rafmacalaba/datause-encoder-data huggingface dataset:
**2 The Study Area and the Survey**

The study area, which is located in Bolangir and Kalahandi Districts, is a patchwork of small river basins and often densely forested hills. The population is highly heterogeneous, with scores of different tribal groups and Hindu communities. What makes this remote rural area particularly interesting for present purposes is its poverty and notorious proneness to drought (Sinha, 1999). In six years of the decade 1996-2005, for example, precipitation was deficient, untimely, or both (van Dillen, 2008). The most important sources of household income are cultivation, animal husbandry, nontimber forestry, government employment schemes and earnings from various forms of migration, where the latter are often a response to drought.

The original survey was carried out in 2001-02 (see van Dillen [2008] for an extensive account). Four administrative blocks (Titlagarh, Saintala, Muribahal and Bongomunda) were selected in Bolangir, and Kesinga, the most north-eastern block of Kalahandi. These five, contiguous blocks have a combined area of approximately 1,900 sq. km and a population of about half a million. Employing a stratification aimed at ensuring a fairly even spatial distribution at the second stage, six clusters of villages were defined in each block and one village was drawn at random from each cluster. Eight households were then selected from each village, yielding 240 households in all. Again, only spatial stratification was employed, households being selected at random from different parts of the village settlement area. Detailed quantitative information was collected on each household’s activities and income portfolio in each of three consecutive six-month periods, namely, for the entire crop year 2000-01 and the _kharif_ season of 2001-02. Also canvassed were the details of the household’s demographic features and wealth. In addition, the team of investigators collected qualitative information in the form of household ‘histories’ of what decisions had been made in periods of drought-induced crises in the past, and why. Data on infrastructure and land use at various spatial and administrative levels completed the picture. Summary statistics of the sample villages are presented in Table 1.

These households were re-surveyed in the early months of 2010. Due to the pressure of time, the primary emphasis was on the immediate past _kharif_ season, when 11 of the 30 villages had an all-weather road, nine more than in 2001-02. The original household questionnaire was heavily redesigned so as to capture more fully the villagers’ activities in the domains of central interest, namely, trade in goods, education and health. The households’ individual members and their salient characteristics in 2004-05, a complete list of which was available from the previous survey round, provided both the basis for, 4
# **Figure 5.9: Hanushek, Ruhose, and Woessman (2017)’s approach versus LAYS**

<!-- Start of picture text --> Human capital derived using Hanushek, Ruhose, and Woessman’s approach applied to TIMSS<br>and Barro‐Lee data versus LAYS<br>0 5 10 15<br>LAYS using TIMSS<br>Source: Authors’ analysis of TIMSS 2015, and Barro and Lee data (2013).<br>Notes: For ln(h), see text for details. For LAYS, the numeraire country is Singapore. Illustration for case where<br>learning starts at Grade 0 for TIMSS. Correlation coefficient is 0.99. Spearman rank correlation is 0.99.<br>2.2<br>2.0<br>1.8<br>1.6<br>1.4<br>1.2<br>ln(h) based on Hanushek, Ruhose and Woessmann approach<br>1.0<br><!-- End of picture text -->

# **_5.5 Quality adjustments using different approaches:  How do they compare?_**

We have illustrated how each of the approaches described above compares to the _LAYS_ approach by graphing one versus the other. But this doesn’t provide a sense of how the different approaches would characterize international gaps in quality-adjusted years of schooling. To provide a sense of this, for each potential approach to measuring schooling, we calculate the average value for the four top-performing countries and the four bottom performers on that measure, and then take the ratio of these two values. The results are reported in Table 5.2. To understand how to interpret it, take the example of Row (1), which examines the simple quantitybased years-of-schooling measure. Row (1) shows that the average years of schooling of 25- to 29-year-olds in the four lowest-performing countries (in this sample) is 7.1, compared with 14.2 among the four highest-performing countries; the global “gap” as measured by the ratio of best to worst performers is therefore 2.0. If one were to just look at scores on the TIMSS 2015 Math assessment, the gap would be 1.6. Unsurprisingly, the approaches that combine quantity and 40
-4-

entity within **che** World Bank which would examine ways to improve data collection efforts in developing countries, .he Living Standards Measurement SL.udy.

# B. LSMS from 1980 to 1985: The Development of LSMS Methodology

In 1980 the World Bank established the Living Standards Measurement Study (LSMS) to explore ways of improving the type and 4uality of household survey data collected by statistical offices in developing countries.- Its ultimate goal is to promote the use of household data as a basis for making policy decisions in these countries. Soon after its inception, LSMS undertook a major review of the state of data collection as it then existed in developing countries with the objective of develoning a new system of data collection at the household level. This work was the major focus of LSMS activities in the early 1980's.

This early LSMS work can be divided into two types: (1) review of past experience in the collection of household data; and (2) theoretical work on various aspects of living standards, with special emphasis on how data should be collected for purposes of doing empirical research. Some of the earliest LSMS reports were assessments of recent experience in collecting household level dati in d ve'Luping countries. These papers, written in the early 1980's, consisted of critical reviews of efforts in Africa, Asia and Latin America (Visaria, 1980; Altimir and Sourrouille, 1980; Scott, de Andre and Chander, 1980; Booker, Singh and Savane, 1980; Wahab, 1980; Musgrove, See Chander, Crootaert and Pyatt (1980) for the original statement of the goals of LSMS.
Barajas, Steiner, and Salazar (2000), Denizer (2000), Claessens, Demirguc-Kunt, and Huizinga (2001), Unite and Sullivan (2002), Claessens and Laeven (2003), Claessens and Lee (2003), Martinez Peria and Mody (2004), Levy-Yeyati and Micco (2007)). The research on foreign bank participation and stability concludes that for the most part foreign banks contribute to banking stability by continuing to lend when faced by financial crises and by lending more under these circumstances than their domestic counterparts (see Goldberg, Dages, and Kinney (2000), Crystal, Dages, and Goldberg (2001), Goldberg (2002), Martinez Peria, Powell, and VladkovaHollar (2005), Detragiache and Gupta (2006), De Haas and van Lelyveld (2006)).

The literature on the implications of foreign bank participation for access to finance has primarily focused on the impact on lending to small and informationally opaque firms. To date, the evidence on this issue is mixed. Using bank-level data, Berger, Klapper, and Udell (2001) and Mian (2006) present results for Argentina and Pakistan, respectively, that suggest that foreign banks limit access and serve only the largest and most transparent firms. On the other hand, also using bank-level data, Clarke, Cull, Martinez Peria, and Sanchez (2005) find evidence that large foreign banks surpass their domestic counterpart in the share and growth of lending to SMEs in Argentina, Chile, Colombia, and Peru. Using firm-level data for India, Gormley (2006) finds that on average firms located in districts with newly established foreign banks are less likely to get long-term financing, but this effect is stronger among the more opaque firms. On the other hand, cross-country research also using firm-level data conducted by Giannetti and Ongena (2005) and Clarke, Cull, and Martinez Peria (2006) indicates that though larger firms benefit more from foreign bank presence, even small companies enjoy greater access to credit when foreign bank participation increases. Finally, taking a more aggregate perspective, using countrylevel data for 86 low and lower income countries, Detragiache, Tressel, and Gupta (2006) find 3
37 World Bank. (2008) _Migration and Remittances Factbook, 2008._ Washington, DC: World Bank.

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- Yang, D, Martinez, C.  (2005) _Remittances and Poverty in Migrants’ Home Areas:  Evidence from the Philippines_ , Unpublished draft manuscript, Department of Economics, University of Michigan, Ann Arbor, USA.
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- Mathieu, E., H. Ritchie, L. Rodés-Guirao, C. Appel, C. Giattino, J. Hasell, B. Macdonald, S. Dattani, D. Beltekian, E. Ortiz-Ospina, and M. Roser (2020). Coronavirus pandemic (COVID-19). Our World in Data. https://ourworldindata.org/coronavirus.

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- National Bureau of Statistics (NBS) (2019). Nigeria - General Household Survey, Panel 2018-2019, Wave 4. Public Use Dataset. Ref: NGA_2018_GHSP-W4_v03_M. Dataset downloaded from `https://doi.org/10.48529/1hgw-dq47` .

- National Bureau of Statistics (NBS) (2022). Nigeria - National Longitudinal Phone Survey (NLPS) 2021-2022. Public Use Dataset. Ref: NGA_2020_NLPS_v12_M. Dataset downloaded from `https://doi.org/10.48529/xeym-xv94` .

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22
|Early marriage|4.3|0.021|24.6|0.041|-20.3***|
|---|---|---|---|---|---|
|Waste Disposal|14.6|0.028|29.7|0.040|-15.0***|
|Drinking water|3.5|0.017|7.9|0.029|-4.4|
|Electricity|13.2|0.026|32.5|0.044|-19.3***|
|Cooking fuel|14.9|0.028|32.5|0.044|-17.6***|
|Housing|13.9|0.026|31.2|0.042|-17.2***|
|Sanitation|12.6|0.028|28.3|0.044|-15.8***|
|Unemployment|1.5|0.007|3.2|0.012|-1.6|
|Legal id|9.0|0.021|23.0|0.032|-14.0***|

Notes: h0 is the proportion of male- and female-headed households deprived in various indicators, SE- standard errors. Asterisks indicate statistical significance at 1% ***, 5% **, and 10% * levels.

_Table A6: Censored headcounts of each indicators in the MPI, by the gender of the household head in Sudan (2018)_

|Sudan|Male-heade|d HH|Female-head|ed HH|Difference|
|---|---|---|---|---|---|
||h0|SE|h0|SE||
|Years of Schooling|2.5|0.004|5.6|0.009|-3.1***|
|School attendance|10.8|0.015|17.7|0.019|-6.9***|
|Food Security|12.1|0.014|21.8|0.021|-9.7***|
|Pregnancy care|3.6|0.009|5.6|0.011|-2.0|
|Physical safety|6.6|0.011|12.1|0.017|-5.5***|
|Early marriage|12.9|0.015|19.3|0.021|-6.4***|
|Waste Disposal|15.7|0.017|25.6|0.025|-9.9***|
|Drinking water|13.2|0.016|21.6|0.018|-8.4***|
|Electricity|14.0|0.016|24.5|0.021|-10.5***|
|Cooking fuel|16.6|0.018|27.0|0.024|-10.3***|
|Housing|16.6|0.018|26.9|0.024|-10.2***|
|Sanitation|13.2|0.015|20.8|0.023|-7.6***|
|Unemployment|7.0|0.012|11.0|0.022|-4.0*|
|Legal id|0.0||0.2|0.001|0.0|
|Bank account|16.0|0.017|26.2|0.025|-10.2***|

Notes: h0 is the proportion of male- and female-headed households deprived in various indicators, SE- standard errors. Asterisks indicate statistical significance at 1% ***, 5% **, and 10% * levels.

36
_Africa’s Macroeconomic Story_

# Source: World Bank _World Development Indicators_ and IMF _World Economic Outlook_ databases.

The transition to a moderate inflation environment occurred much later in countries outside the CFA franc zone. The median inflation rate averaged around 15 percent right up until the mid1990s. Efforts made to correct fiscal imbalances (mitigating the need for inflationary finance) and curb inflationary pressures began to pay off. By the late 1990s, inflation declined to single digits in around half of the countries. For the most part, monetary authorities have been able to contain inflationary pressures stemming from the dramatic increases in food and energy prices in 2008 and 2011. Most countries are expected to bring inflation below 10 percent over the next few years.

Operational aspects of monetary policy have varied significantly across countries making it difficult to analyze policy responses for the region as a whole. That said, two observations are worth highlighting. First, broad money growth over the past decade has been much more stable in the CFA franc zone than in other counties (Figure 18). The rapid expansion in the global credit cycle over the period 2005-07 had little impact on broad money growth in the zone, but had a major impact in most other countries. Second, policy interest rates have begun to play a more prominent role in several countries. Monetary policy responses have been particularly aggressive in Uganda, Kenya and Malawi where policy rates increased sharply in an effort to contain excessive inflationary pressures (Figure 19, left panel).

**Figure 18: Broad Money Growth – Africa Region, 2005-13**

_Percent_ <!-- Start of picture text --> 30<br>20<br>10<br>0<br>2004 2006 2008 2010 2012<br>CFA franc zone Flexible regimes<br><!-- End of picture text --> Source: IMF _Regional Economic Outlook Sub-Saharan Africa_ May 2013.

27
**Figure 2: Increase in remittances after large natural disasters (disaster costs as share of GDP)**

<!-- Start of picture text --> Year before<br>Disaster year<br>Year after<br><!-- End of picture text -->

* These represent the years in which developing countries experienced the high damages as a share of GDP from natural disasters. Damages due to natural disasters were 0.04 percent of GDP in El Salvador in 1986, 0.08 percent of GDP in Honduras in 1998, 0.01 percent of GDP in Guyana in 2004 and 0.01 percent of GDP in Jamaica in 2004.

Source: Authors’ calculations using International Emergency Disasters Database (EM-DAT) and World Development Indicators (WDI), World Bank.

Migrant remittances have an important consumption-smoothing effect and can contribute to financing household investment in concrete housing and communication equipment to increase ex-ante preparedness and to mitigate the impact of disasters in disaster prone areas. Several country studies using household survey data confirm the consumption smoothing role played by remittances in recipient households (see Quartey and Blankson 2004). Yang and Choi (2006) show for the Philippines that remittances help to compensate for nearly 65 percent of the loss in income due to rainfall shocks.<sup>5</sup> Evidence from small-scale surveys conducted after disasters suggest that migrant remittances may have helped recipient households. A survey of households in four villages in Pakistan after a devastating earthquake in 2005 reveals that migrant remittances were important factors in disaster recovery and reconstruction (Suleri and Savage, 2006). The authors suggest quickly restoring banking and financial services to facilitate remittance flows. Remittance-receiving households in the Aceh region of Indonesia were found to have recovered faster from the 2004 Tsunami though because of immediate relief provided by migrant remittances, although remittance transfers were adversely affected due to the disruption of financial services and informal remittance transfer channels (Wu 2006).

> 5 However, it is possible that the loss of the most able household members who migrate may make it difficult for the remaining household members to cope with shocks including natural disasters.

5
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<mark>Bluedorn, J., F. Caselli, N.-J. Hansen, I. Shibata, and M. M. Tavares (2023). Gender and employment in the COVID-19 recession: Cross-Country evidence on “She-Cessions.</mark> _<mark>Labour Economics</mark>_ <mark>, 81: 102308.</mark> <mark>Bundervoet, T., M. E. Dávalos, and N. Garcia (2022). The short-term impacts of COVID-19 on households in developing countries: An overview based on a harmonized dataset of highfrequency surveys.</mark> _<mark>World Development</mark>_ <mark>, 153(May): 105844.</mark> <mark>Bruhn, M., A. Demirguc-Kunt, and D. Singer (2023). Competition and firm recovery postCOVID-19.</mark> _<mark>Small Business Economics</mark>_ <mark>. https://doi.org/10.1007/s11187-023-00750-w</mark> 40
states (FCS) and non-FCS states, or IDA-eligible and IBRD-eligible countries. Such a classification is certainly attractive and simple, though it should be noted that analytical presentations of statistics based on these groupings are readily available in _World Development Indicators_ and in other World Bank databases.

Another widely expressed concern was that thresholds are absolute figures remaining constant in real terms. As a result, if average world income continues to increase along current trends, the high income country threshold will eventually fall below the average world income level. Figure 3 shows the income thresholds and the average world GNI per capita from 1990 to 2013. Some users hold the opposite view, however, arguing that the absolute nature of the thresholds is useful for tracking progress over time. But even then some commentators (e.g., Ravallion, 2012) feel that the threshold levels need updating to reflect the changing world, similar to the need to update poverty lines to reflect changing standards of living and societal preferences.

**Figure 3. Income classification thresholds and average world GNI per capita (current US$, Atlas method, log scale)**

<!-- Start of picture text --> 12,735<br>Upper middle/high<br>threshold 10,779<br>7,620<br>World average<br>4,187 4,125<br>2,465 Lower‐middle/upper‐<br>middle threshold<br>1,045<br>Low/lower‐middle<br>610<br>threshold<br>1990 2014<br><!-- End of picture text --> _Source_ : http://siteresources.worldbank.org/DATASTATISTICS/Resources/OGHIST.xls, and _World Development Indicators_ , accessed November 30, 2015 (NY.GNP.PCAP.CD) 13