rafmacalaba/datause-encoder-data huggingface dataset:
Note that poverty rates estimated with welfare aggregates are _higher_ than poverty rates estimated with total expenditures. This result is a priori surprising, given that the former include food subsidies whereas the latter do not. The reason for this is that, given the official poverty measurement methodology, ignoring food subsidies not only decreases welfare aggregates, but also decreases poverty lines. Indeed, the price of a calorie, which defines a household’s food poverty line, depends on the unit values of food items. Ignoring subsidies implies smaller unit values for subsidized food items, which in turn reduces the price of a calorie and thus the household’s food poverty line. One might think that, on average, this reduction in the food poverty line could approximately compensate for the reduction in household consumption, implying that the net effect of ignoring subsidies might cancel out. This is not the case, however, as we illustrate with a simple example. Assume that ignoring subsidies decreases the value of household consumption by  . Under simplifying assumptions,<sup>14</sup> the food poverty line is also reduced by  . However, this does not mean that the poverty rate is unchanged, because the non-food poverty line is computed as a fraction of the food poverty line. If the imputed share of food is 0.5, this implies that the non-food line is reduced by 0.5  . Overall, the poverty line is reduced by 1.5  , which is a larger reduction than the decrease in the value of household consumption. This explains why poverty rates are lower when ignoring food subsidies.

This explains why, in all regions, the poverty rates estimated with welfare aggregates are higher than those estimated with total expenditures. Yet, if the difference is non-negligible, we deem that the two estimates are sufficiently close to one another for our purposes, namely assessing the poverty trend over the period 2015 to 2017/18 and contrasting the official poverty measurement methodology with more standard alternatives. Nevertheless, when interpreting the results, one should keep in mind that our estimates are computed while ignoring food subsidies.

# **3) Additional results**

Table A1 contains the value of coefficients for the regression imputing food shares as run based on total expenditures in 2015 and 2017/18.

_Table A1: Values of coefficients for the regression imputing food shares_

|VARIABLES|2015|2017/18|
|---|---|---|
|Ln(expenditure/food poverty line)|-0.114<br>(0.00410)|-0.135***<br>(0.00275)|
|[Ln(expenditure/food<br>poverty|||
|line)]^2|-0.0021|-0.00113|
||(0.00175)|(0.00136)|
|Ln(HH size)|-0.113|-0.102***|
||(0.00844)|(0.00601)|
|[Ln(HH size)]^2|0.0181|0.00883***|
||(0.00287)|(0.00215)|
|share of kids|0.0413|0.0283***|
||(0.00549)|(0.00379)|
|share of males|-0.0327|-0.0736***|
||(0.00584)|(0.00392)|
|share of females|-0.0183|-0.0168***|
||(0.00598)|(0.00407)|

> 14 This would be the case if all households have the same composition and all consume the reference poverty bundle.

21
performance of individual Stanbic branches, testing particularly for geographic variation in outcomes. Section V concludes and attempts to draw policy lessons.

# **_II. Background_**

The Uganda Credit and Savings Society, which had been created in 1950 to extend credit to entities without access to loans from commercial banks, was converted into the Uganda Commercial Bank (UCB) in 1965. Before this, three foreign-owned banks (Barclays, Grindlays, and Standard), with clients almost exclusively affiliated with parent companies in Great Britain, dominated commercial lending. The creation of UCB was part of an effort to promote indigenous interests while extending access to financial services to a wider set of recipients.

UCB, however, quickly ran into problems. UCB’s criteria for evaluating creditworthiness tended not to be commercially sound and thus its share of nonperforming loans (NPLs) was far higher than for other commercial banks. Repeated capital injections proved necessary, because of poor portfolio quality and UCB’s pursuit of its mandate to open additional upcountry branches to encourage domestic savings.<sup>11</sup> UCB’s branch network expanded under the activist government policies pursued by Idi Amin in the 1970s. All banking activities of government bodies, parastatals and cooperative unions were transferred to UCB in 1972. Foreign banks were forced to either close up-country branches or sell them to UCB. UCB thus became the sole provider of banking services in many locations, particularly in rural areas. Although financial sector liberalization, which started in 1987 and accelerated in the early 1990s, brought an influx of new banks, UCB’s (now Stanbic’s) branch network remains the largest (see figure 9 in Appendix).

Despite liberalization, Uganda’s banking sector slipped into crisis in the early to mid 1990s.<sup>12</sup> Between 1994 and 1998, half of the sector faced solvency problems (Caprio and Klingebiel, 2003). Despite the entry of Citibank in September 1999, the

> 11 This discussion is based heavily on the description in Kasekende and Sebudde (2002).

> 12 In a cross-country study of banking sector stability, Caprio and Klingebiel (2003) classify Uganda as experiencing a systemic crisis from 1994 to 2003, which they define as much or all of banking capital being exhausted.

5
# The Labor Market Effects of Venezuelan Migration in Ecuador

Sergio Olivieri, Francesc Ortega, Eliana Carranza & Ana Rivadeneira<sup>_∗_</sup> _The World Bank Group_ & _CUNY, Queens College_

> _∗_ We thank Carlos Vayas and Jos ́e Mu ̃noz from _Telefonica Ecuador_ and Juan Mu ̃noz and Ana Aguilera for their advice and knowledge on sampling design. We also thank Leticia Arroyo Abad, Marianne Fay, Jesus Fernandez-Huertas and Tanja Goodwin for their helpful comments. All errors are our own. The affiliation for Eliana Carranza, Sergio Olivieri and Ana Rivadeneira is _The World Bank Group_ . Francesc Ortega is the _Dina A. Perry Professor of Economics_ at CUNY’s- Queens College. Contact e-mails: solivieri@worldbank.org and fortega@qc.cuny.edu.
when they registered. Initially, DrumNet focused on passion fruit, a profitable but challenging crop sold both in export and local markets. The favorable climate and small farms in Kirinyaga favors this fruit crop. Beginning in 2004, the DrumNet team began also to support the production of two other crops in high demand with Kenyan exporters: French beans and baby corn. These crops have additional advantages over passion fruit — they are less capital intensive, simpler to grow, and have shorter growing periods leading to faster economic returns. Because of this, very few SHG members that participated in DrumNet decided to grow passion fruit. Instead, they focused on French beans and, to a lesser extent, baby corn. The type of French beans chosen by DrumNet is the extra fine from the amy variety, exported as fresh produce and preferred by the UK supermarkets. Due to its higher labor requirements, it is better suited for smallholder farms than the bobby type from the paulista variety, mainly produced for canning by larger plantations.

# 3.  Data and Design of Evaluation

The evaluation was conducted in the Gichugu division of the Kirinyaga district of Kenya. First, in December 2003, we collected from the Ministry of Agriculture a list of all horticulture SHGs in Gichugu that had been registered since 2000. There were 96 registered SHGs comprising approximately 3,000 farmers, although many of these 96 were inactive or disbanded groups. After screening out the inactive or disbanded groups (via a brief filter survey to the SHG leader), we were left with 36 viable SHGs for the evaluation.

We randomly assigned the 36 SHGs into three experimental groups of 12 SHGs each: (1) “treatment-credit”: all DrumNet services, totaling 373 individuals, (2) 8
educational attainment and that through the mid-2000s, the rising returns to education in urban China were driven by high returns to college education (Heckman and Li 2004; Cai, Park and Zhao 2008). Most studies suggest a significantly lower return to a year of high school education, and Li et al. (2012) even suggest that the return to high school in urban China was not significantly different from zero. Moreover, as documented by Meng and Zhang (2001), rural migrants faced limited opportunities in higher skill occupations as many cities explicitly reserved some occupational categories for urban residents, and even where this practice was relaxed by the early 2000s, there was often _de facto_ segregation of rural residents into unskilled service and construction sectors, or into other relatively low skill jobs unwanted by urban residents. Finally, evidence from the RCRE survey suggests that migrants earn a significant return to education through middle school attainment, but nearly zero returns to completion of high school education.

From the standard theoretical framework, and the environment in which rural households are making education investment decisions, a reduced form version of the enrollment demand function can be written as: where enrollment demand in period t, Et<sup>∗</sup> , is a function of the shadow price of capital, λt, which captures credit constraints, the expected return or shadow price of schooling, μt, child ability, ψt, productivity shocks, θt, household endowments and characteristics, **X** ht, migrant opportunity, as proxied by the share of village _j_ ’s labor force that is employed and living outside _t_ the home county, Mjt, and the tuition and other costs associated with high school enrollment, Pte. A full model highlighting the potential impacts of an increase in migrant opportunity, acting through changes in the shadow prices of physical and human capital, can be found in de Brauw and Giles (2006).

# **_B. Empirical Approach_**

To understand how migrant opportunity affects the decision to enroll middle school graduates in high school, we need to control for such factors as lifetime wealth, preferences, prices and unobserved ability that might vary with both the probability of school enrollment and off- farm opportunities. From arguments of the enrollment demand function in equation (1), we write the reduced form model of the discrete decision of household _h_ to enroll child _i_ in high school: 8
meter.

The market value of land parcels within the University Campus is close to Rupees 50,000 per square meter For the Cantonment (484 ha) and the Airport (403 ha) the land market value was estimated at Rupees 40,000 per square meter. These two valuable land areas are preventing the extension of the city core in the Northern part of the city. The Cantonment estate, located between kilometer 4 and 6 particularly, is underutilized and may at one point be considered for redevelopment. (See in Annex I a Google Earth image of the area).

Most of the not-so-valuable land is in the Eastern and Southern parts of Ahmedabad. The market prices for these parcels vary between Rupees 3,000 to 15,000 per square meter. It should also be noted that in the very South the parcels are smaller in sizes (0.5 to maximum 2.5 hectares). The parcels derive mostly from the TPS land appropriations regulations.

Along the BRTS corridor in the South and Eastern part of Ahmedabad the land values are still in the low range, Rs. 2,800 to 9,000 per m2.

The Metro Lines (Phases 1 and 2), not immediately planned, in the Western part of Ahmedabad, may have some impact on land values in the future. The areas more affected will be the former Greenbelt area localized next the Phase 2 Metro corridor.

The four estimations, Alternative I (a) and (b) and Alternative II (a) and (b), for the valuations of potentially marketable public land at Jantri and Market Rates are presented below in Table 6 and Table 7.

27
The last grouping, with savings rates between 5 and –16 percent, is mostly made up of small and/or postconflict countries.

The contrast with the two comparator countries, Chile and Malaysia, is striking. Both have substantially higher savings rates than all the focus countries except oil-rich Nigeria and Chad and nonoil-resource intensive Namibia. Chile’s fiscal performance and savings rate have benefited recently from high export revenues in extractive industries (in this case, copper), as well as sound macroeconomic policies and strong domestic institutions.

# Domestic and external debt

In recent years, robust GDP growth, more prudent macroeconomic policies, debt relief negotiated with multilateral and bilateral creditors, and, for major oil exporters, higher oil revenues have enabled many countries to reduce their debt-to-GDP ratios. Twenty-one of the 23 focus countries for which external debt to GDP ratio data are available reduced the ratio over the 2004–05 period—by more than 20 percentage points in the Democratic Republic of the Congo, Ethiopia, Malawi, Nigeria, Sudan, Uganda, and Zambia (appendix 2).

For some countries, such as Nigeria and Zambia, external debt-to-GDP ratios have fallen particularly significantly. Debt relief and high copper export earnings brought Zambia’s down 58 percentage points (to 78 percent) over the 2004–05 period. Nigeria’s external debt-to-GDP ratio fell from 50 percent in 2004 to 22 percent in 2005, as oil windfalls enabled it to pay off nearly all its external debt to multilateral creditors. Several countries have also seen substantial declines in their debt burdens thanks to multilateral debt relief granted under the Heavily Indebted Poor Countries and Multilateral Debt Relief initiatives. In the past several years, 14 focus countries have reached the completion point under the HIPC Initiative, enabling them to begin receiving debt relief.

Economies with high public-debt-to-GDP ratios can result in a crowding out of private credit. The extent of public borrowing from the financial system has obvious implications for the availability of bank credit for private enterprises. High demand for credit from government-owned enterprises and high overall levels of lending to the government pose structural impediments to private sector credit. However, as indicated in appendix table 2.3, where a number of countries have both relatively low public-debt-toGDP and private-bank-credit-to-GDP ratios, there must be other factors that constrain private credit. These are more fully discussed in the next chapter but can include high banking transaction costs and banks’ perceived higher risks associated with lending to the private sector.

6
# **Table 2: Official age and privatization of local SOEs**

The table reports the effects of local politicians’ age on the tendency of privatization by including a series of age dummies of local politicians. The dummy variable _Privatized_ equals to 1 when the SOE experiences privatization in the specific year and 0 otherwise. As privatization is mostly irreversible in our sample, we delete the observations after _Privatized_ has turned 1, leaving 151,269 observations in the regressions. Control variables include macroeconomic conditions and officials’ individual characteristics (education level, prior experience of working in the central authorities and provincial level). Industry and city fixed effects are controlled. All standard errors clustered at the city level are reported in parentheses. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

|Dependent Variable:_Privatized_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_Dum(Age=51)_|-0.008|-0.007|-0.006|-0.006|
||(0.010)|(0.009)|(0.010)|(0.010)|
|_Dum(Age=52)_|<br>0.015|<br>0.015|<br>0.014|<br>0.016|
||(0.011)|(0.011)|(0.011)|(0.011)|
|_Dum(Age=53)_|0.001|0.001|0.001|0.002|
||(0.012)|(0.011)|(0.011)|(0.011)|
|_Dum(Age=54)_|-0.006|-0.006|-0.006|-0.004|
||(0.014)|(0.013)|(0.013)|(0.013)|
|_Dum(Age=55)_|-0.001|-0.002|-0.002|0.001|
||(0.012)|(0.011)|(0.011)|(0.012)|
|_Dum(Age=56)_|0.017|0.017|0.017|0.020|
||(0.016)|(0.016)|(0.016)|(0.015)|
|_Dum(Age=57)_|-0.006|-0.006|-0.007|-0.004|
||(0.014)|(0.014)|(0.014)|(0.015)|
|_Dum(Age=58)_|-0.042**|-0.042**|-0.042**|-0.039**|
||(0.017)|(0.017)|(0.017)|(0.018)|
|_Dum(Age=59)_|-0.047*|-0.049**|-0.049**|-0.043*|
||(0.024)|(0.024)|(0.024)|(0.024)|
|_Dum(Age=60+)_|0.004|-0.002|-0.002|0.008|
||(0.039)|(0.037)|(0.037)|(0.037)|
|_Labor prodt-1_||-0.021***|-0.021***|-0.021***|
|||(0.002)|(0.002)|(0.002)|
|_ROAt-1_||-0.147***|-0.147***|-0.146***|
|||(0.018)|(0.018)|(0.018)|
|_Leveraget-1_||0.044***|0.044***|0.044***|
|||(0.005)|(0.005)|(0.005)|
|_Ln(employmentt-1) _||<br>-0.035***|<br>-0.035***|<br>-0.035***|
|||<br>(0.002)|<br>(0.002)|<br>(0.002)|
|_GDP Per capitat-1_|||-0.001|-0.001|
||||(0.010)|(0.009)|
|_GDP Growtht-1_|||0.001|0.001|
||||(0.001)|(0.001)|
|_College_||||0.025*|
|||||(0.013)|
|_Central Experience_||||-0.019|
|||||(0.020)|
|_Provincial Experience_||||<br>0.004|
|||||(0.009)|
|Industry FE and City FE|Yes|Yes|Yes|<br>Yes|
|N|151,269|151,269|151,269|151,269|
|R-square|0.097|0.113|0.113|0.114|

``` 32 ```
Figure 10: Plants and vertebrates: differences in global cell totals for model-predictions after priority reassignment for small species <!-- Start of picture text --> Se ee 4<br><!-- End of picture text --> Biodiversity Index value Difference [| [-30.1,-0.0903] a (-0.0467,-0.0235] (0.001,0.24] [| (2.49,8.36] [| (-0.0903,-0.0668] (-0.0235,-0.001] (0.24,0.685] [| (8.36,44.3] [| (-0.0668,-0.0467] (-0.001,0.001] ] (0.685,2.49] NA
subset of respondents that completed the survey in all four rounds. A plot of responses from full compliers is shown in Table A4 in Annex A. This suggests that election due to non-response does not appear to have markedly affected survey results.

The two questions with most volatility on a month-to-month basis were access to clean water and access to cooking fuel. Among these two questions, two trends are observed: (1) In February, there appears to have been a one-time shock that made it easier for households to get access to fuel, and (2) beginning in February, a shock lasting through March made it more difficult for households to access clean water. Given that access to fuel depends on market conditions that can fluctuate rapidly (and therefore change the availability and price of fuel commodities), and access to clean water depends on environmental conditions (such as the onset of the dry season) and public or private services that can be disrupted, the higher volatility of responses to these two questions on a month-to-month basis relative to others appears plausible.

# **5. Discussion**

The fact that response rates dropped by about 5 percentage points per round, starting at 68 percent in December and ending at 52 percent in March, is cause for concern that selective response may bias the survey results. This worry is compounded by the finding that those responding were disproportionately women, older participants, and those with alternative phone numbers. At the same time, the fact that trends for responses from full-compliers alone are similar to trends based on all responses suggests that selective attrition did not ultimately bias the survey results.<sup>9</sup> The largest contribution to non-response resulted from individuals who participated intermittently over the course of the survey period. Future studies using mobile data collection might mitigate non-response by sending simple reminders (possibly using SMS) to reduce the number of intermittent compliers. Targeting older, female household members could reduce the number of non-compliers, but such targeting would not be desirable for surveys seeking a representative sample of individuals, e.g. labor force surveys.

> 9 It is not possible to determine, however, whether responses from the 17 percent who _never_ responded would have differed from those of those who did response.

18
skills in general (see, for instance, O'Connor and Lunati, 1999). On the one hand, traditional trade theory suggests that with increased trade skill premiums should decrease in labor-intensive countries. Recent studies, however, show that skill premiums can increase if trade leads to the adoption of better performing skill-biased technologies (Berman et al., 1998; Keller, 2004), or to increased skill-enhancing investments (Feenstra and Hanson, 1997; Harrison and Hanson, 1999). Very few studies have documented these issues in East Asia (see World Bank (2007) on Vietnam; Alatas and Bourguignon (2005) and Abu-Ghaida and Connolly (2003) on Indonesia; Zeufack and Udomsaph (2006) on Thailand; and Heckman and Li (2003) and Fajnzylber and Fernandes (2004) on China) and most of them have not gone beyond the analysis of trends. Additionally, methodologies differ across existing studies making it difficult to compare them. As little is known about the evolution and drivers of skill premiums in East Asia, constructing comparable measures to understand how skill demand evolved in these rapidly growing countries, and linking these changes to variables such as trade and FDI will help shed light on consequences of rapid export-led growth. From a policy perspective, understanding the extent to which skill demand has increased, what has driven the changes, which sectors have become more skill intensive, and which types of workers remain vulnerable, can lead to key policy recommendations ranging from the expansion and improved relevance of education opportunities, to a better targeting of social protection and pro-poor policies in the region and reforms to improve the functioning of labor markets.

This paper focuses on the estimation of skill/industry premiums and the labor force composition (i.e., the proportion of skilled labor by sector) at the national and sectoral level (one or two digit) with the objective to provide a comprehensive description of trends in skill premiums and labor force composition over several years. Although no attempt will be made to explain these trends, a comprehensive description will already allow answering relevant questions such as: Did income inequalities increase or decrease? Can we observe converging or diverging trends in the region regarding the evolution of skill premiums and of the labor force composition? Are changes in skill premiums generalized or industry-related? Is there evidence of labor market segmentation?

3