rafmacalaba/datause-encoder-data huggingface dataset:
survey). These regions have all seen an increase in conflict levels since 2010.<sup>5</sup> In the North East, conflict is largely attributable to Boko Haram. The violent radicalization of Boko Haram members and the resulting military operations have reportedly affected nearly 15 million people since 2009. Boko Haram’s tactics have included multiple modes of attack, including suicide bombings, the seizure and destruction of entire villages, the destruction of basic services, forced displacement, abductions, sexual violence, and forced recruitment.

Since the start of the insurgency in 2009, an estimated 20,000 people have been killed. Nearly 2.1 million people fled their homes during the height of the conflict, of which 1.7 million remained displaced in 2018, a vast majority of them being internally displaced within Nigeria (IOM 2018). An estimated 200,000 people were estimated to reside in neighboring countries in 2017 (OCHA 2017). Our data indicate that 49 percent of households in the North East had been a victim of a conflict event between 2010 and spring 2017. The perpetrator was most often reported to be an insurgent (in 72 percent of the cases). Hence, Boko Haram activity was clearly felt by a large fraction of households.

In the North Central region, the conflict centers around agricultural households and nomadic cattle-herding groups who come into conflict over land and water access. Over the past few years the conflict has been increasing between these two groups. Farmland has been destroyed and forcefully occupied, livestock has been stolen, and crops have been damaged. The conflict has intensified as many northern herdsmen have moved their herding routes toward the south. There have been multiple push factors: the Boko Haram insurgency in the North East, the growth of human settlements in the north more broadly, and the degradation of pastures in the north due to droughts. The death toll has been increasing, with 2,500 fatalities recorded in 2016 alone (International Crisis Group 2017).

Our data indicate that 25 percent of households in the North Central region experienced at least one conflict event between 2010 and 2017. The most common perpetrators of the events are reported to be pastoralists or nomads (45 percent), and the second most common perpetrators are insurgents (21 percent).<sup>6</sup> This indicates that the Boko Haram insurgency also has been active in the North Central region, but to a lesser degree than in the North East.

> 5 Appendix B provides more details on the spatial and time variation of conflict in the three regions with comparisons to external data sources.

> 6 The household survey data does not include nomadic households. Therefore, we only capture one side of the violence in the herder-farmer conflict—namely, the events where the perpetrator was a pastoralist and not the side where the perpetrator was a farmer .

7
estimated treatment effect is very small.<sup>15</sup> Indeed for the IDPoor sample (the portion of the population that was targeted by the program) the estimated effect is zero to two decimal places. In the full sample the estimate indicates that those in the treated communities returned about one percentage point more, a small increment given the average amount returned, and an estimate that is only half the size of its standard error. There are no significant differences between poor and non-poor according to this measure of trustworthiness. Both groups returned about 14 to 15 percent of their total pot to the investor.

Consistent with the remarkably low level of trust exhibited by our subjects compared to previous studies (mentioned above), the results in column 6 indicate low levels of trustworthiness compared to findings in previous trust-game studies. Subjects returned only about 15 percent of the total amount available to them. In their meta-analysis Johnson and Mislin (2011) found that subjects returned about one-third of the total amount available to them on average. Thus subjects in this experiment returned only about half of what has been observed on average in the past. This lack of trustworthiness can help explain the similarly low levels sent in the first round of the trust game. It appears that the communities in which we worked possessed relatively weak norms of trustworthiness, which in turn support relatively low levels of trust at least as measured in our games. If these norms, uncovered in the lab, mirror the norms practiced in the community at large, they point to an important impediment to economic development in Siem Reap.

We also detect no overall effect of LEAP on the behavioral measures of social capital when we account for their co-variance in the final column of Table 6. Here we present the inverse-covariance weighted average treatment effect of the program on all of the laboratory measures (Kling et al. 2004; Clingingsmith et al. 2009). Not surprisingly, given that none of the measures showed any significant effect of the program, the estimate of the average effect is very small. The largest effect, which was among the poor subjects, is only about eight percent of a standard error and not at all statistically significant.

> 15 This result is independent of conditioning on trust (i.e. the amount sent)—unsurprising given that the program had no discernible effect on trust.

21
There remains significant divergence in rates for similar credit products across lenders, which could potentially suggest market segmentation and/or insufficient transparency and disclosure. As mentioned previously, there exist several potentially important sources of household credit outside the formal financial system such as department stores, which are not obliged to report information on their credit activities and are not subject to the same consumer protection laws. However, according to information collected by Condusef that is shown in figures 36 and 37, there is a wide dispersion in the cost of consumer credit both within the banking sector and across different types of lenders (banks, Sofoles, and department stores). This could potentially suggest market segmentation (i.e. limitations on alternative financing sources for lowerincome versus higher-income households) and/or insufficient transparency and disclosure on rates and terms.

<!-- Start of picture text --> Figure 36: Comparative Effective (CAT) Figure 37: Comparative Nominal Credit Card<br>Consumer Credit Rates (December 2005) Rates (December 2005)<br>Elektra 202%<br>Citibank Advantage 40%<br>Fin Alcanza 200% Banamex MC / Visa 39%<br>Credito Familiar 154% Scotiabank Clásica MC / Visa 39%<br>Viana 119% Bancomer MC / Visa 39%<br>Santander MC / Visa 38%<br>Famsa 66%<br>HSBCClasica MC / Visa 37%<br>Santander 45%<br>Banorte MC / Visa 25%<br>Banamex 35%<br>Banamex B smart 25%<br>Fonacot 22%<br>Ixe Visa 17%<br>0% 50% 100% 150% 200% 250%<br>0% 10% 20% 30% 40% 50%<br>Interest rate (%)<br>Interest rate (%)<br>Source: BOM, SHCP and Condusef.<br><!-- End of picture text -->

# **_States_**

Accessibility of financing also varies widely across different Mexican states, which can be partly attributed to differences in the business environment and the level of enforcement of creditor rights. As can be seen in figure 38, there was a wide discrepancy in bank credit volume by state when compared to each state’s GDP or deposits as of 2003 (the last year for which state-level GDP figures are available). This can be partly attributed to statistical problems (i.e. some commercial loans are booked in the capital or in the state where the company’s headquarters are located<sup>54</sup> ), the level of per capita GDP and composition of GDP across states (i.e. agriculture versus industry versus services) and to typical ‘core-periphery’ behavior (i.e. Distrito Federal ‘absorbing’ the financial

> 54 A breakdown of bank loans by type (i.e. commercial, mortgage and consumer) and state is not available.

35
30

# **References**

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Babita, Miriamn, Gabriel Demombynes, Nthabiseng Makhatha, and Berk Ozler. 2002. Estimated Poverty and Inequality Measures in South Africa: A Disaggregated Map for 1996. Processed.

Becker, Gary S. 1968. Crime and Punishment: An Economic Approach. _The Journal of Political Economy._ Volume 66, Issue 2, 169-217.

Behrman, Jere R., and Steven G. Craig. 1987. The Distribution of Public Services: An Exploration of Local Governmental Preferences. _The American Economic Review,_ Volume 77, Issue 1, 37-49.

Black, Donald. 1983. Crime as Social Control. _American Sociological Review,_ Volume 48, Issue 1, 3445.

Blau, Judith R., and Peter M. Blau. 1982. The Cost of Inequality: Metropolitan Structure and Violent Crime. _American Sociological Review,_ Volume 47, Issue 1, 114-129.

Bourguignon, Francois. 1998. Inefficient inequalities : notes on the crime connection. Mimeo, Delta and World Bank.

Bourguignon, Francois. 2001. Crime as a Social Cost of Poverty and Inequality: A Review Focusing on Developing Countries. In "Facets of Globalization" Eds. Shahid Yusuf, Simon Evenett, and Weiping Wu.

Chiu, W. Henry, and Paul Madden. 1998. Burglary and income inequality. _Journal of Public Economics,_ 69, 123-141.

> Cohen, Lawrence E., and Kenneth C. Land. 1987. Age Structure and Crime: Symmetry

> Versus Asymmetry and the Projection of Crime Rates Through the 1990s. _American Sociological Review,_ Volume 52, Issue 2, 170-183.

Deininger, Klaus W., and Lyn Squire. 1996. A new data set measuring income inequality. _World Bank Economic Review._ Volume 10: 565-591.

Demombynes, Gabriel, Chris Elbers, Jean 0. Lanjouw, Peter Lanjouw, Johan Mistiaen, and Berk Ozler. 2002. Producing an Improved Geographic Profile of Poverty. Methodology and Evidence from Three Developing Countries. WIDER Discussion Paper No. 2002/39.
as they arise) we feel that the data are of sufficient quality and interest to merit examination.

The first section describes the data (particularly the data on project performance), establishes the basic specification of the determinants of project performance excluding governance variables, and introduces our classification of governance variables. The second section establishes the positive link between the degree of civil liberties and project performance and argues that this relationship is causal. The third section shows the lack of such a relationship between either political liberties or political regime type. The fourth section discusses the interpretation and implications of the empirical results. The conclusion, as is customary, concludes.

# II Data description, hasic specificrtinn and results

# A Project dat

The data on the performance of projects is assembled by the World Bank's Operations Evaluation Department (OED)<sup>4</sup> . After e wch World Bank loan is fully disbursed--typically 5 to 8 years after the opening of the loan--Bank and borrower country staff write a Project Completion Report (PCR) to assess project performance<sup>5</sup> . As one part of this assessment, two performance 4 We do not differentiate projects financed by the IBRD (International Bank for Reconstruction and Development) loans, which are relatively 'hard" loans at near commercial terms, and IDA (International Development Association) credits, "soft' loans restricted to the poorest countries. Private sector IFC (International Finance Corporation) loans are not included in this analysis.

5 The PCR-recently rechristened the Implementation Completion Report--is usually written by a staff member in the division that supervised the loan, but not by anyone with major project responsibilities. As such, the incentives of the staff to dissemble about project performance, while present, are not overwhelmingly strong.

-2 -
and private), industries and skill categories cannot be accounted for by observed characteristics.

Focusing on the changes between 2004 and 2015, the reductions in the inequality within sectors, industries, and skill levels were the main driver of the reductions in the gender wage gap observed between 2004 and 2015. However, changes in industrial and occupational segregation benefitting women also contributed to the reduction in the gap in all three cases.

Previous work has demonstrated large scope for policy interventions in reducing the gender wage gap in Georgia. For example, Khitarishvili (2016) finds that, in part due to the presence of high degree of industrial and occupational segregation, public sector reforms have enabled swift reductions in the gender wage gap in Georgia. Our analysis further underscores the important role that policy interventions can play. In particular, the findings of the high unexplained share of the intra- and inter- components emphasize that unobserved barriers that limit their labor market earnings remain sizable for women in Georgia. Thus, our policy recommendations include the implementation of measures that will directly contribute to equal wages, such as the laws that mandate equal pay for equal work and ensure stronger representation of women on company boards (World Bank 2016).

Despite its relatively modest role in explaining the gender wage gap in Georgia, the high degree of industrial and occupational segregation is nevertheless problematic to the extent that it reflects strong gender specialization patterns by subject in tertiary education that can widen gender gaps in the long run, especially as Georgia attempts to strengthen its STEM-trained workforce. According to the UNESCO data, the percentage of tertiary-level female graduates in fields such as arts and humanities, education, health and welfare, is twice the percentage among men. Yet for fields such as engineering, manufacturing and construction, information and communication technologies, and services, the percentage of female graduates is less than half that of male graduates. Hence introducing programs that encourage women to pursue these presently male-dominated fields in tertiary education and ultimately employment should be a policy priority. To complement these efforts, targeted policy should also focus on facilitating the transition from school to work.

24
Figure 3: GIRFs for a one percent decline in China’s GDP growth rate <!-- Start of picture text --> china usa euro japan<br>0.0 .00 .00 .00<br>-0.4 -.01-.02 -.01 -.01<br>-0.8 -.03 -.02 -.02<br>-.04 -.03<br>-1.2 -.05 -.04 -.03<br>-1.6 -.06-.07 -.05 -.04<br>-2.0 -.08 -.06 -.05<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>austlia india indns kor<br>.02 .000 .00 .04<br>.01.00 -.004 -.02 .02<br>-.01 -.008 -.04 .00<br>-.02 -.06 -.02<br>-.03 -.012 -.08 -.04<br>-.04-.05 -.016 -.10 -.06<br>-.06 -.020 -.12 -.08<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>mal nzld phlp sing<br>.01 .03 .035 .02<br>.00 .030 .01<br>-.01 .02 .025 .00<br>-.02 .01 .020 -.01<br>-.02<br>-.03 .00 .015 -.03<br>-.04-.05 -.01 .010.005 -.04-.05<br>-.06 -.02 .000 -.06<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>thai oil agri metal<br>.00 0.0 .0 0.0<br>-.02 -0.2 -.1 -0.2<br>-0.4 -.2 -0.4<br>-.04 -0.6<br>-0.6 -.3<br>-.06 -0.8<br>-0.8 -.4 -1.0<br>-.08 -1.0 -.5 -1.2<br>-.10 -1.2 -.6 -1.4<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br><!-- End of picture text --> Note: Please refer to Table A1 in the Appendix for a glossary of acronyms. The lines correspond to the responses of 1985( _red dots_ ), 1995( _short dash_ ), 2005( _long dash_ ), and 2013( _line_ ).

set of variables with a reasonable order that reflects economic theory. Therefore, instead of using the standard IRFs proposed by Sims (1980), we use the GIRFs, which produce shock response profiles that do not vary for different orders of variables.

We will investigate how a negative real GDP shock in China transmits to the Asian countries as well as major developed economies based on the trade weights of 1985, 1995, 2005, and 2013. As explained in Section 2, as the trade linkages strengthen, we see a significant shift in the trade weight matrices of 2005 and 2013 from those of 1985 and 1995. Our focus is on seeing how the change of trade relations affects the propagation of shock.

First, we will examine the impact of a one percent drop in China’s real GDP growth rate on the developed nations of the United States, the Eurozone, and Japan, see the first row 24
labor costs, constraints in land and other factors), as well as no correlation between price of maize and seasonal rain. It is also assumed that the wealth generated during bumper harvests can actually be accumulated by farmers. While these constitute weaknesses of the model, we suspect they are unlikely to invalidate our main conclusion: that a scheme that uses skillful seasonal forecasts to adjust the bundled loan-insurance contract according to expected rains can substantially benefit participating farmers. The example we presented demonstrates that even fairly crude and conservative strategies hold the potential for substantial gains, suggesting that refined approaches may provide greater benefits. There is substantial room to improve seasonal forecast, as the ENSO phenomenon is just one factor affecting seasonal rainfall in southern Africa. There is also room to improve the set of management options in response to a seasonal rainfall forecast (e.g., selecting cultivars with different levels of drought resistance and yield potential, optimizing planting density and fertilizer rates for expected rainfall).

The results presented here depend not only on parameter assumptions, but also on the assumption that future seasonal precipitation will follow the same correlations with ENSO as the 45 years of historical observations used for the model. Future ENSO impacts may not be the same (especially given climatic change). Yet, given the potential for strategic behavior and the potential risk management benefits, one would have to guarantee that future ENSO impacts will not in any way follow the behavior of the past in order to proceed without designing ENSO impacts into the insurance package.

Additional research using more sophisticated forecasts and better characterization of the underlying distributions, correlations, skill and stakeholder preferences and constraints would be necessary before any new contract structure can be implemented in the field. Uncertainty in the 23
# **2.3 Health Calendar**

We collected daily information on symptoms, health consultations, and medications taken in weekly visits over the course of nine weeks from the child’s primary caretaker (usually the mother). Caretakers could use pictorial diaries (Figure B.1 in the online appendix) to mark off symptoms and health-related events. The surveyor then reconstructed the child’s health history during the visit. There was a concern that parents would respond to the observation by the surveyors, for example by watching their children more closely. Ultimately this drawback was outweighed by the benefits of detailed coverage without the problems associated with long recall periods (Das et al. (2011)). The surveyors were carefully instructed not to comment on the child’s health, and to emphasize that they had no medical qualification. While we cannot rule out all Hawthorne effects, we believe our precautions mitigated them, combined with the fact that the calendar data collection had been piloted over six weeks after the baseline in 2012, thus creating some habituation.<sup>11</sup>

**Symptom Calendar.** The list of symptoms was designed in collaboration with Mali Health staff and based on the C-IMCI guidelines by the WHO (see also below). The C-IMCI is designed for use by CHW who have no prior medical experience. It consists of simple rule charts that use important and easy-to-spot symptoms to broadly classify a child’s illness and establish need for care. These properties mean we can collect symptom reports from mothers and surveyors who are not medically trained. The survey explicitly asks about convulsions, fits, or spasms; lethargy or unconsciousness; inability to drink; vomiting; coughing; difficulty breathing; more than three loose stools; blood in the stool; sunken eyes; and unusually hot (cold) skin. We constructed four more symptoms from the “other” category: skin conditions (e.g. spots, rash, itch), cold symptoms, ear pain, and injuries. We observe on average 60

> 11Since the survey took place towards the end of the rainy season, illness rates were declining over the course of the panel, so we cannot assess if a time trend is due to a fading Hawthorne effect. However, in the baseline pilot of the health diaries, initially elevated symptom reporting rates stabilized at a lower level after the first week of surveying and then declined at a slower and more continuous rate (data available on request). This is consistent with data on doctors, who rapidly return to unobserved length and quality of doctor-patient interactions even when observed by other doctors (Das et al. (2008)).

13
# **Supplementary Material**

# **A1 Data Preparation**

## **A1.1 World Bank Enterprise Surveys**

We compile our data set from the World Bank Enterprise Surveys (World Bank, 2020a), a firm-level survey of a representative sample of an economy’s private sector, including both manufacturing and services firms. We used the 2019 surveys, which were conducted in 31 countries in Europe, the Middle East and North Africa, and the Russian Federation and Central Asia. In each country, firms responded to either the Manufacturing survey or the Services survey based on their self-identified sectors. The survey includes productive inputs, performance measures, business indicators, management practice scores, and a green economy module. This module covers energy and environmental impacts as well as energyand climate-related management practices. We use the energy practices in the green economy module to compute the energy management score. This score is designed to capture the extent to which a firm has implemented structured practices aimed at reducing or raising the efficiency of energy use. The green economy module also covers the physical quantities of various forms of energy consumption such as electricity, petroleum, coal, and natural gas. We use these energy data to compute energy intensity in physical terms. To the best of our knowledge, this paper is the first to analyze the impact of general and energy management practices on physical and value measures of energy intensity.

Observations of services firms are generally missing capital stock and share of high-skilled labor. Using the World Bank classification (World Bank Data, 2020), only one country is classified as low-income (Tajikistan); we therefore group countries into three income categories: high, upper middle, and lower middle.

## **A1.2 Merging**

We merge the firm-level responses from all the 31 countries into a combined dataset. For categorical variables such as region that are country-specific and thus inconsistent across surveys, we tagged the possible responses by country throughout the merge to ensure that the responses remained distinct in the final dataset. Additionally, for each country, we converted all financial variables into US dollars using the average market exchange rate in 2018 from World Bank (2020b). After merging, our sample included 20,156 observations.

## **A1.3 Management Measures**

We develop a measure of structured management practices that takes 11 survey questions that quantify practices. These management practices can be divided into operations (1 practice), monitoring (2 practice), targeting (4 practices), and incentives (4 practices). The table below lists these management practices and the scoring scale applied for each. To better align with the methodology from (Bloom et al., 2010a), we map these 11 questions to the 8 questions used by the U.S. MOPS survey (U.S. Census Bureau, 2015). Thus, we combine: 34