rafmacalaba/data-use-mentions-extended huggingface dataset:
highly correlated with telecom usage. Second, we simulate the increase in the probability of using telecom services by assuming that all _nonusers_ would (potentially) benefit from the average welfare gains just described based on the quintile of consumption that they currently belong . Table 6 illustrates that there are limited potential gains in the extensive margin (that is, take up of telecom services). Using the low elasticity as benchmark, households in the fifth quintile would be 0.0014 percentage points more likely to use telecom services (assuming all other covariates constant). The probability of take up is correlated with households’ consumption, but differences are not significant. This result is likely due to the small gains in the short run. However, higher effects could be expected in the long run as competition leads to larger drops in prices. Besides the further drop in prices due to more competition, other indirect effects such as having neighbours who take up telecom services could also have an impact on individual take up.

**Table 6. Estimated increase in probability of using telecom services due to welfare gains**

||**Elasticity = -1.5**|**Elasticity = -2.5**|
|---|---|---|
|**Quintile 1**|n.a|n.a|
|**Quintile 2**|0.0003|0.0001|
|**Quintile 3**|0.0006|0.0002|
|**Quintile 4**|0.0008|0.0003|
|**Quintile 5**|0.0014|0.0005|

Source: own calculations using EDAM 4.

Note: Results shown in percentage points. Quintiles based on the distribution of consumption per capita. Results for the bottom quintile are not shown as it is considered that there is too little information (i.e sample size) to draw reliable estimates.

# Conclusion

In
situation, and (4) making decisions to improve one’s health. The suggested indicators are listed in table 4 of annex 2.

Evaluating empowerment outcomes within different domains/sub-domains (household, community, labor health, and continued education) allows the evaluation team to compare whether empowerment outcomes are the same for different domains, and to assess whether modules contribute to their intended outcomes.

The impact of the project on students will be evaluated on a bi-annual basis, with the first evaluation expected to be completed in 2005. The data collected during this first evaluation will serve as the baseline for future evaluations. The project team is currently in the process of defining the control group.

# 2.3 Applying the Framework within a National Survey

At the national level, empowerment has become embedded in the language of national policy frameworks for poverty reduction.<sup>23</sup> A recent review of some 39 Poverty Reduction Strategy Papers (PRSPs) and Interim Poverty Reduction Strategy Papers (IPRSPs) (Thin 2002), for example, reveals that almost half of this sample explicitly mentions empowerment in some shape or form. The scope of discussion of empowerment issues and strategies within these PRSPs, however, is quite narrow. The three predominant thematic areas of discussing empowerment, echoed in a recent PREM review (Holland and Brook 2004) are governance, gender, and community participation (see annex 3). Governance is discussed particularly in the context of decentralization and service provision. Here, empowerment strategies focus on strengthening agency and “voice” through education, capacity building, and enhanced knowledge, while tackling “responsiveness” through public-sector reform and capacity building among service providers. PRSPs tackling empowerment were also quite likely to look at women’s empowerment, particularly in the political and economic domains, focusing on improved access to education, labor markets, credit, and political participation. Community participation is also widely addressed, emphasizing participation in the project cycle linked to ownership and sustainability, with, in some cases, specific
decomposing the composite real interest rate into its local currency and foreign currency parts provides a clearer idea of vulnerabilities stemming from currency mismatches on the public sector balance sheet. But to be meaningful, this analysis has to be done using nominal debt rather than the discounted present value of debt. The PV of debt obscures a clear picture of debt dynamics because it creates confusion about which r to use. Using nominal debt and actual r to look at dynamics is also applicable to LICs that rely primarily on official, concessional external funding; absent financial repression, domestic funding is likely to be at market terms. In this case, concessionality will show up simply as a low r, and the dynamics will still be meaningful in gauging whether public debt is on a sustainable trajectory.

- Third, _a five-year look back period plus a five-year projection period should be adequate for assessing debt sustainability_ . Identifying country idiosyncrasies—such as the intermittent growth spurts in Ghana on account of new oil discoveries or the rebasing of GDP—and controlling for these to isolate the underlying dynamics is essential for an accurate picture of sustainability.

- Finally, _developing relevant counterfactuals are of the utmost importance_ . Country-specific counterfactuals are not easily dealt with by the current LIC DSF; such an exploration is not part of the “realism tools” added to LIC DSF by the 2017 overhaul. We illustrate this with Ethiopia, the second most populous nation in Africa after Nigeria, based on IMF Country Report 20/150 issued in May 2020 (IMF 2020).

In Ethiopia as with Ghana, the public sector deficit—equal to investment minus the sum of saving and official transfers—has been driving the current account deficit. In fact, over the three years 2017/18 to 2019/20, the private sector ran a significant surplus—private saving plus transfers exceeded private investment—as seen in Tables 1 and 4b of IMF (2020).

The public sector DSA in IMF (2020) uses the 2017 LIC DSF to update the assessment in the 2019
where _V_<sup>�</sup><sup>_h_</sup> is the proportional change in household real income. In the remainder of the paper, we estimate the different components of equation (7) and study them in detail. We also use equation (7) to build ex-post counterfactual distributions. Let _x_<sup>_h_</sup> 0<sup>betheobserved,</sup> ex-ante level of real household income (from the data compiled in the household surveys). The counterfactual real income _x_ �<sup>_h_</sup> 1<sup>is</sup> Much of what we do below hinges on the comparison of the ex-ante and ex-post distributions of income.

Since our propositions are derived from an agricultural household model, which is not often used in the trade literature, it is important to bear in mind some of the key features of our approach when interpreting the results. If we take the standard models at face value, it is clear that several data limitations would prevent us from fully accurately estimating the welfare effects of trade stemming from those models. These limitations are inherent to household surveys. For example, information on returns to capital, especially corporate profits and so on, is often patchy and typically missing altogether in the household surveys. Consequently, it is very difficult to map the results from those standard models to our empirical results which are based on household surveys. There might therefore be some discrepancies between some of our results and standard trade theory. For example, Stolper-Samuelson effects resulting in differential impacts on the returns to capital vs labor, or to skilled vs unskilled labor, may not be captured by our analysis. However, most of the discussion about poverty, inequality and household welfare is (to a large extent) based on household surveys, which remain the dominant instrument for measuring household consumption and income portfolios. Such surveys are thus a natural starting point for analyzing the distributional impacts of trade policy.

Our results also
Studies 54, 1154–1170. https://doi.org/10.1080/00220388.2017.1333108

- Bao, S., Bodvarsson, Ö.B., Hou, J.W., Zhao, Y., 2009. Migration in China from 1985 to 2000. The Chinese Economy 42, 7–28. https://doi.org/10.2753/CES1097-1475420401

- Benjamin, D., Brandt, L., 2002. Property Rights, Labour Markets, and Efficiency in a Transition Economy: The Case of Rural China. The Canadian Journal of Economics 35, 689–716.

- Benjamin, D., Brandt, L., Giles, J., 2005. The Evolution of Income Inequality in Rural China. Economic Development and Cultural Change 53, 769–824. https://doi.org/10.1086/428713

- Biao, X., 2007. How far are the left-behind left behind? A preliminary study in rural China. Population, Space and Place 13, 179–191. https://doi.org/10.1002/psp.437

- Bosworth, B., Collins, S.M., 2008. Accounting for Growth: Comparing China and India. Journal of Economic Perspectives 22, 45–66. https://doi.org/10.1257/jep.22.1.45

- Brandt, L., Rozelle, S., Turner, M.A., 2004. Local Government Behavior and Property Right Formation in Rural China. Journal of Institutional and Theoretical Economics 160, 627–662.

- Brauw, A. de, Li, Q., Liu, C., Rozelle, S., Zhang, L., 2008. Feminization of Agriculture in China? Myths Surrounding Women’s Participation in Farming. The China Quarterly 194, 327–348. https://doi.org/10.1017
cognitive development.<sup>3</sup> The data we use contain measures of SES, including household composition, maternal and paternal education, housing characteristics and household assets; direct assessments of child health, including blood hemoglobin levels and child height and weight, as well as maternal reports of breastfeeding duration; and measures of parenting quality, including parental responsivity and punitiveness, time spent reading to the child, and the number of other children in the household.

Our analysis contributes to the existing literature in a number of ways. First, we document a clear association between SES and cognitive development among poor children in Ecuador. Moreover, we show that the associations between many components of SES and cognitive development become larger with the age of the child. For example, the association between maternal education and the TVIP scores is more than twice as large among older children in our sample, aged 54-71 months, than among younger children, aged 36-53 months. The same is true for household wealth. Although we cannot rule out all other alternatives, a plausible explanation for this finding is that the effect of high SES on cognitive development is cumulative, so that children from poorer households and those with less educated parents fall increasingly behind their peers as they approach school-age.

Second, we show that some measures of child health, in particular hemoglobin levels, and some parenting measures, in particular parental responsivity and punitiveness and whether children are read to, are strongly associated with cognitive development. Health and parenting account for a portion, but not all, of the associations between measures of SES and test scores.

Third, our estimation method explicitly accounts for censoring in the test scores: In our sample, as in many samples of poor children, a fraction of children are unable to make any progress on the test, or receive raw scores that are so low given their ages that their scores cannot be normed. Rather than exclude these children from the analyses—which would result in biased estimates of the associations between cognitive development,
General Government Debt (% of GDP)|38.6|73.2|78.6|110.2|
|GDP in GRD billion|1711|4618|13143|26884|

Source: Estimated on the basis of Bank of Greece, Ministry of Finance, IMF and OECD data.

Estimating the share of different types of investors is complicated by the absence of sectoral financial accounts. An approximate indication can be provided on the basis of the shares of different types of investors in the sources of financing the public sector borrowing requirement and published information on the holdings different types of banks and non-bank financial institutions.

As indicated above, non-bank investors accounted for 27 percent of the increase in public debt between 1986 and 1990. If earlier holdings are ignored, this would represent 17 percent of outstanding debt in 1990.<sup>19</sup> The presence of institutional investors was very weak in 1990. Mutual funds were just starting to emerge and insurance companies were little developed, while pension funds were required to place their financial assets in compulsory deposits with the central bank. Attributing the total of non-bank holdings to retail investors would involve a small amount of overestimation. Foreign investors accounted for 20 percent of total debt, with the remaining 63 percent accounted by the banking system, including the central bank, commercial banks and specialized credit institutions.

Applying the same approach to flows during the period 1991-95, the share of non-bank investors rose to 28 percent of total debt in 1995. Mutual funds started to register impressive

> 18 However, the true level of public debt was probably understated by serious shortcomings in recording and reporting public debt data. 19

This assumption is not unreasonable since holdings before 1986 were negligible. But it also implies that total holdings were not affected by valuation changes. Since the holdings of non-bank investors after 1986 were predominantly in short-term instruments with variable rates of interest, this would also seem reasonable. The only exception was represented by bonds issued with a foreign exchange clause. These were affected by
other Amazônian states. What do these aggregate deforestation rates tell us about the system’s overall effectiveness?

This paper argues that simple before-program/after-program comparisons do not provide a measure of program effectiveness, because commodity prices, weather, and other factors strongly influence year-to-year variation in deforestation rates. Comparisons between deforestation inside vs. outside licensed properties are also problematic guides to effectiveness, because early volunteers for licensing may be systematically different in characteristics or motivation from their more reticent neighbors. Moreover, while we recognize that the program prioritized its enforcement efforts on licensed properties, we hypothesize that it may also have affected behavior in the unlicensed areas. This is because credible operation of the program within the licensed properties might increase the perceived probability of eventual detection and punishment of unlicensed activities.

The paper therefore reviews the methodological challenges in assessing program impact. It then proposes a solution, based on an innovative application of the difference-in-difference approach to geographically explicit data. We hypothesize that the program, if successful, will have a _differentially large_ impact on three types of areas:

- those explicitly targeted for higher enforcement effort

- areas that are more easily observed, e.g. on very large properties

- areas where most deforestation is likely to be unauthorizable, e.g. in contravention of the

- forest reserve requirement.

Using multivariate analysis that holds constant a variety of factors, we find that deforestation in each of these areas declined, _relative to other areas_ , after the program was instituted. The period of analysis however extends only through 2002, and cannot be generalized to the subsequent period. Starting in 2003 there was a change in state administration, with the governorship passing from an environmental advocate to one of the world’s largest soybean farmers.

5
changes in our survey data with the victory dummy from equation (1). Overall, these specification changes leave unchanged the main takeaways from Tables 2 and 3.

# **4.2 Reduced Nepotism**

In many villages, entrenched nepotistic practices sustain the dominance of old village elites, undermining good governance and local democracy (Simanihuruk and Sihombing, 2019; World Bank, 2023). Appendix Table A.8 shows that nepotistic hiring practices in the village bureaucracy correlate negatively with the quality of service provision measured in administrative _Podes_ data (column 1) as well as perceptions of service access and quality in our surveys conducted with citizens (columns 2–3).

In Table 4, we examine the likelihood that relatives of the village head are employed in the village government, as reported by the village heads themselves. At the RD cutoff, we find a substantial, statistically significant decline in this measure of nepotism (column 1). This effect may partly reflect the continued presence of bureaucrats who were relatives of the previous village head (the defeated incumbent) but are unrelated to the new leader, and retained their positions after a turnover. As a result, the presence of bureaucrats related to the village head would be lower in turnover villages than in villages where the incumbent won. The estimate in column 1 of Table 4 suggests that, at a minimum, new leaders do not systematically replace these prior nepotistic appointees with their own relatives.

In columns 2 and 3 of Table 4, we consider two additional measures of nepotism using data collected from bureaucrats, addressing the concern above. We look at the probability that bureaucrats had a parent who ever served as village head (column 2) or a parent who ever served in the village government (column 3). Overall, a large fraction of bureaucrats (27%) had a parent who served in the village govern12
most affected regions include Central India, stretching from Gujrat and Maharashtra in the West to Odisha on the east coast, and eastern states such as Bihar, West Bengal, and Assam. In regions with frequent floods, households and firms internalize them in their decisions and consequently they do not offer themselves to clearly identify the impact of a flood. In line with this, Kocornick-Mina et al. (2020) find permanent relocation of economic activity in response to large floods only

> 3Figure A.1, in the appendix, plots the daily actual and normal rainfall in Kerala from June to December 2018 using data from the Indian Meteorological Department.

> 4 See Raman (2020) on how geographical features of Kerala shaped the course of this disaster.

4
although the number of trained teachers is not.<sup>18</sup> As the time needed to travel to the closest school increases, the probability of children going to school significantly decreases. Finally, a higher average fee charged by schools in the community is associated with lower school attendance. This finding matches the subjective responses of parents: in a 2001 survey, 43 percent of families with children not attending school reported that the major reason for not attending was cost.<sup>19</sup>

# **5. Determinants of School Fees and the Educational Market in Haiti**

Given the apparent importance of fees as a barrier to attendance, we next consider their determinants, using data on the fees charged by each school from the 2003 School Census.<sup>20</sup> To the extent that the fees are determined in a competitive education market, they reflect families’ willingness-to-pay, so that the correlates of fees provide an indication of what characteristics the families of children value when choosing schools. We examine school fees using the following regression framework: where our dependent variable is the logarithm of fees charged by school _i_ in locality _j._ A locality is defined as the urban or rural portion of a _commune, Iij_ is a vector of variables for school _i’s_ infrastructure, _Aij_ is a vector of administrative characteristics of the school, and _Tij_ is a vector of variables related to teacher supply and quality in the school. We measure market concentration

> 18 We define a teacher to be trained if he or she is reported to have a university degree or teaching training certificate (reported in French as _universitaire, normalien,_ or _capiste_ ).

> 19 World Bank (2006), based on analysis of the 2001 _Enquête sur les Conditions de Vie en Haiti_ .

> 20 The descriptive statistics for this data are shown in Table A.7 in the appendix.

12
fees. However, we also find significant differences in the provision of services, such as counseling and physical examinations, that should not affect the cost of the consultation. We cannot rule out that providers treat poorer women worse than they treat wealthier women. But it could also be that some of the differences are driven by the wealthier women being more proactive in demanding care, rather than the providers choosing to exert less effort. Unfortunately, the data provide limited insight into this question. The finding of within‐facility variations in quality merits further research.

The pricing data analyzed in this paper suggest that, if anything, providers discriminate against wealthier women when it comes to out‐of‐pocket charges. Prices not only increase with quality, but also with the patient’s wealth, suggesting that providers seem to assess ability or willingness to pay and charge patients accordingly. Comparing areas with higher and lower ratios of wealthy to poor women, we find greater within‐facility price differentials in the former, suggesting that price discrimination is not only driven by pro‐social cross‐subsidization.

17
50m<sup>2</sup> if conditions are ideal and at sizes around 100m<sup>2</sup> under average conditions. They also report that fires are located at the correct location with a spatial margin of error of less than 100m on average. Unfortunately, the resolution of MODIS is such that the data available in FIRMS only measures whether at least one fire exists in a given square kilometer. As a result, MODIS does not provide any information about the size of the detected fire or the total burned area. Further, it does not distinguish between pixels with only one fire and those with multiple fires.

I combine the FIRMS data on fire presence with remotely sensed landcover data from the European Space Agency’s Copernicus system to determine the land uses on which fires occur.<sup>18</sup> Copernicus land cover data assigns each pixel to a land class based on imaging that measures its reflectivity. Classes include water/ice, urban, wetland, irrigated cropland, non-irrigated cropland, various classes of forest, and various classes of dry shrubland. I use the cropland classes to determine which fires occur on cropland and focus my analysis on these agricultural fires. I assign agricultural fires based on land use in 2006. I supplement the land use data from Copernicus with additional polygon layers from the Harvard Center for Geographic Analysis (CGA) that identify roads and urban areas in India. I verify that my agricultural fires do not include any fires that occur in locations that the CGA defines as urban. Finally, I aggregate the assigned agricultural fires to the district by month level.

Figure 1 shows the annual average number of monthly fires per subdistrict prior to MNREGA. Fires are concentrated in the northwest of the country and along the Indo-Gangetic Plain. However, it is clear that burning occurs on cropland throughout India. Both of these facts are confirmed by Figure 2 showing the distribution of states by mean monthly fires in subdistricts. While fires are used in most states in India