rafmacalaba/datause-encoder-data huggingface dataset:
# **2.2 Data sources**

We use a survey conducted by the World Bank, the University of Wisconsin, and a local NGO, between February and April of 2000. This survey contains, in addition to standard household characteristics, information on income sources, an elaborate section on agricultural production, a land inventory and land acquisition history, as well as a history of land-related investments and questions on the participation in and perception of the titling program. The sample consists of four different groups of rural producers, namely (i) a nationally representative sample of producers in the private sector; (ii) beneficiaries from the govemment's titling program; (iii) land-poor rural households who rely to a significant extent on rural labor and land rental markets; (iv) and recent entrants in the land purchase or rental market. The detailed construction of the sample is explained below.

The basis for this survey is a nationally representative area-sample of 1360 farmers who were first surveyed in 1996 by the Nicaraguan Ministry of Agriculture and Forestry (MAGFOR) in collaboration with FAO. As the number of titling beneficiaries in this group would have been too small to make inferences on the impact of titling, we complement the sample with 461 households who were sampled randomly from a list of beneficiaries who participated in the Government's land titling program between 1994 and 1997. To compensate for the absence of landless and poor households in this sample (which is based on area rather than households), we added 372 rural households with very little or no land who were sampled randomly from a rural household panel first surveyed by FIDEG, a local NGO, in 1996. Finally, 282 of the panel households who had been included in the 1996 MAGFORIFAO survey were no longer present at the plot they had cultivated in 1996. These were replaced with the household who was farming the land under question at the time of the survey in order to obtain information on the characteristics of land buyers and/or renters who recently entered the land market. Taken together, these 4 groups provide a comprehensive picture of the rural sector in Nicaragua.

# **2.3 Descriptive statistics**

Using the data to generate descriptive statistics by group points towards large differences in income sources and levels as well as access to basic infrastructure. Total farmn area varies considerably and is strongly positively correlated with levels of household welfare, even though land productivity varies inversely with holding size, being highest for the smallest producers. The impact, as well as the recent acceleration, of the governments' land titling programs is clearly visible from the data and socioeconomic characteristics. The data also point to the fact that land regularization programs have benefited the poor. More importantly, producers express a clear desire for clarification of land tenure status through registration, implying that the programs may be associated with tangible economic benefits.

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Banks are willing to absorb some losses on their LDC claims in order to clean up their portfolios, but only if such losses do not jeopardize the bank's plans for meeting the guidelines on capital adequacy, which are based purely on book values.

Thus, if we examine which banks have actually reduced their LDC portfolios the most (e.g. via sales in the secondary market), there is a very strong negative correlation between the exposureto-equity ratio and the percentage decline in exposure in the past year. Using the data from Table 3, we find that on an unweighted average the nine money center banks have reduced their exposure by 10.9 percent, while the 20 banks with less exposure have reduced their exposure by an unweighted average of 49.8 percent.34

# 3.4.2. Large banks and debt-equity swaps

There are, moreover, several ways to reduce the LDC exposure, including participation in a comprehensive debt reduction scheme (e.g. the Bolivian buyback), sales directly into the secondary market, and participation in a debt-equity swap program. The latter offers banks the least expensive way to divest their LDC debt, since debt-equity swaps generally allow the creditor to avoid the full market loss implicit in the secondary market (the discount is shared between the debtor and the creditor). However, Nicaragua, Peru, Sudan).

34The nine money center banks are: Bank of America. Citibank, Chase Manhattan Bank, Manufacturers Hanover, Morgan Guarantee, Chemical Bank, Continental Illinois, Bankers Trust, And the First National Bank of Chicago.

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- 5 -

Figure 5. Access to Improved Water Facilities (%), 2004 <!-- Start of picture text --> 120<br>Urban<br>Rural<br>100<br>80<br>60<br>40<br>20<br>0<br>High income Upper middle Lower middle Low income<br>income income<br>Source: World Development Indicators .<br><!-- End of picture text --> Instead of these aggregate data, the current paper uses firm-level micro data. Therefore, the analysis can take into consideration the fact that the quality level of infrastructure services received by individual firms potentially differs, probably depending on their location. The data come from the 2005 Business Environment and Enterprise Productivity Survey (BEEPS) for 27 Europe and Central Asia (ECA) countries.<sup>2</sup> Our sample data include about 4,300 firms in 26 countries, of which the sufficient information is available for our analytical purposes.

The ECA region is of particular interest as far as the economic effects of infrastructure quality is concerned. As World Bank (2006) summarizes the recent infrastructure developments in the region, many transition economies have experienced sharp declines in the national incomes and outputs since the collapse of the Soviet Union. As a result the existing infrastructure facilities, which were constructed to provide almost universal access in the Soviet period, turned out excessive. Without appropriate maintenance and investment due to a lack of financial resources, the quality of infrastructure tends to deteriorate considerably in these countries.

> 2 It covers Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Moldova, Latvia, Lithuania, Macedonia, Poland, Romania, Russia, Slovakia, Slovenia, Tajikistan, Turkey, Ukraine, Uzbekistan, and Yugoslavia. One country, Azerbaijan, is excluded from our empirical model, because there is no firm cost information collected in the country.
# **_Agreement vs. disagreement_**

The evidence above suggests that the relationship between women’s participation in decision-making and empowerment proxies holds regardless of whether the wife or husband is the respondent: we are getting a signal from the husband’s answers. However, until now we have studied pooled husband and wife responses, regardless of what _their_ spouse says, and have not considered whether their own spouse agrees or disagrees with them.

In general, the DHS data show that disagreement across spouses is common and systematic. First, differences across spouses’ responses are statistically significant in 17 out of 20 country surveys. Detailed tables showing responses given by married respondents and their husbands/partners are included in the Appendix (Tables A.2a and A.2b). The share of couples disagreeing overall is around 47 percent for who makes decisions over large purchases and about 40 percent for use of husband’s earnings. However, looking at disagreement as a binary variable can cloud important differences, as women’s agency could vary substantially depending on whether disagreement assigns more or less power to the woman. That is, the direction of the disagreement might be more important for the measurement of agency than simply looking at spousal response discrepancy.

Figures 4a and 4b present matrices which show the share of couples’ responses for decision-making over large purchases and husband’s earnings (averaged across countries).<sup>26</sup>

> 26 Figure 4a and 4b include observations for which we have an answer on the part of the man and the woman and we exclude the cases in which the answer is not the man, the woman or together.

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as it assumes away such heterogeneous eects of a policy on relative mobility. The insight that the schooling expansion at the primary level may create an unintended bottleneck at the secondary level which in turn can lead to adverse distributional eects on historically disadvantaged groups is relevant for policymakers in many other countries. This is because primary school expansion from the 1970s onward has been dramatic in most of the developing countries but the expansion at the secondary and higher levels has lagged far behind.<sup>20</sup> The more general lesson here is that expanding educational opportunity at a lower level without concurrent targeted policies at the next level may result in crowding out of the children from disadvantaged backgrounds (e.g., ethnic and religious minorities, lower caste, and girls). The potential conicts between the rank-based mobility model and the models based on BeckerTomes (years of schooling) are likely to be important for the evaluation of other government policies, and in many other countries.

The remainder of the paper is organized as follows. Section (2) discusses the related literature on intergenerational mobility and the eects of better access to schools to put the paper in perspective. Section (3) contains a description of the census 2000 data and the variables in our analysis. Section (4) lays out the empirical strategy and the estimating equations for the linear and quadratic mobility models. Section (5) reports the main estimates of the eects of Inpres schools on the mobility equations for sons and daughters, including estimates of the eects of Inpres schools on relative and absolute mobility. This section also oers robustness checks using dierent comparison groups, and mother's schooling in place of father's schooling as an indicator of children's family background. Section (6) reports the estimated eects on relative and absolute mobility of sons and daughters. Section (7) is devoted to uncovering the mechanisms behind the puzzling absence of any eect on girl's completed years of schooling notwithstanding the strong eects found at the primary level. This section also provides evidence on the sources of gender-based crowding out observed at the secondary level. Section (8) provides evidence using alternative models of mobility, based on schooling ranks and years of schooling normalized by generation specic standard deviation. This section also discusses how to advise policymakers when dierent models give conicting

> 20In 2016, the secondary completion rate was only 35 percent in the low income countries as classied by the World Bank. The corresponding rate for OECD countries was 96 percent. See chapter 2 in World Bank (2018).

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Figure B1 Tari Cuts in Textiles Capital, Real Wages, Employment Baseline (left) & Cobb-Douglas (right) <!-- Start of picture text -->
-2 0 5 10 15 -2 0 5 10 15<br>Year Year<br>Textiles Other Traded Textiles Other Traded<br>Non-traded Non-traded<br>-2 0 5 10 15 -2 0 5 10 15<br>Year Year<br>Textiles Other Traded Textiles Other Traded<br>Non-traded Non-traded<br>-2 0 5 10 15 -2 0 5 10 15<br>Year Year<br>Textiles Other Traded Textiles Other Traded<br>Non-traded Unemployment Non-traded<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1 -.1<br>-.15 -.15<br>-.2 -.2<br>-.25 -.25<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1 -.1<br>-.15 -.15<br>-.2 -.2<br>.01 .05<br>0<br>0<br>-.01<br>-.05<br>-.02<br>-.1<br>-.03<br>-.15<br>-.04<br>-.05 -.2<br><!-- End of picture text -->

Notes: responses of capital, real wages and employment to a full elimination of taris on textiles. Simulation results from the baseline Leontief specication for the production technology on the left and from the CobbDouglas specication on the right.

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**7 Summary and Conclusions**

Replacing one km of _kutcha_ track by one km of PMGSY road yields an estimated increase in the net price obtained for paddy of Rs. 7.95 per quintal. Given a typical stretch of 5 km to reach the next level of the road network, a household selling one tonne would therefore realize an increase in net revenue of almost Rs. 400, or about 5 percent of the price ruling in the main markets in _kharif_ 2009. To this gain must be added those arising from the marketing of other crops, especially cotton and vegetables, the estimated reductions in whose unit transport costs are, on average, larger than that of paddy. There are also the reductions in the farm gate prices of bought-in inputs such as fertilizers and agro-chemicals. Our measure of the net price relates to the village’s place in the road and marketing network, whatever be the actual location of the villagers’ transactions. Although rather noisy owing to the small number of observations in each village, it is conservative, in that the nearest alternative location to the farm gate is chosen when the sale is actually at the gate. The focus group interviews suggest that an all-weather connection leads to sharper competition among marketing intermediaries and more joint marketing by farmers to sell in their main market. We therefore suspect that the changes in behavior reported in the focus group interviews imply larger gains than those yielded by the cross-section econometric analysis.

All but two villages have their own primary school, and given that a village does so, the absence of an all-weather road will do little to hinder primary school children from getting to the school itself, even if its hamlets are a bit scattered. What its absence can do, however, is to prevent the teachers from doing so, especially during the monsoon; for they usually live elsewhere. This is confirmed by the survey data: in _kharif_ 2009, primary school children in villages with an all-weather road each lost, on average, 1.9 days involuntarily, in those without, 8.6 days, a difference that is not only substantial, but also statistically highly significant. In contrast, only one of the 30 villages has a secondary or high school, so that the connection will normally affect the attendance of pupils and teachers alike. The corresponding average numbers of days lost involuntarily were 2.9 and 9.4, respectively, a difference that is not only statistically highly significant, but also leads to the suspicion that it is the teachers’ attendance, at all levels, that is most furthered by an all-weather road. Secondary and high school pupils enjoyed virtually no savings in time making the daily round trip according to the cross-section comparisons, but between 17 and 30 minutes a day (depending on the definition of the cohort) in ‘before’ and ‘after’ comparisons in the villages receiving a PMGSY road after January 2007. The respondents in the 35
# **6.2 DHS**

This section reports the results of the analyses performed on the combination of DHS survey and the road survey. I mapped the GPS locations of DHS survey clusters together with the road survey vector dataset and other geographic raster data layers.

Table 4 shows the estimates of the first-stage regression - following the equation (2). The dependent variable is the endogenous variable _LogDistGRs_ - log of distance to the nearest road in good condition. It is instrumented with _LogDistRoads_ 1903 _s_ - log distance to the nearest primary military and postal road in 1903.

Table 4: First-stage regression.

||Coef.|s.e|
|---|---|---|
||Logdist. good road||
|Logdist. Road 1903|0.423***|(0.0252)|
|Logdist. regional center|-0.316***|(0.0446)|
|Logdist. capital city|0.612***|(0.0846)|
|Altitude|0.00101***|(0.0001)|
|Slope|-0.0716***|(0.0199)|
|Population Density 2015|0.00438***|(0.00025)|
|Nightlights Composite|-1.652***|(0.0971)|
|Aridity|-0.00689|(0.00665)|
|Individual controls|Yes||
|Household controls|Yes||
|Region dummies|Yes||
|Observations|3,801||
|R-squared|0.350||
|First stage F-stat|186.38||

Individual controls include: age, age squared, gender, years of education, individual weights. Household controls include: owns land usable for agriculture, wealth index, number of children of age 5 and lower, number of family members. Robust standard errors in parentheses. ***p _<_ 0.01, **p _<_ 0.05, *p _<_ 0.1 Table 4 shows that _LogDistRoads_ 1903 _s_ has a strong, statistically significant correlation with the endogenous variable _LogDistGRs_ . The First-stage F-statistics also prove that _LogDistRoads_ 1903 _s_ is a strong instrument.

The coefficients in Table 5 represent the average marginal effects on the probability 22
is not as consistent in predicting a positive role of social capital. For example, Olson’s (1982) economic analysis of interest groups and Banfield’s (1958) sociological analysis of “amoral familism” find a negative relationship between social capital and economic growth.

Putnam (2000) has in fact recognized that social capital may display positive or negative externalities and proposed the distinction between bonding and bridging social capital in order to study these divergent effects. Bonding social capital emerges when trust and cooperation are restricted within groups, so that negative externalities may ensue for other people. Bridging social capital emerges when new linkages between groups arise, so that cooperation increases. Recent exploratory findings in the economic literature seem to confirm that bonding and bridging social capital can be considered as two distinct variables, and that the former has a negative effect on economic growth and the latter has a positive effect (Knudsen et al. 2010; Beugelsdijk and Smulders 2009).

Moreover, some studies show that family ties, usually included in bonding social capital, causally reduce trust in people, which is the basic indicator for bridging social capital (Alesina and Giuliano 2009; Ermish and Gambetta 2008) while other studies show that both family ties and trust in people are positively correlated with people’s life satisfaction (Alesina and Giuliano 2007, Helliwell and Wang 2010).

Two main problems thus clearly emerge. First, how to distinguish between the bonding and bridging dimensions starting from a variety of proxies for social capital, and how the bonding dimension is related to the bridging dimension. Second, how these two dimensions of social capital are related to individual life satisfaction: in particular, whether they relate positively in any case, thus departing from the mixed results on the relationships of bonding and bridging social capital with economic growth.

In addressing these two problems, and by using a large world dataset, this paper obtains two main surprising results. First, proxies for bonding and bridging social capital appear to describe two sides of the same latent variable, rather than two distinct latent variables. This would imply that bonding and bridging cannot be conceived as two distinct dimensions of social capital but should instead be seen as two opposite sides of a single dimension of social capital, which is a broader concept possibly including other dimensions. This result prompts us to call the bonding/bridging dimension the _locus of socializing_ , in order to emphasize that it is where people mostly enjoy social relationships, whether within their kinship and existing ties, or across new social ties.

2
international standards [World Bank (2009)]. A number of other countries in the region have also followed suit, including Nigeria, South Africa, Tanzania and Uganda [Kasekende et al. (2009)].<sup>31</sup> However, even as countries which in the past have excessively abided by pro-cyclical fiscal austerity entered the new policy paradigm of countercyclical fiscal expansion, steps should be taken to ensure that these stimuli do not lead to another cycle of debt overhang, which in the past dramatically curtailed the growth of public investments and capital accumulation in the region. In this regard, and to the extent that empirical results show that hypothetical returns of fiscal expansion in terms of aggregate output and income growth would be much lower if deficits are largely run to finance public consumption instead of investments, the design and implementation of these stimulus packages, and more generally the shift towards greater recourse to fiscal policy, should be guided by the productivity of investments and efficiency considerations.

> 31 The South African government implemented a fiscal stimulus package worth Rands 787 billion (about US$100 billion), largely to finance public investments and infrastructure projects. At the same time, the South African Reserve Bank eased monetary policy through cutting of its policy rate by cumulative 500 basis points. Similarly and in order to support the implementation of counter-cyclical policy measures, the governments of Tanzania and Uganda raised expenditures in the 2009-10 budget by 30 percent and 20 percent, respectively. The Federal Government of Nigeria also implemented a fiscal stimulus of 1.6 trillion Naira to enhance growth and avoid job losses. At the same time, many countries, which did not formally implement fiscal stimuli offered tax rebates to corporations in order to expand output growth in a context of global contraction. This is particularly the case for countries such as Cameroon, Mali, and the Democratic Republic of Congo [Kasekende et al. (2009)].

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_Sectoral concentration and infrastructure endowment of the provinces_ As mentioned in the data source subsection, the Turkish Statistical Agency provides some socio-economic and infrastructure variables (some with a five-year gap) at the provincial level. We can complement these variables with the share of each of the 8 manufacturing sectors within each province and perform a Factor Analysis to determine the types of socio-economic and infrastructure variables significantly correlated with each sector. Factor Analysis is a class of multivariate statistical methods whose purpose is to analyze the interrelationships among a large number of variables by defining a set of common underlying dimensions known as factors (Hair and al. 1998). The two basic models used are _common factor analysis_ and _components analysis_ . The component factor model is appropriate when the primary concern is about determining the minimum number of factors needed to account for the maximum portion of the variance represented in the original set of variables, and when prior knowledge suggests that the specific and error variance represent a relatively small portion of the total variance. In contrast, when the primary objective is to identify the shared variance of the original variables, and the researcher has little knowledge about the amount of specific and error variance and therefore wishes to eliminate this variance, the common factor model is most appropriate.

Appendix 2 presents the variables included in the Factor Analysis. We use a simple fiveyear average (1980, 1985, 1990, 1995 and 2000) to adjust the database as suggested in the _data source_ subsection, and we adopt the common factor approach under STATA to analyze the correlation matrix. Figure 3 presents the plan formed by the two most significant factors.

Figure 3: Plan formed by the two most significant factors <!-- Start of picture text --> Factor loadings<br>v24 v22<br>v32 v38 v39 v27 v18 v21<br>v25<br>v44<br>v33Z33 v14 v30<br>Z31 Z36 Z32<br>v31<br>v41<br>v42<br>v36v20 v40<br>v35<br>v12<br>Z37<br>Z35Z34 v34<br>Z38<br>-1 -.5 0 .5 1<br>Factor 1<br>1<br>.5<br>Factor 2<br>0<br>-.5<br><!-- End of picture text --> 10