rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Loan Characteristics

||PACS|SHG|Formal Sources|Informal Sources|
|---|---|---|---|---|
|Number of Loans since April 06|861|175|454|1,051|
|Median Borrowed Amount|10,000.0|7,000.0|20,000.0|20,000.0|
|Mean Borrowed Amount|15,734.3|8,725.1|44,565.0|26,522.4|
|Std Borrowed Amount|29,993.2|6,871.5|141,475.3|40,678.1|
|Maturity (Months)|24.32|14.80|15.55|17.11|
|Annual Interest Rate (%)|16.85|19.07|20.10|31.71|
|Pct. of loans that equire Physical Collateral (%)|98.14|4.00|91.41|8.09|

Source: Household survey conducted in three districts in AP, India in 2007. Formal Sources includes Basix / Bartiya Samruddi / KBS Bank or NGO, commercial banks, rural bank and government bank. Informal Sources refers to friend/relative, local money lender, landlord/employer, local grocery store, pawn broker, SHG/ Raitu Mitra and others. Physical Collateral includes land, house, gold, crops or livestock (the rest of loans required guarantor, fund-savings or other collaterals).

Table 3: Perceptions of Access to credit and repayment

||_PACS _|<sup>_Comm._</sup><br>_Bank_|_MFI_|_PACS -_<br>_Comm.Ba_|_PACS -_<br>_MFI_|_Comm._<br>_Bank -_|
|---|---|---|---|---|---|---|
|Perceived probability of ... (0-10)|||||||
|Repayment rescheduled if drought|7.078|5.448|2.446|1.629***|4.631***|3.002***|
|Repayment forgiven if drought|6.564|4.621|2.015|1.943***|4.549***|2.606***|
|Loan amount lower than requested|6.441|5.109|3.604|1.332***|2.838***|1.506***|
|New loan after full repayment|4.961|6.276|6.818|-1.315***|-1.857***|-0.542**|
|New loan even if not full repayment|3.696|3.650|4.253|0.046|-0.557**|-0.603**|
|Share deducted from loan as bribe|4.722|2.903|2.985|1.819***|1.737***|-0.082|
|Share cap./savings misused and lost|4.801|2.374|3.576|2.426***|1.225***|-1.201***|
|Repayment of loan in case of droughts but good harvest*|4.962|6.186|6.613|-1.224***|-1.651***|-0.427**|
|Rank of repayment of loan if not enough funds to repayall(1 to 3)*|2.486|1.907|1.607|0.579***|0.879***|0.300***|

Source: Household survey conducted in three districts in AP, India in 2007. An asterisk (*) indicates that the sample is restricted to those with more than one source of credit.

12
<!-- Start of picture text --> 4,400,000<br>1,200,000 ———_—_—_—§<br>2& 1,000,000 _— _—___<br>§ a 1981<br>g 92005<br>$ 800,000 — ——<br>a<br>ao<br>= 600,000 —<br>2<br>c<br>400,000 —<br>200,000 —<br>0<br>Agricufture Industry Services<br><!-- End of picture text --> <!-- Start of picture text --> rT<br>:<br><!-- End of picture text -->
_Figure 1: Trends Growth and Inflation Rates in CAMU Countries, 1981-2018_ _Figure 2: Trends Growth and Inflation Rates in WAMU Countries, 1971-2018_ <!-- Start of picture text --> 25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>1981 1986 1991 1996 2001 2006 2011 2016<br>-5.0<br>-10.0<br>CAMU-GDP Growth CAMU-Inflation Rate<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>1971 1976 1981 1986 1991 1996 2001 2006 2011 2016<br>-5<br>-10<br>WAMU-GDP Growth WAMU-Inflation Rate<br><!-- End of picture text --> _Figure 3: Comparison of Median Growth and Inflation Rates of CAMU Countries, 1981-2018_ _Figure 4: Comparison of Median Growth and Inflation Rates of WAMU Countries, 1981-2018_ <!-- Start of picture text --> 7.0 8.0<br>6.0 7.0<br>6.0<br>5.0<br>5.0<br>4.0 4.0<br>3.0 3.0<br>2.0 2.0<br>1.0<br>1.0<br>0.0<br>0.0<br>Central Congo, Gabon Cameroon Chad Equatorial<br>African Rep. Guinea<br>Republic<br>Growth Inflation Inflation Threshold Growth<br>Source: UNSD, WDI and ECOWAS database.<br><!-- End of picture text --> <!-- Start of picture text --> 8.0<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Growth Inflation Inflation Threshold<br><!-- End of picture text --> Note: Aggregate indicators are based on weighted averages – Weights are derived from GDP, 2010 USD constant prices.

The fact that the CFA franc is pegged to the euro has broadly helped containing inflationary pressures but also cost the independence of the regional monetary policies. In comparison with other African countries, inflationary pressures have been broadly contained in the Franc zone countries, excluding the period that followed the 1994 devaluation and outliers such as Equatorial Guinea and Guinea-Bissau. However, this result comes at the expense of the autonomy of the monetary policy as, according to the Impossible Trinity of Mundell, a country loses the autonomy of its monetary policy when it adopts a fixed exchange rate regime and it allows the free circulation of capital flows. The loss of the independence of the monetary policy can be observed by comparing the dynamics of the monetary policy rates of the regional central banks and the one of the European Central Bank (ECB) – at least until before the 2014 negative oil price shock for CAMU countries. In addition, this limited margin on the monetary policy is also Page | 11
||PPR|Working Paper Series|||
|---|---|---|---|---|
||Title|Author|Date|Contact|
|WPS36|Means and Implications<br>of Social<br>Security Finance in Developing||||
||Countrles|Douglas J. Puffert|August 1988|R. Blade-Charest<br>33754|
|WPS37|Black Market Premia, Exchange Rate<br>Unification<br>and Inflation In<br>Sub-Saharan<br>Africa|Brian Pinto|July 1988|R. Blade-Charest<br>33754|
|WPS38|<br>Intergovernmental<br>Grants In<br>Developing<br>Countries|Larry Schroeder|||
|WPS39|Fiscal Policy In Low-income<br>Africa|Stephen A. O'Connell|<br>July 1988|R. Blade-Charest<br>33754|
|WPS40|Financial Deregulation<br>and the||||
||Globalization<br>of Capital Markets|Eugene L. Versluysen|August 1988|R. Blade-Charest<br>33754|
|WPS41|Urban Property Taxation in<br>Developing<br>Countries|William Dillinger|August 1988|R. Blade-Charest<br>33754|
|WPS42|Government<br>Pay and Employment<br>Policies<br>and Government<br>Performance in||||
||Developing<br>Economies|David L. Lindauer|August 1988|R. Blade-Cha-est<br>33754|
|WPS43|Tax Administration<br>in Developing<br>Countries: Strategies<br>and Tools||||
||rf Implementation|Tax Administration<br>Division|||
|WPS44|The Size and Growth of Government<br>Expenditures|David L. Lindauer|||
|WPS45-<br>WPS60|Assigned to WDR||||
|WPS61|Student Performance<br>and Schools Costs<br>in the Philippines'<br>High Schools|Emmanuel Jimenez<br>Vicente Paqueo<br>Ma. lourdes de Vera|August 1988|T. Hawkins<br>33678|
|WPS62|Status of Universities in the Arab<br>Countries of the Middle East and<br>North Africa|George 1. Za'rour|||
can be expected to grow further in the near future, directly through additional large-scale privatizations and indirectly through follow-up equity issues by already privatized companies.

# **IV.4** Foreign Direct Investment

Direct investment (FDI) was clearly the most common vehicle used by foreign investors in privatization transactions, especially in sma!l- and medium-sized transactions. FDI accounted for the remaining 371 recorded privatizations with foreign participation during 1988-1992, worth $14.5 billion. Hence, privatizations were certainly one of the reasons underlying the rapid increase in FDI over the last five years, accounting for an average of abl,ut **11** percent of total FDI to the developing world during the period.

_Europe and Central Asia_ received the largest share of its FDI inflows from privatizatiors, reaching an average of almost 32 percent during the period. This reflects the strong dependence of the transforming economies in Eastern Europe on privatizations as the main vehicle to attract foreign investors. While the available FDI information generally does not appear very reliable for the Eastern European countries<sup>21</sup> , they seemed to have received almost 85 percent of their FDI from the sale of their SOEs.

_Latin American_ countries showed a strong growth in privatization revenues stemming from foreign investors, in particular during 1990 and 1991, when privatizations accounted for over a quarter of total FDI flows. Over the period as a whole, privatization FDI still accounted for over 16 percent. Especially in the cases of Argentina and Venezuela, privatization FDI was particularly strong, accounting for a share of 36 percent and 41 percent respectively over the period (see Table A-4). Mexico, on the other hand, as the strongest privatizer of the region in terms of sales volume, only showed an average share of slightly more than 5 percent, primarily because the sizable privatization of the banking sector during 1991 and 1992 was for all practical purposes limited to domestic investors, generating no FDI inflows at all.

In _East Asia, South Asia, Sub-Saharan Africa_ and _North Africa and the Middle East,_ privatizations only contributed marginally to FDI inflows. For one, no indication could be found that foreign participation in the existing privatization programs is particularly strong. This is due to the fact that in many countries strong restrictions are imposed regarding the participation of foreigners in privatizations. In East Asian countries, on the other hand, FDI flows have been very large over the last years, rendering privatization transactions marginal. In addition, the capital markets in these countries are already so

> 21 For HLugary, for example, the IMF does not report any FDI inflows until 1991, uhilc privatization transactions with foreign participation are recorded since 1989.

21
New York and American Stock Exchanges, the first section of the Tokyo Stock Exchange, the London Stock Exchange and the U.K. unlisted securities market.

Monthly retums adjusted for dividends and stock splits, are transformed into US dollar retums using end-of-month exchange rates. The emerging markets exchange rates are from the _Emerging Markets Database_ while the developed markets exchange rates are from _I_ _nternational Financial Statistics_ which is published by the International Monetary Fund. To compute excess retums I use the US Treasury Bill returns from Ibbotson Associates (1993).

Tables 2 and 3 provide some summary statistics on the emerging markets in the sample. _'Te Emerging Markets Database_ does not include all of the stocks traded in the emerging markets. Rather, the database consists of a sample of stocks from each market. The stocks are chosen on the basis of trading activity, capitalization, and diversity across market sectors [see Intemational Finance Corporation (1993)]. On average, the stocks in the IFC sample represent 57% of the total capitalization of the respective markets.

Construction of the _Emerging Markets Database_ began in 1981. Firms were chosen at that time on the basis of 1980 data [Errunza and Losq (1985, p. 562)]. While this poses no particular problems for retums after 1980, there may be a survivorship bias induced for the retums before 1981. That is, firms which disappeared between, say, 1975 and 1980 would not be included in the database. An actual portfolio strategy might have included those assets in the sample. As we can see from Table 2, eleven of the twenty emerging markets have data prior to 1981. Errunza and Losq (1985) investigate the issue of survivorship biases in a sample of eight of these emerging markets. They apply the selection criteria to assets as of the beginning of the 10
- 38 -

World Bank. 2007. Addressing climate change-driven increased hydrological variability in environmental assessments for hydropower projects—a scoping study. Mimeograph.

Wong, Heung, Wai-cheung Ip, Riquan Zhang, Jun Xia. 2007. Hon-parametric time series models for hydrologic forecasting. _Journal of Hydrology_ , Vol. 332, pp. 337-347.
Table A3 — Robustness check: Regression results of logarithm of monthly earnings from the latest job

||Full Samp|le<br>Tr|immed and Weig|hted Sample|
|---|---|---|---|---|
|Years of schooling|0.0873***|(5.61)|0.0832***|(4.50)|
|Age|0.496*|(1.95)|0.757***|(2.92)|
|Age squared|-0.0119*|(-1.94)|-0.0185***|(-2.98)|
|TEEP cohort*TEEP province|0.0339|(0.11)|0.191|(0.71)|
|Years of schooling*TEEP cohort*TEEP province|<br>-0.0196|(-0.58)|-0.0344|(-1.09)|
|TEEP cohort|-0.0291|(-0.08)|0.0105|(0.03)|
|Years of schooling*TEEP cohort|-0.0408|(-0.74)|-0.0811|(-1.37)|
|Female*TEEP cohort*TEEP province|0.240**|(2.16)|0.224*|(1.84)|
|Female|-0.488***|(-2.94)|-0.388**|(-1.99)|
|Female*Years of schooling|0.0275*|(1.75)|0.0209|(1.18)|
|Female*TEEP cohort|0.107|(0.78)|0.189|(1.21)|
|Mother’s years of schooling|0.0105|(1.00)|0.00905|(0.85)|
|Mother’s age|-0.000131|(-0.06)|-0.00277|(-1.27)|
|Log(consumer durables)|0.0488|(1.60)|0.0169|(0.48)|
|Height|0.00557*|(1.79)|0.00698*|(1.97)|
|Number of students per classroom in high school|-0.000624|(-0.70)|0.000799|(0.69)|
|If phone interview|0.232***|(4.60)|0.244***|(3.24)|
|Inverse Mills ratio|-0.0445|(-0.12)|0.376|(0.92)|
|School fixed effects|yes||yes||
|Number of observations<br>|1,716||1,592||

Source: TEEP Tracking Survey. Notes: t values are in parentheses.

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

44
according to our estimates, increase costs by the smallest farmers by 17.2%, largely because they would forego the use of machinery (the cost share of which would drop to 17.13%) and adopt more labor-intensive methods of production. By comparison, an increase in average distance is estimated to not have any impact on the smallest farm size group but to affect the largest ones (with a one-standard deviation change increasing cost by 8.27% without affecting relative cost shares). These effects vary by crop as illustrated by the fact that slightly different findings emerge for farms with rice and wheat only. A similar pattern is observed for wheat where higher levels of fragmentation are estimated to reduce the amount of family labor applied by large farms. Again, with few exceptions of minor magnitude and low overall relevance, the mean fragment-fragment distance is not estimated to have any significant effect on cost shares for most farmers.

# **5. Conclusion and implications for policy**

Our analysis contributes to the literature in three ways. First, we document that fragmentation increases cost of production in India even once potential endogeneity due to market participation or crop choice is accounted for. Second, our findings suggest that the main mechanism underlying this phenomenon is a substitution of labor for mechanical and chemical technology. Finally, analysis by farm size group highlights that fragmentation is not neutral from a distributional perspective, as associated cost increases are disproportionately borne by those in the smallest farm size class. A more consolidated holding structure would thus contribute to equity by benefiting poor and marginal farmers most.

Such potential equity benefits, together with the limited scope for market-based land consolidation due to high coordination costs could, and have been, used to argue for public support to consolidation programs. Yet, the mixed record of such efforts in India and elsewhere suggests that interventions are unlikely to be viable or sustainable unless there is net migration out of agriculture, land ownership records are unambiguous and up to date, and active land markets allow public efforts to focus on complementing private ways of helping to equalize returns to land rather than substituting for them. Any initiatives in this direction should thus be preceded by measures to allow operation of land rental, improve land records, and establish low cost mechanisms to register transfers so that records remain authoritative and up to date.

13
To complement the analysis with these last outcomes, we additionally examine whether, in the past year, children worked while school was in session. To construct this outcome variable, we rely on the following two questions asked to _working_ children: "Were you enrolled in the past 12 months?" and "Did you sometimes work [in this occupation] while also attending school (i.e. during the school year)?"

# _Outcomes as defined for the Mexican data_

We classify children as attending school if they "currently" attend school, regardless of the level attended. We classify them as attending school regularly if they currently attend school and did not miss any school days during the 4 weeks prior to the interview. We classify them as working if, during the week prior to the interview, they worked, had a job but did not work, or worked in the household business, on the household property, or on the household farm. We classify them as working for pay if they worked in the week prior to the interview for a wage or salary.

41
Table 5: Compliance with treatment assignment.

||(1)<br>**Attended Therapy** _at_<br>_least one session_|(2)<br>**Received Cash**|
|---|---|---|
|IPT-G|0.559|0.000|
||(0.034)|(0.000)|
|IPT-G+|0.516|0.865|
||(0.030)|(0.016)|
|Control mean|0.000|0.000|
|Observations|1913|1914|
|_t-tests (p value)_|||
|H0: IPT-G=0|0.000|.|
|H0: IPT-G+=0|0.000|0.000|
|H0: IPT-G = IPT-G+|0.343|0.000|

Notes: This table shows the impact of treatment on compliance with treatment assignment; Estimates obtained by regressing compliance on treatment indicators; Standard errors are clustered at the club level in parentheses.

35
probability of exit, remaining a non-exporter, survival, and entry, as well as changes in export intensity.

Results in Tables 5a-c are consistent with the Multinomial Logit results reported in Table 3 (and the logit results in Table 4), although the results on export intensity are a bit stronger than those in Table 3. EPA treatment significantly increases the probability of survival or entry and reduces the probability of remaining a non-exporter regardless of which propensity score matching we used.

Regarding export intensity, the relevant results appear in the last two rows of each panel. The penultimate row contains the results for the whole sample of firms, whereas the last row contains the results for the sub-sample of firms that were exporters in 2006. For the whole sample, only the Kernel Matching approach yields statistically significantly positive effects of exportpromotion treatment. For the sub-sample of exporters in the initial year of the sample, two out of the three matching techniques (Kernel and Stratification) yield statistically significant positive effects.

The aforementioned results seem to weakly contradict the findings of Görg et al (2008) who in a sample of Irish firms found that grants tend to have an impact on the intensive margin, but not the extensive margin. Even though Görg et al (2008) do not differentiate between entry and survival it seems unlikely that their results would become statistically significant if they were to differentiate between the two rationales for observing exporting firms.

In order to check whether the differences in results could partly be explained by institutional differences in the activities of export promotion agencies in Ireland and Latin American countries, we explore differences in the responses to a recent World Bank survey of export promotion agencies between Enterprise Ireland (Ireland’s EPA) and the corresponding average across all LAC EPAs.<sup>6</sup>

> 6 Note that the program studied in Görg et al (2008) although run by the same agency (Entreprise Ireland which before 1998 was known as Forbairt) is not a program aimed at promoting exports, but rather at encouraging investment in technology and training.

12
# Table 5: Program exposure

|||<br>HW|<br>WS|||<br>TSS|<br>M||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|High<br>(3 channels)|Medium<br>(>= 2 channels)|Low<br>(>= 1 channel)|Number of<br>channels|High<br>(3 channels)|Medium<br>(>= 2 channels)|Low<br>(>= 1 channel)|Number of<br>channels|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|HWWS (β1)|0.071***|0.212***|0.373***|0.656***|0.068***|0.193***|0.251***|0.512***|
||(0.012)|(0.024)|(0.033)|(0.061)|(0.012)|(0.024)|(0.031)|(0.057)|
|TSSM ( β2)|0.006|0.036*|0.100***|0.142***|0.026**|0.080***|0.141***|0.247***|
||(0.008)|(0.021)|(0.025)|(0.047)|(0.010)|(0.019)|(0.029)|(0.050)|
|HWWS + TSSM (β3)|0.061***|0.254***|0.442***|0.758***|0.103***|0.266***|0.345***|0.714***|
||(0.011)|(0.026)|(0.026)|(0.055)|(0.014)|(0.024)|(0.027)|(0.057)|
|p-values β1= β2|0.000|0.000|0.000|0.000|0.000|0.000|0.002|0.000|
|for F-test β1= β3|0.500|0.180|0.061|0.160|0.030|0.015|0.006|0.004|
|β2=β3|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_Observations_|3,619|3,619|3,619|3,619|3,619|3,619|3,619|3,619|
|_R-squared_|0.071|0.168|0.238|0.245|0.077|0.141|0.186|0.191|
|_Control Mean_|0.001|0.014|0.135|0.150|0.004|0.040|0.224|0.269|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects.

41
econometric method used in this paper to analyze the effect of ethnicity, relevant economic, demographic, and environmental factors were taken into account. The questions to be addressed in this regard are: To what extent are the indigenous people at a disadvantage health care-wise and in what particular health services are they disadvantaged? Is the health care gap due to indigenous culture _per se_ as opposed to the impact of socioeconomic factors such as wealth, health insurance coverage, education and access cost?

The paper is structured as follows. First, it presents background information, laying out a stylized comparison of the indigenous versus non-indigenous population. Second, it describes the methodology and the data used for the analysis. Third, the econometric results are presented and analyzed. Finally, in the conclusion, the report summarizes its findings and their implications.

# **The indigenous population and the health challenge.**

As elsewhere, the health status of Mexico's indigenous population has lagged behind that of other population groups. A World Bank appraisal report (2001) highlighted this health gap. On this, the National Development Program for the Indigenous Population (NDPIP), 2001-2006, indicate that the infant mortality rate among the indigenous people is 58 percent higher than the national average. And their life expectancy appears to be only about 69 years in contrast to the national average of 74 years.

Infectious diseases, malnutrition, vector-borne diseases and other illnesses associated with poverty and underdevelopment have been a major cause of the health gap. The NDPIP 2001-2006 reveals that chronic malnutrition among indigenous children less than 5 years old is 44 percent compared with the national average of only 17.7 percent. Moreover, the indigenous maternal mortality rate appears to be about triple that of non-indigenous mothers. Further, the World Bank appraisal report (2001) indicates that in the 1990s the national infant mortality due to infectious diseases stood at only 27.3 percent, while the rate for the indigenous population reached 83.6 percent.

Access to health services by the indigenous population remains a key issue, although data indicates that significant progress has been made in this regard. In 1991 there were only 537 primary health care centers and four secondary level health units in communities largely populated by indigenous groups. In 1997, these figures rose to 2,095 and 31 units, respectively. Nevertheless, these communities apparently continue to be at a disadvantage. For example, in the indigenous communities in Oaxaca the number of doctors per 1000 indigenous people is only 0.13 compared to 0.93 for the state as a whole. In regard to hospital beds, the figures for Oaxaca, Veracruz, and Puebla are only 0.07, 0.11 and 0.19 beds per 10,000 inhabitants in the indigenous areas, respectively - compared to the national average of 0.79. Further, the NDPIP has pointed out that 87 percent of the inhabitants of communities classified as indigenous are not covered by social security services.

4