rafmacalaba/datause-encoder-data huggingface dataset:
can Economic Journal: Applied Economics 1(3): 1-32.

De Mel, Suresh, David McKenzie and Christopher Woodru¤ (2009b) “Measuring microenterprise pro...ts: Must we ask how the sausage is made?”, Journal of Development Economics 88(1): 19-31 Di Falco, Salvatore and Erwin Bulte (2009) “The Dark Side of Social Capital: Kinship, Consumption, and Investment”, Mimeo. University of Kent.

Du‡o, Esther, Michael Kremer and Jonathan Robinson (2010) “Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya”, American Economic Review forthcoming.

Du‡o, Esther and Christopher Udry (2004) “Intrahousehold Resource Allocation in Côte d’Ivoire: Social norms, separate accounts, and consumption choices” , NBER Working Paper no. 10498.

Emran, M. Shahe, AKM Mahbub Morshed and Joseph Stiglitz (2007) "Micro...nance and Missing Markets", Mimeo. George Washington University.

Fafchamps, Marcel, Christopher Udry, and Katherine Czukas (1998). "Drought and Saving in West Africa: Are Livestock a Bu¤er Stock?", Journal of Development Economics, 55(2): 273-305, April Fafchamps, Marcel, David McKenzie, Simon Quinn and Christopher Woodru¤ (2010) “Using PDA consistency checks to increase the precision of pro...ts and sales measurement in panels”, Journal of Development Economics, forthcoming.

Grimm, Michael, Flore Gubert, Ousman Koriko, Jann Lay and Christophe Nordman (2010) “Does forced solidarity hamper entrepreneurial activity? Evidence from seven West-African countries”, Mimeo. International Institute of Social Studies, Eramus University Rotterdam.

Harrison, Glenn, Morten I. Lau, and E. Elisabeth Rutstrom (2007). "Estimating Risk Attitudes in Denmark: A Field Experiment", Scandinavian Journal of Economics, 109(2): 341-68.

Jakiela, Pamela and Owen Ozier (2011) "Does Africa need a rotten kin theorem? Experimental evidence from village economies", Mimeo. University of Washington, St Louis.

Karlan, Dean and Jonathan Zinman (2010) “Expanding Microenterprise Credit Access: Using Randomized Supply Decisions to Estimate the Impacts in Manila”, Mimeo. Yale University.

Laibson, David (1997) "Golden Eggs and Hyperbolic discounting", Quarterly Journal of Economics 112(2): 443-77.

Lee, David (2009) “Training, Wages, and Sample Selection: Estimating Sharp Bounds on 40
Ganimian et al. (2021) to project what benefits might be and assess whether these projected benefits exceed reported costs.

For each study, we gather information about the country’s labor force participation, average wages, and real wage growth to calculate the total expected lifetime earnings of a child.<sup>19</sup> We assume children will work between the ages of 22 and 65, which should be a conservative assumption for the number of active years in the labor market, given that labor market participation tends to start much earlier than age 22 in low- and middle-income countries. Because most studies reported program costs in US dollars, we first convert wage data into US dollars using official exchange rates from the International Financial Statistics of the International Monetary Fund.<sup>20</sup> We discount this lifetime stream of earnings to the year that preprimary program costs were incurred, using two different discount rates – 3 percent and 5 percent. While 3 percent reflects the standard in the literature on economic evaluation in high-income countries, some researchers have argued that the higher economic growth rates in low- and middle-income countries would make rates as high as 5 percent more appropriate (Haacker et al, 2020).

Next, we rescale the present value of earnings to account for the benefits of preprimary interventions, combining study-specific estimates of the improvement in cognitive skills following a preprimary intervention with estimates from the literature on the extent to which improvements in cognitive skills observed during the preprimary period translate into improvements in earnings. To be conservative, we select each study’s lowest estimated treatment effect that is significantly different from zero. To translate average effects on cognitive skills into increases in earnings, Kline and Walters (2016) use a value of 13 percent per standard deviation increase in cognitive skills in their study of the Head Start program in the United States, while Galasso and Wagstaff (2018), focusing on low-income countries, use a value of 4.3 percent. Thus, for example, we can multiply the present value of earnings by (0.13 x study-specific average

> 19 We omit this data collection and calculation for Ganimian et al., (2021) which directly reports benefit to cost ratios. Because of lack of information from the general population on monthly wages in Mozambique, we also cannot calculate benefit-to-cost ratios corresponding to Martinez et al., (2017) which found significant impacts on children’s cognitive skills from an expansion of community preschools and did report per child costs.

> 20 As studies did not indicate whether they had used official exchange rates or exchange rates adjusted for purchasing power parity to convert program costs to US dollars, we assume they used official exchange rates and therefore use official exchange rates to convert wages to US dollars.

23
However, monga is perhaps more than seasonality of income and poverty in greater Rangpur. A World Bank study shows that the greater Rangpur was the most lagging region in Bangladesh in 2005 in terms of poverty reduction ((Narayan, Yoshida and Zaman 2007). This is indeed echoed in Table 3. In 2005, for example, greater Rangpur had a rural poverty rate in 2005 as high as 61 percent compared to only 45.1 percent poverty at the national level Extreme poverty was as high as 47.9 percent compared to the national rural average of 31.1 percent. Although Rangpur experienced a greater reduction of poverty over time (say between 2000 and 2005), the region is still lagging compared to the rest of the country in term of overall poverty figures.

More importantly, the Rangpur region is lagging in terms of other socio-economic indicators of household and community –level welfare. For example, household per capita income in Rangpur was about two-thirds of that in the rest of the country and this proportional share has remained constant over the years (Table 4). Households of greater Rangpur drew income more from farm than non-farm sources, thereby being more vulnerable to seasonality of agriculture. Greater Rangpur is also lagging in terms of access to credit and other infrastructures. More households draw wage income from agriculture in Rangpur (13.5 percent) than in the rest of the country (9.4 percent). However, agricultural wage workers fare worse in Rangpur than their counterparts in the rest of the country. For example, in 2005, the daily real wage rate for male agricultural workers in Rangpur was only 46 Taka compared to 64 Taka in the rest of country.<sup>17</sup>

> 17 The higher wage rate in rest of the country reflects of a much more integrated wage market with outside the region which may reflect a higher rate of out-migration, implying an active shortage of labor with consequential higher wage rates.

13
Table 8 extends the analysis for poverty rates by incorporating the gender dimension in the racial differentials. The evidence is consistent with negligible differences across groups. Table 9 additionally present the combined perspective of age and race. We observe that the impact of the fiscal system is poverty-increasing for children (0-15), youth (16-17) and non-elderly adults (18-59) and very povertydecreasing for the elderly, both for AfroBrazilians and whites. Interestingly, the impact for AfroBrazilian elderly is higher (-19.7 p.p.) than for white elderly (-11.1 p.p.).

**Table 8. Moderate poverty headcount rates and Gini index considering both pre‐ and post‐fiscal income aggregates, separately by household head’s gender and race**

|**Race/gender of the head**|**Prop. of**<br>**individuals**<br>**(%)**|**Pre-fiscal**<br>**income**|**Post-**<br>**fiscal**<br>**income**|**Change in**<br>**poverty**<br>**(p.p.)**|
|---|---|---|---|---|
|White female head|18.2|22.0%|21.6%|-0.4|
|White male head|21.7|17.6%|17.0%|-0.6|
|AfroBrazilian female head|28.5|45.9%|45.3%|-0.6|
|AfroBrazilian male head|30.4|37.9%|37.0%|-0.9|

Source: authors’ estimations based on PNAD-C 2019. Pre-fiscal income considers the household-level market income plus pensions, while the post-fiscal income considers the household-level consumable income. Afro-Brazilians are those declared to be either Black or Pardos. Proportion of households indicate the number of households in each group as a share of the total number of households in PNAD-C. Categories that account for less than 1 percent of the population not shown.

**Table 9. Moderate poverty headcount rates considering both pre‐ and post‐fiscal income aggregates, separately for distinct groups of individuals in terms of race and age**

|**Race/age of the survey respondent**|**Prop. of**<br>**individuals**<br>**(%)**|**Pre-fiscal**<br>**poverty**<br>**rate**|**Post-**<br>**fiscal**<br>**poverty**<br>**rate**|**Change in**<br>**poverty**<br>**(p.p.)**|
|---|---|---|---|---|
|White aged 0-15|9.2|31.6%|34.6%|3.0|
|AfroBrazilian aged 0-15|13.2|56.0%|59.2%|3.1|
|White aged 16-17|1.1|28.4%|31.2%|2.8|
|AfroBrazilian aged 16-17|1.9|52.1%|54.5%|2.4|
|White aged 18-59|25.1|16.5%|17.6%|1.1|
|AfroBrazilian aged 18-59|34.8|36.8%|38.0%|1.2|
|White aged 60+|6.9|19.0%|7.9%|-11.1|
|AfroBrazilian aged 60+|6.8|38.8%|19.0%|-19.7|

Source: authors’ estimations based on PNAD-C 2019. Notes: Pre-fiscal poverty indicates the poverty headcount rate considering the household-level market income plus pensions, while the post-fiscal poverty indicates the poverty headcount rate considering the household-level consumable income. Afro-Brazilians are those declared to be either Black or Pardos. The proportion of individuals indicates the number of individuals in each group as a share of the total number of individuals in PNAD-C. Categories that account for less than 1 percent of the population not shown.

35
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where σ _jt_ is the standard deviation of asset _j_ in year _t_ , x _jit_ is the average quantity per household of asset _j_ in wealth class _i_ in year _t_ , and μ _jt_ is the overall (national) average quantity per household of asset _j_ in year _t._ The calculated values of σ _jt_ are presented in Figure 4. In terms of the average quantity of AWMs the degree of dispersion among wealth classes increased between 1995 and 2001, but declined thereafter (Panel A, Figure 4). In terms of the average quantity of CTVs the degree of dispersion also increased between 1995 and 2001, but then declined to a even lower level than that in 1995 (Panel B, Figure 4). Looking at the average quantity of PCs across wealth classes we find that the degree of dispersion was consistently increasing over time (Panel C, Figure 4). This is consistent with the findings reported in the previous section that the upper middle and richest classes were the ones who mostly acquired this durable good during this period (Panel C, Figure 2).<sup>19</sup> Finally, when it comes to the average quantity of cars we find that the degree of dispersion increased between 1995 and 2001, but declined between 2001 and 2003 (Panel D, Figure 2). The analysis suggests that in essence the story of Bulgaria is that of rising divergence in assets distribution among wealth classes between 1995 and 2001, followed by convergence between 2001 and 2003.

# **4. ETHNIC AND GEOGRAPHIC DIMENSIONS**

Bulgaria is composed of three main ethnic groups: Bulgarians, Turks and Roma.<sup>20</sup> The Turks and the Roma are ethnic minority groups that account for about 6-7 percent and 3-4 percent of the total population, respectively.<sup>21</sup> There is a significant disparity in the living standards between Bulgarians and the two ethnic minorities. The latter faces a higher degree of vulnerability to poverty and risk than the average population. The disparity in poverty levels

> 19 Despite the declining prices of computers over time, the possession of this durable by poor households is still low as it is not a necessary item and it costs more than many other durable goods. 20 As per the Bulgarian law ethnicity for everybody is defined based on self-declaration.

> 21 The estimates of the population shares of these ethnic groups are based on the household survey data used in this paper. The ethnical structure of the population has been quite stable over the last decade, as the survey data suggest.

- 10 -
As a next step, we test key variables, particularly those relating to economic vulnerability, as to their suitability as predictors for explaining the differences of projected and observed GDP in year 5 post event. Based on the literature review and discussion above, the following variables listed in table 5 are assessed.

Table 5: Predictor variables used in the analysis<sup>7</sup>

|**Predictors**|**Variables**|**Source**|
|---|---|---|
|Direct impact and risk|Direct monetary losses|EMDAT, 2009, Munich Re,<br>2008 as compiled by<br>Okuyama, 2009|
||Losses in percent of GDP|Okuyama, 2009|
||Losses in percent of capital stock|Own calculations|
|Exposure|GDP|WDI, 2008|
||Capital stock|Sanderson and Striessnig, 2009|
||Total number of population|WDI, 2008|
|Hazard|Hazard type:<br>Storm, Flood, Earthquake,<br>Drought, others|EMDAT, 2008<br>Munich Re, 2008|
|Economic vulnerability|Indebtedness|WDI, 2008|
||Income level|WDI, 2006|
||Land area|WDI, 2008|
||Literacy rate|WDI, 2008|
||Aid|WDI, 2008|
||Remittances|WDI, 2008|
||Small island development state<br>(SIDS)|WDI, 2008|

In the following, we first use multivariate models, then employ general linear regression modeling approaches (GLM) using fixed factors, covariates and mixed models as independent variables and Diff(5) as the dependent variable.

> 7 We did not look at physical vulnerability factors (for example, the quality of building stock in an economy) as predictors, as those do not seem to be of importance in isolation and are accounted for in the direct impact variable.

18
# **Table of Contents**

|**1. Introduction ..............................................................................................................................1**|
|---|
|**2. Overview of Previous Surveys .................................................................................................3**|
|**3. The MENA Survey ....................................................................................................................4**|
|**4. Main Survey Results .................................................................................................................6**|
|4.1.<br>Overall Extent of SME Lending in MENA ...................................................................... 6|
|4.2.<br>Strategic Approach to SME Banking ............................................................................... 8|
|4.3.<br>SME Products ................................................................................................................. 10|
|4.4.<br>Risk Management ........................................................................................................... 11|
|**5. A Preliminary Analysis of the Dataset ..................................................................................14**|
|**6. Summary of Findings and Policy Implications ....................................................................18**|
|**Figures...................................................................................................... ...20**|
|**Tables...................................................................................................... ....43**|
|**Appendix Table 1: Descriptive Statistics of the Variables Used in Regression Analysis ......54**|
|**Appendix Table 2: Correlations between variables used in Regression Analysis .................55**|
|**Appendix Table 3: Definition and Sources of variables used in Regression Analysis ...........56**|
|**References .....................................................................................................................................56**|
# **<u>FIGURES</u>**

<!-- Start of picture text --> Figure 1 ‐ GDP per capita growth (annual %)<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>‐2<br>‐4<br>‐6<br>‐8<br>Fast growing countries Comparison group<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> 45
Table A10: IV regression: The effect of migration on land tenure arrangements, using the residual approach: Acreage, Extended Controls

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|3.144***|2.646***|0.498|-1.769*|-1.483**|-0.231|0.788|
||(1.036)|(0.802)|(0.574)|(0.974)|(0.715)|(0.863)|(0.505)|
|bank branches (number)|-0.0162|-0.0110|-0.00516|-0.00393|0.0230|0.0179|0.0101|
||(0.0242)|(0.0207)|(0.0129)|(0.0231)|(0.0176)|(0.0183)|(0.0120)|
|road length (km)|-0.00162**|-0.00152**|-0.000104|0.000953|0.000469|0.00171***|-0.000942**|
|2012 year|-6.523*|-2.673|-3.850*|-4.397|8.686***|22.22***|5.205**|
||(3.905)|(3.418)|(2.157)|(3.428)|(2.206)|(3.039)|(2.172)|
|Observations|722|722|722|722|722|722|722|

Note: This table uses the residual shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The agroecological controls include soils, length of the growing season (in days) and reference ETo (a measure of water stress). The dependent variable is as percentage of acreage of rented in land. The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
8

# Table (1.2) SELECTED MACROECONOMIC INDICATORS FOR SUB-SAHARAN AFRICA

|INDIC|ATOR<br>1973-81|1982-85<br>1986-89|
|---|---|---|
|Investm|ent to GDP ratio<br>21.5|18.7<br>17.1|
|Domes|tic Savings to GDP ratio<br>13.0|8.8<br>9.1|
|Resour|ce Balance to GDP ratio<br>-8.3|-9.7<br>-8.0|
|Import|s to GDP ratio<br>37.2|37.0<br>34.5|
|Debt S|ervice to Exports ratio<br>9.6|18.4<br>26.4|
|REER (|1980=100)<br>1/<br>95.5|113.5<br>89.4|
|Terv.|of Trade Index<br>106.3|91.6<br>80.8|
|Rate of|change of CPI (inflation)<br>16.5|17.7<br>20.5|
|Black m|arket exchange rate premium (%) 2/<br>128.9|221.9<br>90.9|
|Fiscal D|eficit to GDP ratio<br>5.3|7.4<br>7.8|
|Externa|l Shock 3/<br>0.1|-5.3<br>-2.2|
|Externa|l Financing (net flows in 1980 US$mn)||
||Total 4/<br>7830(29%)|3839(-28%)<br>4635_(25%)_|
||Public<br>7136(2s%)|3357(-30%)<br>4272(31%)|
||Private<br>694(5S%)|482(-13%)<br>363(.03%)|
|Notes: I/|Index of the period average exchange rate of the currency to a weighted|geometric average of exchaAge rates|
||for the currencies of selected partner countries and adjusted for relative|price movements in national price|
||of the home country and its partners. An increase in the index reflects a|n appreciation.|
|_V_|Includes only Ethiopia. Ghana, Kenya, Malawi, Sudan, Tanzania, Zaire|and Zambia.|
|3/|The total effect of extemal shocks as_%_of GDP is computed as the sum|of the real interot rate effect and|
||the terms of trade effect. The interest rate effect is calculated as -(r-rba|se)*(debt/GDP)beg, where r is|
||the real interest rate computed as (i-dplp)l(l.dp/p); rbase is the average|real interest rate of base period;|
||it is the ratio of interest payments to total debt; interest payments are ca|lculated by adding public interest|
||payments to private interest paymenu; private interest payments are pro|xied by multiplying private debt|
||by L (L equals three-month annualized LBOR plus one percent); the pr|ivate debt is cstimated by subtracting|
||public and publicly guaranteed debt from total debt; dp/p is world inf<br>change of the ONP deflator of the US), and (deWODP)beg is the ratio o|lation (proxied by the percentage<br>f debt to GDP of the year preceding|
||the beginning the end period. Debt data corvspond to total disbursed g|uarantee: and non guiranteed debt.|
||The effect of terms of trade is computed as [((PX/PXbase)-1)Q(/ODP)|begl-[((PMIPMbase)-l)*(M/CDP)begl, where PX|
||and PM ate the average export and import price indices deflated by US|ONP deflator, respectively; PXbase and|
||PMbase are the average price indices of the base period; X and M are e|xports of ONFS and imports of ONFS|
||respectively; and (XIGDP)beg and (MIGDP)beg are the ratios of X and<br>the begining of the end period. All the variables are denominated in cur|M to GDP respectively at the year preceding<br>rent US dollars.|

4/ The periods used are 1970-1980, 1983-1985 and 1986-1989 respectively. The figures in parentheses refer to average annual growth rates.

Source: World Bank (BESD), OECD 1990 Report, Pick's Currency Yeaebooks.