rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 17b: Industry interaction terms – Doing Business city only**

|||Idt Cd<br>Firm|Doing Busine|ss city only|Idt<br>Industry||
|---|---|---|---|---|---|---|
|ISIC2 Code|Rank on the<br>Ease of GE|nusry oe<br>(ISIC2) -<br>coefficient|Industry*Ran<br>k GE|Rank on the<br>Ease of GE|nusry<br>Code (ISIC2) -<br>coefficient|<br>Industry*Ran<br>k GE|
|Manufacture of tobacco products|-0.231***|0.00|0.00|-0.0908***|-0.47|0.00|
|Manufacture of textiles|-0.231***|-0.664***|0.00418**|-0.0908***|-0.581*|0.00|
|Manufacture of wearing apparel;<br>dressing and|-0.231***|-0.746***|0.00353**|-0.0908***|-0.57|0.00|
|Tanning and dressing of leather;<br>manufacture|-0.231***|0.11|-0.00322*|-0.0908***|0.36|-0.00750*|
|Manufacture of wood and of<br>products|-0.231***|-0.39|0.00|-0.0908***|0.12|0.00|
|Manufacture of paper and paper<br>products|-0.231***|-0.22|0.00248**|-0.0908***|0.90|-0.01|
|Publishing, printing and<br>reproductionof recorded|-0.231***|-0.282**|0.00|-0.0908***|0.14|0.00|
|Manufacture of coke, refined<br>petroleumproducts|-0.231***|0.505***|0.00|-0.0908***|0.800***|0.00|
|Manufacture of chemicals and<br>chemicalproducts|-0.231***|-0.217*|0.00179*|-0.0908***|0.12|0.00|
|Manufacture of rubber and<br>plastics products|-0.231***|0.04|0.00|-0.0908***|0.04|0.00|
|Manufacture of other non-metallic<br>mineralproducts|-0.231***|-0.39|0.00|-0.0908***|-0.42|0.01|
|Manufacture of basic metals|-0.231***|-0.33|0.00|-0.0908***|-0.54|0.01|
|Manufacture of fabricated metal<br>products, except|-0.231***|-0.756***|0.00529***|-0.0908***|-0.50|0.00|
|Manufacture of machinery and<br>equipment n.e.c.|-0.231***|-0.29|0.00|-0.0908***|-0.24|0.00|
|Manufacture of electrical<br>machineryandapparatus|-0.231***|-0.25|0.00|-0.0908***|0.21|0.00|
|Manufacture of radio, television<br>and communication|-0.231***|-1.512***|0.0111***|-0.0908***|-2.450***|0.0184**|
|Manufacture of medical, precision<br>and optical|-0.231***|-0.993***|0.00|-0.0908***|-0.47|0.00|
|Manufacture of motor vehicles,<br>trailersand|-0.231***|0.809**|-0.00897***|-0.0908***|0.29|0.00|
|Manufacture of other transport<br>equipment|-0.231***|-0.537*|0.00689***|-0.0908***|-0.03|0.00|
|Manufacture of furniture;<br>manufacturingn.e.c.|-0.231***|-0.41|0.00|-0.0908***|0.09|0.00|
|Recycling|-0.231***|-0.916**|0.01|-0.0908***|-1.14|0.02|
|Construction|-0.231***|-1.598***|0.0174*|-0.0908***|-0.21|0.01|
|Sale, maintenance and repair of<br>motor|-0.231***|-0.292***|0.00|-0.0908***|0.60|0.00|
|Wholesale trade and commission<br>trade, except|-0.231***|0.75|-0.01|-0.0908***|0.01|0.01|
|Retail trade, except of motor<br>vehicles|-0.231***|-0.73|0.01|-0.0908***|0.06|0.00|
|Hotels and restaurants|-0.231***|-0.13|0.00|-0.0908***|0.36|0.00|
|Supporting and auxiliary transport<br>activities; activities|-0.231***|-0.11|0.00|-0.0908***|-0.728***|0.00|
|Computer and related activities|-0.231***|-0.30|0.01|-0.0908***|-0.25|0.00754***|
|Robust standard errors in parenthe|ses *** p<0|.01, ** p<0.05, * p|<0.1||||

50
# 2. Framework

We discuss five sources of validity evidence as outlined by _The Standards for Educational and Psychological Testing_ (American Educational Research Association, American Psychological Association, & National Council on Measurement in Education, 2014). The central premise of validation is that the tool itself is not validated; rather, the scores from the tool are validated for a particular purpose (Kane, 2013), which applies to a given population of observers and users. This purpose could be for system diagnostics, teacher selection, or teacher evaluation. Sources of evidence are described by Ho (2018) using a “5 Cs” mnemonic: _content, cognition, coherence,_ and _correlation._<sup>_1_</sup>

1. **Content** : There is theoretical and substantive evidence that _Teach_ ’s areas, elements, and behaviors measure teaching practices in low- and middle-income countries.

2. **Cognition** : There is evidence that Teach items are interpreted accurately by raters and aligned with the content.

3. **Coherence** : There is evidence that _Teach_ is internally consistent and produces precise scores that can differentiate between teachers with similar, but not equal, ability. Scores do not vary substantially when two different observers view the same lesson.

4. **Correlation** : There is evidence that the _Teach_ score is related to other metrics that have been found in the literature to be related to teaching practices (concurrent) and with student learning (predictive).

> 1 Validation frameworks ask for five types of evidence, including evidence that using _Teach_ achieves its intended purpose in producing positive student learning outcomes. Ho (2018) describes this as a fifth ”C”, Consequences. Development papers like this one should provide a theory of action for positive consequences but typically defer empirical evidence of positive consequences until after longitudinal outcomes accumulate.

4
# 6. Robustness checks

The empirical strategy employed in the paper to incorporate demand factors could be challenged on statistical grounds. When job opportunities are assessed through the potential wage of the woman making the decision to participate, as in one of the strategies traditionally used to study female labor force participation in industrial countries, quite a lot of effort goes into addressing the potential wage endogeneity. Wages are observed only for women who work – hence participate in the labor force – and are also affected by individual characteristics bearing no correlation with the availability of job opportunities. Corrections of that sort are not introduced in the other traditional strategy to study female labor force participation in industrial countries, which treats unemployment rates or job losses by husbands as exogenous indicators of job opportunities – or lack thereof. The question is whether the strategy this paper to estimate model D, hence to derive its main conclusions, could be subject to biases requiring correction.

Introducing local employment levels ( Lj ) as a determinant of labor force participation by women raises two potential problems. The first one, _spurious correlation_ , arises when the number of individual observations from the NSS available for some localities is limited. In those localities, a working woman captured by the NSS sample is by definition a labor force participant ( Pij∗ = 1 ) but she also boosts the observed local employment level ( Lj ). The employment shares in Model D are calculated at the substratum level for the rural sample. Although the NSS data is representative for the rural sample at that level, sample sizes are in some cases small. As a result, the number of women working in a particular type of job at the substratum level could be small too, in which case the marginal effect of local employment on labor force participation would be overestimated.

The standard approach to deal with this problem is to use instrumental variables. Fortunately, the Census collects information on the economic activities of individuals in a manner that is similar to that of the NSS, but is totally independent from it. More specifically, the Census allows to compute district-level shares of cultivators, agricultural laborers, household industry workers and others, relative to the population aged 6 years and above. These shares can be computed for both the rural and urban areas of each district using data from the 2001 and 2011 Censuses. These Census district shares involve very large numbers of people, so are unlikely to be affected by sample size issues. Therefore, Census employment shares at the district level arguably provide a valid instrument for the substratum employment shares in the NSS.

To implement this approach, in the first stage we perform Ordinary Least Squares (OLS) regressions for the NSS substratum level employment shares, based on usual status. The corresponding district-level employment shares from the Census, the rank variable and the interactions between the two, serve as the explanatory variables in these first-stage OLS 26
There are no direct data available for the efficiency of public investment in infrastructure in DRC; using the average value for the 30 Sub-Saharan African countries (excluding South Africa) in the sample compiled by Dabla-Norris et al. (2012, Table 1), the efficiency parameter for public investment,  , is set at 0.37.<sup>16</sup> The parameter  1 is set at the low value of 0.05 initially, which implies that  0, which is solved for residually, is equal to 0.33. The rate of depreciation of public capital, <sup>P</sup> , is set at 0.035, a fairly standard choice.

External public debt as a share of GDP, D<sup>G</sup> /Y, is set equal to 27.1 percent, which is equal to the ratio of general government gross debt to GDP in 2010, as estimated in the IMF’s WEO database. Thus, all public debt is assumed to result from foreign borrowing. From the estimates compiled by Boyce and Ndikumana (2012, Table 1), the stock of private capital flight for DRC in the same year represented a staggering 258.4 percent of GDP.<sup>17</sup> Thus, the economy’s net stock of external debt, as a share of GDP, can be calibrated at 27.1 - 258.4 = 231.3 percent. By this metric, in the initial equilibrium the country is a net creditor to the rest of the world. However, as noted earlier, due to market imperfections only the public debt matters in the determination of the risk premium.

The world risk-free interest rate (in foreign-currency terms), r<sup>WR</sup> , is set initially at 0.017, which corresponds to the difference between recent averages on nominal yields on U.S. treasury 30-year bonds and an average rate of U.S. inflation of 2.0 percent. To estimate the country risk premium, and given that there are no data available for DRC, the spread on sovereign bonds issued by Kenya on international financial markets is used. Recent averages

> 16Dabla-Norris et al. (2012) define their metric on a range of 1 to 4 with an average value of 1.47 for the 30 countries; this value was simply divided by 4 to obtain an indicator between zero and unity. 17The average for Sub-Saharan Africa in the same year was 78.7 percent.

24
|Road Maintenance and Rehabilitation Project(02)|Bernard A. Becq|
|---|---|
|Road Maintenance and Rehabilitation Project(02)|Bernard A. Becq|
|Road Maintenance and Rehabilitation Project(02)|Bernard A. Becq|
|Human Resources Project|Emanuela Di Gropello|
|Human Resources Project|Emanuela Di Gropello|
|Human Resources Project|Emanuela Di Gropello|
|Human Resources Project|Emanuela Di Gropello|
|Human Resources Project|Emanuela Di Gropello|
|Human Resources Project|Emanuela Di Gropello|
|Human Resources Project|Emanuela Di Gropello|
|Human Resources Project|Emanuela Di Gropello|
|Road Maintenance and Rehabilitation Project||
|Road Maintenance and Rehabilitation Project||
|Road Maintenance and Rehabilitation Project||
|Power Development Project|Eduardo H. Zolezzi|
|Power Development Project|Eduardo H. Zolezzi|
|Power Development Project|Eduardo H. Zolezzi|
|Power Development Project|Eduardo H. Zolezzi|
|Power Development Project|Eduardo H. Zolezzi|
|Power Development Project|Eduardo H. Zolezzi|
|Power Development Project|Eduardo H. Zolezzi|
|Power Development Project|Eduardo H. Zolezzi|
|Agricultural Credit and Export Development Project|Daniel Moreau|
|Agricultural Credit and Export Development Project|Daniel Moreau|
|Agricultural Credit and Export Development Project|Daniel Moreau|
|Agricultural Credit and Export Development Project|Daniel Moreau|
|Agricultural Credit and Export Development Project|Daniel Moreau|
|Agricultural Credit and Export Development Project|Daniel Moreau|
|Agricultural Credit and Export Development Project|Daniel Moreau|
|Agricultural Credit and Export Development Project|Daniel Moreau|
|Agricultural Credit and Export Development Project|Daniel Moreau|
|Road Maintenance & Rehabilitation Project(02)||
|Road Maintenance & Rehabilitation Project(02)||
|Road Maintenance & Rehabilitation Project(02)||
|Road Maintenance & Rehabilitation Project(02)||
|Road Maintenance & Rehabilitation Project(02)||
|Road Maintenance & Rehabilitation Project(02)||
|PrimaryEducation Development Project|Jacob H. Bregman|
|PrimaryEducation Development Project|Jacob H. Bregman|
|PrimaryEducation Development Project|Jacob H. Bregman|
|PrimaryEducation Development Project|Jacob H. Bregman|
|PrimaryEducation Development Project|Jacob H. Bregman|
|PrimaryEducation Development Project|Jacob H. Bregman|
|PrimaryEducation Development Project|Jacob H. Bregman|
|PrimaryEducation Development Project|Jacob H. Bregman|
|PrimaryEducation Development Project|Jacob H. Bregman|
|Power Sector Development Project(02)|Susan G. Goldmark|
line. While there is a larger concentration of sampled clusters in and around major urban centers, the survey provides a complete coverage of the country including rural and more remote areas. Poverty rates are seen to vary considerably across space. On average however, poverty levels tend to be higher in more remote areas that located further away from the urban centers. Note that this does not imply that poverty is less of a concern in urban centers. In fact, the majority of the poor plausibly reside in urban centers due to the higher concentration of population there, i.e. despite the lower prevalence of poverty.

||2008 census|IHS3|
|---|---|---|
|# regions|3|3|
|# districts|32|31|
|# Wards/TAs|353|279|
|# EAs|12,575|767|
|# Households|2,859,720|12,271|

Table 2: Descriptive statistics of 2008 census and IHS3

# **3.2 Population census**

The enumeration for the 2008 Population and Housing Census for Malawi was carried out between June 8 and 28, 2008. It covers regular households, institutions, and the homeless. For the purpose of this exercise, institutions and the homeless are omitted. The total population was found to be 13,077,160. Table 2 reports the number of households in the census and number of sampled households in the survey at different levels of aggregation. The most disaggregated level at which poverty will be estimated is the Traditional Authority (TA) level, which we may also refer to as the‘small area level.

The administrative boundary data are taken from the National Statistical Office (NSO) of Malawi in order to match the boundaries with the census data at the enumeration area level. Malawi has 12,666 enumeration area boundaries from the 2008 census. The IHS3 household survey has 12,271 observations from 768 enumeration areas.<sup>6</sup>

> 6See the background report for more details on the design, sample and survey results at: http://microdata.worldbank.org/index.php/catalog/2939/download/41224

15
(such as education, gender, and social identity) and local economic conditions. A second key question explores whether participation in non-farm activities leads to improved household welfare, specifically focusing on consumption patterns and poverty reduction. The study also investigates the constraints faced by rural micro-enterprises, seeking to understand both the qualitative and quantitative measures of enterprise performance and the various barriers that might impede their growth. Finally, the research examines whether the historical predictors of rural non-farm employment identified in earlier periods (1999-2011) remain relevant in more recent years, utilizing contemporary data from 2015 to assess the stability and evolution of these relationships. This multi-faceted approach allows for a comprehensive understanding of both the historical patterns and contemporary dynamics of rural non-farm employment in India.

# **2 Data**

This analysis uses data from five distinct sources. The historical data on individual employment and household consumption is obtained from nationally representative employment surveys (focusing on the Rajasthan component) ; an alternate household and individual employment survey is used to gauge the more recent relationship between individual characteristics and rural non-farm employment. The data on micro-enterprise performance is gathered from an establishment level survey of micro-enterprises. Data on bank branches and bank credit are obtained from the Basic Statistical Returns (BSR) published annually by the Reserve Bank of India; and finally, data on annual district-level rainfall is obtained from the ICRISAT’s district-level database.

## **2.1 Employment-Unemployment Surveys**

Our primary analysis exploits rich data collected by various rounds of the National Sample Survey Organization’s (NSS) household surveys. These are nationally representative surveys conducted every 5 years, covering every state and district in India, with a sample size exceeding 80,000 households in each round. The data includes individual characteristics such as labor force participation, sector of employment, and employment type, in addition to gender, age and education levels. Household characteristics such as household location (urban or rural), landholdings, religion, caste and size are also included. Each household (individual) is accorded a weight, reflecting the inverse of the frequency of being sampled. We use the sampling weights to aggregate the individual (household) data to the level of districts and construct district-level characteristics of interest such as urbanization, and the 7
three aspects. First, in all the economies, income earned by refugees exceeds aid received, challenging the common perception that refugees are passive recipients of aid. Second, despite significant amounts of aid being spend on refugees, in all the economies included in this study, aid is insufficient (on average) to bring refugee to or above the global poverty line. Finally, given a choice as how to best to bring refugees to the global poverty line, increasing refugee self-reliance holds more potential than increasing aid. In LICs, refugee incomes would need to increase by 62 percent on average to attain the poverty line. Aid, by contrast, would have to increase by 115 percent. In MICs, the difference is even greater, with refugee incomes needing to increase by 17 percent to reach the global poverty line; whereas aid would need to increase by 221 percent. This shows that aid, while essential in reducing the poverty levels experienced by refugees, is surpassed by the potential contribution refugee economic activity can have in increasing incomes and reducing poverty.

**Figure 8: Composition of refugee income needed to reach the poverty line**

<!-- Start of picture text --> 3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>Chad Ethiopia Niger Uganda Kenya Colombia Costa Rica Jordan West Bank<br>& Gaza<br>IPL 2.15 3.65 6.85<br>Income Aid Shortfall<br>US$<br><!-- End of picture text -->

# 5. Conclusion

This paper presents a straightforward approach to measuring refugee self-reliance, one that has several advantages over alternative measures that are available. It allows for refugee self-reliance to be determined through a quantifiable and widely understood metric: income generation, while anchoring refugee self-reliance within existing poverty measurements and SDG 1.1 (the elimination of extreme poverty). Theoretically, it offers an appropriate measure with which to conceptualize refugee needs, capturing both income and, at least partially, non-income factors in its measurement, while also appropriately accounting for ‘independence from aid’ by focusing on earned income only.

macro-level, location and economic environment are key. Refugees in middle-income countries are far more likely to be self-reliant than those in lower income countries, while refugees residing in urban and non-camp settings typically demonstrate far higher levels of self-reliance than those in rural and camp environments, reflecting the presence of greater economic opportunities outside camps and in urban 15
Table 5. Explaining inter-occupational inequality, 1984-1999 (dependent variable: annual change in Gini; percentage points)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|∆tariff|-0.118<br>(0.097)|-1.490<br>(0.033)|-5.707<br>(0.009)|-5.428<br>(0.015)|
|Ln (GDP per capita)|-0.060<br>(0.816)|0.448<br>(0.236)|0.456<br>(0.320)|0.526<br>(0.265)|
|∆tariff*ln (GDP per<br>capita)||0.168<br>(0.057)|0.688<br>(0.008)|0.648<br>(0.015)|
|Trade union members<br>as % of labor force<br>(TUMMBR)||0.002<br>(0.920)|||
|Percentage of workers<br>covered by collective<br>bargaining (TUCVGE)|||-0.002<br>(0.855)|-0.002<br>(0.887)|
|∆tarf*TUMMBR||0.001<br>(0.915)|||
|∆tarf*TUCVGE|||-0.012<br>(0.197)|-0.010<br>(0.271)|
|Globalization variable||||-5.850<br>(0.506)|
|Constant|0.651<br>0.780|-4.221<br>(0.205)|-4.132<br>(0.331)|-4.855<br>(0.270)|
|R<sup>2</sup>adjusted|0.005|0.02|0.06|0.05|
|F value (p)|1.6(0.19)|1.7(0.15)|2(0.1)|1.7(0.14)|
|Number of<br>observations|233|176|79|79|

Note: Levels of significance given between brackets. Coefficients significant at less than 5% level are shaded.

43
# **Table 10. Impact of migration to urban or rural areas**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Variables|OLS-IHHFE|Second stage<br>2SLS-IHHFE|OLS-IHHFE|Second stage<br>2SLS-IHHFE|
|Mover to rural (1=mover to rural, 0=stayer)|0.335***|0.560***|||
||(0.079)|(0.124)|||
|Mover to urban (1=mover to urban, 0=stayer)|||0.118|0.651***|
||||(0.080)|(0.233)|
|Unmarried|0.003|0.002|0.006|-0.002|
||(0.002)|(0.003)|(0.005)|(0.007)|
|Unmarried male|-0.003|-0.004|0.002|0.004|
||(0.003)|(0.003)|(0.006)|(0.006)|
|Age category (reference: aged 10-14)|||||
|Aged 15-24|0.003|0.001|0.003|-0.006|
||(0.002)|(0.002)|(0.004)|(0.006)|
|Aged 25-34|0.001|-0.001|0.003|-0.006|
||(0.003)|(0.003)|(0.006)|(0.008)|
|Aged 35-49|0.000|0.000|0.003|-0.003|
||(0.002)|(0.003)|(0.006)|(0.006)|
|Aged 50-65|-0.000|0.001|0.012**|0.012*|
||(0.003)|(0.003)|(0.005)|(0.006)|
|Aged 66 plus|0.003|0.005|0.005|0.012|
||(0.003)|(0.004)|(0.007)|(0.009)|
|Number of effective years of schooling completed|0.000|0.000|0.001**|0.001|
||(0.000)|(0.000)|(0.001)|(0.001)|
|Constant|-0.018***||-0.018**||
||(0.004)||(0.008)||
|Cragg-Donald Wald F statistic||7.573||4.103|
|Sargan's statistic||3.548||7.022|
|Sargan p-value||0.738||0.319|
|Endogeneity test of mover (Hausman Chi-square)||5.373||5.162|
|Endogeneity test of mover (Hausman p-value)||0.0204||0.0231|
|Observations|11,034|10,783|11,069|10,824|
|Number of households|2,523|2,319|2,490|2,290|
|Initial Household Fixed Effects|||||
|Robust standard errors in parentheses|||||
|*** p<0.01, ** p<0.05, * p<0.1|||||
|Source: Authors’ calculations with UNPS.|||||

31