rafmacalaba/datause-encoder-data huggingface dataset:
# **<mark>Table A1: Business Training Does Not Affect Reporting</mark>**

|**Dependent variable:**Businesspractice score from audit less businesspractice scor|e from survey|
|---|---|
|Randomly Assigned to Training Treatment|0.013<br>(0.027)|
|Sample Size|200|
|Mean Difference between Audit and Survey|‐0.065|
|SD of Difference|0.189|
|Robust standard error in parentheses.||
|*, **, *** denotes significance at the 10, 5, and 1 percent levels respectively.||
|Note: data are from Sri Lanka only||

|**Table A2: Robustness to Form of Aggregating Business**|**Practices**<br>Log Sales|Log Sales|Log Sales|Log Sales|Log Sales|Log Sales|
|---|---|---|---|---|---|---|
|Business Practices Score|1.361***|||0.614***|||
||(0.096)|||(0.089)|||
|First Principal Component of Business Practices||0.120***<br>(0.008)|||0.057***<br>(0.008)||
|Average of Z‐scores of Business Practices|||0.628***<br>(0.044)|||0.287***<br>(0.041)|
|Log(Paid Workers)|0.793***|0.776***|0.788***|0.553***|0.545***|0.550***|
||(0.027)|(0.027)|(0.027)|(0.027)|(0.027)|(0.027)|
|Log(Owners Hours)|0.106***|0.113***|0.107***|0.078***|0.079***|0.078***|
||(0.030)|(0.029)|(0.029)|(0.027)|(0.027)|(0.027)|
|Log(Capital Stock)||||0.082***|0.081***|0.082***|
|||||(0.009)|(0.009)|(0.009)|
|Log(Inventories)||||0.232***|0.231***|0.232***|
|||||(0.011)|(0.011)|(0.011)|
|Sample Size|4474|4474|4474|4474|4474|4474|
|Business Practices Mean|0.353|‐0.073|‐0.203|0.353|‐0.073|‐0.203|
|Business Practices SD|0.196|2.468|0.433|0.196|2.468|0.433|
|**Implied Effect of a 1 S.D. change in businesspractices**<br>Notes:<br>Robust standard errors in parentheses, *, **, *** indic<br>respectively<br>Regressions also include dummies for zero or missing<br>All regressions include country dummies and industry<br>owner and firm characteristics (sex, age, education, di|**0.267**<br>ate signific<br>values of e<br>sector dum<br>gitspan, ra|**0.296**<br>ance at the<br>ach contro<br>mies. Col<br>ven test sc|**0.272**<br>10, 5, and<br>l.<br>umns 4 thr<br>ore, age of|**0.120**<br>1 percent<br>ough 6 also<br>firm).|**0.141**<br>levels<br>control f|**0.124**<br>or|

37
|Diesel|0.74|2.70|6.16|-0.04|-0.14|-0.26|
|---|---|---|---|---|---|---|
|LPG|0.70|2.56|5.86|0.03|0.08|0.12|
|Other petroleum|0.31|1.31|3.33|0.03|0.07|0.10|

The impacts on prices of other commodities are mainly influenced by two factors: (i) the price of electricity and (ii) the price of fossil fuels which will be increased due to the fuel tax imposed to finance the renewable energy subsidy. Under the subsidy case, prices of all commodities except those of fossil fuel intensive sectors (e.g., construction, transportation, chemicals, mining, petroleum products) would decrease.

In the case of mandate, most prices are seen to be decreasing. This is because the renewable electricity mandate increases electricity prices. It causes demand for goods and services to fall (please see Table 4) thereby causing demand curves shifting left and thus resulting in lower prices.

# **4.4 Impacts on household consumption**

The impacts on household consumption of goods and services are presented in Table 4. Household consumption of goods and services decreases due to increased share of renewable energy in the national electricity grid in Morocco no matter whether the policy instrument is a subsidy or a mandate. The exception is electricity consumption under the subsidy case which increases due to reduction in electricity prices. The decrease in household consumption of other goods and services under the subsidy case has to be attributed to the fuel tax imposed to petroleum products to finance the renewable subsidy. In the case of mandate, the same effect comes from the increased electricity price that causes reduction in household demand for goods and services. Note that the percentage reductions in household consumption of goods are smaller under the subsidy case as compared to that under mandate case. This is consistent with other results and also implies that the subsidy is a more efficient policy than the mandate.

**Table 4: Impacts of Renewable Energy Targets on Household Consumption in 2020**

(% change from the baseline)

|Sector||Subsidy<br>|||Mandate<br>||
|---|---|---|---|---|---|---|
||H|HW|HWS|H|HW|HWS|
|Agriculture|-0.09|-0.27|-0.54|-0.12|-0.33|-0.59|
|Forestry|-0.09|-0.25|-0.48|-0.13|-0.33|-0.58|
|Other mining|-0.09|-0.28|-0.62|-0.12|-0.36|-0.68|

20
Several researchers have argued that the effect of the legal system per se on property rights is limited, and that differences across countries in their enforcement depends on a broader range of cultural and social considerations. Thus, according to the **_Culture view_** , differences in culture, defined as a system of beliefs, can help explain the differences in investor protection. Stulz and Williamson (2003) and LLSV (1999) both use religion as a proxy for culture. We use four categories of classification for the religion variable: Catholic, Protestant, Muslim and Other Religions. The data on religious composition is taken from LLSV (1999) to create four corresponding dummy variables, each of which takes the value 1 if the religion it represents is the dominant religious group (highest percentage of practicing population) in the country and 0 otherwise<sup>5</sup> .

Easterly and Levine (1997) show that ethnic diversity is also an important determinant of rent seeking and social polarization, affecting property rights and social institutions.<sup>6</sup> We term this the **_Ethnic Diversity view._** Empirically, to capture these broader ethnic diversity effects we use a measure of ethnic fractionalization of the country compiled by Alesina et al (2003). Unlike measures used in the earlier literature, this measure captures important aspects of ethnicity like racial origin and skin color.<sup>7</sup> The explanatory power of the different variables may be affected due to nonlinearities arising from the way these variables are constructed. Unlike legal origin, the other theories posit determinants of property rights that cannot be directly observed. These determinants are accordingly proxied by variables that have been independently scaled for other purposes. Conventionally, these proxies are then entered linearly in an equation that predicts a variable of interest. However, there is no reason to believe that the scales are designed to detect material effects of the determinant on the variable of

> America’s agriculture is conducive to small farms, so that more egalitarian institutions emerged. We focus on the AJR (2001) measure of settler mortality and latitude and not on agricultural endowments because the data on settler mortality and latitude is available for a broader cross-section of countries.

> 5 Since we need a categorical variable to do variance decomposition, we use dominant religion dummies calculated from the percentage of the population of each religion within each country instead of using the percentages directly.

> 6 As noted by Alesina et al (2003), ethnic fractionalization has also been found empirically to predict lower levels of trust, less efficient public services and less favorable economic outcomes in US localities, 7 In an earlier version of the paper we used a measure of ethnic fractionalization from Easterly and Levine (1997) that Alesina et al (2003) show relies largely on linguistic distinctions.

7
10 suggests that these households depend heavily on other household members' income for support. If the average dependency ratio was the same as in male-headed households, the per capita income of these households would be poor indeed. Female heads appear to have less income-earning capacity than their male counterparts. Female heads have, on average, less education than their male counterparts, which is not surprising given that (1) female heads are older, and older cohorts tend to have less education; and (2) within older cohorts, women tend to have less education than men (Lam, et al., 1992). Note that the female head's older age on average does not necessarily mean more work experience, given the need to take time out from paid employment in order to have children. Female heads are much less likely to be economically active. This partially explains why these households tend to get a lower share of income from the household head.

In sum, female-headed households are well identified in household survey data in Brazil. They are, however a heterogeneous group, containing rich and poor, widows and divorc6es, and including a number of male earners. Despite their heterogeneity, in the aggregate this household type does possess some characteristics significantly different from male-headed households. One of those is the tendency for these households to be overrepresented among the poor. Previous studies, including Fox (1982), Merrick and Schmink (1983), and Pastore, et al. (1983), analyzing data from the 1960s, 1970s, and 1980s, and using various definitions of poverty, all found that female-headed households had a 30-50 percent greater chance of being in poverty than did male-headed households. We now turn to an analysis of the causes and consequences of this association using our survey data. We focus especially on female-headed households with children, as this aspect in particular has been underanalyzed in previous studies.
-41-

significant increases in agricultural output or generalized rural development with higher returns to land and higher wages for unskilled rural labor.

# V.2 Urban Bias.

Persistent imbalances in inter-sectoral resource allocation between rural and urban areas also play a fundamental role in the generation of poverty. A proper study of urban bias in social and infrastructure spending requires systematic data-gathering to quantify the associated flows. Here I restrict myself to a listing of five areas where this bias is most directly observed: health, education, housing, public infrastructure, and subsidies for goods and services.

Aspe and Beristain (1984) studied the regional distribution of government resources for health and education. With regards to education they find that while the share of public sector expenditure in education in GDP rose steadily from 1960 to 1978, resources allocated to elementary education in the rural areas have fallen, despite the growth of the younger-aged population during that period (pp. 291-300); a privileged share of expenditures was devoted to higher education, located mostly in urban areas. Thus, independently of the rural poor's ability to demand education (see section VI.1 below), the supply of educational facilities has been insufficient68. A related phenomenon is observed in the provision of health services. On the whole, they note that: 'The greatest significance of this study is a negative one: the (government's, S.L.) educational and health policies have not been corrective and have not diminished the disparity in income, but have, on the contrary, confirmed and reaffirmed these conditions" (p. 323). Similar results are obtained by Moore (1984) with regards to the provision of public housing who notes that: "In spatial terms it is evident that housing policy has been concentrated in the capital city, to a lesser extent in other metropolitan areas, and is almost absent in the rural context" (p. 352). And still the same type of biases are found against the poorest states with the higher share of rural population in the allocation of public infrastructure investment (cf. World Bank, 1989a, pp. 13-31).

Urban bias also appears in the pattern of subsidies. This hurts the rural poor in three ways. One, urban inhabitants appropriate the bulk of food 680f course, issues as to the quality of education are equally relevant.
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Source: UN Comtrade, via WITS (ISIC2 classification).

Notes: *** indicates a significance level of 1%, ** of 5% and * of 10%. Charts display sub-sectors of “Manufacturing of Fabricated Metal Products, Machinery and Equipment” (division 38 in ISIC2). Vertical specialization is the share of parts and components in a sub-sector's trade. The size of the bubbles denotes sub-sector's share in trade of ISIC2 division 38. Sub-sectors of ISIC2 division 38: 31 Manuf. of electrical industrial machinery and apparatus 11 Manuf. of cutlery, hand tools and general hardware 32 Manuf. of radio, television and communication equipment and apparatus 12 Manuf. of furnitures and fixtures primarily of metal 13 Manuf. of structural metal products 19 Manuf. of fabricated metal products except machinery and equip n.e.c. 21 Manuf. of engines and turbines 22 Manuf. of agricultural machinery and equipment 23 Manuf. of metal and wood working machinery 24 Manuf. of special industrial machinery and equipment except metal and wood working machinery 25 Manuf. of office, computing and accounting machinery 29 Machinery and equipment except electrical not elsewhere classified 39 Manuf. of electrical apparatus and supplies n.e.c.

41 Shipbuilding and repairing 42 Manuf. of railroad equipment 43 Manuf. of motor vehicles 44 Manuf. of motorcycles and bicycles 45 Manuf. of aircraft 49 Manuf. of transport equipment n.e.c.

51 Manuf. of professional and scientific, and measuring and controlling equip. n.e.c 52 Manuf. of photographic and optical goods 53 Manuf. of watches and clocks
Table 2: Average student performance for participating and nonparticipating schools

||Schools<br>_Educ_|in_Mais_<br>_ação_|Nonparti<br>Sch|cipating<br>ools|Mean dif<br>between|ferences<br>groups|
|---|---|---|---|---|---|---|
||2007|2011|2007|2011|2007|2011|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Panel A|: Averages fo|r schools wit|h fifthgrades|||
|Portuguese grade|164|179|176|192|−11.9***|−13.3***|
|Mathematics grades|180|195|194|212|−14.2***|−17.6***|
|Dropout rates|5.9|3.1|2.1|1.1|3.8***|1.97***|
|No. observations|611|611|17263|17263|||
||Panel B|: Averages fo|r schools wit|h ninthgrade|||
|Portuguese grades|222|233|230|241|−8.4***|−8.6***|
|Mathematics grades|230|236|243|249|−13.4***|−13.1***|
|Dropout rates|12.0|7.2|5.2|3.6|6.8***|3.6***|
|N|555|555|13404|13404|||

_Sources: Prova Brasil_ and INEP.

_Note:_ Table reports the average students' performance ( _Prova Brasil_ 's test scores and dropout rates) for the schools that joined _Mais Educação_ in 2008 and remained until 2011 (columns (1) and (2)) and for nonparticipating schools (columns (3) and (4)). Columns (5) and (6) report the mean differences of the variables between the two groups. _T_ tests were performed to detect the mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
11 bias, i.e. capturing an ethnicity or wealth effect as opposed to the (relative) welfare loss associated with the commodity price decline. Data permitting, the asset variables are also lagged to 2000, to be consistent. This also mitigates potential simultaneity bias with health shocks.

There is a large difference in the number of coffee trees owned across coffeegrowing households. To allow for differential effects among smaller and larger coffee farmers, coffee growers in the sample are divided in five quintiles based on their number of coffee trees in 2000.<sup>9</sup> The omitted category is the non-coffee growers. In Ruvuma, quintile categories for cashew growers, based on their number of cashew trees, and a category for tobacco growers are further included.<sup>10</sup> To mitigate potential endogeneity problems arising from the self-reporting of drought shocks as registered in the directly administered shock module in the household questionnaire, an index of a household’s qualitative assessment of the rainfall amount across its plots is used.<sup>11</sup> According to this measure, 21 percent of all households in Kilimanjaro experienced rainfall much below normal on their plots in 2003 and 42 percent rainfall somewhat below normal. Drought shocks are much less frequent in Ruvuma with 4 percent of all households experiencing rainfall much below normal in 2003 and 34 percent rainfall somewhat below normal. To better capture actual exposure

> 9 In Kilimanjaro, those in the lowest quintile have on average 40 trees, which more than doubles from quintile to quintile to reach 1326 trees among farmers in the highest quintile.  In Ruvuma, coffee growing households have on average three times as many trees as in Kilimanjaro with those in the highest quintile owning on average 5 times as many trees as those in the lowest quintile.

> 10 This categorization is based on tree ownership in early 2002, since recall data in Ruvuma extend only two years back.  The tobacco group was not further disaggregated—only 4  percent of the households grew tobacco.

> 11 In particular, households were asked in the production module for each plot whether the rainfall was much below normal, somewhat below normal, normal, somewhat above normal, much above normal.  A plot size weighted average of these rainfall assessments was calculated and rounded off to the nearest integer to obtain a qualitative assessment for each household.
Policy Research Working Paper 5011

# **Abstract**

This paper evaluates the impact of the Thailand Village and Urban Revolving Fund on household expenditure, income, and assets. The revolving fund was launched in 2001 when the Government of Thailand promised to provide a million baht (about $22,500) to every village and urban community in Thailand as working capital for locally-run rotating credit associations. The money —about $2 billion in total—was quickly disbursed to locally-run committees in almost all of Thailand’s 74,000 villages and more than 4,500 urban (including military) communities. By May 2005, the committees had lent a total of about $8 billion, with an average loan of $466. Using data from the Thailand Socioeconomic Surveys of 2002 and 2004, each of which surveys almost 35,000 households, the authors find that the borrowers were disproportionately poor and agricultural. A propensity score matching model finds that Fund borrowing in 2004 was associated with, on average, 1.9 percent more income, 3.3 percent more expenditure, and about 5 percent more ownership of durable goods. These results are broadly consistent with the results from instrumental variables models (where the identifying instrument was the inverse of village size), which however show a smaller (marginal) effect. Households that borrowed both from the revolving fund and from the Bank of Agriculture and Agricultural Cooperatives gained substantially more in terms of higher income than those who borrowed from either one or the other or from neither.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the cost-effectiveness of rural financial institutions. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 6: Regression analysis of firm profits and formalization

|||**Firm**|**Profts**||**Formalization**|
|---|---|---|---|---|---|
|Variables|(1)|<br>(2)|<br>(3)|(4)|(5)<br>(6)|
|Education|6.517***|6.279***|5.930**|5.310*|0.127***<br>0.142***|
||(0.723)|(0.726)|(2.891)|(2.960)|(0.019)<br>(0.021)|
|Age|3.133**|3.502***|6.328|5.022|0.012<br>0.046|
||(1.019)|(1.026)|5.436|(5.403)|(0.033)<br>(0.034)|
|Age<sup>2</sup>|-0.027**|-0.028**|-0.032|-0.014|0.000<br>0.000|
||(0.012)|(0.012)|0.069|(0.069)|(0.000)<br>(0.000)|
|Wealth|-0.015|-0.018|14.40***|14.73***|-0.001<br>-0.002|
||(0.049)|(0.028)|(4.408)|(4.352)|(-0.004)<br>(0.004)|
|PopDensity|1.251**|0.843***|1.561|2.388|-0.018<br>-0.070|
||(0.615)|(0.626)|(2.944)|(2.923)|(0.017)<br>(0.062)|
|PopDensity<sup>2</sup>|||||0.002<br>0.002|
||||||(0.004)<br>(0.004)|
|Manufacturing||18.59***||51.85**|1.669***|
|||(7.087)||(26.22)|(0.181)|
|Commerce||22.50***||-0.944|1.331***|
|||(6.829)||(29.63)|(0.189)|
|Services||119.8***||-45.77|2.191***|
|||(6.591)||(31.96)|(0.191)|
|City Dummies|NO|YES|NO|YES|NO<br>NO|
|<br>R<sup>2 </sup>/Pseudo R<sup>2</sup>|0.062|0.055|0.136|0.118|0.039<br>0.153|
|Obs|1897|1897|247|247|2143<br>2143|

_Notes:_ Our unit of observation is individual. Standard errors are in parentheses. Column (1) and (2) and profit regressions for informal firms; Column (3) and (4) are profit regression for formal firms; Column (5) and (6) are probit regressions for the probability of formalizing. Significance codes: ‘*’ 10%, ‘**’ 5%, and ‘***’ 1%.

# **3 Model Economy**

The economy comprises _J_ cities (or locations) and a mobile population of size _Nj_ in each city _j_ . Individuals choose between wagework, informal entrepreneurship and entrepreneurship. We assume as in Evans and Jovanovic (1989), Paulson et al (2006) and others, that each individual is endowed with an initial wealth _z_ and an intrinsic level of entrepreneurial ability _θ ∈_ [0, +∞), drawn from a fixed, continuously differentiable, city distribution _Gj_ ( _θ_ ). There is a large number of industries (or sectors), indexed by _i_ . Each sector produces a homogeneous final consumption good. Production of each good requires labor and capital, both of which are freely mobile across cities. Workers are hired locally and paid city-specific wages, whereas capital is owned globally and is rented at the same price everywhere. We assume that the total output _yij_ of an entrepreneur with ability _θij_ , operating in industry _i_ within city _j_ is given by: 20