rafmacalaba/datause-encoder-data huggingface dataset:
India increased the real value of their urban poverty lines by more than 40 percent, while China increased the real value of the rural poverty line by more than 75 percent.<sup>2</sup> Over time, many countries have increased the real value of their national poverty lines, in recognition that their economies had grown so significantly that the concept of basic needs in their societies had fundamentally changed.<sup>3</sup> In these cases, the national absolute lines again behave like relative lines in that (over longer periods of time) they increase with economic growth.

Townsend (1979) recognized well this point that basic-needs lines are not necessarily fixed over time, because “...the necessities of life are not fixed. They are continuously being adapted and augmented as changes take place in society and its products” (p. 915). Sen (1983) expressed this differently by arguing that a concept of poverty fixed in terms of functioning at some minimum level in a society may not be fixed in terms of the expenditures required to perform this function. For example, participating in the labor market may be viewed as a minimal social functioning; the cost of this functioning though may require only clothing and food in a poor society, while in a richer society it may require internet access, vehicle, cellphone in addition to clothing and food. Paraphrasing Sen, Atkinson states that “...a standard that is _constant_ in the space of functionings ... would imply a standard in terms of consumption that varies over time and across countries” (World Bank, 2017, p. 139). To reflect this concern, Atkinson recommends that the “World Bank should introduce a _societal_ headcount ratio measure

> 2 In India, the 2011 value of the poverty line in urban areas increased from 33 rupees per person per day to 47 rupees. The change in rural poverty lines was significantly less, about 19 percent, increasing from 27 to 32 rupees. China also increased the real value of its poverty line several times in the late 1970s. 3 After 15 years of keeping the real value of its national poverty line constant, the Government of Nepal increased the line in 2011 (Government of Nepal, 2012). Similarly, the Government of Jordan increased the real value of the poverty line by about 10 percent in 2011 (World Bank, 2009, Jolliffe and Serajuddin, 2017). For more examples of countries that changed the value of their national poverty lines, see the appendix in Jolliffe and Prydz (2016).

3
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 7. Benchmarking water and sanitation indicators**

||Unit|Fragile<br>states|Central<br>African<br>Republic|||Low-income<br>countries|
|---|---|---|---|---|---|---|
|||Mid-2000s|1994|2000|2006|Mid-2000s|
|Access to piped water|% pop|17.9|2|3|2|9.3|
|Access to standposts|% pop|9.4|15|19|23|17.1|
|Access to wells/boreholes|% pop|54.5|73|68|70|39.3|
|Access to surface water|% pop|18.1|10|9|5|34.2|
|Access to septic tanks|% pop|11.2|1|1|2|4.7|
|Access to improved latrines|% pop|29.0|24|24|76|18.3|
|Access to traditional latrines|% pop|36.8|42|51||38.5|
|Open defecation|% pop|23.1|33|24|23|38.3|
||||**2000**|**2005**|**2009**||
|Domestic water consumption|liter/capita/day|29.6|—|6.3|6.5|50.9|
|Revenue collection|% sales|94.6|61|61|86|94.1|
|Distribution losses|% production|31.5|39|53|51|34.8|
|Cost recovery|% total costs|57.2|—|46|—|89.5|
|Operating cost recovery|% operating costs|80.1|—|64|—|125.2|
|Labor costs|connections per<br>employee|168.8|63|49|62|175.9|
|Total hidden costs as % of revenue|%|169|185|187|148|67|
|||**2004**|**2009**|Non<br>water|-scarce<br>resources|Other<br>developing<br>regions|
|Average effective tariff|U.S. cents per m<sup>3</sup>||57|57|80|3.0–60.0|

_Source:_ AICD water supply and sanitation database (http://www.infrastructureafrica.org/aicd/tools/data).

Access figures calculated by the AICD using data from Demographic and Health Survey (1994) and Multiple Indicators Cluster Surveys (2000 and 2006), as published by the WHO/Joint Monitoring Program (JMP) in March 2010.

_Note:_ Domestic water consumption is calculated as a weighted average of the consumption in Bangui and seven other centers served by SODECA. A country is considered to have non-scarce water resources if the renewable internal freshwater resources per capita are greater than 3,000 millimeters.

m<sup>3</sup> = cubic meters.

= Data not available.

More than three-fourths of the population relied on latrines as of 2006, but due to data constraints and the difficulty of tracking investments made by nongovernmental organizations (NGOs), it is not possible to make a clear separation between improved and traditional latrines. In rural areas, it is mainly VIP latrines or septic sanplat or double-vented types that are installed by NGOs. Devices for handwashing have been installed in community centers by the Department of Health and NGOs working in the sector (World Bank, 2010g). After 2006 a substantial number of households had access to shared latrines constructed by humanitarian agencies.<sup>7</sup>

> 7 Shared latrines have expanded dramatically in recent years: 27 new shared latrines were built in 2007, 5,352 in 2008, and 1,527 in 2009 (World Bank 2010g).

21
_Child laborers are highly concentrated within the North_ . If the South constituted its own country, nearly all 7-14 year olds would not be working (92.2%), while if the North formed its own country, one third of children would be engaged in labor activities (33.1%). In the North, about half of the children of this age are only attending school (50.6%) and 18.9% are only working while 14.1% combine work with school and 16.3% are doing neither. The fact that only 1.6% of Southern children are working while not schooling compared to 18.9% of Northern children illustrates the extent of the latter group’s strong marginalization (Table 2). Children affected by harmful child labor (conflicting with their education) live therefore nearly entirely in the North.

_Children’s usefulness or value on the farm increases as they get older or (more accurately) physically bigger and stronger_ . Child labor rises incrementally from 9.9% for 7-8 year olds to 15.4% for 9-11 year olds followed by 19.6% for 12-14 year olds. Such trends are also found internationally (Edmonds 2008). In addition, there is an increasing demand for older children to only be working, as age-specific data in Figure 2 shows (see appendix).

_There appears to be a strong intergenerational relationship between child labor and parental education as well as agricultural livelihoods (Table 2)_ . The kinds of livelihoods that parents pursue seem closely related to whether children work or not, as nearly all child laborers are working in family farming, with only 6.3% engaged in non-agricultural work. This, together with the fact that only 3.8% of child laborers receive payment for their work (see Table 2), does not offer strong evidence for the argument that parents primarily employ their children as a source of additional revenue. Otherwise there would likely be a larger share of child laborers working outside of family farming for higher earnings in other sectors. In addition, children are three times more likely to be involved in harmful child labor (without schooling) who live in households owning land (10.3%) compared to those in landless households (3.2%).

11
as they were not in the initial pool of pre-listed beneficiaries to receive the transfer. We assume that these households are comparable to the treated households.

The Proxy Means Test has been used in previous studies as a criteria to identify treated and control groups of a cash transfer (for example see Stoeffler et al. (2020)). The challenge with this methodology of identification is that we do not have the true LPMT score of the control households. Using the CIMS data, it is possible to recreate a proxy LPMT score, but only partly. Indeed, as described in Appendix 1, the LPMT score is calculated as a sum of 10 different characteristics which include household characteristics, assets, work status and disability status. Out of the 10 characteristics, 3 are unavailable in the CIMS data, two of which we are able to proxy. We do not have any information on whether the household uses bottled or sachet water for drinking. We also do not have the information on (1) whether the head of the household is literate in English or Sierra Leonian language and (2) whether the head of the household has an informal contract. For these two characteristics, we use information on the education level and work with a formal contract of the respondent (as opposed to the household head) as proxies. Among the Round 1 beneficiaries, 32 percent are non-household heads and among the R2 beneficiaries, 45 percent are non-household-heads. We are therefore able to recreate an LPMT score that can take a total of 9 instead of 10, but with potential errors because of the two proxies.

The original LPMT was calculated using a simplified tool collected by program administrators.<sup>10</sup> The LPMT threshold used by NaCSA to determine the eligibility of a household for the ECT was 7 out of 10. As described in Figure 2, households that scored 7 or above have necessarily passed the original LPMT because these households have a true score of 7 to 10. However, there are potentially households that

> 10 The data for the LPMT tool was collected by Statistics Sierra Leone (Stats SL) in presence of NaCSA and the Anti-Corruption Commission (ACC). Independent spot checks of the ECT confirm that most participants of the exercise thought the process was fair and transparent (Sanford 2023).

13
- World Bank. 2014. " _A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals_ ." Washington, DC: World Bank.

- ----------. 2016. " _Poverty and Shared Prosperity 2016: Taking on Inequality_ ." Washington, DC: World Bank.

- ----------. 2017. "World Development Indicators."  Accessed in December 2017.

- ----------. 2018. " _Poverty and Shared Prosperity Report 2018: Completing the Poverty Puzzle_ ." Washington, DC: World Bank.

- ----------. 2019. "PovcalNet."  Accessed on February 22, 2019.

- World Bank; International Monetary Fund. 2016. " _Global Monitoring Report 2015/2016: Development Goals in an Era of Demographic Change_ ." Washington, DC: World Bank.

# **APPENDIX A: GENERAL FIGURES**

<!-- Start of picture text --> FIGURE A 1. PREDICTED GLOBAL POVERTY HEADCOUNT AND GINI COEFFICIENT<br>Panel A. Poverty headcount Panel B. Gini coefficient<br>(Base period: 1980–2014) (Base period: 1980–2014)<br>75 70<br>60<br>57.5<br>45<br>45<br>30<br>15 32.5<br>0<br>20<br>1980 1990 2000 2010 2020 2030 2040<br>1980 1990 2000 2010 2020 2030 2040<br>Years<br>Years<br>Poverty headcount o utcomes derived from 500 random draws per year in each of the 150 Aggregated Gini index stats der ived from 500 random draws pe r year in each of the 150<br>selected countries. The 2015 and 2030 corresponding results are: i) median = 11.42 and 8.84; selected countries. The 2 015 and 2 030 corresponding res ults are: i) median = 38.92 and 38.21;<br>ii) mean = 11.45 and 8.89; iii) skewness = 0.16 and 0.38; iv) standard deviation = 0.52 and ii) mean = 38.93 and 38 .23; iii) s kewness = 0.23 and 0.16; iv) standard deviation = 0.55 and<br>0.93. Estimated number of persons living in extreme poverty corresponding to the median 1.99.<br>headcount = 811 and 724 million people.<br>96% confidence interval 80% confidence interval<br>(percentage)<br>(All economies) (All economies)<br>Poverty headcount Gini index proxied<br>(weighted by population)<br><!-- End of picture text --> 96% confidence interval 80% confidence interval 40% confidence interval Median Poverty headcount of 3 percent Year = 2030 _Source:_ Penn World Table 9.0, World Development Indicators, World Bank PovcalNet, United Nations, and the author’s estimates.

Page **32** of **45**
food price increases are likely to deepen their poverty, especially in the short term. In addition, those slightly above the poverty line tend to be marginal net purchasers (i.e., on average they are net buyers, but the purchases represent a small share of total consumption). Staple food price increases are likely to push some of these households into poverty.

Changes in poverty depend on the size of the price increase as well as the net benefit ratio for a given household. We estimate the change in household welfare (consumption per adult equivalent) by multiplying the household-level NBRs calculated in the preceding section by the actual price changes that have occurred in Uganda from 2007 to 2008. From the price data in Figure 2, one may observe a seasonal pattern in food prices, with prices tending to be lower in January than at other times of the year. To control for these seasonal effects we use the price changes during the most recent 12 months for which data are available, which should purge the seasonal component from the price changes. To facilitate comparison with the previous analysis of Benson et al. (2008), price data were drawn from the same set of six markets used in that study.

The region-specific price data from FoodNet are shown in Table 2. As seen in the table, the price data are not complete for cassava and sweet potatoes. First, cassava and sweet potato prices are only available through May 2008. Therefore, for cassava and sweet potatoes we use the observed price increases for May 2007 to May 2008 instead of October to October. The incomplete price information that is available for cassava roots and cassava flour indicates that the percentage change from May 2007 to May 2008 is a close approximation of the increase from October 2007 to October 2008, including the sharp increase in cassava prices in the Northern region in mid-2008. Second, there are no cassava or sweet potato price data more recent than April 2007 for the Eastern region. This is a major gap, especially because the average budget shares of these foods in Eastern region are 6% and 8%, respectively (see Table 1). In the absence of better information, we close this gap in the data by using the simple average of the percentage price changes in the Central and Western regions. This assumes that the large cassava and sweet potato prices observed in the Northern region did not occur in the Eastern region, and is thus a conservative estimate of the change in prices for these foods in the Eastern region.

One of the more striking observations from Table 2 is the enormous heterogeneity of price changes by region and commodity. As noted earlier, Uganda is not tightly integrated with world food markets, in large part because of its landlocked status and poor 12
These reforms affected all types of ECED establishments; however, we focus on preschools due to data availability.<sup>3</sup> Preschools are non-mandatory, formal ECED establishments intended for children between the ages of 4 and 6. They offer academic preparation for primary education. Figure 2, Panel B shows the upward expansion of preschools overtime. Between 2003, when the first reforms were enacted, and 2014, the average number of preschools per 1,000 children of eligible age almost doubled, from 3.7 in 2003 to 6.6 preschools in 2014. As a result, the preschool enrollment rate (3-6) years more than doubled during the same time, from 25.7 percent in 2003 to 57.9 percent in 2014 (World Development Indicators, 2022). Figure 2, Panel A illustrates the geographical variation of preschool access across the country. In the next section, we elaborate how we exploit spatial and temporal variations in preschool access to estimate its impact on firm productivity.

# 3. Empirical strategy and data

We combine several data sets to estimate the impact of access to preschools on firm-level outcomes. We use PODES, the village census, and the annual National Socioeconomic Survey (SUSENAS) to construct a measure of preschool density: the number of preschools per 1,000 children of eligible age (3 to 6 years old). Although preschools are intended for children aged 4 to 6, following Halim et al. (2022), we define preschool density with a broader age range, as age requirements are not strictly enforced. PODES is fielded approximately every three years. We use data from 2002 to 2014. SUSENAS is annual, and we merge corresponding years to PODES data. We measure preschool availability at district, rather than village level, as this is the lowest level geographical identifier available in all datasets that we use. Since decentralization reform in 1999, districts have often split over time. In 1993, there were 290 districts; by 2014, there were 511 districts. To ensure equal comparisons across time, we harmonize district boundaries as they existed in 1993. As PODES is not available annually, we follow Halim et al. (2022) and infer preschool data from in-between PODES years using the closest upper year available. For instance, year 2007 is sandwiched between PODES 2005 and 2008, so we infer preschool data from the

> 3 Specifically, we are looking at _Taman Kanak-kanak_ (TK) and _Raudhatul Afthal_ (RA).

7
Table 1. RLMS: Selected health and nutrition variables

||Total expenditure(in ro|ubles)|||
|---|---|---|---|---|
||2007|2008|2009|2010|
|income shock|14597.05|21039.59|12214.22|13081.52|
|non-income shock|10614.79|11659.98|11841.92|12331.89|
|sample average|11448.86|13594.98|11922.06|12491.12|
||Total food expenditure(in|roubles)|||
||2007|2008|2009|2010|
|income shock|4811.995|5895.166|4422.716|5205.508|
|non-income shock|4076.366|4095.325|4159.266|4472.185|
|sample average|4230.441|4466.629|4215.97|4627.951|
||health care expenditure(in|roubles)|||
||2007|2008|2009|2010|
|income shock|455.4994|551.2735|414.8871|513.1008|
|non-income shock|440.6567|455.4952|501.4874|494.6954|
|sample average|443.7655|475.2541|482.8477|498.6049|
||Doctors visits||||
||2007|2008|2009|2010|
|income shock|9.312712|7.505747|8.557823|8.744292|
|non-income shock|9.083679|7.393584|8.593731|7.898586|
|sample average|9.137302|7.417315|8.586074|8.077966|

Source: RLMS and authors' calculations

# 5. <u>Methodology – Propensity score matching</u>

# <u>5.1. Difference-in-difference with propensity score matching</u>

In order to estimate the impact of an income shock sustained during a crisis year, we employ a difference-in-difference propensity score matching estimator (see Heckman et al, 1999), which extends simple before-after comparisons to determine the treatment effect based on the presumption that the outcome variable can also change over time due to reasons unrelated to income shocks. Such an approach requires longitudinal data and builds on the assumption of time-invariant linear selection effects. Following Heckman et al (1998), we implement a conditional difference-in-differences estimator. This method combines a propensity score matching approach with the difference-in-difference empirical strategy.<sup>8</sup> This technique relaxes the linear assumption when controlling for observables relative to standard difference-indifference modeling and controls for unobservables by exploiting the longitudinal dimension of the data. This further feeds into Smith and Todd (2005), who show that the difference-indifferences matching estimator performs the best among non-experimental matching based estimators.

> 8 As such, this method builds on propensity score matching (PSM). It is explained in the next section (as this is our robustness check).

9
on the developmental side, many of the items in the list have been widely emphasized in recent years as center pieces of the financial development agenda. These include the promotion of access (which comes at the very top of the list), microfinance related issues, the informational framework (registries, auditing), the general design of payments systems and the institutional framework for payments oversight.

Other “in” topics, such as the various ways in which the state can influence financial development and the performance and management of public programs and institutions (including housing, social security, and central banks), are somewhat more surprising. It is remarkable, however, that various items of pro-market activism come at the top of the list, perhaps reflecting the increasingly prominent role of the state as a catalytic agent of change. It is also worth noting that finding ways to improve the functioning of stateowned financial institutions (including through better performance assessments and better resource management) also figure prominently. A possible interpretation is that FSAPs are increasingly grasping with the broader role of the state in financial development, going beyond the setting up of the enabling environment. These issues may have been somewhat side-lined initially, perhaps due to their complexity or political sensitivity.

Similarly, the list of what is cooling off includes items that one would associate with somewhat “resolved” or “outdated” issues, such as wholesale payments (the big wave of RTGS systems introduction seems to have passed), checks, deposit insurance, and crisis management (less crises have occurred more recently). But in other cases, the list is more surprising as for the closure of state financial institutions, various aspects of monetary management (such as liquidity and reserve management, and money and foreign exchange market development), capital markets (as already noted above), and enforcement and capacity building in the judiciary.

In each of these cases, various interpretations are of course possible: i) the underlying issues have largely been resolved and no longer require attention; ii) the underlying issues have not been resolved but there is no point in repeating over and over the same recommendations; or iii) the underlying issues have not been resolved but the assessing institutions have limited their role or capacity in this area. Take for example the case of public financial institutions. FSAP teams may have learnt to accept realities on the ground whereby improving these institutions might be more productive than trying to close them. On the other hand, the apparent phasing out of recommendations in the area of capital markets perhaps reflects a supply constraint rather than a demand shift from the membership.

10
# **VIII.  How Did the Firms Rate the Fluency in International Languages of Locally Educated MBAs?**

## **Figure 4**

<!-- Start of picture text --> MBA Survey Question 6C: How would you rate<br>the fluency in international business languages<br>of locally educated MBAs?<br>6%<br>13%<br>28%<br>30%<br>LACKING (%) INADEQUATE (%)<br>ADEQUATE (%) GOOD (%)<br><!-- End of picture text --> Figure 4 shows that unlike the areas discussed above, 19% of the responding firms found the language skills of locally trained MBAs to be either lacking or inadequate, while 58% of respondents found the language skills of locally trained MBAs to be adequate or good. Comparing the statistics for technical training and communication skills, there is a greater proportion of respondents who were dissatisfied with the language skills of local business graduates, than the proportion of respondents who were dissatisfied with the other skill areas.

At the regional level, two regions stand out: 36% of East Asian respondents and 30% of Latin American respondents were dissatisfied with the language skills of local MBA graduates (with a rating of lacking or inadequate). The dissatisfied responses from other regions were significantly lower. Considering the level of business ties that Latin America and East Asia have with the industrialized countries, such a response is a strong indication of the need to concentrate on developing the skills of local MBAs in international business languages.

By contrast, firms in the other regions found the fluency in international business languages of the local MBAs to be satisfactory. 63% of the firms responding from Europe, 69% of the firms responding from the Middle East, and 71% of the firms responding from South Asia rated the fluency of local MBAs in international business languages to be either adequate or good.

16