rafmacalaba/datause-encoder-data huggingface dataset:
# **3 Empirical Strategy**

This evaluation of the CADENA program focuses on the effect of insurance payments on ex-post production decisions and other coping mechanisms. To identify this effect, we limit our sample to municipalities that were insured through drought index insurance policies between the years of 2005 and 2013. Using weather data provided by CONAGUA, we match policies to their corresponding weather stations and calculate the total precipitation recorded at that station within each of the three phases designated in the policy. We then subtract the policy-specific precipitation threshold from the realized precipitation to obtain deviations from the threshold for each of the three phases, _Xmcsti_ = _precipmcsti − thresholdmcsti_ , where _m_ indexes the municipality, _c_ the crop, _s_ the weather station, _t_ the year, and _i_ the phase. A policy results in payment if precipitation at the corresponding weather station falls below the pre-determined threshold in any of the three phases.

In an ideal setting, we would observe insurance payments and all relevant outcomes for precisely the area covered by the policy. The running variable in this setting would be the minimum deviation from the threshold at a given weather station over the three phases of the policy: _Xmcst_ = min _i∈{_ 1 _,_ 2 _,_ 3 _}{Xmcsti}_ . However, we only observe payment and outcome variables at the municipality level, so we collapse the observations to the municipality-crop-year level and define the running variable as the minimum deviation from the threshold over all of the weather stations associated with a given municipality, _Xmct_ = min _s∈S_ ( _m_ ) _{Xmcst}_ . Given the data limitations, we have to account for the fact that there is substantial heterogeneity in the intensity of the treatment among municipalities receiving payment because some municipalities may have received payouts on 25% of their insured area and others may have received payment on 100%. In addition, there exists variation in terms of the percentage of agricultural land in the municipality that is insured via CADENA, since the policy is limited to land cultivated by producers with less than 20 hectares of land. Thus, the treatment variable is defined as the total number of hectares (across all weather stations linked to a given municipality) that received a payout divided by the number of hectares of land devoted to that crop in a given municipality: _Tmct_ =<sup>_hapaidmct_</sup> _hamc_ 4
effects on households’ access to banking services. Table 5 reports the marginal effects when estimating our baseline Probit model of households’ access to banking services (Eq. 2) by maximum likelihood with standard errors clustered by district and year (see Petersen, 2009). We report first a regression with individual/household characteristics, district level bank branch expansion by other banks and district fixed effects (Model 1). Next, we differentiate banks by their ownership structure (i.e. foreign banks, government banks and domestic private banks). We run these regressions with and without the dummy variable associated with Equity Bank presence (Models 2 and 3); note that the domestic banks category includes Equity Bank in those models. Finally, instead of using an indicator denoting the presence of a certain type of bank, we use the number of branches for each bank ownership type. Again, we run these regressions with and without the dummy variable associated with Equity Bank presence (Models 4 and 5), and again, the domestic banks’ branches include those of Equity Bank.

Table 5 shows, _first,_ that the presence of Equity Bank in a specific district is strongly positively related to the residents’ probability of having a bank account, which goes beyond the effect of bank expansion and presence of other commercial banks (Models 1, 3 and 5). This effect is highly statistically significant regardless of the model specification. The coefficient in Model 1 suggests that the penetration of Equity Bank in a new district increases the probability of having a bank account by roughly 4 percentage points after controlling for the total number of other banks’ branches. Model 3 also suggests that the impact of the presence of Equity Bank in a specific district goes beyond the impact of the presence of a standard domestic bank by around 2 percentage points. As noted, according to the FinAccess survey, the proportion of people having a bank account in Kenya was 14 percent in 2006 and 23 percent in 2009. Therefore, the magnitude associated with Equity Bank presence on financial access is also economically meaningful. This finding is consistent with the notion that the business model of Equity Bank, which targeted middle- and low-income segments of the population using such strategies as the local language requirement in its branches, has paid off in terms of greater financial inclusion.

_Second_ , we find that the presence of domestic private banks (including Equity Bank), government banks or foreign banks all have a positive impact on access to bank accounts at the district level (Model 2). However, we find a hierarchy in the effects of different types of banks: the effect of foreign banks is 2.5 percent (and statistically insignificant), the effect of government banks is 3 15
7 outcomes but rather vice versa. Countries that shifted payment system may have done so out of a concern over underlying health system weaknesses causing poor outcomes. Section 5 introduces our data, including our payment method classificatory variables. Inevitably there is some arbitrariness about these at the margin, so we have assessed the robustness of our results to alternative classifications. Section 6 presents our results, both of the specification tests and our estimates of payment method impacts. Section 7 contains our conclusions.

# **2. Provider payment reforms in the ECA countries**

In almost all the former communist countries of the ECA region, the health care sector was organized exclusively along the lines of the centrally planned Semashko model during the communist era.<sup>8</sup> Characteristically, this meant general revenues financing in addition to out-of-pocket payments—the latter primarily taking the form of payments for drugs and “gratuities” paid by patients to providers. The state normally owned the whole network of health care providers with no participation of a private sector. Salaries were the prevalent form of paying medical doctors and other health professionals. Providers were organized in a tiered system and historical, line-item budgeting was used to reimburse hospitals; that is, budgets or block grants were allocated to hospitals according to population-based and, mainly, capacity norms (such as the number of beds, the most commonly used criterion). From year to year, the historical budget accruing to a given hospital could be adjusted by some inflation factor, yet there was rarely any reallocation across spending categories.

> 8 This section draws heavily on Dixon et al. (2004) and the Health Systems in Transition ( _HiT_ ) series, downloadable from http://www.euro.who.int/observatory/Hits/TopPage.
# TABLE 1: SUMMARY OF MEASLES VACCINATION STUDIES

|STUDY<br>i<br>-<br>~<br>~|ILOCATION<br> <br>__<br>_<br>__<br>___<br>__|ISTUDYPERIOD<br>___<br>__<br>__<br>_|STUDY POPULATIOM|MAIN FINDINGS<br>[M.IN|
|---|---|---|---|---|
|THE KASONGO<br>PROJECT TFAM<br>l<br>_______________|ZAIRE|1974-1977<br>_____________|255 VACCINATED CHILDREN<br>3 CONTROL GROUPS OF<br>UNVACCINATED CHILDREN|30-49%<br>REDUCTIQN IN<br>7-35 MONTH<br>MORTALITY|
|AABY, ET AL.<br>} _______________|GUINPA-BISSAU<br>______________|1980-1981<br>_____________|803 VACCINATED CHILDREN<br>193 UNVACCINATED CHILDREN|72% REDUCTION<br>IN 6-35 MONTH<br>MORTALITY|
|GARENNE AND<br>CANTRELLE|SENEGAL|LATE 1960s|Krk|31% REDUCTION<br>IN 6 MO.-<br>3<br>YR. MORTALITY|
|CLEMENS, ET AL.|MATLAB,<br>BANGLADESH<br>___ _ _ _ _ _ _ _ _ _ _ _|1982-1984<br>_____________|536 DEATHS<br>1072 AGE-SEX MATCHED<br>CONTROLS<br>__________________________|36% REDUCTION<br>_IN_10-60<br>MONTH<br>MORTALITY|
|KOENIG, ET AL.|MATLAB,<br>BANGLADESH|1982-1985<br>l____________|8135 VACCINATED CHILDREN<br>8135 MATCHED UNVACCINATED<br>CHILDREN|UP TO 46%<br>REDUCTION IN<br>9-60 MONTH<br>MORTALITY|
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but child-specific substitution effect, which is brought on by the reduction in the opportunity cost of schooling for the eligible child. The displacement effect is also positive for these children, as it refers to situations in which they displace their siblings from school. The ambiguity of the effect on ineligible siblings arises from the opposing (positive) income and (negative) displacement effects of the transfer on these children.

We then take the predictions of the model to data from Cambodia, where a program known as the CESSP Scholarship Program (CSP) makes very modest transfers, equivalent to between 2 and 3 percent of the total expenditures of the average recipient household, conditional on school enrollment for children of middle-school age. The results show that children who received scholarships were about 20 percentage points more likely to be enrolled in school, and 10 percentage points less likely to work for pay. However, the school enrollment and work of _ineligible_ siblings was largely unaffected by the program. These results are robust to a variety of specification checks.

These findings have important implications for the way in which we think about household decisions regarding school enrollment and child labor, and for the design of cash transfer programs. We highlight three. First, the very large effect of the CSP program on the behavior of recipients confirms that scholarship and conditional cash transfer programs may be an effective way of increasing school enrollment in low income countries (see Filmer and Schady 2008, 2009a on Cambodia; Chaudhury and Parajuli 2008 on Pakistan). This is important because most of the evidence on these programs refers to Latin America, where income levels are generally higher and institutions stronger than in many African and Asian settings where such programs are now being implemented.<sup>1</sup> Second, and as a cautionary counterpoint, our model suggests that very narrow age ranges for benefit eligibility could potentially lead to the unintended displacement of _some_ poor children from school. Although this displacement did not take place in Cambodia (where sibling enrollment was unchanged), it is certainly a theoretical possibility, and one which appears to have been observed in

> 1 The literature from Latin America is extensive—see, among others, Schultz (2004) and Behrman et al. (2005) on Mexico; Schady and Araujo (2008) on Ecuador; Attanasio et al. (2005) on Colombia; Glewwe and Olinto (2004) on Honduras; Maluccio and Flores (2005) on Nicaragua. Fiszbein and Schady (2009) review these and other studies.

3
Table 10 further extends these regressions to consider the specialization and diversity differences across urban and rural areas of districts. Interestingly, we see employment growth for the district as a whole is most closely connected to both traits in urban areas and to rural specialization. The latter may be especially related to the movement of organized plants to rural areas in many Indian districts (e.g., Ghani et al. 2012a), while the former is more closely associated with traditional city dynamics that can favor both industrial forms.

To summarize, there is a very clear and robust relationship between specialization and growth in the Indian manufacturing data over the 1989-2010 period. Similar to the productivity estimations, these relationships remain non-causal in nature. They do, however, provide new guidance as to how Indian specialization and diversity link to local growth, which we can begin to compare with other countries.

# **5. Conclusions**

The industrial specialization and diversity trends documented in this paper for India are very exciting. They show evidence of a rapid change from specialized, undiversified manufacturing districts prior to India’s liberalization to a pattern today that has a distribution more closely resembling that of the United States. While India’s economic geography remains in flux, the very rapid shifts during the 1990s have segued to more modest adjustments during the last decade. Looking forward, modern industries are quite spatially concentrated in India, which has the corollary that most districts derive their highest specialization from a traditional industry. In terms of economic consequences, local specialization and diversity are jointly associated with stronger production functions. The marginal impacts of these benefits are mostly located outside of the industry leading the local specialization. In terms of manufacturing employment growth, the greatest gains over the 1989 to 2010 period are observed in districts that had the highest initial specialization levels, with diversity perhaps showing a non-linear role.

We see several very promising avenues for further study. First, there remain some intriguing issues and question marks within the patterns that we have outlined. We have noted the general movements of Indian districts towards less specialization and greater diversity; we have also observed, however, that districts with higher initial specialization exhibited greater manufacturing employment growth. These facts are not necessarily at odds with each other, as 22
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# **APPENDIX 1:  GENERAL EQUILIBRIUM SIMULATIONS**

## **Table 1:  BASE CASE (BASED ON 2010)**

|YEAR|1|2|3|4|5|6|7|8|
|---|---|---|---|---|---|---|---|---|
|REAL GDP|100|100|98.7|102.4|109.7|114.2|119.8|124.6|
|NOMINAL GDP|100|120.4|129.6|146.4|175.9|192.3|233.3|241|
|PRICE LEVEL|100|120.4|131.3|143|160.3|168.4|194.7|193.4|
|INFLATION|0|20.4|9|8.9|12.1|5|15.6|-0.7|
|TAX REVENUES/GDP|22.3|21.5|21|20.3|20.2|19.5|19.8|19|
|GOV. EXPENDITURES/GDP|36.6|33.6|30.4|28.9|27.8|27|26.5|26.7|
|GOVERNMENT DEFICIT|-14.3|-12.1|-9.4|-8.6|-7.6|-7.5|-6.7|-7.7|
|INTEREST RATE|21.6|6.3|5.6|3.9|4.1|2.6|2.7|1.6|
|EXPORTS/GDP|15.7|16.1|17.2|18.7|18.4|20.8|20.1|24.3|
|IMPORTS/GDP|21|20.7|20.7|19.9|19.2|18.5|18|17.2|
|TRADE DEFICIT/GDP|-5.2|-4.5|-3.5|-1.2|-0.8|2.4|2.2|7.1|

|SHARE OF SECTOR PAY|ING TAX|ES (normaliz|ed)|||||||
|---|---|---|---|---|---|---|---|---|---|
|PERIOD<br>2|4|6<br>8||||||||
|SECTOR 1<br>100|100|100<br>100|<br>SEC|TOR 1=LI|GHT MAN|UFACTU|RING|||
|SECTOR 2<br>100|100|100<br>100|<br>SEC|TOR 2=HE|AVY IND|USTRY||||
|SECTOR 3<br>100|100|100<br>100|<br>SEC|TOR 3=EL|ECTRICI|TY,WATE|R, SEWAG|E||
|SECTOR 4<br>100|100|100<br>100|<br>SEC|TOR 4=TR|ANSPOR|T||||
|SECTOR 5<br>100|100|100<br>100|<br>SEC|TOR 5=HO|TELS, H|OUSING, H|EALTH S|ERVICES||
|REAL INCOME (PERIOD)|1/|1|2|3|4|5|6|7|8|
|Urban quintile 1||3.68|3.58|3.91|3.93|4.42|4.52|4.69|4.92|
|Urban quintile 2||5.71|5.25|5.39|5.44|5.71|5.88|5.87|6.19|
|Urban other||31.72|29.5|25.06|25.33|26.11|26.94|26.59|28.06|
|Med farm Sindh||3.45|3.91|5.32|5.41|6.87|6.98|8.38|8.46|
|Med farm Punjab||16.73|16.77|17.53|18|19.1|19.66|21.37|21.85|
|Med farm OthPak||4.07|3.6|3.69|3.8|3.89|4.02|4.26|4.37|
|Small farm Sindh||3.18|2.88|2.95|3.03|3.18|3.28|3.52|3.61|
|Small farm Punjab||5.56|5.93|5.82|5.94|6.63|6.79|7.49|7.67|
|Small farm OthPak||3.42|3.37|3.58|3.65|4.17|4.26|4.75|4.86|
|Landless Farmer Sindh||1.2|1.2|1.36|1.39|1.64|1.67|1.92|1.95|
|Landless Farmer Punjab||1.07|1.29|1.52|1.55|1.87|1.91|2.2|2.24|
|Landless Farmer OthPak||0.46|0.48|0.55|0.55|0.67|0.68|0.78|0.8|
|Waged rural landless farmer|s Sindh|2.58|2.34|2.28|2.32|2.36|2.44|2.45|2.56|
|Waged rural landless farmer|s Punjab|3.32|2.91|2.65|2.7|2.58|2.67|2.55|2.69|
|Waged rural landless farmer|s OthPak|2.43|2.81|2.93|2.98|3.09|3.19|3.24|3.38|
|Rural non-farm quintile 1||3.01|2.93|2.91|2.93|3.22|3.31|3.4|3.57|
|Rural non-farm quintile 2||3.65|3.63|3.68|3.81|4.14|4.38|4.4|4.79|
|Rural non-farm other||7.33|7.31|6.6|7.17|7.87|8.44|9.14|9.9|
|TOTAL||102.57|99.7|97.73|99.91|107.53|111.03|117.01|121.88|

1/ These should be interpreted as index numbers that have relative significance, but are not connected to current monetary values.

## SECTORAL CAPITAL PERIOD 8 1/

(Stock; relative to base case) SECTOR 1 100 SECTOR 3 100 SECTOR 5 100 SECTOR 2 100 SECTOR 4 100 30
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30
Figure 4 Dominican Republic and Haiti: Output Gap, 1963–2007 **___** Dominican Republic ___ Haiti --- United States <!-- Start of picture text --> 0.15<br>0.1<br>0.05<br>0<br>-0.05<br>-0.1<br>1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: Author’s calculations based on World Bank data. See Appendix B for calculation method.

The evolution of bilateral trade between the Dominican Republic and Haiti, using the IMF’s _Directions of Trade_ statistics, suggests that measured trade between the two countries remains very low. There are a number of factors that may help to account for this. The first is that any assessment of the extent of trade between Haiti and DR faces a major problem—the coverage of the data. Frontiers between the two countries are highly permeable and informal trade is of great significance, being equivalent according to some estimates to as much as total recorded trade. Nevertheless, even allowing for such effects, the consensus remains that trade between the two countries accounts for only a small fraction of their total trade.

One reason why this may be so is related to the non-complementary production structures of these economies, with exports consisting of goods and services (primary products, tourism in the case of the Dominican Republic) heavily in demand by the industrial countries, whereas imports consist mainly of raw materials and finished investment and consumer goods that are not produced domestically. Alternatively, the low level of trade may be explained by the relatively poor transportation and communications networks between the 15