to connect a commitment to verification and payments linked to them.

In the _verification_ table, observations are verifications by some agency in a given municipality, which are always linked to a specific commitment. The key information available for each verification is the date when they happen, the initial value verified, any adjustments to the original value and the final value. One commitment might generate one or more verifications, so each verification is uniquely identified by the variable id_liquidacao_bd and can be connected to commitments by the variable id_empenho_bd.

Finally, for the _payment_ table, each observation is a payment made to a specific entity. Payments can often (but not always) be linked back to a specific verification. The key information available in the payments table is the date of payment and values. The dataset also includes variables with the names and “document number” of payees - these are often unique national identifiers for individuals (CPF) and firms (CNPJ) but are not available for all payments or municipalities. Again, one verification event can lead to one or multiple payments, so payments are uniquely identified by the variable id_pagamento_bd and can be linked back to verification events by id_liquidacao_bd and to commitments by id_empenho_bd.

# **2.3 Coverage and descriptive statistics**

Our dataset on municipal procurement and budget execution currently covers seven of Brazil’s twenty-seven states, highlighted in Figure 1: Ceará (CE), Minas Gerais (MG), Paraíba (PB), Pernambuco (PE), Paraná (PR), Rio Grande do Sul (RS), and São Paulo (SP). These are large states that cover a substantial share of the total number of municipalities (55%), population (48%), and GDP (49%) of the country according to 2020 data. Notably, our dataset only covers states in the South, Southeast and Northeast regions - data from the states in the North and

> 16For a detailed discussion of expenditure functions see Manual do Orçamento (in Portuguese).

7
management may vary across schools within and across provinces. Less experience teachers and school principals are more likely to be placed in schools with fewer resources and higher proportion of poor children (CIPPEC 2004, Llach 2006).<sup>4</sup> At the class level, the sorting of students across schools will determine the potential pool of classmates (social capital) that children can be exposed to. Lastly, differences in performance are due to the students’ own characteristics – such as ability, effort and parental background. Of these forces, I will focus on the third, that is, the extent to which the composition of classmates explains differences in pupils’ achievement, by exploiting differences within schools across classes. All other differences will be controlled for.

The composition of peers – and by extension, the degree of segregation across schools – is the result of choices made by families and the recipient schools. Parents choose which school to apply to, and school authorities choose the pupils they accept, based on their preferences and certain policy restrictions. These choices may be determined by observed and unobserved characteristics of all participants.

Students are segmented in a first stage when choosing between private and public schools. The ability to pay fees, in a context of scarce scholarships, clearly contributes to the disparity in incomes across public and private schools. In urban Argentina 65% of children in the upper quintile of the income distribution attended private establishments, while only 7% of children in the lowest quintile did so.<sup>5</sup> Within the pool of public schools, there is still some degree of segregation of students. According to the 1994 Federal Education Law children should be allowed to apply to any school, irrespective of their place of residence. The selection of pupils by the school should be on a first-come-first-served basis, with priority given to children with siblings or parents in the same small monthly fee to cover repairs and maintenance. Private schools are mainly funded by fees charged to students and by subsidies from the central government. Subsidies are in the form of salaries for teachers, and vary between 20% and 100% of the total salary, the latter mainly in the case of schools run by churches.

> 4Within the public sector, teachers can choose where to be posted, with priority given according to a points system based on their tenure, training, and evaluations from school directors. Some authors have argued that schools with better-off pupils are able to capture more resources from the State (better infrastructure, more computers and books, better teachers) because high- and middle-income families can exert greater pressure on the educational system (Llach 2006, Veleda 2005).

> 5Source: _Encuesta de Calidad de Vida_ 2001, carried out by the National Ministry of Social Development (SIEMPRO). The sample is representative of urban areas with populations above 5,000 inhabitants across the whole country.

5