rafmacalaba/datause-encoder-data huggingface dataset:
# **3. ESTIMATION STRATEGY AND RESULTS**

# **3.1. Sample Attrition and Balance**

Figure I summarizes the study sample and attrition. We began with a sample of 2,284 respondents who were in school at baseline and formed the experimental sample for our study of conditionality. Of this sample, 2,186 were tracked successfully for the Round 3 household survey and 2,089 were successful interviewed in all three rounds, a tracking rate of over 90%. Of the 983 subjects randomly sampled for the school survey in Round 3, enrollment data are available for 922 of them. We were less successful in locating attendance ledgers; of the 821 girls who were selected for the Round 3 school survey and reported being enrolled in school in 2009, legible ledgers are only available for 384.

Table I examines attrition across the two treatment arms and control groups separately by each of our data sources: household surveys, achievement tests, and school surveys. The regression analysis indicates that there is no significant differential attrition between the two treatment arms. Study participants in both treatment arms, however, were equally more likely to take the achievement tests than the control group. Similarly, ledgers are more likely to be found for treatment girls. Appendix Table B.1 shows, for each of these two outcome variables, that the baseline characteristics of those lost to follow-up do not differ between the control group and either of the two treatment arms. Thus, the analysis of the available samples should give us unbiased estimates of differential program impacts on schooling outcomes, marriage, and childbearing.

In Table II, we test the balance of the experiment, using baseline data for the sample used in the analysis, i.e. those successfully interviewed during all three rounds. Panel A shows balance on household attributes, and Panel B on individual characteristics. Overall, the experiment appears well balanced between the treatment and control groups over a broad range of outcomes; Column (4) shows that the two treatment arms only differ in age and highest grade attended at baseline – two 17
**Table 1. Expected signs of independent variables** **_xk,it_ and** **_yl,it_ on pump fossil fuel prices** **_pit_ .**

|Variable|Sign|Variable||Sign|
|---|---|---|---|---|
|GDP purchasing power dollars per inhabitanti,t-1|+|Presidential systemi,t|||
|Net oil surplusi,t-1||Presidential system democraciesi,t|||
|Land in km<sup>2</sup><br>i,t||Presidential system non-democraciesi,t|||
|Opennessi,t|+/|Voting system is proportional representationi,t||+|
|Road gas consumption per million inhabitantsi,t-1|+/|Voting system is proportional representation<br>democraciesi,t|in||
|Public health % GDPi,t-1|+|Voting system is proportional representation<br>non-democraciesi,t|in||
|Control of corruptioni,t|+|Elections in democraciesi,t||0/|
|/Years country has democratic or non-democratic<br>systemi,t|+|Elections in non-democraciesi,t|||
|Years country has democratic systemi,t|+|Legislative elections in democraciesi,t||0/|
|Years country has non-democratic systemi,t||Legislative elections in non-democraciesi,t|||
|Years of executive in officei,t||Party<br>of<br>executive<br>controls<br>all<br>houses<br>democraciesi,t|in|<br>0/|
|Years of executive in office in democracyi,t|0/|Party of executive controls all houses in no<br>democraciesi,t|n-||
|Years of executive in office in<br>non-democracyi,t||Herfindahl Index of government party seats<br>parliament in democraciesi,t|in|<br>0/|
|Regime<br>change<br>from<br>non-democracy<br>to<br>democracyi,t||Herfindahl Index of government party seats<br>parliament in non-democraciesi,t|in|<br>|
|Regime<br>change<br>from<br>democracy<br>to<br>non-<br>democracyi,t|||||

# **4. The Data**

The data are assembled on the basis of several data sets, related to energy pricing, and to economic and political characteristics of countries. The data for the dependent variable in equation (1) are annual diesel and premium gasoline prices, measured in November, with up to 10 observations for each country during the 1991-2010 period (GTZ, several years). We have merged these with data for premium gasoline, diesel and kerosene prices, for a large set of countries, for the period 2002- 2008, from the IMF, some of which overlap with the GTZ data (Coady _et.al_ , 2010). We also have a set of data for prices on regular gasoline, but these cover far fewer countries than those for premium gasoline, and will not be further analyzed in this paper.

Table 2 sums up our available fuel data. We see that mean fuel prices are highest in high income OECD nations, except that average prices of regular gasoline in OECD and nonOECD high-income countries are very close. Second, prices in middle income countries are below those in the least developing countries; the latter presumably have less means to subsidize (most often imported) fossil fuels. Third, net oil exporters (found in all income groups) generally have the lowest average prices, consistent with the arguments offered above.

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was informed that his or her responses would not be disclosed to the other spouse. The migrants were interviewed face-to-face while their wives were interviewed over the phone. Cross reports were obtained—the interviews documented not only the subjects’ responses to questions about themselves but of their spouse as well. For example, not only was the husband asked about his income but also his wife was separately queried on his income in Qatar. Detailed data were collected on the migrant’s demographics, expenses, income, life and work satisfaction, and remittances sent home, in addition to savings and loan decisions. Similar data were collected from the migrant’s spouse about herself and of the household remaining behind in India.

A local survey firm, staffed with migrants from Kerala, was hired to recruit and subsequently interview the couples between August and December 2010. Recruitment focused on migrants mostly residing in Doha’s Industrial Area. Male interviewers visited migrants at their dormitory-styled residences where they shared a room with three to four other co-workers. After obtaining consent to participate in the study, each migrant was asked to provide contact information and times suitable for a future interview with him and his wife in India.<sup>11</sup> Table 1 provides the summary statistics of the key variables reported by the migrants and their wives. The average annual foreign income earned in Qatar was the equivalent of US$ 6,407 and the average of US$ 2,900 or 47 percent of income was remitted home on an annual basis. The wives tended to underreport their husbands’ earnings, averaging US$ 4,807 per year according to their account or 79 percent of the husbands’ reported income.<sup>12</sup> The mean

> 11. The interviewer would randomly select one migrant to be interviewed per room. A total of eighteen such dormitories were visited which varied in size, the largest of which housed migrants working for a local taxi and bus operator which constituted nearly 50 percent of the sample. The migrants were informed that a female staff member would interview their spouses. If a migrant refused to provide contact information, the survey staff would not arrange for an interview. The firm did not document the number of migrants that declined to provide their spouse’s contact information but they informed us that the fraction was minimal.

> 12. Such a figure could arise if migrants were instead overreporting their income. We also have comparable data from a separate one-sided survey of married male migrants from Kerala ( _n_ = 204 ) collected in the first quarter of 2012, almost fifteen months after the survey for the current study. The average annual income reported

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does not markedly change the distribution of partially built-up cells: while the levels are different between the two figures, the shape of the distribution is similar. This is true even though each transition matrix is estimated from two distinct sets of observations.

Next, we expand on the above analysis to investigate the effect of adding or removing a nearby road. We use the data from 1976 and 1986 since they are comparable. We define four categories: cells not close to a road in either 1976 or 1986; cells close to a 1976 road that no longer existed in 1986; cells close to a road that did not exist in 1976 but was present in 1986; and cells close to a road that existed both in 1976 and 1986. Abandoning a road may signal that the road was no longer economically useful which, if true, should predict less growth and less subsequent buildup. In contrast roads that remained in existence across both years should be correlated with a preexisting and persistent economic usefulness. Hence, we expect them to be most associated with economic activity and thus most likely to attract population and buildup. Roads that were added in 1986 should occupy an intermediate position: they were not initially deemed useful but were added later, perhaps when sufficient funds became available to add less essential roads. For the analysis, we construct four transition matrices from the 1975-2014 data on these four categories of cells. We then predict buildup in 2053 and 2092 as before. To save space, we present in Figures 4a and 4b only the results for unbuilt and fully built cells, respectively. Indeed, as noted earlier, the actual and forecasted distributions of partially build cells are fairly stable across time. We see that, as anticipated, cells near a road are predicted to receive a lot more buildup over time. The buildup forecast is particular strong for locations that had a road in 1986. Whether or not they also had a road in 1976 matters less, contrary to what we expected. Cells near an abandoned road do less well, but still better than cells with no road in either years.

# **4.4 Agglomeration**

Having discussed roads, we now turn to agglomeration effects. We first examine the extent of correlation between built-up levels _yit_ , _yit_<sup>_n_1</sup> and _yit_<sup>_n_2.</sup> To this effect we estimate a simple OLS regression of the form: 17
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25. In order to account for the differences in natural resource endowments, Balassa (1986b) used the ratio of primary imports to total imports as an explanatory variable. This differs from Bergaten/Cline who used crude oil production per capita, arable land per capita and a dummy variable for presence of significant iron ore reserves.

26. A critical difference between Bergsten/Cline (1985) and Balassa (1986b) lay in the variable accounting for the distance with trading partners. While Bergsten/Cline used a simple average of the _c.i.f./f.o.b._ *mport ratio and an index of air transport costs, Balassa (1986b) used the transport costs adjusted for inter-country differences in commodity composition of imports. 1/ 27. Balassa (1988), then, undertook re-estimations of his model for the sake of comparison with Bergsten/Cline (1985). In other words, Balassa (1988) re-estimated his model using some of the Bergsteli/Cline assumptions. More specifically, it "involved (a) combining the per capita income and population variables; (b) using a single variable for the EEC; and (c) employing the unadjusted cif-fob ratio for the transport cost variable" (except for ignoring the air cargo rates due to unavailability of data). The results thus obtained showed the dummy variable for Japan to be negative and statistically significant at the 7Z level. Balassa (1988), then, concluded that "adopting the Bergsten-Cline specification reduces but does not eliminate the statistical significance of the Japanese dummy variable." This exercise, however, did not exactly duplicate the Bergsten-Cline study, and that the difference in the results _1/_ Balassa presented results of both versions using the unadjusted and adjusted transport cost variables.
**Table 1: FPM coefficients, actual and theoretical transfers**

|Population|FPM<br>coefficient|Actual<br>transfers|Theoretical<br>transfers|Obs.|
|---|---|---|---|---|
|6,793-10,188|0.6|13.91|13.11|5211|
|10,189-13,584|0.8|18.20|17.65|3982|
|13,585-16,980|1|22.48|22.28|2931|
|16,981-23,772|1.2|26.77|26.78|3931|
|23,773-30,564|1.4|31.12|31.17|2312|
|30,565-37,356|1.6|35.35|35.60|1412|
|37,357-44,148|1.8|39.55|39.80|907|
|44,149-50,940|2|43.81|44.43|542|
|Total|-|23.42|23.13|21168|

_Notes:_ Actual and theoretical FPM transfers are in hundred thousand Brazilian _reais_ at 2000 prices.

**Table 2: Preschool enrollment and household income, 2001**

||% of total<br>enrollment|Mean|10th percentile<br>Household inc|Median<br>ome (mont|90th percentile<br>hly)|
|---|---|---|---|---|---|
|Enrollment in public preschool|0.68|586.93|120|400|1200|
|Enrollment in private preschool|0.32|1079.26|280|950|3980|

_Notes:_ Data come from _Pesquisa Nacional por Amostra de Domicilios_ (PNAD). Household income is measured in Brazilian _reais_ at current prices.
signed for the unemployed poor and was implemented at different points in time in different municipalities. Eligibility for the SR was determined using a Poverty Index Score calculated from answers to the SISBEN, which is a questionnaire collecting individual- and household-level demographic and dwelling information (Casta ̃neda et al. 2005).<sup>6</sup> The SISBEN was conducted at the municipal level in a similar way to the population census, with door-to-door interviews in lower-income neighborhoods. This was an unprecedented system, and nothing of this magnitude had ever been implemented in Colombia. As explained by officials, not all municipalities had the resources or infrastructure in place to conduct a census of the poor (SISBEN), so implementation dates varied across municipalities. Importantly, these dates are not correlated with the pre-reform informality rates in the municipalities. To identify the results, we use this variation in implementation dates and the eligibility indicator from when the system was introduced.

The government agency that designed the SISBEN indicates that although the instrument was designed to select beneficiaries for different social programs, during the period of our study, it was almost exclusively used for the SR, the largest program in terms of beneficiaries and costs (DNP, 2003).<sup>7</sup> This exclusive use was so extensive that initially, many people thought that being interviewed for the SISBEN was equivalent to becoming eligible for the SR. In reality, households were eligible for the SR only if they had a Poverty Index Score below a certain threshold, as determined by the answers to the SISBEN.

If a worker is formally employed, the worker and the employer are required to make contributions to the CR regardless of the worker’s occupation or the SISBEN score of his household.

Independent workers can also enroll in the CR by paying 5% of their wages.<sup>8</sup> Employers who do

> 6In the first SISBEN, the Poverty Index Score was generated using a principal components methodology that took into account demographic and dwelling characteristics at the individual and household levels. It was designed to capture long-term living conditions and was assigned at the household level.

> 7For details on how the SISBEN has been used, see Camacho and Conover (2011) and Camacho and Conover (2013).

> 8From 1995–2007, the cost to non-independent workers was 12% of a person’s wages, 25% of which were paid

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Gertler, M. and P. Karadi. 2015. “Monetary Policy Surprises, Credit Costs, and Economic Activity.” _American Economic Journal: Macroeconomics_ , 7(1), pp.44-76.

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[2004b] find that infrastructure quantity and quality both have a robust impact on economic growth and income distribution. The authors use a large panel data set covering more than 100 countries and spanning the period 1960-2000, and conduct a variety of specification tests to ensure that the results capture the causal impacts of infrastructure quantity and quality on growth and inequality.

The relevance of infrastructure as a determinant of logistics costs is enormous. Logistics costs, being defined as the costs incurred to take a given good from the producer to the consumer, are heavily determined by the availability and quality of infrastructure. Infrastructure influences directly transport costs and indirectly the level of inventories and consequently financial costs, the main components of logistics costs. A variety of studies [World Bank, 2006] emphasize the fact that infrastructure stock and quality, by lowering logistics costs, has a significant impact on countries’ competitiveness.

It has been widely shown that poor infrastructure contributes to Latin America’s low rankings on competitiveness indexes. Several indexes, aggregating infrastructure variables, have been developed. These include the World Economic Forum’s Growth and Business Competitiveness index and the International Institute for Management Development’s World Competitiveness Yearbook<sup>2</sup> . These indexes use data and firm surveys to rank countries’ ability to create and maintain an environment that sustains enterprise competitiveness. The World Bank’s investment climate assessments survey firms about the environments in which they operate, including the performance of infrastructure. More than half of the respondents in Latin America consider infrastructure to be a major or severe obstacle to the operation and growth of their business.

**FIGURE 1: BUSINESSES THAT CONSIDER INFRASTRUCTURE A SERIOUS PROBLEM, BY REGION**

<!-- Start of picture text --> Latin America<br>Middle East & North Africa<br>Sub-Sahara Africa<br>South Asia<br>Europe and Central Asia<br>East Asia & Pacific<br>0 10 20 30 40 50 60<br>Percent of firms<br><!-- End of picture text -->

> 2 See http://www.webforum.org/ for the World Economic Forum’s indices and http://www02.imd.ch/wcc/yearbook/ for the World Competitiveness Yearbook.

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# **Table 21: Cross-country covariates associated with the loan penetration**

This table summarizes the results of OLS regressions of the (logarithm of) number of loans per thousand adults in commercial banks on different country characteristics. Each column in each panel represents the result of one regression. The definition of each variable is shown in Appendix 1. Robust standard errors are in parentheses. ***= significant at the 1% level; ** = significant at the 5% level; * = significant at the 10% level.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|1.05***|1.05***|0.99***|1.04***|0.57**|0.60***|1.01***|1.08***|1.04***|1.02***|0.93***|0.66***|0.97***|
||(0.09)|(0.09)|(0.09)|(0.09)|(0.21)|(0.19)|(0.13)|(0.11)|(0.10)|(0.09)|(0.10)|(0.10)|(0.11)|
|Pop. Density (log)||-0.05<br>(0.08)||||||||||||
|Gini|||0.01<br>(0.01)|||||||||||
|Inflation||||-0.01<br>(0.01)||||||||||
|Elect. consumption|||||0.40*<br>(0.22)|||||||||
|Phone lines||||||0.46***<br>(0.17)||||||||
|Road density|||||||0.06<br>(0.16)|||||||
|Contract enforcement||||||||0.00<br>(0.00)||||||
|Absence of violence|||||||||0.06<br>(0.16)|||||
|Creditor Rights Index||||||||||0.07**<br>(0.04)||||
|Creditor Inf. Index|||||||||||0.14***<br>(0.05)|||
|Branches per adult (log)||||||||||||0.66***<br>(0.13)||
|Branches per km<sup>2</sup>(log)|||||||||||||0.10<br>(0.08)|
|Constant|-3.4***|-3.2***|-3.0***|-3.2***|-2.3**|-0.7|-3.4***|-3.8***|-3.2***|-3.6***|-2.9***|-1.81**|<br>-2.53*|
||(0.77)|(0.78)|(0.84)|(0.77)|(0.91)|(1.30)|(0.96)|(1.00)|(0.82)|(0.75)|(0.79)|(0.71)|(0.99)|
|N|65|65|33|64|53|64|42|65|65|65|65|64|64|
|R<sup>2</sup>|0.73|0.74|0.75|0.74|0.69|0.77|0.74|0.74|0.74|0.75|0.76|0.80|0.74|

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