rafmacalaba/datause-encoder-data huggingface dataset:
the least proportion of individuals classified as extreme poor identified as international extreme poor, followed by Bhutan, Maldives, Pakistan, Nepal, Bangladesh, and India. However, when we use the standardized consumption aggregates to do the same exercise, Bangladesh and India have virtually the same poverty rate, whereas the poverty rate is three percentage points higher in India when using the national aggregates. The international poverty rate for South Asia as a region also falls when using the standardized consumption aggregates, from 19.6 percent to 18.3 percent – a fall of 1.3 percentage points.

Figure 1 shows how standardization affects the distribution of per capita consumption in each of the South Asian countries. For India, standardization has a significant impact on the distribution of the consumption aggregates, with the distribution shifting to the right and away from the $1.90 poverty line. On the other hand, it has more minor impacts on the distribution of consumption aggregates in Bangladesh and Pakistan, shifting them very slightly to the left. This reinforces the observed decline in India’s poverty rates when using the standardized aggregates, and the small increases in poverty rates for Bangladesh and Pakistan.

Using the standardized national consumption aggregates significantly reduces the number of poor in India and the region. Table 12 presents the estimates of the number of international extreme poor in each country using the national consumption aggregates and the standardized consumption aggregates.

**Table 11. International extreme poverty rate in South Asia** **<u>using the standardized and national consumption aggregates</u>**

||**Year**|**Standardized**<br>**consumption**<br>**aggregates**|**National**<br>**consumption**<br>**aggregates**|**Difference**<br>**(1)- (2)**|
|---|---|---|---|---|
|||**(1)**|**(2)**|**(3)**|
|Bangladesh|2010|19.44|18.52|0.92|
|_- Rural_|_2010_|_23.86_|_22.74_|_1.12_|
|_- Urban_|_2010_|_7.01_|_6.67_|_0.34_|
|Bhutan|2012|3.49|2.17|1.32|
|India<sup>(1)</sup>|2011/12|19.61|21.56|-1.95|
|_- Rural_|_2011/12_|_24.20_|_24.83_|_-0.63_|
|_- Urban_|_2011/12_|_8.13_|_13.38_|_-5.25_|
|Maldives|2009|6.10|7.25|-1.14|
|Nepal|2010|14.51|14.89|-0.38|
|Pakistan|2011/12|9.24|7.93|1.31|
|Sri Lanka|2012/13|2.06|1.92|0.14|
|South Asia||18.31|19.6|-1.29|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). India’s consumption aggregates reported are based on the Uniform Recall Period (URP).

The standardization of the consumption aggregates would reduce the international poverty rate in South Asia by 1.3 percentage points or almost 18.5 million extreme poor, with India witnessing a reduction in the headcount by 40 million. Other countries, like Bangladesh and Pakistan would see a large increase in the poverty numbers if consumption aggregates were standardized. Pakistan would increase the number of extreme poor from 10.3 million to 12 million, and Bangladesh would see its number of extreme poor go from 27.5 million to 28.9 million, mainly driven by the rural poor. India would increase the number of extreme poor by over 22 million nationwide. Most of 17
# Figure 5. Cumulative impulse responses of global inflation over time

A. Global inflation following oil price shock B. Global inflation following global supply shock <!-- Start of picture text --> 0.4 0.0<br>0.2 -0.2<br>0.0 -0.4<br>12 24 36 12 24 36<br>1970-85 1986-2000 2001-22 1970-85 1986-2000 2001-22<br>C. Global inflation following global demand D. Global inflation following global interest-<br>shock rate shock<br>0.8 0.0<br>0.6<br>-0.2<br>0.4<br>-0.4<br>0.2<br>0.0 -0.6<br>12 24 36 12 24 36<br>1970-85 1986-2000 2001-22 1970-85 1986-2000 2001-22<br><!-- End of picture text --> Note: The impulse responses (in percentage point) are based on three sub-sample global FAVAR estimations (1970-85, 1986-00, 2001-22). Three solid lines with different colors indicate median responses to a 1-standard-deviation global shocks for the corresponding three sub-sample periods. Horizontal axis indicates month(s).

28
# Figure 4. Financial sector developments

# A. Number of EMDE in post-crisis booms and credit crunches

<!-- Start of picture text --> Number of EMDEs<br>25<br>20<br>15<br>10<br>5<br>0<br>2002-07 2008-10 2011-18 2002-07 2008-10 2011-18<br>Credit booms Credit crunches<br>C. Net capital inflows<br>Percent of GDP Percentage points<br>2.0 Equity Debt FX volatility (RHS)<br>24<br>1.5<br>18<br>1.0<br>0.5 12<br>0.0 6<br>-0.5 0<br>E. Bank assets<br>Percent of GDP<br>2000 2007 2016<br>120 Advanced economy 2016 average<br>100<br>80<br>60<br>40<br>20<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>EMDEs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> B. EMDEs: Bank credit to private sector <!-- Start of picture text --> Percent of GDP<br>2000 2007 2016<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EMDEs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

# D. Banks’ profitability

<!-- Start of picture text --> Percent Percent<br>Return on equity<br>25 Return on assets (RHS) 3<br>20<br>15<br>2<br>10<br>5<br>0 1<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

# F. EMDE-based banks operating in EMDEs

<!-- Start of picture text --> Number of branches and subsidaries<br>600 Within region<br>Other EMDEs<br>400<br>200<br>0<br>Before After Before After Before After<br>GFC GFC GFC GFC GFC GFC<br>SSA ECA EAP<br><!-- End of picture text --> Source: Institute of International Finance, International Monetary Fund, World Bank.

Note: EMDEs = Emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa, and GFC = global financial crisis, 2008/09.

A. Credit booms (crunches) are episodes when private credit to GDP ratio exceeds (falls below) its long-term trend by 1.65 times one standard deviation of a cyclical component obtained with the HP filter. Sample includes about 140 EMDEs with private sector credit data. Weights are based on nominal GDP measured in U.S. dollars at market exchange rates.

B. Unweighted averages. Based on the private credit by deposit money banks and other financial institutions to GDP (%) from the World Bank’s Financial Development and Structure Dataset.
_There is an obvious difference between rural and urban postings. Working in rural areas involves helping the poor... in urban areas, one can learn, have more income, have good schools for one’s children._

--Health worker in Ethiopia

# **1. Introduction**

Health services depend critically on the size, skills, and commitment of the health workforce. Human resources have been a long-standing policy concern in both developed and developing countries. A number of recent reports have pointed out that human resources comprise a fundamental constraint to improving health outcomes and reaching the Millennium Development Goals (Joint Learning Initiative, 2004; USAID, 2003; 2002; World Bank, 2004). While human resource challenges take many dimensions— shortages, geographic imbalances, international migration, low skills and inappropriate skills mix, poor performance and low motivation, etc.—this paper focuses on the specific issue of geographical imbalances. Geographic imbalances in the health workforce have been a persistent feature of nearly all health systems. Given the obvious relationship between the number of health workers and the capacity to deliver services—both in terms of volume and quality, the distribution of health professionals has implications for equity in access to health services. But the distribution of health professionals is also an efficiency issue: the under-provision of cost-effective interventions in some areas implies that overall health outcomes could be improved through a reallocation of resources.

Our analysis is based on a survey of final year nursing and medical students in Ethiopia. The survey included detailed questions on student background and characteristics, the use of contingent valuation methods to elicit monetary valuations of different types jobs in the health sector, and survey-based and experimental approaches to measuring intrinsic motivation. Together, these data shed new light on the factors that influence the willingness to work in rural areas among a new cohort of health workers.

Ultimately, the difficulties in attracting and retaining staff in rural facilities are rooted in the preferences and choices of health professionals. Evidence from various countries suggests that, while financial rewards are important, they are not the only consideration. In choosing where to work, other considerations come into play, including training opportunities, career development prospects, living conditions, workload, colleagues and working conditions, and social, family and security concerns, etc. (Hays, Veitch, Cheers, & Crossland, 1997; Kamien, 1998; Peters, Yazbeck, Sharma, Ramana, Pritchett, & Wagstaff, 2002; Shields, 2004). Hence, the general preference for work in urban and affluent areas is not surprising. Work in rural areas is often associated with reduced access to training, limited professional interaction with peers, reduced exposure to technical sophistication, heavy responsibilities and workload, social isolation, poor 1
# **3.4 Follow-up Surveys and Attrition**

After the two rounds of baseline, we conducted six-monthly surveys every April and October from 2009 through 2012, followed by additional surveys in April 2013 and April 2014. Altogether this provides 12 rounds of data, including 2 to 3 rounds pre-intervention, 2 rounds during the intervention, and then 7 rounds post-intervention covering four years after the subsidy ended. Each survey round collected operating data for the previous month, along with details of worker hiring and other information. Appendix 4 describes in more detail how key variables were measured. For firms which closed down, we collected information on the current activities of the owner, while for those who could not be interviewed we attempted to obtain basic information on whether the business still existed and the number of employees through observation and discussions with neighbors and family members.

The multiple rounds of follow-up surveys offer several advantages over standard firm studies which rely on a single follow-up. First, they enable us to trace out the trajectory of impacts, to determine whether the treatment effects vary over time. Second, by pooling together data from multiple waves, we can average out seasonality and increase power (McKenzie, 2012). Third, they give us multiple chances to interview firm owners, since owners who may not be available one round may be able to be interviewed in a subsequent round. In order to benefit from all three advantages, we pool together rounds 4 and 5 to capture average effects during the intervention, rounds 6 and 7 to capture average effects in the first year after the subsidy ended, rounds 8 and 9 to capture average effects in the second year after the subsidy ended, and rounds 10, 11, and 12 to capture average effects in years 3 and 4 after the subsidy.

Survey attrition was low for a panel of this length with microenterprises. Round by round attrition rates averaged 5.6 percent for whether the business was in operation, and 9 percent for whether it had a paid worker (see Appendix 5). Table 2 provides summary information on data availability by time period and treatment status after we pool together several data rounds as described above. Data are available for 95 percent of the firms during the intervention period, 95-97 percent in the first year after the subsidy, 92-99 percent in the second year after the subsidy, and 96-98 percent in years 3 to 4 post-subsidy. There is no significant difference in attrition rates by treatment status, except for the second year post-treatment where we have slightly higher data availability for the control group. The last four columns of Table 1 also show that the sample responding to the last 12
work was more important for men than women. In R1, 44% of men and 36% of women said they would have been unemployed in the absence of the scheme; in R2 the percentages were 38% and 13%. Work on own land or in the house was the most common answer given by women.

Recall that the unemployment rates for rural Bihar in 2009/10 were 16% for men and 32% for women, with an overall rate of 18%. So the unemployment rates expected by our BREGS participants in Table 5 are appreciably higher than these numbers, especially for men. Male BREGS participants appear to be self-selected workers with well above average unemployment rates, although this is less evident for women.

The survey design allowed us a test of the reliability of reported forgone incomes for the main relevant activity, namely casual work. We compare the reported month-specific forgone wage for casual work with the mean wage earnings actually received by casual workers (in the week before the survey) for that same month. Sample sizes entail that the test is only feasible for May and June of 2009 and 2010. Table 6 presents the results. Mean reported forgone earnings are lower, but the difference is small. Overall we find a fairly close correspondence and more so in R2 than R1, suggesting that there may have been some learning. (Recall also that the response rate was higher in R2.) The variance is greater for actual than for counterfactual wages. These results give us greater confidence that the counterfactual questions were understood and that the answers are sensible.

On average, workers had to give up workdays equivalent to 40-45% of the total BREGS employment received. While BREGS provided the sampled households with 18,900 person days of employment in R1, we calculate that 7,700 days of other employment were given up to take on this BREGS work. In R2, 20,400 person days of employment were provided, but 9,300 days had to be given up. Forgone employment is higher for men. In R1 the share of gross employment that was accounted for by forgone work was 0.42 for men, versus 0.36 for women. In R2, the corresponding ratios were 0.51 and 0.31.

There are three distinct types of participants. The density functions in the left-hand side panel of Figure 7 have three distinct modes. One is around zero, which is the overall mode. These participants would have not had any days of work had they not worked on BREGS. A second mode is around 0.6 and the third and smallest mode is about 0.9.

Forgone income tends to be slightly lower than employment, reflecting the lower wages from casual work on the labor market as compared to working on BREGS. The mean ratio of 19
acquire their education in their origin or host country;<sup>7</sup>

- the log of tertiary educated in 1990, ln(H90), so as to capture convergence or divergence forces;

- the log of public subsidy per student as percentage of GDP per capita in 1990, ln(�90); so as to control for the local education policy;

- the age structure of the population within a country. It can be argued that the growth rate of human capital is stronger in countries where the population turnover (replacement of old cohorts by young ones) is important. Since migrants usually belong to young cohorts, it is important to control for the population structure in our regressions. We account for that possibility by using the demographic share of individuals aged 15 to 24, S15=24, and the share of those 49 to 55, S49=55 (Source: United Nations Population Division);

- the urbanization rate, UR90; which accounts for access to schooling facilities within a country (Source: World Development Indicators);

- the level of remittances in 1990, R90; which potentially alleviates liquidity constraints impeding human capital investments (Source: World Development Indicators);

- a set of regional dummies REGDi (Sub-Saharan Africa, Latin America, Middle East and North Africa, East Asia and Paci...c, Eastern Europe and Central Asia) capturing regional unobserved characteristics. South Asia is used as reference.

This speci...cation is similar to that used in Beine et al (2007a) but is more general it in several respects. We add new control variables (including regional ...xed e¤ects) and use new UNESCO estimates of public expenditures in tertiary education. We expect the inclusion of the latter variable to improve our assessment of the incentive mechanism.

> 7In Appendix 6.3, we estimate our model using alternative measures of the skilled emigration rate (mJ;t) and human capital stock (HJ;t) which control for the age of entry of migrants.

14
|P106794|2008|2008||0 Closed|Cameroon|
|---|---|---|---|---|---|
|P106794|2008|2009||0 Closed|Cameroon|
|P106794|2008|2010||0 Closed|Cameroon|
|P106794|2008|2011||0 Closed|Cameroon|
|P106794|2008|2012||0 Closed|Cameroon|
|P106794|2008|2013|2/4/2013|1 Closed|Cameroon|
|P106794|2008|2014|10/27/2013|2 Closed|Cameroon|
|P106834|2009|2009||0 Closed|Rwanda|
|P106834|2009|2010||0 Closed|Rwanda|
|P106834|2009|2019|5/9/2019|1 Closed|Rwanda|
|P106851|2008|2008|6/28/2008|1 Closed|Congo,Republic of|
|P106851|2008|2009|8/31/2008|2 Closed|Congo,Republic of|
|P106851|2008|2009|2/28/2009|3 Closed|Congo,Republic of|
|P106851|2008|2009|6/29/2009|4 Closed|Congo,Republic of|
|P106851|2008|2010|12/29/2009|5 Closed|Congo,Republic of|
|P106851|2008|2010|6/28/2010|6 Closed|Congo,Republic of|
|P106851|2008|2011|3/26/2011|7 Closed|Congo,Republic of|
|P106851|2008|2012|11/5/2011|8 Closed|Congo,Republic of|
|P106851|2008|2012|3/11/2012|9 Closed|Congo,Republic of|
|P106851|2008|2013|12/26/2012|10 Closed|Congo,Republic of|
|P106851|2008|2013|6/26/2013|11 Closed|Congo,Republic of|
|P106851|2008|2014|12/30/2013|12 Closed|Congo,Republic of|
|P106851|2008|2014|6/2/2014|13 Closed|Congo,Republic of|
|P106855|2009|2009|5/23/2009|1 Closed|Ethiopia|
|P106855|2009|2010|9/28/2009|2 Closed|Ethiopia|
|P106855|2009|2010|1/28/2010|3 Closed|Ethiopia|
|P106855|2009|2010|5/3/2010|4 Closed|Ethiopia|
|P106855|2009|2011|2/28/2011|5 Closed|Ethiopia|
|P106855|2009|2012|10/11/2011|6 Closed|Ethiopia|
|P106855|2009|2012|4/7/2012|7 Closed|Ethiopia|
|P106855|2009|2013|7/11/2012|8 Closed|Ethiopia|
|P106855|2009|2013|5/17/2013|9 Closed|Ethiopia|
|P106855|2009|2014|12/4/2013|10 Closed|Ethiopia|
|P106857|2008|2013|1/18/2013|1 Closed|Malaysia|
|P106857|2008|2014|12/29/2013|2 Closed|Malaysia|
|P106862|2008|2008||0 Closed|World|
|P106862|2008|2009||0 Closed|World|
|P106862|2008|2010||0 Closed|World|
|P106862|2008|2011||0 Closed|World|
|P106862|2008|2012||0 Closed|World|
|P106870|2011|2011|2/19/2011|1 Closed|Nicaragua|
|P106870|2011|2012|8/13/2011|2 Closed|Nicaragua|
|P106870|2011|2012|3/28/2012|3 Closed|Nicaragua|
|P106870|2011|2013|11/20/2012|4 Closed|Nicaragua|
|P106870|2011|2013|6/26/2013|5 Closed|Nicaragua|
|P106870|2011|2014|2/1/2014|6 Closed|Nicaragua|
|P106870|2011|2015|8/31/2014|7 Closed|Nicaragua|
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