rafmacalaba/datause-encoder-data huggingface dataset:
adjusted consumption levels was smaller than the revision of the PPP-adjusted value of international poverty line and, as a result, the poverty estimates under the new line are higher. In contrast, for Nepal, the 2011 PPPs implied a revision of the PPP-adjusted welfare aggregates that was significantly higher than the revision of the poverty line, and the resulting poverty estimates using 2011 PPPs is thus lower than the estimates based on 2005 PPPs. It is noteworthy that notwithstanding the changes in levels, across all three countries the trends in poverty between different surveys are broadly maintained. This is as expected since the revision to PPP-adjusted consumption and income is uniform across years.

Figure 6 (a) compares the latest country-level poverty estimates based on the $1.25 line and 2005 PPPs with the corresponding estimate using the $1.90 line and 2011 PPPs. Deviations from the 45-degree line reveal the difference in poverty between the estimates. Generally, there is a strong bivariate correlation between the estimates. The distribution of the differences in the poverty estimates gives a sense of the degree of changes across all countries. Figure 6 (b) summarizes such changes across these poverty estimates, illustrating how the changes are concentrated close to zero, but with some clear outliers where changes are large. Some of these changes are considerable, while for the majority, the changes to country-level estimates are less jarring.<sup>58</sup> The distribution of the absolute differences in the estimates suggests that for more than half the estimates, the change is smaller than 2 percentage points, while for three-quarters of the estimates in the poverty headcount changes by five percentage points or less. For about 10 percent of the countries, this change is greater than 8 percentage points. Some of the largest changes come in countries that either used imputed (regression-based) 2005 PPPs and who only participated in the 2011 ICP, or where there has been considerable revisions in underlying consumption aggregates.

# **_5.2 The regional poverty profile with the 2011 PPPs_**

Globally, we estimate that 897 million people, or 12.7 percent of the world population, live in extreme poverty in 2012 under the $1.90 poverty line at 2011 PPPs. The highest regional poverty rate is in Sub-Saharan Africa, where 42.7 percent of the population is estimated to live line in for Armenia was $1.88 – close to the $1.90 poverty line. The poverty equivalent lines for Botswana and Nepal are $1.61 and $2.11, respectively.

> 58 With the caveat that the absolute changes are different from changes in percentage points.

35
**Table A.11: Robustness of results to inclusion of facilties controls**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|**(11)**|**(12)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||**Effort**|||Correct<br>**Diagnosis**|Correct|||**Treat**|Correct<br>**ment**|||
|<br>**Panel A: Representative sampl**|Time<br>spent<br><br>**e, with SP, case**|Checklist<br><br>**and market**|IRT Score<br><br>**fixed effects**|Gave<br>diagnosis<br><br>|<br>diagnosis<br>(conditional)<br>|diagnosis<br>(uncondition<br>al)<br>|Correct<br>treatment<br>|Palliative<br>treatment<br>|Unnecessary<br>treatment<br>|<br>treatment<br>only<br>|Antibiotic <br>|<sup>Number of</sup><br>medicines<br>|
|Is a private provider|1.207***|7.826***|0.635**|0.197**|-0.023|0.039|0.143*|0.082|0.115|-0.009|0.153*|0.861***|
||(0.363)|(2.494)|(0.305)|(0.085)|(0.126)|(0.038)|(0.073)|(0.085)|(0.076)|(0.027)|(0.081)|(0.285)|
|Facilities index|0.012|1.679***|0.129*|0.051**|0.014|0.010|0.034*|0.026|0.038**|-0.001|0.029|0.203***|
||(0.112)|(0.600)|(0.072)|(0.023)|(0.033)|(0.011)|(0.018)|(0.022)|(0.018)|(0.004)|(0.021)|(0.078)|
|R-squared|0.356|0.265||0.233|0.362|0.161|0.410|0.379|0.267|0.280|0.275|0.313|
|Number of observations|634|634|220|420|171|420|420|420|420|420|420|420|
|**Panel B: Dualpractice sample**|**, with SP, case a**|**nd district f**|**ixed effects**||||||||||
|Is a private provider|1.218***|9.185***|0.751***|0.030|-0.035|-0.004|0.166**|-0.121|-0.010|0.021|-0.146*|-0.070|
||(0.259)|(1.927)|(0.213)|(0.081)|(0.135)|(0.069)|(0.072)|(0.076)|(0.058)|(0.028)|(0.080)|(0.207)|
|Facilities index|-0.206|-0.928|-0.033|-0.040|-0.029|-0.029|-0.066*|-0.016|0.002|0.001|-0.038|-0.249**|
||(0.157)|(1.148)|(0.111)|(0.041)|(0.079)|(0.037)|(0.038)|(0.040)|(0.034)|(0.014)|(0.045)|(0.115)|
|R-squared|0.321|0.244||0.219|0.199|0.092|0.315|0.304|0.157|0.051|0.144|0.197|
|Number of observations|272|272|114|164|73|164|164|164|164|164|164|164|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. All regressions include a constant and controls for provider qualifications, age, gender, and patient load. Observations are standardized provider-patient interactions. Dual sample refers to providers who operate both public and private clinics. Market fixed effects are used for the representative sample, and district fixed effects for dual practice sample. Columns (1)-(3) include all cases and can be compared with Table 3. The remaining columns include Unstable Angina and Asthma cases only - compare Columns (4)-(6) with Table A6; and Columns (7)(12) with Table 4. In column (12) the dependent variable is total number of medicines recommended to the patient (dispensed and/or prescribed).
# Trading in Clusters and the Future of Small-Scale Trade in the Borderlands of the Great Lakes Region of Africa<sup>∗</sup>

C ́elestin B. Bucekuderhwa<sup>_a_</sup> , Charles Kunaka<sup>_b_</sup> , Nyembezi Mvunga<sup>_c_</sup> and Douglas Amuli Ibale<sup>_a_</sup> _a_ Laboratoire d’Economie Appliqu ́ee au D ́eveloppement (LEAD)/Universit ́e Catholique de Bukavu (UCB) _b_ Lead Transport Specialist, World Bank, Washington DC _c_ Economist, World Bank, Lusaka, Zambia

**Keywords** : Cluster, Small scale Traders, Impact, LATE, Income, Poverty. **JEL codes** : D02, D31, I32, C92 , F18, P25

> ∗The paper benefitted from support provided by the Umbrella Facility for Trade trust fund that receives contributions from the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. Correspondence: C ́elestin B. Bucekuderhwa (Bucekuderhwa.Bashige@ucbukavu.ac.cd); Charles Kunaka (ckunaka@worldbank.org); Douglas AMULI Ibale (amuli.ibale@ucbukavu.ac.cd)
- Timmer, M. P., de Vries, G. and de Vries, K. (2014). Patterns of Structural Change in Developing Countries. GGDC Research Memorandum 149. Groningen: Groningen Growth and Development Centre.

- World Bank (2012). Job Generation and Growth Decomposition Tool: Understanding the Sectoral Pattern of Growth and its Employment and Productivity Intensity. Washington, DC: World Bank.

- World Bank (2016). Live Long and Prosper: Aging in East Asia and Pacific. Washington, DC: World Bank.

- World Bank (2018a). World Development Report 2019: The Changing Nature of Work. Washington, DC: World Bank.

- World Bank (2018b). Malaysia Economic Monitor: Realizing Human Potential. Washington, DC: World Bank.

- World Bank (2018c). Productivity Unplugged: The Challenges of Malaysia’s Transition into a High-Income Country. Washington, DC: World Bank.

- World Bank (2019a). Agricultural Transformation and Inclusive Growth: The Malaysian Experience. Washington, DC: World Bank.

- World Bank (2019b). Breaking Barriers: Toward Better Economic Opportunities for Women in Malaysia. Washington, DC: World Bank.

World Bank (2019c). JobsStructure Tool. Washington, DC: World Bank.

- World Bank (2020). Management Practices in Malaysia – The Good, the Bad and the Ugly. Washington, DC: World Bank.

- World Bank (forthcoming). Aiming High: Navigating the Next Stage of Malaysia’s Development. Washington, DC: World Bank.

28
Dissecting further the role of new exporters for export growth, Panel E of Figure 4 shows a strong pattern during the steady growth period: the contribution of new exporters was accounted for mostly by the more long-lasting exporters, i.e., those that continued to export in _t+1_ in all LAC countries.<sup>23</sup> Longer-term export growth decompositions would be more likely to show significant contributions of firm entry to export growth. That is indeed the finding obtained for Chile by Alvarez and Fuentes (2009) and for Costa Rica by Lederman, Rodriguez-Clare, and Xu (2011) and may be pursued in future research.

This evidence for firms in LAC countries which operate in export markets is in line with the evidence from the industry dynamics literature (focused on firms operating in domestic markets): new firms start small and suffer a high probability of exit in their initial years (Caves, 1998). It is also consistent with evidence from the growing export dynamics literature (e.g., Eaton, Kortum, and Kramarz, 2007; Lederman, Rodriguez-Clare, and Xu, 2011). Entry and exit into export markets should theoretically be influenced by two forces: fixed costs to enter export markets and uncertainty about the profitability of export markets. While fixed costs deter entry, the possibility of success improves it. Therefore, one can expect to see higher entry rates when either fixed costs are low or uncertainty is high. In those cases, however, exit rates are also likely to be high because weaker (possibly less productive) firms enter when entry costs are low or when the probability of a high payoff is high. What the evidence for the LAC region suggests is that the sunk costs of entering export markets that play such a crucial role in the models of heterogeneous firms and trade pioneered by Eaton and Kortum (2002) and Melitz (2003) do not seem to be very large in practice, or at least appear to play a less dominant role than uncertainty about export success.

# **_5. Export Entrepreneurship across LAC Countries along the New Product Dimension_**

Export entrepreneurship is present also when incumbent exporters export new products. To examine this dimension of entrepreneurship, we focus on incumbent exporters (as defined in Table 1) in each country and consider products defined at the 6-digit HS disaggregation level.<sup>24</sup>

> 23 The decomposition of the contribution of new exporters during the crisis period is not presented due to the lack of data for 2010 for most LAC countries which would be necessary to calculate the survival of new exporters into the year following 2009. 24 Brazil, New Zealand, and Spain are not used in this section given that we do not have access to the raw exporter-level customs data for those countries necessary to compute the measures used here. Egypt is also not used as a comparator given that its exporter-level customs data is provided with a 4-digit HS (instead of a 6-digit HS) level of disaggregation.

21
cost nor the idea that absence is viewed by all participants as a necessary (if unfortunate) result of poverty.

# **_Teacher absence slows student learning_**

Recent studies examine the issue of teacher absenteeism using US data, although without the benefit of an experimental design.<sup>10</sup> In their study of a large urban school district, Miller, Miller, Murnane, and Willett (2007) find that a substantial share of absences are discretionary, and that higher absences lead to significantly lower student achievement. Another recent study from North Carolina that used a rich longitudinal data set of teachers and students found that teacher absences are associated with lower student achievement in primary school (Clotfelter, Ladd, and Vigdor, 2007). Moreover, absence is more prevalent in schools serving disadvantaged children. The authors found that schools in the poorest quartile averaged almost one extra sick day per teacher than schools in the highest income quartile, and schools with persistently high rates of teacher absence were much more likely to serve low‐income than high‐income students. This suggests that in the US, teacher absenteeism compounds the disadvantages already faced by students in poor communities; as we will show below, the same is true in developing countries.

Surprisingly, some of the best evidence on absence and student learning comes from a handful of developing‐country studies. These studies have the advantage of direct observation of teacher attendance by study teams, which gets around problems of inaccurate administrative records. One such study is by Das _et al_ (2007), who focus on primary schools in Zambia. That study involved repeated surprise visits to the same schools over the course of the year, together with measurement of the students’ learning gains. By correlating each student’s learning gains with the absence of his or her teacher, the authors conclude that absence has a surprisingly large effect: each additional 5 percent increase in teacher absence reduces learning by 4 to 8 percent of a year’s learning for the typical student. The study controls for many other observable inputs into student learning, such as classroom equipment and even family‐provided inputs. This makes it more likely that the learning effect really is due to absent teachers and not to differences in some other input that is correlated with teacher absence.

> 10 One note on terminology:  The term “absenteeism” is widely used in this literature.  In this chapter, we prefer the term “absence”, which we view as less judgmental, but sometimes use “absenteeism” when referring to high levels of apparently volitional absence, or when citing work that uses the term.
The research collection gathers surveys for a subset of heterogeneous countries. Most of the countries are in the Sub-Saharan Africa region, but there are also 4 countries in the South Asia region, 3 countries in the Europe and Central Asia region, 2 countries in the East Asia Pacific region, 2 countries in the Latin America and the Caribbean region, and 1 country in the Middle East and North Africa region. The coverage of income groups is also of great interest. Thirteen countries are in the low-income group, 11 countries are in the lower middle-income group, and 6 countries are in the upper middle-income group. Table 2 summarizes the selected countries and corresponding surveys.

**Table 2. Selected countries and corresponding surveys for further analysis**

|**Collection**<br>**Country**|**Region**|**Income group**|**Survey name**|**Survey year**|**Community**<br>**questionnaire?**|
|---|---|---|---|---|---|
|Albania|ECA|Upper middle<br>income|Living Standards Measurement<br>Survey|2012|No|
|Burkina Faso|AFR|Low income|Continuous Multisectoral Survey|2014|No|
|Cambodia|EAP|Lower middle<br>income|Socioeconomic Survey|**2019/20**<br>**(LSMS+)**|No|
|Ethiopia|AFR|Low income|Socioeconomic Survey|2011/12,<br>2013/14,<br>2015/16,<br>**2018/19**<br>**(LSMS+)**|Yes|
|Iraq|MENA|Upper middle<br>income|Household Socioeconomic Survey|2012/13|No|
|LSMS<br> <br>Malawi|AFR|Low income|Integrated Household Survey|2010/11,<br>**2016/17,**<br>**2019/20**<br>**(LSMS+)**|Yes|
|collection<br>Mali|AFR|Low income|Integrated Agricultural Survey|2014/15,<br>2017/18|No|
|Nepal|SAR|Low income|Living Standards Survey|2010/11|Yes|
|Niger|AFR|Low income|National Survey on Household<br>Living Conditions and Agriculture|2011/12,<br>2014/15|Yes|
|Nigeria|AFR|Lower middle<br>income|General Household Survey|2010/11,<br>2012/13,<br>2015/16,<br>2018/19|Yes|
|Tanzania|AFR|Low income|National Panel Survey|2010/11,<br>2012/13,<br>2014/15,<br>**2018/19**<br>**(LSMS+)**|Yes|
|Uganda|AFR|Low income|National Panel Survey|2010/11,<br>2011/12,<br>2013/14,<br>2015/16,<br>2018/19|Yes|

5
Fifteen households in each sampled commune also answered a household questionnaire. A welfare ranking implemented by commune authorities was used to divide households into the poorest, middle and richest thirds of each commune’s households. Five were then randomly selected from each of these equal sized groupings.<sup>12</sup> The household ranking is undoubtedly subjective, but stratified sampling on this basis should ensure a sample that is reasonably representative of each commune’s socioeconomic groups.

The household survey does not attempt to measure household income or consumption expenditures. However, using extensive information on household characteristics common to the SIRRV and the nationally representative Vietnam Living Standards Survey (VLSS) of 1998, we use regression techniques to predict consumption expenditures for SIRRV households in 1997.<sup>13</sup> This variable is then aggregated to form a commune level welfare indicator which we use in Figure 1 (Section 3.3) and to divide communes into those below and above median mean predicted consumption, which we will refer to as poor and non-poor communes, respectively.<sup>14</sup> Finally, a project level database detailing what the project did, when and how, was also constructed for each surveyed project area. Project differences can then be taken into account in determining impacts.

# **_3.3 Initial conditions of poor and non-poor communes_**

Pre-project, 48 percent of all sample communes had a market whose frequency

> 12  Since the groups were equal and equal-sized samples were drawn from each, weights are not required for constructing commune level variables from the household data.

> 13  The consumption model includes 80 explanatory variables (not counting 58 province dummies) and has an R<sup>2</sup> of 0.687.  Mean consumption for the 1998 VLSS rural sample is 2515.605 thousand dongs (with a standard deviation of 1467.065), and mean predicted consumption for 1997 SIRRV households is 2332.896 (with a standard deviation of 1110.707).  Full details are available from the authors.

> 14  This is the only variable used in the paper that is built up from household level information.

15
Probability density functions for _u_ and _ε_ <!-- Start of picture text --> −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 −4 −2 0 2<br>x x<br>3.0 0.6<br>2.5 0.5<br>2.0 0.4<br>1.5 0.3<br>dens.uhat(x) dens.epshat(x)<br>1.0 0.2<br>0.5 0.1<br>0.0 0.0<br><!-- End of picture text --> Figure 3: Normal-mixture density (Red) versus normal density (Blue) estimates for: (a) area error _ua_ (left panel); (b) household error _εah_ (right panel) household error is accurate.

The log poverty line is set at 9 _._ 5 which yields an aggregate poverty rate of about 28 _._ 3 percent. County level estimates of poverty and inequality are obtained using both EB and nonEB estimates under the assumption of normal errors and under the less restrictive assumption of normal mixture errors. For each of the specifications we obtain 422 estimates of poverty and inequality which we then compare to the true estimates derived from the full census. We will judge the accuracy of the estimates on the basis of two summary statistics: the aggregate bias and the RMSE (both obtained by aggregating the county level differences between our estimates and the true rates over the 422 counties). The results are presented in Table 8.

||Bias|RMSE|
|---|---|---|
|EB|0.029 (0.048)|0.045 (0.059)|
|NON-EB|0.024 (0.044)|0.042 (0.056)|

Table 8: Bias and RMSE for estimates of county-level poverty using 2010 data from the United States: (a) Normal-Mixture distribution (left number); (b) Normal distribution (right number) We observe a rather large bias when estimating county level poverty under the assumption of normal distributed errors; almost 5 percentage points given a national poverty rate of about 28 percent. By relaxing the normality assumption, and assuming normal mixture distributions instead, we are able to reduce this bias to just below 3 percent which denotes a non-trivial improvement. We see similar reductions in the RMSE. Working with EB or non-EB estimates makes little to no difference in this empirical application. This was to be expected given the smallness of the location effect. Note that the fact that some bias still remains suggests that a degree of model misspecification still persists; these could include mis-specifications in the structural model but also forms of heteroskedasticity that are currently being ignored.

23
studies that do show such patterns in Muslim employment either had not set out to specifically measure the Muslim effect (in that it is a control variable, as in Lanjouw and Shariff’s study) or did not put it within a clear theoretical framework to explain the differences. An exception to this was Khandker’s (1992) analysis of segmented markets in Bombay, where he measures the effect of background variables, religion being one of them. Regardless, until the report of the Sachar Commission (2005) few analyses had systematically documented the “Muslim effect” in employment. The report of the Sachar Commission brought out the manner in which Muslims are concentrated in certain occupations and alluded to discrimination playing a major part in this.

There is no doubt that the issue of minority status and employment is fraught with political overtones, and few writings have addressed the issue empirically. This paper aims to address that gap through an empirically grounded analysis using national data to understand the relationship between minority status and access to self-employment, and the possible social correlates self-employment. This paper builds on a conceptualization of the labor market not merely as a market in the economic sense but a site where cultural and social relations play out (see Das, 2005; 2006; Das and Desai, 2003). It is here that the opening quote by Karl Polanyi gains additional relevance. Without labeling the Indian labor market “segmented” or “discriminatory” because we do not have tools to test those labels empirically, this paper moves from the assumption that social, historical and cultural factors play a major role in the functioning of this market. We ask how minority status – which is more than a numerical construct – it is a social construct – plays out in labor market outcomes.

The paper is organized in six sections. This introduction comprises Section I. Section II is a discussion of the conceptual underpinnings of the paper and its application to India. Section III lays out the key hypotheses, data and methods. Section IV and V describe the results of the sociometric analysis and discuss their implications for the questions we have posed. The final section is a conclusion that sets out areas for greater empirical exploration.

**II. The Idea of “Ethnic Enclaves” and Its Application to the Indian Labor Market:** The role of ethnicity in labor market outcomes has long engaged the attention of sociologists interested in social inequality.  In hypothesizing about how minority status could affect employment allocation, we use the work of Portes et al (Wilson and Portes, 1980; Portes and Jensen, 1989; 1992; Wilson and Martin, 2001) and their idea of “ethnic labor markets”. Some of their arguments can be applied more readily in the Indian context than others and therefore need adaptation. The main thrust of the argument made by Portes, Wilson and their colleagues is that immigrants who enter the US labor market are discriminated

3
years, it is possible to use the CPI year average over a short period of time (as in Persson and Tabellini, 2003) as a representative score for a country in a given time period.

**Constituent Parts (data sources).** As Table 1 indicates, the data sources of the CPI are numerous. The sources mainly consist of surveys of experts (smaller-N, e.g. the Economist Intelligence Unit) or surveys of business leaders (Institute for Management Development has a larger-N) and tend to be conducted by well-established international institutions, such as the Economist Intelligence Unit, the World Economic Forum, the World Bank etc. The advantage is that many of these groups (e.g. business leaders) are likely to experience actual high-level political corruption, although the CPI has the disadvantage of not ascertaining the perception of voters, except in a minor capacity.<sup>9</sup>

> 9 Since 2001 the CPI has generally not included surveys of the general public as part of its index.

11