rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 7: Impact of proximity to large farms on practices market integration, and subjective welfare</u>**

||**Area cult.**|**Sold crops**|**Rec. credit**|**Employ’t**|**Sit. now**|**vs 3a ago**|
|---|---|---|---|---|---|---|
||||||**better**|**worse**|
|||||**Panel A**|||
|Any new lg. farm <= 25 km|0.009|0.005|-0.002|0.018|-0.025|0.044**|
||(0.107)|(0.025)|(0.008)|(0.019)|(0.026)|(0.021)|
|Any new large farm 25-50 km|0.077|0.033|0.004|0.027|0.021|-0.010|
||(0.095)|(0.027)|(0.008)|(0.019)|(0.027)|(0.022)|
|Any new lg. farm 50-100 km|-0.056|-0.033|-0.007|-0.046**|-0.014|0.017|
||(0.089)|(0.027)|(0.010)|(0.020)|(0.027)|(0.022)|
|Observations|12,202|12,202|12,202|12,202|12,202|12,202|
|R-squared|0.215|0.274|0.126|0.144|0.172|0.152|
|||||**Panel B**|||
|New lg. farm area <= 25 km|0.008|0.003|0.000|0.002|-0.007|0.008**|
||(0.021)|(0.005)|(0.001)|(0.004)|(0.005)|(0.004)|
|New lg. farm area 25-50 km|0.021|0.002|0.001|0.005|0.001|0.004|
||(0.017)|(0.004)|(0.002)|(0.003)|(0.004)|(0.004)|
|New lg. farm area 50-100 km|-0.004|-0.004|-0.001|-0.001|0.004|-0.001|
||(0.016)|(0.004)|(0.001)|(0.003)|(0.004)|(0.003)|
|Observations|12,202|12,202|12,202|12,202|12,202|12,202|
|R-squared|0.216|0.274|0.127|0.143|0.172|0.153|

_Note:_ Other control variables that are included throughout but not reported are farm size, household composition (no. of children, adults and old people), headship, head’s age, and highest education in the household and a time dummy. EA fixed effects are included throughout and standard errors are clustered by EA. *** p<0.01, ** p<0.05, * p<0.1.

22
look after the welfare of the people and ensure their security. The post-1998 democratization, and the administrative decentralization that followed, created an environment of flux. In many parts of rural Indonesia, this has led to tensions between the way things were done in the past— where local village and sub-district elites held the purse strings and were not held accountable by villagers—and new normative systems which emphasize widespread autonomy over decisionmaking, and checks and balances on forms of political power. Local culture and custom, and appeals to custom and tradition ( _adat_ ), have been utilized by elites seeking to cling to power.<sup>33</sup> The clash between two normative worlds, and the resistance of elites to change, can and does lead to tensions.

Elites have the autonomy to capture the KDP process in the way they do other projects; whether they do or not is a partial measure of KDP’s efficacy in providing constraints on elite power. If elite roles are significantly different between KDP and non-KDP projects, we can assume that KDP is creating incentives for _compliance_ with more participatory and open decision-making processes. In contrast, if the difference in elite involvement is minimal (and especially if elite involvement in KDP and non-KDP projects is high) we can assume that there is _resistance_ at play; i.e., that elites develop strategies to maintain disproportionate involvement in development decision-making and resource distribution. Is conflict then, more likely to result from compliance (where elite roles change significantly, and hence decision-making presumably becomes more uncertain) or from resistance (where elites try to maintain their disproportionate role)?

Table 1 below shows how changes in elite involvement correlate with the reported extent to which KDP forums triggered conflict. The results show a clear trend for East Java, with subdistricts with little change in elite involvement considerably more likely to report conflict. This suggests that where resistance to a changed role exists among elites, conflict is more likely. The evidence from NTT is less clear.

> 33 On a cross-national analysis of the use of culture symbolism as a mechanism for the maintenance of power, see Wolf (1999).

18
profits than the mean for crossovers, almost 80 percent of non‐crossovers that make less than crossovers think they make the same or more.

**Figure 3: Non‐crossovers beliefs on profitability (%) – Mixed methods data set**

<!-- Start of picture text --> 14<br>12<br>5<br>5<br>42<br>22<br>Non‐crossovers that make less Non‐crossovers that make the same or<br>more<br>Believe make more money Believe make same money<br>Believe make less money<br><!-- End of picture text -->

# _Other explaining factors_

While information is clearly one barrier to crossing over for some women, we now take a deeper look at the characteristics of the entrepreneurs themselves for other explanations. In order to examine these characteristics, we run a series of regressions using the mixed methods data set. The results are in Table 6. In what follows we discuss two sets of OLS regression results.<sup>18</sup> Recall that the mixed methods data set has two sets of non‐crossover women: those who were matched to the crossovers on a small set of pre‐business characteristics and a group of randomly selected women. Given these two groups, we discuss results for both of them combined (columns A‐C) and for the matched sample (columns D‐F). Summary statistics for the variables used in the regressions can be found in Appendix table 1.

> 18 We run also probit models, obtaining similar conclusions, although the results in these models are more extreme given the small sample size and the rare outcome.

16
Virtually all these cross-country studies, both early and recent, have three common ch&racteristics. First these studies cover developing countries of mn regions, the number varying between _35_ and _95_ countries. Except Dollar (1991b), none examine regional variations. Second, except Bhalla and Lau (1991) and Harrison (1991), all rely on cross-sectional period averages of time series data. Third, except arrison (1991), all studies use a siln3e measure of openness to test whether openness explains cross-country differences in economic performance. This paper seeks to depart from those studies in the same three Icipects: it focuses on Sub-Saharan Africa, uses annual time series cross section data and tests several measures of openness.

8
<!-- Start of picture text --> So<br>-—- Emerging markets mean<br>— India \<br>w - I \<br>fo) 7 ~~ AN<br>‘ 2 ‘\.<br>i cr<br>x oO /y\ /<br>o oO<br>= (Ne?<br>Te) /<br>fo) “ /<br>7<br>o 7<br>1970 1980 1990 2000 2010<br><!-- End of picture text -->
on average 2-3 months. Individuals received unemployment benefits during the course of their studies. Workers in the training component tended to have higher education levels and to be female. The wage subsidy component of the program was structured so that the subsidy was increasing in the time the worker stayed with the firm. The public works component was targeted at the longer-term unemployed and many of the jobs available were low-skill jobs working on infrastructure improvements. Workers in either the wage subsidy program or the public workers program had an incentive to participate in the wage subsidy program at least 6 months to qualify for another 12 months of unemployment benefits.

The ALMP programs are evaluated using a difference-in-difference matching approach, where the main outcome of interest is labor force status. The samples are drawn from the 18th wave of the Polish Labor Force Survey that included a supplement with four years of historical information on individual labor market histories (monthly from 1992 to 1996). The treated group were individuals who were offered participation in the programs by their local labor office and who accepted the offer. Sample sizes in the three types of programs (training or retraining, intervention works or public works) were 241, 532 and 93. The control group consisted of 7,784 individuals who had been registered at least once as unemployed since January 1992. The matching procedure pairs treated individuals with control individuals who have the exact same labor force history and are matched on certain demographic characteristics (gender, marital status, education, region and age). The matching impact estimates turn out to be sensitive to which variables are used in the matching analysis, for example, to whether the selection of matches also takes into account local labor market conditions.

The impact estimates indicate that the training/retraining program increased the average employment probability for both men and women. Participation in the non-training ALMP programs did not affect women‟s employment probabilities but had a negative effect on men‟s employment probabilities, which the authors attribute to benefit churning rather than stigmatization of intervention and public works participants. That is, males appear to have taken intervention works and public works jobs between two spells of unemployment benefit receipt. Overall, the study concludes that ALMP training/retraining programs in Poland raised women‟s employment rates over the short and medium term.

22
earthquake were skull fractures with intracranial hemorrhage, cervical spine injuries with neurological impairment and internal injuries to the lung, liver and spleen.

Although disasters frequently disrupt water and sanitation services, which may lead to an increase in infectious disease, we focus in the remainder of the paper on injuries, rather than on infectious diseases, since the former are likely to have more serious long-term consequences.

# C. Injury Classification Schemes

If injuries associated with disasters are to be included in benefit-cost analyses of disaster risk reduction, they must be classified according to standard coding schemes. The two primary, internationally recognized, schemes for classifying injuries—the International Classification of Diseases (ICD9-CM) and the Abbreviated Injury Scale (AIS), both describe physical impairments according to the nature of the injury and the part of the body where it occurs. The AIS also describes injury severity, defined in terms of threat to life. In the ICD-9, the primary system for coding medical diagnoses, injuries (800999) are classified by the nature of the injury (e.g., fractures, internal injuries) and the site of the injury (e.g., skull, leg, thorax). Three-digit ICD-9 codes are listed in Table 5. Digits 4 and 5 pinpoint the site/nature of the injury more exactly.<sup>8</sup>

**-** **<u>Table 5 International Classification of Diseases (ICD 9)</u>**

|**ICD – 9 Three-Digit Injury Codes**||
|---|---|
|Fractures (800-829)|Skull (800-804)<br>Neck and Trunk (805-809)<br>Upper limb (810-819)<br>Lower Limb (820-829)|
|Dislocations(830-839)||
|Sprains and Strains of Joints and Muscles(840-848)||
|Intracranial Injury (850-854)||
|Internal Injuryof Thorax, Abdomen and Pelvis (860-869)||
|Open Wounds (870-897)|Head, Neck and Trunk (870-879)<br>Upper limb (880-887)<br>Lower limb (890-897)|
|Injuryto Blood Vessels(900-904)||
|Late effects of injuries andpoisonings(905-909)||
|Superficial injuries(910-919)||
|Contusions(920-924)||
|CrushingInjury (925-929)||
|Effects of foreign bodies enteringthrough orifices(930-939)||
|Burns(940-949)||
|Spinal Cord Injuries(950-957)||
|Unspecified injuries(958-959)||
|Poisonings (960-979)||

8 Medical diagnoses require at least 4 digits. To illustrate, ICD-9 category 824 describes an ankle fracture, and 824.1 an open fracture of the medial malleolus (end of the tibia).

7
cognitive skills taxonomy. To the best of our knowledge, this specific methodology for aggregating scores and discovering latent traits has never before been used to assess the so-called “big five” personality traits and their relationship with employment outcomes. While Glewwe _et al_ . (2013) also use the Rasch model, they employ a different set of dimensions to measure non-cognitive skills. Similarly, Ajwad _et al_ . (2014) and Cunningham _et al_ . (2016) use factor analysis to estimate personality traits, while Acosta _et al_ . (2015) use simple averages across different categories.

Following this introduction, Section 2 presents an overview of the Bangladeshi labor market and reviews the literature on non-cognitive skills and labor market outcomes. Section 3 describes the data and methodology used to calculate personality trait scores. Section 4 presents the findings of the analysis, and Section 5 discusses these findings and their implications.

# **2. Literature Review and Background on the Bangladeshi Labor Market**

## a) _Literature Review_

There is a growing interest in the economic literature in exploring the relationship between noncognitive skills, or personality traits, and labor market outcomes. Introducing a psychological taxonomy of personality traits has increased the consistency with which non-cognitive skills are defined and measured in the economic literature. The most common form of classification is the so-called “big five” personality traits. These include (i) “openness to experience,” (ii) “conscientiousness,” (iii) “extraversion,” (iv) “agreeableness,” and (v) “neuroticism,” which can be conversely defined as “emotional stability.” This taxonomy represents very broad personality traits, summarizing a large number of distinct and highly specific facets of personality (Almlund _et al_ ., 2011). While the big five is a commonly used taxonomy, various other measures are also used in different studies ( _ibid._ ; John and Srivastava, 1999).

The majority of studies that assess the relationship between non-cognitive skills and labor market outcomes focus on developed countries. Using data from Swedish military enlistments, Lindqvist and Vestman (2011) find that non-cognitive skills are a better predictor of employment and annual earnings than cognitive ability. Similarly, using data from the United States, Heckman _et al_ . (2011) find that even though non-cognitive skills have little effect on wages after controlling for years of education, they are strong predictors of educational attainment. Carneiro and Heckman (2003) find 4
MEXICO: IMPACT EVALUATION OF SME PROGRAMS USING PANEL FIRM DATA<sup>**1**</sup> Gladys Lopez-Acevedo (Senior Economist, LCSPP) Monica Tinajero (Consultant, World Bank) Keywords: small and medium enterprises, program impact evaluation JEL codes: L25, L53, O25, O54 Contact information: gacevedo@worldbank.org 1 This country study was funded by a research grant RF-P105213-RESE-BB from the World Bank’s Research Committee for the project “Evaluating Small and Medium Enterprise Support Programs in Latin America” managed by LCSPP and directed by Gladys Lopez-Acevedo and Hong Tan. We acknowledge the close collaboration offered by Abigail Durán and Adriana Ramirez from the headquarters of the Mexican National Statistics Office ( _Instituto Nacional de Estadistica y Geografia—_ INEGI). We also acknowledge the contributions and background paper on the “Review of SME Programs” from Jorge Mario Soto, Hugo Fuentes and Victor Aramburu (ITESM). We acknowledge the technical guidance from Hong Tan and his paper on “Evaluating SME Support Programs in Chile,” forthcoming as a World Bank Research paper. We thank David McKenzie in particular for his expert guidance on key methodological issues that arose over the course of the regional study.
# Contents

# Foreword

|1.|Introduction.1||
|---|---|---|
|2.|Economic Theory:<br>Balancing Public and Private Roles in<br>Water Supply ...................................|2|
|3.|Institutional Arrangements for Private Participation<br>in Water Supply..................................|_5_|
|4.|The Urban Water Supply Sector in Guinea:<br>Recent History|10|
|5.|The Restructuring of Guinea's Water Supply Sector .12||
|6.|Conclusions<br>....................................|24|

# Figare

Breakdown of the Full Cost of Water ...................... 19 Table Water Rates and Costs, 1989-98 .......................... 20

# Annexes

- Annex 1 Contractual Arrangements for Private Sector Participation in Water Supply Operations

- Annex 2 Summary of Key Legal Documents Governing the Guinea Water Supply Sector

Author's Note: Thelma Triche is a consultant with the Water and Sanitation Division, Urban Infrastructure Department of the World Bank.

Acknowledgements: The author wishes to thank Mike Garn for his invaluable guidance in every stage of the preparation of this paper. The assistance and cooperation of Alain Locussol and Richard Verspyck and the insightful comments of Arturo Israel are greatfully acknowledged. Barbara de Boinville and John Green provided editorial assistance.
Table S2: Effect of Extending DMSP-OLS Baseline with More Recent VIIRS Data.

||ln(GDP)<br>(1)|ln(GDP)<br>(2)|ln(GDP)<br>(3)|ln(GDP)<br>(4)|ln(GDP)<br>(5)|ln(GDP)<br>(6)|
|---|---|---|---|---|---|---|
|ln(lights/area)|0.055***<br>(0.017)|0.169***<br>(0.050)|0.218***<br>(0.049)|0.228***<br>(0.030)|0.124***<br>(0.028)|0.109***<br>(0.027)|
|PWT Version|10.0|10.0|10.0|10.0|10.0|10.0|
|Stray Light-Corr. VIIRS|✓|✓|✓|✓|✓|✓|
|Top 3 Clusters (City Core)||✓|||||
|Stable Lights (VIIRS, ‘14-‘20)|||✓|✓|✓|✓|
|Inter-Calibrated (OLS, ‘92-‘13)||||✓|✓|✓|
|Satellite Average (OLS)||||||✓|
|Satellite Fixed Efects||||✓|✓||
|Observations|1,194|1,188|1,194|7,363|3,399|2,752|
|Countries|180|179|180|180|180|178|
|Years|14-20|14-20|14-20|92-20|05-20|05-20|
|Within R-Squared|0.032|0.075|0.125|0.151|0.079|0.068|
|R|obust standa<br>*** p_<_0.01|rd errors in<br>, ** p_<_0.05|parenthes<br>, * p_<_0.1|es|||

_Notes._ All specifications include country and year fixed effects. Robust standard errors, clustered by country, are in parentheses. _p_ -value for the null hypothesis that the estimated effect differs across night lights data sets in brackets. “Within R-Sq.” is adjusted and within all fixed effects. In comparison with Table S1 Column 6, which presented results using DMSP-OLS alone, Column 6 uses the same OLS specification but extends the sample through 2020 using VIIRS. This column becomes Table S3 Column – 1 in the text. Note, two countries are dropped in comparison with Column 5 Bermuda and the British – Virgin Islands because they are smaller than the resolution of OMI, and hence not included in the NO2 sample. In Column 6, the satellite average is computed across OLS sensors only.

DMSP-OLS specification from Table S1 Column 6, which starts in 2005 where OMI NO2 data begins, with Column 3. Column 6 now uses satellite averages, for comparability with Henderson et al. (2012). This is equivalent to Table S3 Column 2, below. Including country-specific time trends finally yields Table 2, Column 2, the baseline night lights specification used in the text.

# **B.2 Results without Country Time Trend**

Table S3 conducts the same estimation as Table 2 in the text, but now omitting country-specific time trends. Thus, whereas Table 2 depicted the business cycle, i.e. cyclical relationships between GDP and each signal, Table S3 presents trends, i.e. longer-run economic growth relationships. In particular, the baseline night lights specification in Column 2 is described in Section B .1 above, and is equivalent to Table S2 Column 6. The basic difference when considering trend economic growth is that night lights maintain statistically significant explanatory power across S11
a regulatory environment that fosters new product development and consumer protection, and (iii) fostering competition in the market.

Historical data are only available from approximately 550 IMD weather stations. These are insufficient to adequately cover the 150 million hectares of arable land, and they are rarely located in rural areas. In areas underserved by private weather providers, such as NCMSL, the government could help increase the density of automated rainfall gauges, which can help ameliorate basis risk and reduce the delay before payouts can be calculated and paid.<sup>7</sup> By reforming the regulatory environment, the government could also improve the ability of the industry to underwrite contracts, thereby lowering reinsurance costs.<sup>8</sup> It could also devote resources to agronomic research to improve crop models that could lower basis risk by maximizing the correlation between the weather index and crop production.

Finally, the government could foster competition in the sector by scaling up WBCIS while providing incentives to insurance companies to lower the premiums. In areas where the correlation between rainfall and yields are well established or where basis risk is not a concern, WBCIS could be offered alone, but in other areas WBCIS could be perhaps combined with the modified NAIS. This way WBCIS would absorb weather risk, while NAIS could cover the residual production risk from other perils (pests, etc).

Although traditional financial markets do not allow households in developing countries to fully smooth out fluctuations in their living standards, the state and central governments could use weather insurance to hedge against fluctuations in the cost of social programs caused by weather events.<sup>9</sup> For example, relief aid and principal or interest waivers are likely to increase after a drought or a flood. Similarly, participation in the National Rural Employment Guarantee Act increases during bad monsoons as many of the rural poor decide to leave their land fallow to
how these programs operate in the context of government implementation, including delays in starting programs (the time between applying and starting an intervention was 3-12 months for most firms), and sometimes delays of several months in payment to providers and firms (see Appendix 2). In the control group, firms were told that due to the high number of applicants, random selection was used as a fair way to choose which firms could access the program, and that they had not been chosen.

In all four treatment groups, firms were allocated points based on attendance and task completion, with firms given a Total Points target (a priori) that they needed to achieve in order to become eligible to apply for grants.<sup>6</sup> Firms were told that if they achieved the Total Points score necessary for eligibility they would be entered into a random draw for grants (after the program ended). In practice, program implementation delays meant that none of the firms in our study knew whether they would receive a grant, nor had any received a grant, until after the conclusion of our final follow-up survey. Nevertheless, we note that the possibility of a grant may, and was indeed intended to, increase the incentive of firms to comply with the interventions.

Next, we summarize the four treatment interventions, with further details on their content and implementation provided in Appendix 2. All four treatments were offered free to firms. The cost of offering each treatment was designed to be approximately the same (US$2,000) for business training, insourcing and outsourcing, while business consulting was anticipated to be twice this (US$4,000).

# _Business Training_

Firms in this treatment group were provided with a mix of online and in-class training based on the IFC Business Edge curriculum adapted to the local context. Courses were managed and implemented by the Enterprise Development Centre (EDC) of the Pan-Atlantic University in Lagos. This organization has been operating in Nigeria since 2003. The program includes five core modules on financial management, general operations, writing a business plan, marketing management, and human resources; as well as optional modules on enterprise governance, personal productivity, and tourism/hospitality. Each topic had an online and in-person component. The online module had to be completed first before an individual could attend the in-person

> 6 There were two types of grants potentially available: (a) matching grants that could pay for technical services; and (b) performance grants that could pay for goods and equipment. Both were typically for 10 million Naira (US$27,000).

8