rafmacalaba/datause-encoder-data huggingface dataset:
One of the challenges of donor-led statistical capacity building is to develop statistical systems that can function independently. External international aid should not lead to perpetual dependency. The strategy proposed in this paper creates a sustainable trajectory for developing NSSs that meets the growing demands of local and global data users.

The paper is organized as followings. Section 2 discusses the evolution of approaches to statistical capacity building and reviews measures of statistical capacity. Section 3 discusses the main properties of the SPI and provides some descriptive results. Section 4 presents the methodology for assessing demand for and supply of data. Section 5 discusses the policy implications of our analysis. Section 6 summarizes the paper’s main conclusions.

# **2. Evolution and Measurement of Statistical Capacity**

Over the past 20 years, new data sources and increasing user demands have redefined what is meant by a capable NSS. As a result, assessment and measurement tools have also evolved.

# **_Evolution of Statistical Capacity_**

The Millennium Summit in 2000 triggered a new conceptual framework for improving official statistics in developing countries. Before the summit, key statistics produced by many developing countries were of poor quality or nonexistent. Domestic resources allocated for statistics were inadequate, limiting the production of censuses and surveys. Technical and financial aid for statistics was almost exclusively bilateral and uncoordinated, often to support specific areas reflecting the priorities of donors, not countries.

The Millennium Declaration called for a new approach to statistical capacity in developing countries to improve development outcomes and track performance. The approach emphasized country-led statistical development plans, monitorable results, and coordinated donor support to address shortcomings in domestic resources for both the supply of and demand for statistics. New global institutions, such as the Partnership in Statistics for Development in the 21st Century (PARIS21) and the UN body to coordinate statistical activities, were established to promote cooperation between the international donor community and statistical producers in developing countries and advocate for improved statistics in key international forums.

The Sustainable Development Goals (SDGs), adopted in 2015, introduced a set of indicators to be produced by NSSs. Global leaders encouraged official statisticians to tap into the exponential 4
<u>Table 6 - In-sample direct estimates vs. out-of-sample small area estimates</u>

||(1)<br>Male LFP|(2)<br>Female LFP|(3)<br>Male<br>Unemp|(4)<br>Female<br>Unemp|
|---|---|---|---|---|
|**Average estimated mean**<br>**squared error (x1,000)**|||||
|Direct (in sample only)|9.329|5.494|0.288|0.161|
|SAE (in sample only)|3.331|4.991|0.179|0.093|
|SAE (out of sample only)|6.736|10.458|0.379|0.202|
|**Coverage rate**|||||
|Direct (in sample only)|0.971|0.853|0.409|0.215|
|SAE (in sample only)|0.988|0.990|0.952|0.976|
|SAE (out of sample only)|0.974|0.956|0.940|0.979|
|**Estimated mean absolute error**|||||
|Direct (in sample only)|0.061|0.071|0.022|0.017|
|SAE (in sample only)|0.028|0.040|0.009|0.006|
|SAE (out of sample only)|0.046|0.073|0.013|0.009|
|**Estimated rank correlation**<br>|||||
|Direct (in sample only)|0.471|0.623|0.363|0.324|
|SAE (in sample only)|0.541|0.750|0.497|0.264|
|SAE (out of sample only)|0.225|0.312|0.309|0.207|
|**Estimated Pearson correlation**|||||
|Direct (in sample only)|0.493|0.659|0.418|0.385|
|SAE (in sample only)|0.553|0.770|0.415|0.614|
|SAE (out of sample only)|0.164|0.298|0.163|0.131|
|In-sample municipalities|1,072|1,072|1,072|1,072|
|Out-of-sample municipalities|569|569|569|569|

Coverage rate is equal to one if the true census value is within 1.96 standard errors of the point estimate.

There are several patterns to note. First, looking at estimated mean squared error, estimates for non-sampled municipalities are approximately half as precise as those from sampled municipalities, and - except for male LFPs - are less precise than the direct estimates for sampled municipalities. Second, for unemployment rates, the coverage rate is markedly better for the out-of-sample small area estimates relative to the direct estimates. Third, small area estimates are far less accurate out of sample than in-sample. For example, for female LFP estimated absolute deviation is approximately twice as large and correlations are nearly twice as low for out-of-sample municipalities compared with in-sample municipalities. Comparisons between survey-based insample municipalities and out-of-sample small area estimates are more mixed. With the exception of female LFP, out-of-sample estimates have slightly lower estimated mean absolute deviation than in-sample survey estimates, but far lower correlations.

21
# **7. TABLES AND CHARTS**

**Table 1. Characteristics of the hypothetical individuals:**

||(1)|(2)|
|---|---|---|
|Year of arrival|1975|1985|
|Age at arrival|25|25|
|Level of education|At least college degree|At least college degree|
|Age as of Census 1980|30|-|
|Age as of Census 1990|40|30|
|Age as of Census 2000|50|40|

**Table 2. Summary Statistics of Datasets Extracted from the US Census Samples**

||**DE**|**CADE 1970-19**|**79**|**DECADE**|**1980-1989**|
|---|---|---|---|---|---|
||**Census 1980 **|**Census 1990 **|**Census 2000 **|**Census 1990 **|**Census 2000**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|**Individuals in the sample**||||||
|**(unweighted)**|41,234|39,445|35,880|75,851|78,745|
|**Total number of people**<br>**(after weighting)**|824,680|847,573|750,289|1,668,342|1,704,575|
|**Age interval**|16-40|26-50|36-60|16-50|26-60|
|**Median age**|30|40|49|32|41|
|**Years since arrival**||1970-1974||1980|-1984|
|six to ten|41%|40%|38%|47%|43%|
|||1975-1979||1985|-1989|
|one to five|59%|60%|62%|53%|57%|
|**By education:**||||||
|None or preschool|4%|7%|8%|8%|6%|
|Grade 1, 2, 3, or 4|9%|9%|7%|6%|4%|
|Grade 5, 6, 7, or 8|25%|20%|21%|17%|19%|
|Grade 9|5%|5%|4%|6%|6%|
|Grade 10|4%|3%|2%|3%|3%|
|Grade 11|4%|2%|2%|3%|2%|
|Grade 12|19%|20%|21%|23%|24%|
|1 to 3 years of college|9%|13%|13%|13%|14%|
|4+years of college|21%|21%|22%|21%|21%|

28
# _Infrastructure for Basic Services_

In developing countries the infrastructure for the provision of water and energy is poor or nonexistent. These services thus have to be acquired by household members. In most settings the burden of provision of these services for the household falls largely on the female members.'<sup>2</sup> Since these are "outside chores" that are time intensive, a few obvious questions come up:

- Do "outside" chores constrain women from allocating time to income-generating work more than they do men?

- Or, do they increase the total work burden of women more than men.

The existing literature provides mixed results on this. Kumar and Hotchkiss (1988) find that a deterioration of access to forest wood tends to lower the allocation of women's labor to onfarm activities in the hills of rural Nepal. However, the Nepal region is a case of a peasant agricultural system with little hired labor. Thus it is not surprising that increased demands on women's time through a worsening of access to forest energy results in a reduction in their time on the farn. Ilahi and Jafarey (1998) extend this to all of rural Pakistan. Here rural markets for labor exist. Their results indicate that while worsening access to firewood increases the time women have to allocate to collection activities, there is no countervailing drop in time allocated to income-generating work. The case of water collection is somewhat different. Ilahi and Grimard (1998) find that more difficult access to water in rural Pakistan does result in significantly less time allocated to earning activities. This is understandable when one considers the fact that water is quite inelastically demanded (it has little substitutes) whereas firewood has a number of substitutes (coal, kerosene, dung etc.).

> 12 There is some evidence that certain activities are also carried out by men. For instance Fafchamps and Quisumbing (1998) who analyze intra-household time use pattems in three rural districts in Pakistan find that water collection is in the domain of women while wood collection for energy are largely male activities.

9
_Page 20_ workers. Research Personnel presented in the table are full-tlme equivalent and expressed as a proportion of million porpulation.

Data on China and Talwan were supplemented/obtained, respectively, from the following sources: I China Statistical Information and Consultancy Service Center, Beijing, China: Statistics Abstract, 1988. Ii) Republic of China, Council for Economic Planning and<sup>Development,</sup> Taiwan Statistical Data Book, 1988.
**Table 9: Determination of employment and earnings at the regional levels – using years of school as an indicator of human capital**

||Central R<br>Coefficient<br>|egion<br>t-statistics|Eastern<br>Coefficient|Region<br>t-statistics|Zagreb R<br>Coefficient|egion<br>t-statistics|Adriatic W<br>Coefficient|est Region<br>t-statistics|Adriatic So<br>Coefficient|uth Region<br>t-statistics|
|---|---|---|---|---|---|---|---|---|---|---|
|**_Estimation of incom_**|**_e equations (depe_**|**_ndant varia_**|**_ble: monthly e_**|**_arnings (in t_**|**_housand kuna)_**|**_)_**|||||
|age|0.124|9.73|0.094|6.62|0.060|3.4|0.122|4.56|0.044|2.18|
|age*age|-0.001|-8.46|-0.001|-4.98|0.000|-1.63|-0.001|-3.6|0.000|-0.93|
|years of school|0.333|56.61|0.283|40.01|0.344|49.38|0.302|27.15|0.300|30.71|
|sex (male=0,|||||||||||
|female=1)|-0.628|-19.26|-0.571|-14|-0.525|-15.17|-0.900|-15.95|-0.810|-16.18|
|constant|-3.287|-11.79|-2.252|-6.9|-1.944|-5.01|-2.418|-4.06|-1.018|-2.2|
|**_Estimation of selecti_**|**_on equations_**||||||||||
|age|0.198|39.2|0.198|34.33|0.280|46.89|0.263|33.27|0.230|35.93|
|age*age|-0.003|-41.25|-0.002|-35.68|-0.004|-50.54|-0.003|-35.65|-0.003|-37.89|
|years of school|0.022|6.33|0.079|21.13|0.058|15.51|0.047|8.9|0.072|16.61|
|sex (male=0,|||||||||||
|female=1)|-0.218|-9.93|-0.412|-16.16|-0.148|-6.69|-0.215|-6.9|-0.196|-7.24|
|married|0.235|9.97|0.241|8.97|0.239|9.26|0.134|3.73|0.186|6.28|
|household head|0.104|4.01|0.198|6.79|0.183|7.27|0.188|5.3|0.317|9.99|
|constant|-3.655|-38.27|-4.464|-41.36|-5.563|-49.4|-5.040|-33.31|-5.094|-41.38|
|Log likelihood|-31949.15||-23680.53||-27311.09||-15275.03||-21660.24||
|Number of|||||||||||
|observations|19519||16479||18379||9355||14416||

18
_Source: EUROSTAT, WB staff calculations._ Although poverty is multidimensional, we focus on a single dimension with the purpose of maximizing comparability among the various data sources used. Thus, in the context of this work we focus exclusively on the monetary dimension of poverty, using one main measure, namely, the 2011‐anchored AROP (at risk of poverty). This measure uses a relative poverty line for each member, but keeping it constant for all the years within the analysis. A country’s poverty line is defined as 60% of its equivalized median income anchored on the 2011 value. Figure 2 shows the trends from 2004 to 2014 for GDP per capita and anchored monetary poverty (using an anchored relative poverty line for each member state). Since the survey coverage changes over time due to the expansion of the EU membership, we compute separate lines for each cohort of member states in terms of comparable data availability. The figure shows that although GDP per capita is already above its pre‐crisis level, the relative anchored poverty level is at higher or at the same pre‐crisis level, suggesting that growth during the recovery has not been inclusive. The case for the Southern EU member states is particularly alarming.

**Figure 2.** Although GDP per capita has recovered, anchored relative poverty rates are still higher. _Source: EUROSTAT, WB staff calculations._

<!-- Start of picture text --> Average Anchored Relative Poverty Rate GDP per inhabitant, PPS<br>0.4 35<br>0.35<br>30<br>0.3<br>25<br>0.25<br>20<br>0.2<br>15<br>0.15<br>10<br>0.1<br>0.05 5<br>0 0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Central EU Northern EU Central EU Northern EU<br>Southern EU Western EU Southern EU Western EU<br>Thousands<br><!-- End of picture text --> Furthermore, absolute poverty levels remain high across the EU. In order to compare absolute poverty levels between countries, we define the median of the absolute national poverty lines of all EU Member States as the absolute poverty line to use across countries. This gives an absolute poverty line of US$21.70 per day (in PPP). Using this measure, poverty remains high across the EU, and further stresses the large disparities across countries. Figure 3 below compares the poverty rates that result from using absolute measure and relative measures.

**Figure 3.** Poverty rates between member states are extremely different using an absolute poverty line, compared to a relative measure.

4
- Nguyen, T. 2008. “Information, Role Models and Perceived Returns to Education: Experimental Evidence from Madagascar.” _Job Market Paper_ . Cambridge, MA: Massachusetts Institute of Technology. Available at: https://bit.ly/2Omx17q

- Orkin, K., R. Garlick, M. Mahmud, R. Sedlmayr, J. Haushofer, and S. Dercon. 2024. “Aspiring to a Better Future: Can a Simple Psychological Intervention Reduce Poverty?” _NBER Working Paper_ 31735. February version available at: https://shorturl.at/GPRST

- Papageorge, N. W., S. Gershenson, and K. M. Kang. 2020. “Teacher Expectations Matter.” _Review of Economics and Statistics_ 102(2): 234–251.

- Rakshit, S., and S. Sahoo. 2023. “Biased Teachers and Gender Gap in Learning Outcomes: Evidence from India” _Journal of Development Economics_ 161 (103041).

- Ray, D. 2006. “Aspirations, poverty and economic change.” in A. Banerjee, R. Benabou, and D. Mookherjee (eds.). _Understanding Poverty_ . pp. 409–22. Oxford: Oxford University Press.

- ———. 2016. “Aspirations and the Development Treadmill.” _Journal of Human Development and Capabilities_ 17(3): 309–323.

- Ridley, M., G. Rao, F. Schilbach, and V. Patel. 2020. “Poverty, depression, and anxiety: Causal evidence and mechanisms”. _Science_ 370(6522).

- Riley, E. 2024. "Role models in movies: the impact of Queen of Katwe on students' educational attainment." _Review of Economics and Statistics_ 106 (2): 334–351.

- Rojas Valdés, R. I., Lybbert, T. J., and B. Wydick. 2022. “Can Hope Elevate Microfinance? Evidence from Oaxaca, Mexico.” _Oxford Economic Papers_ 74(1): 236–264 _._

- Ross, P. H. 2019. “Occupation Aspirations, Education Investment, and Cognitive Outcomes: Evidence from Indian Adolescents.” _World Development_ 123: 104613.

- Ross, P. H., P. Glewwe, D. Prudencio, B. Wydick. 2021. “Developing Educational and Vocational Aspirations through International Child Sponsorship: Evidence from Kenya, Indonesia, and Mexico.” _World Development_ 140: 105336.

- Saltiel, F. 2023. “Multi-Dimensional Skills and Gender Differences in STEM Majors” _Economic Journal_ 133(651): 1217–1247.

- Seager, J., T. M. Asaduzzaman, S. Baird, S. Sabarwal, and S. Tauseef. 2022. “Gender, Growth Mindset, and Covid-19: A Cluster Randomized Controlled Trial in Bangladesh”. _Review of Economic Analysis_ 14(2): 183–219.

- Serra, D. 2022. “Role Models in Developing Countries.” In U. Dasgupta and P. Maitra (Eds.) _Handbook of Experimental Development Economics (forthcoming)_ . August 21 version, available at: PDF.

- Urzúa, S. 2012. “La rentabilidad de la educación superior en Chile. Revisión de las bases de 30 años de políticas públicas” (The Economic Returns to Higher Education in Chile: Revising the Foundations of 30 Years of Public Policies). _Estudios Públicos_ 125: 2–52.

32
17 spreads. Put in another way, the current fiscal outlooks for some of these countries are in the spirit of accidents waiting to happen.

Nor are emerging markets completely exempt from these risks. The levels of debt that are regarded as prudent in emerging markets—about 40 percent of GDP—is generally considerably lower than in the advanced economies, with their much deeper financial markets and better track records of debt management. Emerging markets tend to have lower revenue ratios, they sometimes are more dependent on financing by nonresidents and have a much more uneven history of debt defaults. According to the IMF, countries such as Brazil, Hungary, India, Pakistan, Poland, Egypt, and Thailand, among others, already have debt levels above 40 percent of GDP, sometimes substantially so. Poor developing countries are vulnerable as well, partly because they depend on aid provided by the rich industrial countries, both in the form of cash grants (about 4 percent of GDP on average) and others forms of concessional financing; and also because they are particularly vulnerable to capital account volatility, which complicates fiscal management.

**Table 2. Advanced economies’ gross financing needs, 2013**

(In percent of GDP)

||**Maturing debt**|**Budget deficit**|**Total**<br>**financing need**|**Gross debt**|**Average**<br>**maturity**<sup>**a**</sup>|
|---|---|---|---|---|---|
|Australia|3.1|3.1|6.2|29.1|5.6|
|Belgium|15.8|2.8|18.7|100.9|7.3|
|Canada|13.2|3.4|16.6|87.1|5.6|
|France|13.4|4.0|17.4|93.5|6.7|
|Germany|7.9|0.4|8.3|80.4|6.4|
|Greece|17.0|4.1|21.1|175.7|8.2|
|Ireland|5.6|6.7|12.4|123.3|12.1|
|Italy|25.2|3.2|28.4|132.3|6.4|
|Japan|48.9|9.5|58.4|243.5|6.4|
|Portugal|17.8|5.5|23.3|123.6|4.8|
|Spain|13.5|6.7|20.2|93.7|5.5|
|Sweden|3.5|1.4|4.9|42.2|5.4|
|United Kingdom|5.9|6.1|12.1|92.1|14.4|
|United States|18.1|5.8|23.9|106.0|5.5|

**Source: IMF Fiscal Monitor October 2013.** **_a_** In years.

# **_Extreme vulnerabilities_**

The above discussion does not pretend to present a comprehensive listing of the many ways in which various factors are likely to put pressures on public resources over the next 10-20 years and beyond. (No effort is made, for instance, to explore the ramifications of a war in the Middle East, through its impact on oil prices, investor and consumer confidence and what, thus far anyway, remains a fragile economic recovery from the global financial crisis.) Indeed, it is not inconceivable that well before the impact of population aging and climate change kick in with all their force, there is some other
P a g e <u>|</u> **3**

Without government action, slum populations can be expected to grow in urban India even in the face of rapid income growth. Table 1 illustrates a variety of scenarios for populations under the poverty line in India. These scenarios use the population projections from McKinsey (2010) and draw from the range of estimates of the elasticity of poverty headcount percentages with respect to economic growth. (Murgai and Zagha: 2010). Even with sustained rapid income growth, millions in cities are likely to remain poor and be unable to afford access to formal housing. This is because the urban population in India is due to grow considerably over the next 20 years. Even with rapid income growth, and much lower poverty incidence, the numbers of poor people in cities will continue to be substantial. Even with the arguably implausibly optimistic scenario of reaching a 2% poverty incidence in India’s cities, 11 million people will be poor. A more likely scenario suggests that twenty years from now, somewhere between 30 and 40 million people in urban India will be poor, even if the economy performs very well over the long term.

|**Scenarios of # of Po**<br>**Elasticity of**<br>**Poverty with**<br>**respect to Income**<br>**Growth**|**or in Cities**<br>**Income**<br>**Growthpa **|**Period**<br>**in Years**|**# of Urban**<br>**Poor 2030 M**|**Urban**<br>**Poverty**<br>**incidence**|**Total**<br>**Urban**<br>**Population**<br>**2030 M**|
|---|---|---|---|---|---|
|**0.5**|**8%**|**20**|**65**|**11.1%**|**590**|
|**1**|**8%**|**20**|**28**|**4.7%**|**590**|
|**1**|**6%**|**20**|**43**|**7.3%**|**590**|
|**1.2**|**10%**|**20**|**11**|**1.9%**|**590**|

**Table 1: Number of people under the poverty line in urban India under different poverty elasticity assumptions Source: Authors’ calculations.**

This paper outlines a new strategy in four main sections:

- 1) Shaping the market to ‘right-size’ the need for subsidies. The supply of urban real estate markets in India is deeply constrained by misguided or outdated government policies. These policies have a dramatic effect on prices and the quite unsurprising result is by far the largest slum population of any country in the world. India’s vast and growing slums are not an inevitable result of fast urban growth or poverty. Vietnam’s cities are growing at twice the rate in India, per capita income is roughly
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spring of 2020. Other potential reasons for discrepancies include the significant informality in the labor market in developing countries, and the difficulty that GDP projections face in incorporating the role of labor market institutions and labor market policy responses to the pandemic.

Interestingly, both household and firm survey measures of business revenues are more aligned with projected GDP growth. Using recent firm survey data and the HFPS question “Has the revenue from that business decreased since the pandemic started?”, the two panels of Figure 10 show that when available, business sales and revenue data are more correlated with the downward adjustment of the WEO GDP projections. This suggests that GDP projections are better at capturing the impact of the pandemic on business operations, which are more likely to be at least somewhat formalized or interact more closely with the formal economy.

Another important external source of information comes from the labor market indicators reported by the ILO. These indicators are based on national labor force surveys which ascertain labor market outcomes for all adults in the household and are based on larger samples than the phone surveys, which we employ for our analysis. We therefore compare the phone survey measure, the share of stopped working, with the ILO employment-topopulation ratio data available for the same time frame, quarter 1 and quarter 2 of 2020.<sup>9</sup> Comparing the phone survey measure, the share of stopped working, with the ILO employment-to-population ratio data available for the same time frame, unfortunately only 12 of the HFPS countries that have the share of stopped working variable for this period overlap with the quarterly ILO data, which also are limited in terms of current availability. Figure 11 shows that most of the countries seem to have higher values for the labor market change measured by the HFPS data than for the labor market changes measured by the ILO data, although the two measures are strongly correlated (0.74).<sup>10</sup>

> 9The data come from the ILO collection STLFS Short-term labor force indicators, updated December 3, 2020.

> 10Looking at measure of stop work for specific broad sectors, industry and services, a very similar picture emerges as for the general stop work measure; however, the country subset is even smaller.

23