rafmacalaba/datause-encoder-data huggingface dataset:
y0 represents the cotton productivity function before the reform. Average productivity is, say, E(y0), the average of y for levels of φ > φ.

Consider the effects of the failure of the outgrower scheme. If cotton productivity is negatively affected, the productivity curve shifts down to y1. Assuming a fixed cut-off <u>y, the</u> 0 7 cut-off for the unobservables increases to φ . This induces the “exit” of those farmers with 0 relatively low levels of φ, between φ and φ. Average productivity drops to E(y1). But the decline in individual productivity is larger. The right quantity is the average productivity, computed along the curve y1, and integrating over values of φ above the cutoff before the reform φ. This is given by E(yr).

The difference in differences model described so far estimates changes in average productivity given by E(y0) − E(y1). To estimate the true effect at the farm level, E(y0) − E(yr), we need to account for the role of unobserved cotton effects.<sup>8</sup> Entry-exit effects have been extensively considered in industrial productivity analysis.<sup>9</sup> In this paper we extend this literature by developing a method to deal with entry and exit in the estimation of agricultural production functions and crop choices. Furthermore, whereas the industrial organization literature relies on longitudinal surveys, our method can be used in repeated cross-sections.

Our solution to this problem is to construct proxies for the unobserved productivity parameter. Our method exploits the idea that since households with high φ are more productive in cotton, they are also more likely to devote a larger share of their land to cotton production. This means that we could use land cotton shares as a proxy for the unobservable φht in (3). In practice, consistent estimation requires that we purge these shares of the part explained by observed determinants of cotton choice. 7It is not necessary to assume that <u>y</u> remains fixed after the reform. Our intuition remains unchanged. 8Notice that omitting φ not only leads to inconsistencies because of the entry-exit effects, but also may induce correlation between some variables in the vector x and the error term in the difference-in-differences model. For example, the choice of inputs, such as labor or pesticide use, will depend on φ (so that higher levels of unobserved productivity may be positively correlated with input use) The model in (3) takes care of these biases.

> 9See Olley and Pakes (1996) and Pavcnik (2002) for models of industrial productivity with entry and exit with panels of firms.

16
Table 7: Growth in mean per capita income conditional on initial parameters, 1992–2014

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||**1992-2014**|**1992-2000**|**2000-2014**|**2000-2005**|**2005-2010**|**2010-2014**|
|||**a. C**|**onditional o**|**n initial pov**|**erty**||
||_−_0_._009***|_−_0_._033***|_−_0_._022***|_−_0_._032***|_−_0_._036***|_−_0_._003|
|_ln yit−τ_|(0.003)|(0.008)|(0.003)|(0.007)|(0.008)|(0.010)|
|_lP_|_−_0_._001|_−_0_._022***|_−_0_._002|0.007*|_−_0_._010*|0.006|
|_nit−τ_|(0.002)|(0.005)|(0.002)|(0.004)|(0.005)|(0.006)|
|Obs.|2,361|2,361|2,361|2,361|2,361|2,361|
|R<sup>2</sup>|0.103|0.039|0.344|0.318|0.084|0.024|
|||**b. Conditio**|**nal on initia**|**l poverty an**|**d inequality**||
|_l_|0.005|0.014*|_−_0_._014***|_−_0_._000|_−_0_._022**|0.003|
|_n yit−τ_|(0.003)|(0.008)|(0.004)|(0.008)|(0.010)|(0.013)|
|_lP_|0.008***|0.009**|0.002|0.021***|_−_0_._002|0.009|
|_nit−τ_|(0.002)|(0.005)|(0.002)|(0.004)|(0.007)|(0.008)|
|_lG_|_−_0_._045***|_−_0_._155***|_−_0_._031***|_−_0_._130***|_−_0_._058***|_−_0_._016|
|_nit−τ_|(0.005)|(0.015)|(0.011)|(0.026)|(0.017)|(0.024)|
|Obs.|2,361|2,361|2,361|2,361|2,361|2,361|
|R<sup>2</sup>|0.222|0.170|0.363|0.379|0.098|0.025|
||**c. Condit**|**ional on init**|**ial poverty **<br>|**and inequali**<br>|**ty and extra**<br>|**controls**<br>|
|_l_|_−_|_−_|_−_0_._003|_−_0_._004|_−_0_._021**|0.003|
|_n yit−τ_|_−_|_−_|(0.004)|(0.010)|(0.009)|(0.014)|
|_lP_|_−_|_−_|0.016***|0.035***|0.007|0.015|
|_nit−τ_|_−_|_−_|(0.003)|(0.007)|(0.006)|(0.010)|
||_−_|_−_|_−_0_._038***|_−_0_._112***|_−_0_._054***|_−_0_._031|
|_lnGit−τ_|_−_|_−_|(0.006)|(0.016)|(0.014)|(0.025)|
||_−_|_−_|0.007***|0.011***|0.007***|0.012***|
|Public sector payroll|_−_|_−_|(0.001)|(0.003)|(0.003)|(0.004)|
||_−_|_−_|_−_0_._000|0.000|_−_0_._003*|_−_0_._000|
|Public investment|_−_|_−_|(0.000)|(0.001)|(0.002)|(0.003)|
||_−_|_−_|_−_0_._001|0.000|_−_0_._008***|0.007**|
|Public transfers/subsidies|_−_|_−_|(0.001)|(0.002)|(0.001)|(0.003)|
||_−_|_−_|0.033***|0.059***|_−_0_._019|_−_0_._062**|
|Growth in CCT coverage|_−_|_−_|(0.011)|(0.015)|(0.025)|(0.025)|
|Obs.|_−_|_−_|1,793|1,793|1,910|2000|
|R<sup>2</sup>|_−_|_−_|0.440|0.403|0.234|0.075|

_Source_ : World Bank calculations.

_Note_ : The table presents the estimates of equation (7) and extensions, weighted by municipal population at the initial year of each period under study. The dependent variable is the annualized growth rate in the mean per capita income of municipalities over the period. _ln yit−τ_ , _lnPit−τ_ , _lnGit−τ_ , and all public expenditure variables are for the initial year and are in log-scale. All monetary variables are in real per capita terms at August 2014 prices. The growth rate in CCT coverage is the annualized growth rate in the number of beneficiary families in each municipality over the period. The empty cells in panel c indicate that models conditional on CCT data were not estimated because the data are available only from 2000 onward. The intercepts are shown in tables 37–40 in the ancillary file. Robust standard errors are in parentheses. *** _p < ._ 01, ** _p < ._ 05, * _p < ._ 1 27
22 Netherlands is somewhat less specialized (covering ̳Efficiency and equity‘ and ̳Economic evaluation‘), but is still more specialized than Canada and the United States. Interestingly, the topics of the most-cited papers in the US are not on topics such as ̳Medical insurance‘ and ̳markets in health care‘, but rather the topics ̳Determinants of health and ill-health‘, ̳Health and the economy‘, and ̳Health statistics and econometrics‘.

# **Journals and edited volumes**

Table 12 shows the top-100 journals in health economics on the basis of their _h_ -index for health economics articles only. Also shown are number of articles, total citations of these articles in Google scholar, the _I_<sup>3</sup> index with  =0.5, and the normalized _h_ -index. The JHE dominates Health Economics on both the _h_ -index and the normalized _h_ -index, which in turn dominates PharmacoEconomics and the other specialized health economics journals on both criteria. The JHE fares worse than many journals on the normalized _h_ -index: over 60% of health economics articles in Econometrica have been cited 60 times or more, as is the case for health economics articles in the J Political Economy, the Quarterly J of Economics and the J Economic Literature; several other general journals also fare better than the JHE on the normalized _h_ -index though less spectacularly than these top journals. These are all general journals, of course; their editors have the freedom to change the fraction of articles they publish on health economics according to the quality of submissions—an option that is not open to the editors of JHE and other specialized health economics journals.

Table 13 shows the top-25 edited volumes in health economics based on the _h_ -index. The Handbook of Health Economics dominates the others by quite a wide margin on both the _h_ -index and the normalized _h_ -index; at 76 percent, the Handbook‘s normalized _h_ -index is better than any journal, suggesting a remarkably adept choice of chapters and authors by the editors as well as a consistently high quality of papers. Two other collections also fare well on both the _h_ -index and the normalized _h_ -index, namely ―The economics of health‖ and ―Health sector reform in developing countries: Making health development sustainable‖.
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it was only the worsening of the crisis that would eventually put an end to this unfavorable trend (Chart 10).

Besides, the spread of FX-denominated products risks was exacerbated by loosening credit conditions and standards. The loan-to-value (LTV) ratio for mortgage loans increased constantly and in many cases exceeded 100 percent. The banking system‘s average LTV ratio for the housing loan portfolio was above 70 percent at the end of 2008, while in 2004 it was only around 50 percent. The average payment-to-income ratio also increased, which was supported by the ever lower documented mortgage loans. Finally, increasing reliance on brokers as a sales channel also meant higher risks, because loans granted via their intermediation were characterized by significantly higher default rates (Banai et al., 2010a).

**Chart 10. Currency structure of household loans**

<!-- Start of picture text --> HUF Bn Per cent<br>8 000 80<br>7 000 70<br>6 000 60<br>5 000 50<br>4 000 40<br>3 000 30<br>2 000 20<br>1 000 10<br>0 0<br>HUF<br>EUR<br>CHF<br>JPY<br>Others<br>Share of FX loans as a percentage of total household loans (right-hand scale)<br>Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan-05 Mar-05 May-05 Jul-05 Sep-05 Nov-05 Jan-06 Mar-06 May-06 Jul-06 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08<br><!-- End of picture text --> Source: MNB.

The currency structure of the portfolio carried risks in itself. The question arises as to whether lending to households was affected by excessive credit expansion. As previously, we applied the method of Kiss et al. to find an answer. In the context of a sudden spike in lending, in 2003 the credit/GDP ratio rapidly reached the level of the equilibrium path. Owing to a moderate downturn in net flows in 2004–2005, the level of the ratio at the end of 2005 was still only slightly above the equilibrium. In 2006, however, developments in the portfolio deviated from the path justified by macroeconomic variables both in terms of dynamics and level. This growth accelerated further in 2007–08. Therefore, during the two years preceding the crisis there was clearly a credit boom in the household segment. Besides the hidden risks associated with the poor currency structure, the size of the accumulating portfolio imposed an excessive burden on households. This implied mounting risks to their debt servicing capacity (Chart 11).

# **_II. 3. Financing risks_**

Owing to the credit boom and the currency structure of the loan portfolio, severe risks were building on the financing side as well. Since a number of studies (including Banai et al., 2010b) and several issues of the _Report on Financial Stability_ have addressed this issue extensively, this paper is intended to merely touch upon the most important features.

12
Table D.9. Main Results Restricting the Sample to Tracts without Sudden Altitude Changes with Respect to Their Neighbors

||Night Light Arcsine|Wealth Index|Years of Education|
|---|---|---|---|
||(2013)|(2007)|(2007)|
||(1)|(2)|(3)|
|Guerrilla control|-0.146***|-0.120***|-0.309**|
||(0.0240)|(0.0439)|(0.137)|
|Observations|2,572|2,561|2,562|
|Bandwidth (Km)|2.103|2.103|2.103|
|Dependent mean|3.743|0.118|6.924|

_Note:_ The table presents main results without considering segments that have a difference in altitude of more than 100 masl with respect to their neighbors. Column 1 shows the effect of whether a census tract was under guerrilla control on the arcsine of night light luminosity from NOAA. Column 2 does the same but uses as dependent variable a standardized score of household wealth. Column 3 shows as dependent variable years of education of the population older than 18 years. The unit of observation in all columns is the census tract. Information from Columns 2 and 3 was obtained from the Population Census of 2007. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table D.10. Effects of Guerrilla Territorial Control on Main Outcomes for Individuals Who Have Always Lived in the Same Place

||Wealth Index|Years of Education|Literacy Rate|
|---|---|---|---|
||(2007)|(2007)|(2007)|
||(1)|(2)|(3)|
|Guerrilla control|-0.132***|-0.402***|-0.0261***|
||(0.0356)|(0.112)|(0.00563)|
|Observations|3,621|3,633|3,633|
|Bandwidth (Km)|2.266|2.266|2.266|
|Dependent mean|-0.0280|6.785|0.817|

_Note:_ The table presents main results for the sample of people who have always lived in the same place. The unit of observation in all Columns is the census tract. The information was obtained from the Latin American Public Opinion Project survey (LAPOP). Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

84
MIGEPROF and the research team set the treatment sample size at 640 couples based on budget constraints. The research team first conducted a desk-randomization to assign villages to treatment or control groups using Stata. Then in all study villages, public VSLA lotteries were used to rank eligible couples from 1 to 30 (the maximum VSLA size). The first 60% of couples from each VSLA lottery were assigned to treatment, stratified by sector and number of lottery applicants. The implementing NGO then organized the 640 treated couples into 32 training groups based on location, combining members from nearby VSLAs.

## **2.5 Representative Baseline Community Survey**

To assess selection into the program, we randomly sampled and interviewed 686 couples at baseline from a complete household listing of study villages. VSLA couples were similar to randomly selected non-VSLA couples on most characteristics, though slightly younger, more educated, less economically vulnerable, and more progressive on gender issues (see table A2 for a comparison on key characteristics).

# **3 Data and Estimation**

## **3.1 Data Collection**

Data collection included two rounds of panel surveys. We conducted baseline surveys from November 2017 to February 2018, before the intervention (March–August 2018), and endline surveys six to eight months after completion (February–March 2019).<sup>10</sup> Of the 2,042 baseline study couples, 98.3% of women and 97.5% of men were re-interviewed at endline, with no differential attrition across treatment arms.<sup>11</sup> Women’s and men’s surveys covered household demographics, socio-economic characteristics, time use, economic activity, decision-making, well-being, gender attitudes and norms, experience of IPV, and likelihood of perpetrating violence in hypothetical scenarios (men only). Surveys were administered by enumerators of the same sex as the respondent in private settings (empty classrooms, village offices, or churches), with strict privacy protocols requiring interviews to pause

> 10A World Bank qualitative review of implementation was conducted in January 2020.

> 11Village leaders from each 5-person village leadership committee were also interviewed about gender attitudes, infrastructure, and program awareness.

11
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28
CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA subtract the minimum from the maximum for each 30x30 arc second cells (approximately 1x1 km) in a pixel, and define it as the terrain roughness.

## Merging of data sets

Data sets of different resolutions (grid sizes) are aggregated at the IFPRI SPAM data resolution (5x5 minutes, approximately 9x9 km on the equator), exported to standard-format data sets, and then merged with the SPAM database. In aggregation, we control for differences in land area (mainly due to oceans or lakes), such that: where _areak_ is land area (km<sup>2</sup> ) at a 1-km resolution from GRUMP (CIESIN). The final data set includes 42 countries, which are listed in table A.1.

# 2 Descriptive statistics

## Crop production

Low-input/rain-fed crop systems dominate crop production in Sub-Saharan Africa. For the 42 SubSaharan African countries in the SPAM sample, 63 percent of the $53 billion average annual production (1999–2001) of the 20 major crops is cultivated under such systems (table 2.1). The remainder of the production is nearly evenly split between high-input/rain-fed (20 percent) and irrigated systems (16 percent).<sup>11</sup> For the six major cereal crops (wheat, rice, maize, barley, millet, and sorghum), the overall distribution is similar: 60 percent were under low-input/rain-fed, 25 percent under high-input/rain-fed, and the remaining 15 percent under irrigated systems. But, when we look at three major cash crops (cotton, coffee, and groundnuts), 40 percent were produced under the irrigated system, which enables the highest yields among production systems. For example, table 2.2 shows that the potential yield of cash crops in kilogram per hectare (kg/ha) under the irrigated production system is as many as 15 times higher than that under low-input/rain-fed production. A summary of each of the 20 crops is given in table A.5.<sup>12</sup>

> 11 Again, 20 crops are aggregated using the medians of country-level individual crop prices.

> 12 See You and others (2007a) for a summary of country-level crop production.

5