rafmacalaba/datause-encoder-data huggingface dataset:
# **Reference**

Acemoglu, D., S, Johnson and J. A. Robinson (2005) “Institutions as a Fundamental Cause of Long-Run Growth.” Handbook of Economic Growth 1A: 386-472.

Alfaro, L., and F. Kanczuk (2019) “Undisclosed Debt Sustainability.” _NBER Working Paper_ 26347.

Banisar, D. (2006) “Freedom of Information around the World 2006: A Global Survey of Access to Government Information Laws.” Privacy International. http://www.freedominfo.org/ Beck, T., G. Clarke, A. Groff, P. Keefer and Patrick Walsh (2001) "New tools in comparative political economy: The Database of Political Institutions." _World Bank Economic Review_ 15(1): 165-176.

Bellver, A., and D. Kaufmann (2005) “Transparenting Transparency.” MPRA Paper 8188, University Library of Munich, Germany.

Blinder, A., C. Goodhart, P. Hildebrand, D. Lipton, and C. Wyploszet (2001) “How the Central Banks talk?” Geneva Report on the World Economy 3, ICMB.

Berliner, D. (2014) “The Political Origins of Transparency.” _The Journal of Politics_ 76(2): 47991.

Calderon, C., and A. Zeufack (2018) “Debt Trends and Risks in Sub-Saharan Africa.” Washington, DC: The World Bank, Manuscript, November.

Calvo and Mendoza (1996) “Petty Crime and Cruel Punishment: Lessons from the Mexican Debacle.” The American Economic Review 86(2): 170-75.

Chinn, M. D. and H. Ito (2006) "What Matters for Financial Development? Capital Controls, Institutions, and Interactions" _Journal of Development Economics_ 81(1): 163-192.

Chinn, M. D. and H. Ito (2008) "A New Measure of Financial Openness." _Journal of Comparative Policy Analysis_ 10(3): 309-22.

Clare, A. D. and R. Courtenay (2001) “What Can We Learn About Monetary Policy Transparency from Financial Market Data?” _Bundesbank Series 1 Discussion Paper_ No. 2001,06.

De Gregorio (2008) “Transparency and Communications in Modern Central Banking.” At the 10<sup>th</sup> Annual General Meeting of the Interamerican Economics and Finance Journalist Association (July).

Dincer, N, N., and B. Eichengreen (2007) “Central Bank Transparency: Where, Why, and with What Effects?” _NBER Working Paper_ 13003.

Edwards, S. (1998) “The Mexican Peso Crisis: How Much Did We Know? When Did We Know It?” _The World Economy_ 1-30.

16
Figure 4: Heterogeneity in impacts of partial school reopening on adult work hours <!-- Start of picture text --> All<br>Women<br>Men<br>No HH agriculture<br>}*<br>Any HH agriculture<br>Rural<br>Urban<br>Below mean wealth<br>Above mean wealth<br>No child age 0-4<br>Any child age 0-4<br>1-2 HH adults<br>3+ HH adults<br>-5 0 5 10 15<br><!-- End of picture text --> The figure summarizes estimated coefficients and 95% confidence intervals for the effect of _Post ∗ Treat_ from Equation 1 for sub-samples with specified characteristics. Only coefficients for treatment households are shown. The outcome is total work hours in the 7 days prior to the interview. Results are reported in Table 2. Brackets indicate significant differences between pairs of characteristics. Data include observations from May-November 2020. Household characteristics are from the first time they are observed. Wealth is measured by an index based on housing and asset ownership.

are engaged in agriculture.<sup>13</sup> The sample may also not be representative of all rural households with schoolchildren: sample households must have a mobile phone and are on average better off than the population (Pape et al. 2021). This may further blur the urban/rural distinction in these data. Low take-up of formal childcare services and low wage employment in the data may also limit heterogeneity by urban location.

Adults in poor households increase work hours by more than those in wealthy households, measuring wealth by an index based on housing and asset ownership, though the difference is not significant. Below-mean-wealth households in the analysis sample are around 30 percent more likely to engage in agriculture than wealthier households and so may have been better able to increase work in the short term after the partial reopening, but differences in agricultural hours do not fully explain the difference (Table A4). Wealthier households may also have had resources to better absorb increased childcare burdens during school closures and thus been less affected by the reopening.

We also consider differences by household composition. Households with below-school-age chil13. The difference remains insignificant when restricting urban households to those in counties with the largest cities in Kenya (Column 4), though even there 25% of ‘urban’ households engage in agriculture.

14
- 14 -

economies is not the same among commodities. Whether infrastructure after all has a positive or negative impact depends on the price elasticity and economies of scale.

|Table 4. Correlation between com|modity-spec<br>|ific growth a<br>|nd infrastr<br>|ucture devel<br>|opment, 2<br>|001-04<br>|
|---|---|---|---|---|---|---|
||Coff|ee|Coc|oa|Mil|k|
||Obs.|Cor.|Obs.|Cor.|Obs.|Cor.|
|Road density2000|70|0.077|54|-0.022|167|-0.251|
|Rural access index1993-2003|71|-0.030|55|0.080|163|-0.020|
|Teledensity2000|73|0.160|55|0.061|175|-0.183|
|Rural teledensity2000|54|0.185|39|0.134|128|-0.094|
|Electricity consumption2000|46|-0.003|34|0.069|125|-0.173|
|Agri. Power consumption2000|10|0.079|8|0.004|61|-0.138|
|Water access1990|55|0.088|39|0.001|128|-0.075|
|Rural water access1990|55|0.063|39|-0.054|128|-0.094|

Source: Author's caluculations based on _WDI_ , ITU (2002), IEA _Energy Statistics_ , Roberts et al. (2006), _FAOSTAT_ , and IMF _Primary Commodity Prices_ .

<!-- Start of picture text --> Figure 6. Coffee export price and quantity, 2001-05<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>0.0001 0.001 0.01 0.1 1 10 100 1000<br>ln (Export volume in thousands of tons)<br>Source: Author's calculation based on Eurostat and IFS .<br>Figure 7. Cocoa export price and quantity, 2001-05<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>0.0001 0.001 0.01 0.1 1 10 100 1000<br>ln (Export volume in thousands of tons)<br>Export price in constant 2000 euro<br>Export price in constant 2000 euro<br><!-- End of picture text --> Source: Author's calculation based on _Eurostat_ and _IFS_ .

Source: Author's calculation based on _Eurostat_ and _IFS_ .
percent. The regression analysis indicates much smaller price elasticities with respect to calories than with respect to food consumption, suggesting that households traded off quality for quantity of calories consumed. As additional evidence of this trade-off, the regression estimates show that the price increases are associated with changes in the composition of food consumption, namely decreases in dietary diversity and a movement towards staple foods. This result is similar to that of McKenzie (2006) who finds a shift in food consumption towards staples after the devaluation of the Mexican Peso in 1994.<sup>11</sup> The analysis in this paper also documents the statistical relationships between commonly used nutrition indicators and various measures of food security, and thereby contributes broadly to work on the efficacy of household-level indicators like dietary diversity.<sup>12</sup> This is in line with the growing recognition of the importance of household- and individual-level consumption data in developing sub-national interventions to reduce chronic food insecurity (Barrett, 2010).<sup>13</sup> In addition to contributing more broadly to the food security literature, we also believe that this analysis fills a critical gap in the knowledge base for policy makers. The dearth of data and analysis available on Afghanistan poses acute challenges to political leaders, lawmakers, and humanitarian organizations interested in creating programs and policies to alleviate poverty and food insecurity.

The next section in this paper briefly describes the Afghan economy and its ties to international markets during the period of the food crisis. Section 3 provides details on the survey, the methodology and the sample. More findings on the household response to the price effects are described in section 4, along with discussion of robustness tests, and section 5 concludes.

# **2 Afghanistan**

Over the past decade, the Afghan economy has experienced strong growth, with real GDP growth averaging approximately 10.8% per year between 2003 and 2009. But after decades of war and political instability, landlocked Afghanistan remains one of the world’s

> 11 See Ruel _et al._ (2009) for a more detailed review of the literature on the effects of economic crises on household wellbeing.

> 12 See Ruel (2003) for a review of studies examining the efficacy of dietary diversity measures.

> 13 For example, select research examines reductions in maternal and child nutritional status following the Indonesian crisis and the CFA devaluation (Martin-Prevel _et al._ , 2000; Block _et al._ , 2004). See Ruel _et al._ (2009) for a more detailed review of the literature on the effects of economic crises on household and individual wellbeing.

5
A firm‟s information environment also plays an important role in determining the cost of capital by allowing investors to better recognize the value of the firm (Merton, 1987‟s investor recognition hypothesis) and have a better estimation of the risk and prospects for the firm (Barry and Brown, 1985). A large literature on credit markets has shown that asymmetric information prevents efficient allocation of lending, driving a wedge between lending and borrowing rates and also resulting in credit rationing (e.g. Stiglitz and Weiss, 1981).

There are two primary sources of information on firms in developing countries: The first is the information on large, publicly traded firms, which are in turn influenced by both formal laws and informal practices in the country. The second source is information on small businesses through the exchange of information between lenders via credit bureaus. We examine the international evidence on both aspects below.

In countries with developed capital markets such as the US, disclosure regimes involve audited balance sheets, income statements, and cash flow statements with supporting disclosures, all of which provide credible information on publicly traded firms. However, sophisticated disclosure regimes are expensive, and not all countries require the same level of disclosures of their firms. Finance scholars agree that the quality of accounting standards in a country serves as a measure of information quality, at least on the large public firms. LLSV (1998, 1999) compile an index of accounting standards across countries and find that the quality of accounting standards varies by legal origins. They show that countries with an English legal tradition have better accounting standards than French or German Civil Law countries. Levine, Loayza, and Beck (2000) show that cross-country differences in accounting standards also explain other institutional differences such as the differences in financial development. Leuz, Nanda, and Wysocki (2003) also establish a link between a country‟s legal and institutional environment and the quality of accounting earnings reported to investors.

While accounting standards are a measure of information quality at the country level, a second stream of literature has focused on measuring the information environment at the firmlevel. Three approaches are of note here. First, papers by Durnev and Kim (2005) and Klapper and Love (2002) use data on the quality of governance practice compiled by Credit Lyonnais Securities Asia (CLSA) across 27 countries, and compiling indicators of a firm‟s disclosure 31
|**Specifcation 2**<br>**Coefcient**<br>**Std. Err.**<br>-1.81_∗∗∗_<br>0.36<br>0.75_∗∗∗_<br>0.08<br>0.28_∗∗∗_<br>0.06<br>0.00<br>0.00<br>-0.02<br>0.02<br>-0.04<br>0.05<br>-0.13<br>0.20<br>0.15_∗∗_<br>0.05<br>0.10_∗_<br>0.04<br>0.21_∗∗∗_<br>0.05<br>0.38_∗∗∗_<br>0.08<br>0.10_∗_<br>0.04<br>0.31_∗∗_<br>0.10<br>0.13_∗_<br>0.06<br>0.05<br>0.15<br>0.23_∗_<br>0.11<br>0.37_∗∗∗_<br>0.11<br>0.13_∗∗_<br>0.05<br>0.00<br>0.00<br>0.22<br>0.46<br>0.18<br>0.43<br>0.12<br>0.35|
|---|
|**Specifcation 1**<br>**Std. Err.**<br>0.59<br>0.08<br>0.06<br>0.00<br>0.02<br>0.05<br>0.19<br>0.07<br>0.04<br>0.06<br>0.08<br>0.04<br>0.10<br>0.07<br>0.13<br>0.11<br>0.15<br>0.12<br>0.11<br>0.09<br>0.08<br>0.03<br>0.00<br>0.46<br>0.40<br>0.29<br> but the results are not shown.|
|**Variable**<br>**Coefcient**<br>Intercept<br>-4.01_∗∗∗_<br>Year 2008<br>0.89_∗∗∗_<br>Listen to radio every day/past week<br>0.22_∗∗∗_<br>Distance to district headquarters<br>0.00<br>Population density of enumeration area<br>-0.03<br>Ethnic minority in ward<br>-0.05<br>Ethnic fractionalization<br>-0.19<br>NGO projects in community<br>0.15_∗_<br>Community exposure to war violence<br>Belong to a religious group<br>0.11_∗∗_<br>Belong to a cooperative<br>0.20_∗∗∗_<br>Belong to a political party<br>0.37_∗∗∗_<br>Household member had malaria last year<br>0.13_∗∗_<br>Experience with registering deaths<br>0.30_∗∗_<br>Health clinic in community has working fridge<br>0.30_∗∗∗_<br>Clinic in community: district hospital<br>0.10<br>Clinic in community: maternal/child post or community post<br>0.30_∗∗_<br>Clinic in community: private<br>0.38_∗_<br>Satisfed with health clinics: Level 1<br>0.04<br>Satisfed with health clinics: Level 2<br>0.19<br>Satisfed with health clinics: Level 3<br>0.47_∗∗∗_<br>School in community provides free textbooks<br>Satisfed with education: Level 1<br>Satisfed with education: Level 2<br>Satisfed with education: Level 3<br>Local councillors spending money well<br>1.28_∗∗∗_<br>Identifed local councillor’s name<br>Health transfers from central to local government<br>0.05<br>Council’s administrative capacity<br>0.01<br>Random Efects<br>Enumeration Areas (N=623)<br>0.21<br>Wards (N=154)<br>0.16<br>Local Councils (N=19)<br>0.09<br>_∗p < ._05; _∗∗p < ._01; _∗∗∗p < ._001<br>Other socio-demographic variables are included in the model|

26
Goldberger, A. (1989), “Economic and Mechanical Models of Intergenerational Transmission,” _American Economic Review_ , vol. 79, no.3, 504‐513.

Gong Cathy Hongge, Andrew Leigh and Xin Meng (2012), “Intergenerational Income Mobility in Urban China”. _Review of Income and Wealth_ . Series, Number 3, p. 481‐503 Hannum, Emily and Albert Park Ed. (2007), Education and Reform in China, Routledge. Hansen, M. H. (1999), Lessons in Being Chinese: Minority Education and Ethnic Identity in Southeast China, Seattle, University of Washington Press.

Harvey, A (1976), "Estimating Regression Models with Multiplicative Heteroscedasticity". _Econometrica_ , pp. 461 465.

Hertz, T (2001): Education, Inequality and Economic Mobility in South Africa,Ph.D. Disser‐tation, University of Massachusetts.

Klein, R and F. Vella (2009), “A Semiparametric Model for Binary Response and Continuous Outcomes under Index Heteroscedasticity", _Journal of Applied Econometrics_ , v. 24, iss. 5, pp. 736‐62 Klein, R and F. Vella (2010), “Estimating a Class of Triangular Simultaneous Equations Models Without Exclusion Restrictions”, _Journal of Econometrics_ , 2010, 154 (2), 154‐164 Lanjouw, P. and G. Feder (2001), “Rural Non‐farm Activities and Rural Development,” Rural Strategy Background Paper # 4, World Bank.

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42
Table 4: Fixed-effects regression results for employment elasticity of growth: product market

|Variabl|es|ECA|WE|PECA|PWE|EU10|BalTur|CIS|
|---|---|---|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Distance to fron|tier||||||||
|Output|Coeff.|0.209|0.295|0.015|0.048|0.391|0.539|0.062|
||t-stat.|3.3|2.9|0.6|0.8|4.9|1.5|2.0|
|Other variable|Coeff.|0.002|0.000|0.001|0.001|0.003|-0.005|0.001|
||t-stat.|1.9|0.2|2.4|0.8|1.2|-1.2|2.0|
||N|162|109|586|107|60|41|66|
||R2|0.45|0.59|0.34|0.92|0.82|0.42|0.83|
|Credit regulatio|n||||||||
|Output|Coeff.|0.232|0.332|0.068|0.122|0.434|-0.068|0.131|
||t-stat.|4.2|4.6|3.5|1.6|5.5|-0.3|1.8|
|Other variable|Coeff.|0.006|0.003|0.000|0.001|0.003|0.013|0.003|
||t-stat.|2.0|1.5|0.1|0.3|0.9|1.5|0.7|
||N|210|209|810|175|110|44|56|
||R2|0.37|0.53|0.24|0.61|0.62|0.38|0.51|
|Business regulat<br>|ion<br>||||||||
|Output|Coeff.|0.354|0.413|0.120|0.144|0.422|||
||t-stat.|5.5|6.3|3.6|2.9|5.3|||
|Other variable|Coeff.|0.009|0.003|0.002|-0.003|0.008|||
||t-stat.|1.5|0.7|0.7|-0.8|1.1|||
||N|143|187|362|99|110|||
|Note: The depen<br>parameter estim<br>fixed-effects.|R2<br>dent varia<br>ate of out|0.53<br>ble is em<br>put growt|0.60<br>ploymen<br>h. All re|0.24<br>t growth<br>gressions|0.72<br>. The outp<br>also inclu|0.62<br>ut coeffic<br>de countr|ient is the<br>y and year||

15
**15**

Table 3:

|**Se,tor Compositi'n**<br>(Current<br>Prices)|||
|---|---|---|
|Year<br>Agriculture<br>e<br>:<br>.|Light<br>Industry<br>e|Heavy<br>Industry|
|1952<br>56.9|27.8|15-3|
|1957<br>43.3|31.2|25.5|
|1962<br>38.8|28<br>9|32.3|
|1965<br>37.3|32.3|30.4|
|1970<br>33.7|30.6|35.7|
|1975<br>30.1|30.8|39.1|
|1980<br>30.8|32.6|36.6|
|1985<br>34.3|30.7|35.0|
|Source:<br>State<br>Statistic|al<br>Bureau,<br>Guo|miinshouru<br>tor.rii<br>ziliao|
|huibiax,<br>p.11.|||

Judging from China's sector composition, the program of reforms has reached its intended goal of accelerating the development of heavy industry. Nonetheless, China has paid a high price for its achievement. The economy is highly inefficient due to two major problems: (1) low allocative efficiency arising from the deviation of the industrial structure from the pattern dictated by the comparative advantages in the economy; and (2) low technical efficiency linked to the meager incentives for workers and managers to apply themselves and excel.

Low allocative efficiency At the current stage of China's economic development, capital is still relatively scarce and labor relatively abundant. If prices were determined by market competition, capital would be relatively expensive and labor relatively inexpensive. Thus, the comparative advantages of the Chinese economy lie in its labor-intensive sectors. If investments were guided by market forces, profit incentives would have induced entrepreneurs to adopt technologies which demand labor but not capital, and to allocate more resources to labor-intensive industries.
level of tension that may invite violence. Violent gang activities are prevalent and 73 percent of the youth answered that their neighborhood had experienced gang fights in the last 12 months. Concomitantly, the lack of safety in the neighborhood is clearly expressed by the dwellers in general. Only 15 percent of the youth and 16 percent of the adults feel safe in their neighborhood. Finally, 47 percent of the youth and 55 percent of the adults do not feel safe in their own home.

We also address the issue of why the youth would engage in violent activities. Slightly less than half of the youth population (47 percent) answered that family problems are the main reason for violent activities. Other 23 percent suggest that lacking opportunities are the main reason to engage in violent activities.

The risk of domestic violence is also present in the neighborhoods. Around 13 percent of the youth claim that they grew up in a household with violence and their father or brothers were the principal committers of domestic violence. Thirteen percent may seem low, but given the fact that only 7 percent grew up with their father in the household, this number holds a significant meaning. (see section 3.1).

# **3.5 Risk of Unemployment**

A good job is a well-paid job that, as a bare minimum, lifts the household out of income poverty. Hence, sufficient income to pay for basic needs such as food, shelter, education and health care are key to sustainable poverty reduction at the household level. Unfortunately, the data from the three poor urban neighborhoods in Fortaleza reveal that the dwellers do suffer from lack of employment opportunities.

The youth and adults face substantial risk of being unemployed and underemployed. The data show that 58 percent of the youth population did not work at any point during the last 12 months and almost half of the adult respondents (48 percent) did not work during the last 12 months.

20
our partner bank), (ii) SME loan from our partner bank, (iii) SME loan from a financial institution different from our partner bank, and (iv) consumption loan from any financial institution. We define these four dummies for three different time periods: (i) the pre-application period, comprising 6 months before the loan application, (ii) the immediate post-application period, comprising 6 months after the loan application, and (iii) the medium run, comprising two years after the loan application, that is, loan outcomes 24 months to 31 months after the SME applied for a loan. The immediate post-application period allows 6 months for loans to be processed and disbursed and also provides some time for applicants to potentially shop around with other banks for other loan offers. Given that the maximum loan term reported by our partner bank was 18 months, we then define the medium run as starting at 24 months, i.e. 18 months after the initial 6 months, to arrive at a time when applicants may want to renew their loans. We chose the endpoint of 31 months since this is the last month for which we have data for the full sample.<sup>15</sup> Table A3 in the appendix provides summary statistics for our background variables as well as pre-application loan use and credit scores. Column 1 in panel A shows that the loan applicants in our sample were on average 39 years old and 50 percent of them were female. Average annual business revenues were about US$12,000. Close to 20 percent of applicants would not have received a loan offer from our partner bank based on the conventional screening method. At the time of the application, 22 percent of loan applicants did not have credit with a formal financial institution.

Column 1 in panel B of table A3 reports Equifax data for the pre-application period. About 52 percent took out a new SME loan from any financial institution during the 6 months preceding the loan application and about 43 percent took out a new consumption loan. Almost all new SME loans came from banks other than our partner institution, which reflects the fact that our partner institution was not very active in the SME segment before the EFL pilot.

# **4. Identification strategy**

We estimate the effect of being offered a loan using the EFL tool on loan use and repayment behavior using a non-parametric regression discontinuity (RD) design. Let �� be the psychometric

> 15 As a robustness check, we also used two alternative definitions of loan use: (i) a dummy equal to one if either the DNI or RUC associated with a loan applicant has any SME loan amount outstanding from a given financial institution and (ii) the total volume of SME loans from all financial institutions. Our main results with these alternative measures are reported in the appendix and are similar to the results using the loan increase dummy.

11