rafmacalaba/datause-encoder-data huggingface dataset:
model from using median consumption compared to using mean consumption. For both the full sample and the 2011 sample, the fit as assessed by the r-squared statistic is better for the median specification. Or, alternatively, the root mean square error (RMSE) of the observed values from the predictions is about 10 percent less when the median is used rather than the mean.<sup>22</sup> Panel B indicates that the median also provides a better fit than the mean when considering log-log specification of the model.

We also examine log-log specifications of this bivariate model because they minimize proportionate differences (and not level differences) in national poverty lines. While the levels specification equally weights a dollar deviation between the fitted line and the observed national poverty line regardless of the wellbeing level of a country, the log-log specification will weight this deviation more heavily for a poorer country where a dollar deviation is a substantially larger percentage difference from the national poverty line. For this reason, we find the log-log fit as meriting more emphasis in evaluating differences across specifications.

Furthermore, in columns 3 and 6, when we include both the mean and the median in the specification, for both samples the mean is statistically insignificant while the median remains largely unchanged from the value it takes when included alone. We interpret this as suggesting that the median has a stronger signal in explaining national poverty lines relative to mean consumption. Our findings align with the view of Birdsall and Meyer (2015) and Stiglitz et al. (2010) who both argue that median household consumption (or income) is a better measure of material wellbeing in a country than mean household consumption (or income). A candidate explanation for preferring the median is that because consumption and income distributions

> 22 The RMSE is 1.1 on the full sample when using the median, and this increases to 1.25 when using the mean on the full sample.

15
Policy Research Working Paper 10559

# **Abstract**

Capabilities to track fast-moving economic developments re-main limited in many regions of the developing world. This complicates prioritizing policies aimed at supporting vulnerable populations. To gain insight into the evolution of fluid events in a data scarce context, this paper explores the ability of recent machine-learning advances to produce continuous data in near-real-time by imputing multiple entries in ongoing surveys. The paper attempts to track inflation in fresh produce prices at the local market level in Papua New Guinea, relying only on incomplete and intermittent survey data. This application is made challenging by high intra-month price volatility, low cross-market price correlations, and weak price trends. The modeling approach uses chained equations to produce an ensemble prediction for multiple price quotes simultaneously. The paper runs cross-validation of the prediction strategy under different designs in terms of markets, foods, and time periods covered. The results show that when the survey is well-designed, imputations can achieve accuracy that is attractive when compared to costly–and logistically often infeasible–direct measurement. The methods have wider applicability and could help to fill crucial data gaps in data scarce regions such as the Pacific Islands, especially in conjunction with specifically designed continuous surveys.

This paper is a product of the Development Data Group, Development Economics, the Agriculture and Food Global Practice; and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bandree@worldbank.org and upape@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_Brazilian spatial labor dynamics Page 19_

**Table 7A.  Summary Statistics, all Brazil**

|**Variable**|**Obs**|**Mean**|**Std.Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Delta ln Wage (2000/1991)|4267|-0.0101|0.2935|-1.5242|0.8495|
|ln Teacher qualification in 1991|4267|1.3938|2.1088|-4.6052|4.2370|
|Years of Schooling in 1991|4267|3.9181|1.4556|0.4425|9.6572|
|Total rainfall|4266|1337.3190|425.4345|368.6636|3361.7090|
|Government Accountability|4267|0.9121|0.2832|0.0000|1.0000|
|Delta ln Employment (2000/1991)|4267|0.1422|0.2224|-0.6961|1.8755|
|lnTransport Cost to São Paulo in 1995|4266|7.0420|0.8469|2.3026|9.2603|
|ln Transport Cost to State Capital in<br>1995|4256|5.8180|0.7780|1.3863|8.6910|
|Delta ln Transferences (2000/1991)|4267|0.8083|0.3221|-1.1823|2.4240|
|Delta ln Market Potential (2000/1991)|4,258|0.4093|0.1375|-0.4691|2.0949|
|ln Population in 1991|4,267|9.5287|1.0888|6.6214|16.0821|
|Dependency Ratio in 1991|4267|0.4775|0.0466|0.3738|0.6205|
|ln Transport Cost to São Paulo in 1968|4266|7.6264|0.7874|3.2189|9.6385|
|ln Transport Cost to State Capital in<br>1968|4256|6.2548|0.7959|1.3863|8.6910|
|ln Proportion of Elderly in 1991|4267|-2.2502|0.2621|-3.9145|-1.5332|
|Illiteracy Rate in 1991|4267|35.5004|17.8266|2.4800|87.0500|
|Urbanization Rate in 1991|4267|53.6356|23.1683|2.1562|100.0000|
|Population Density in 1991|4267|93.6426|510.3321|0.0896|12199.7700|
|ln 5 to 15 over 15 to 55 years old Ratio|4267|-0.7491|0.2303|-1.6218|-0.0354|
|Repetition Rate in 1991|4267|4.2935|0.2071|2.3542|4.5756|
|ln Employment Rate in 1991|4267|-0.3760|0.1411|-1.2429|0.1601|
|Delta ln Education Market Potential<br>(2000/1991)|4251|0.9737|0.1736|0.0535|2.7704|
|Percentage of Good Soils|4266|0.7904|0.3071|0.0000|1.0002|
|Rainfall - First Principal Component|4266|0.0000|2.2685|-4.2315|6.9861|
|Rainfall - Third Principal Component|4,266|0.0000|1.5749|-2.6324|9.2440|
|ln Wage in 1991|4267|8.0265|0.4963|6.6139|9.8121|
|Proportion of Natives in 1991|4267|0.6565|0.1790|0.0855|1.0134|
|Homicides per Capita in 1991|4265|0.0016|0.0011|0.0000|0.0090|
|Employment in Farming Activities in<br>1991|4,267|0.1777|0.0854|0.0000|0.5437|
|Bank Dummy (1991)|4,267|0.7492|0.4335|0.0000|1.0000|
||4267|9.5287|1.0888|6.6214|16.0821|
|Average Temperature|4266|22.3050|2.8451|13.8325|27.8109|
|Fertility Rate in 1991|4,267|3.6449|1.1885|1.7600|8.6800|
# **Methodology**

## **_Level of analysis: minimum comparable areas (MCA) 2000-2001_**

Conceptually, the analysis was performed at the level of the municipio. However, some municipios fissioned or recombined between 2000 and 2001, and our data include elements from before and after this redefinition. To integrate the data, we use the _minimum comparable areas (MCA) —_ where an MCA consists of the smallest set of municipios that has a consistent boundary over time. Of 5480 MCAs, 45 consist of aggregates of multiple municipios comprising in total 125 municipios of year 2001. To assemble municipal-level categorical data to MCAs we use the majority value; to integrate continuous data we use sums for absolute and means for relative information.

## **_Data_**

<u>Environmental institutions:</u> The data is at the _municipio_ level and taken from the Brazilian Municipal Environmental Survey conducted in 2001 (IBGE 2002). We look at two environmental institutions. The first is the presence of a dedicated environmental secretary within the municipal administration. The municipal environmental secretary ( _Secretaria Municipal de Meio Ambiente_ , SMMA) is the local branch of Brazil’s national system of the environment ( _Sistema Nacional do Meio Ambiente_ , SISNAMA) in charge of implementing municipal environmental policy. The second is the presence of an _active environmental council_ . In Brazil, a municipal environmental council _(Conselho Municipal de Meio_ _Ambiente_ , CMMA) has the function of advising and assessing the municipal executive power, on environment-related subjects (MMA 2006). In this paper, we an active environmental council as onel that is reported to meet more than once a year and at least half of whose members are from civil society.

Municipal environmental councils are found in 34% of the _municipios_ and 26% provided information on meeting frequency (IBGE 2002). 8.86% these municipal environmental 4
<!-- Start of picture text --> School Participation & Progression - (Adult)<br>Study cauntrycode Type of intervention ES (05% Cl) ‘Outcome variable<br>High Income - Adult<br>(Rossin-Slater and Wust 2020/NK Preschool Construction = 0.15 (0.04, 0.28) T, Years of schooling (30-60 y.0.)<br>(Rossin-Slater and Wust 2020/NK Preschool Construction 0.04 (0.02, 0.08) T, More than compulsory education (30-00 yo.)<br>(Haynes and Mogstad 2011) NOR Subsidized Childcare (Expansion) 0.07 (0.01, 0.02) T, Years of completed education (33-28 yo.)<br>(Haynes and Mogstad 2011) NOR Subsidized Childcare (Expansion) 0.03 (0.01, 0.04) T, Attended college (23-36 yo.)<br>(Haynes and Mogstad 2011) NOR Subsidized Childcare (Expansion) 0.03 (0.01, 0.05) T, High school dropout* (33-36 y.o.)<br>(Bailey et al 2020) USA (HS) - Subsidized Preschool 0.09 (0.02, 0.76) T, Professional or doctoral degree (25-54 y.o.)<br>(Bailey et al 2020) USA (HS) - Subsidized Preschool + 0.13 (0.06, 0.20) T, Years of schooling (25-54 y.0.)<br>(Bailey et al, 2020) USA (HS) - Subsidized Preschool 0.06 (-0.01, 0.13)T, High school completion (25-54 y.o.)<br>(Bailey et al, 2020) USA (HS) - Subsidized Preschool 0.07 (-0.00, 0.14) T, Attended college (25-54 yo.)<br>(Bailey et al, 2020) USA (HS) - Subsidized Preschool + 0.12 (0.05, 0.19) T, Completed college (25-54 y.o.)<br>(Cameiro and Ginja 2014) USA (HS) - Subsidized Preschool 1.25 (-0.15, 2.65) T, High school diploma (20-21 yo.) Male<br>(Cameiro and Ginja 2014) USA (HS) - Subsidized Preschool 0.12 (40.88, 0.6571, Ever in college (20-21 y.o.) Male<br>(Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.02 (-0.17, 0.21)T, College graduation (22 ya.)<br>(Heckman et al 2070) USA Subsidized Preschool He 1-40 (0.42, 2.37) T, High school graduation (19 yo.) Female<br>(Heckman et al 2070) USA Subsidized Preschool —_—_—) 1.20 (0.49, 1.92) T, Grade point average (19 y.o.) Female<br>(Heckman et al 2070) USA Subsidized Preschool _}_— 0.79 (0.23, 1.35) T, Highest grade completed (19 y.o.) Female<br>(Heckman et al 2070) USA Subsidized Preschool 0.04 (-0.11, 0.20)T, High school graduation (19 yo.) Male<br>(Heckman et al 2070) USA Subsidized Preschool 0.11 (40.47, 0.68)T, Grade point average (19 y.o.) Male<br>(Heckman et al 2070) USA Subsidized Preschool 0.08 (-0.38, 0.55)T, Highest grade completed (19 y.o.) Male<br>NOTE: Weights are from random effects analysis<br>“2.65 o 2.65<br><!-- End of picture text -->
still small (approximately 600,000 units), although it is expected to grow as rent control barriers are removed. The private rental sector is particularly significant in Warsaw (27.8 percent of households) and other larger urban areas (21.6 percent).

- The survey data indicate existence of only a marginal rental sector for Romania of 2.4 percent of the housing stock. This share is supported by the findings of the recent 2002 census about tenure structure among occupied units (although the large number of unoccupied units confuses the picture<sup>24</sup> ). However, high levels of tax evasion and the apparently widespread stigmatization of tenants arising from one of the most radical privatization programs suggest underreporting incentives for households living in rental units. A Bucharest city government survey of 2003 arrived at an estimate of the private rental share of 5 percent, more than 2.5 times the level recorded by the survey data (although in absolute terms the difference is only 2–3 percent). The public rental sector share, in contrast, has plausibly fallen to less than 2 percent of the housing stock.

- Similarly, the survey data are also likely to underestimate the size of the Russian private rental sector (at 3.6 percent). Puzanov (2004) estimates the sector size at about 5 percent; Kalinina and Klepikova (2002) describe private rental as the fastest growing real estate sector in Russia. The widespread tax informality and illegality of most rental arrangements in Russia is probably the largest factor contributing to under-reporting. The tenure status of the large reported public rental stock in Russia is undefined. Current legislation vests tenants with the free “callable” right to privatize, so public tenants can be interpreted as “pending” owners who have not yet exercised their privatization option. However, the new housing legislation recently adopted (the Housing Code) has stipulated a time limitation on these decisions. They would also curb the current practice of tenants in newly built municipal rental apartments privatizing their new units shortly after they get allocated to them.

- The survey data appear to capture well the proportions of the rental sector in Serbia. Official sources suggest a rental sector share among households of 7 percent, of which 5 percent are private tenants. The highest “free or other” share in the sample also concurs with official estimates.<sup>25</sup> It reflects the effect of numerous war refugees, who frequently live with families or in in-kind rental relationships, and a lack of cash in large segments of the population.

> 24  This could explain a part of the large number of unaccounted-for non-owner-occupied housing units that emerged from the 2002 Census. Of the 1.5 million units, only 212,000 are public rental units, leaving a gap of almost 1.3 million dwellings. These units should be vacant (possibly up to 700,000 of them), perform functions as second homes, or be part of the private rental sector.

> 25  See Zivanovic (2003).

18
# **Long duration of unemployment**

Poor employment opportunities along with skill and location mismatches lead to long duration of unemployment, which is the mirror image of low exit rates from unemployment, which prevail in transition economies. As a result, transition economies have seen the growth in the incidence of long-term unemployment. Presently the long term unemployment in CEE accounts for over 50 percent of total unemployment, which is much more than in EU, where it accounts for 40 percent. (Figure 9). After all, in the flexible, well performing EU labor markets the incidence of long-term unemployment at around 25 percent is still much lower. It is noteworthy, that in every EU transition economy the incidence of long-term unemployment is higher than the already high EU average. In Latvia, where long term unemployment is the least prevalent among European transition countries, it still accounts for 45 of total unemployment. Bulgaria is an extreme case, with two-thirds of all the unemployed suffering from long-term joblessness.

**Figure 9: Long-term unemployment looms large in all European transition economies**

## **Long-term unemployment, 2002 as percentage of total unemployment**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>OECD EU Hungary Latvia Poland Czech R. Estonia Lithuania Romania Croatia Slovenia SlovakiaSerbia and MontenegroBulgaria FYR Macedonia<br>percent<br><!-- End of picture text --> Source: Eurostat, OECD (2003) and national Statistcal Offices; Bank staff calculations.

The problem of long duration of unemployment is even more severe in the Southeastern transition economies, such as Croatia and FYR Macedonia, where stringent employment protection legislation has for long discouraged employers from hiring. This has led to the accumulation of a large pool of workers who find it extremely difficult to enter or re-enter the labor market (World Bank, 2003c; Rutkowski, 2003c).

To conclude, the high incidence of long-term unemployment in most transition economies points to a depressed, stagnant labor markets and possibly to structural mismatches. Once unemployed, people have little chances to find new employment. Long duration of 24
**Table 1. Gross capital formation, national and foreign savings**

|Foreign savings in|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|Average|
|---|---|---|---|---|---|---|---|---|---|---|---|
|<br>Mongolia<br>National savings|3.1|-9.8|7.7|5.7|7.4|6.1|9.4|7.2|-3.9|0.3|3.3|
|<br>Mongolia|26.8|37.9|27.5|31.3|28.8|30.0|22.8|30.8|40.4|35.2|31.1|
|Gross capital formation (|% of GD|P)||||||||||
|Mongolia|29.9|28.1|35.2|37.0|36.2|36.1|32.2|38.0|36.5|35.5|34.5|
|Cambodia|14.5|14.8|11.7|17.4|16.9|18.7|20.1|25.2|25.8|..|18.3|
|Vietnam|28.1|28.3|29.0|27.6|29.6|31.2|33.2|35.4|35.6|..|30.9|
|Azerbaijan|29.0|34.2|33.4|26.5|20.7|20.7|34.6|53.2|53.5|..|34.0|
|Kazakhstan|16.1|15.6|15.8|17.8|18.1|26.9|27.3|25.7|26.3|27.5|21.7|
|Kyrgyz republic|25.2|21.7|15.4|18.0|20.0|18.0|17.6|11.8|13.8|20.4|18.2|
|Uzbekistan|29.2|21.7|20.2|17.5|16.3|19.6|20.3|20.2|24.5|25.1|21.5|
|Ghana|21.2|24.8|23.1|20.9|24.0|26.6|19.8|22.9|27.9|29.6|24.1|
|Uruguay|15.2|15.2|15.9|15.1|14.0|13.8|11.5|13.1|13.3|..|14.1|
|East Asia and Pacific|36.5|34.8|30.8|29.5|29.9|30.8|31.5|33.1|34.4|..|32.4|
|Low Income Countries|20.9|21.1|20.8|21.9|21.3|21.5|21.9|22.3|23.0|..|21.6|
|Foreign aid (% of gross cap|ital formati|on)||||||||||
|Mongolia|56.9|84.9|59.5|66.4|63.5|57.8|57.1|51.4|44.3|..|60.2|
|Cambodia|82.9|65.8|92.4|45.2|64.6|59.3|59.2|46.5|38.0|..|61.5|
|Vietnam|13.6|13.1|14.9|18.0|18.2|14.2|11.0|12.6|11.4|..|14.1|
|Azerbaijan|10.5|13.6|8.1|14.0|12.8|19.7|16.2|7.8|3.8|..|11.8|
|Kazakhstan|3.7|4.1|6.4|5.8|5.7|2.5|2.8|3.4|2.3|..|4.1|
|Kyrgyz republic|50.1|62.5|94.2|125.8|78.4|69.0|65.8|88.1|84.9|..|79.9|
|Uzbekistan|2.2|4.4|5.2|5.2|8.3|6.9|9.6|9.5|8.3|..|6.6|
|Ghana|44.3|28.9|40.7|37.9|50.3|45.6|53.3|54.7|54.9|..|45.6|
|Uruguay|1.1|1.0|0.7|0.7|0.6|0.6|0.9|1.1|1.3|..|0.9|
|East Asia and Pacific|1.5|1.3|1.9|2.1|1.7|1.3|1.1|0.9|0.7|..|1.4|
|Low Income Countries|14.6|12.1|12.8|10.9|11.0|11.9|13.4|13.3|11.9|..|12.4|
|FDI (% of GDP)||||||||||||
|Mongolia|1.4|2.4|2.0|3.3|5.7|4.2|7.0|10.4|5.8|9.8|5.2|
|Cambodia|8.4|5.9|7.8|6.6|4.0|3.9|3.6|1.9|2.7|..|5.0|
|Vietnam|9.7|8.3|6.1|4.9|4.2|4.0|4.0|3.7|3.6|..|5.4|
|Azerbaijan|19.7|28.1|23.0|11.1|2.5|4.0|22.3|45.1|41.0|..|21.9|
|Kazakhstan|5.4|6.0|5.2|9.4|7.0|12.8|10.5|6.8|9.5|..|8.1|
|Kyrgyz republic|2.6|4.7|6.6|3.6|-0.2|0.3|0.3|2.4|3.5|..|2.6|
|Uzbekistan|0.6|1.1|0.9|0.7|0.5|0.7|0.7|0.7|1.2|..|0.8|
|Ghana|1.7|1.2|2.2|3.2|3.3|1.7|1.0|1.8|1.6|..|2.0|
|Uruguay|0.7|0.6|0.7|1.1|1.3|1.5|1.4|3.7|2.4|..|1.5|
|East Asia|3.9|4.0|4.1|3.3|2.6|2.7|2.9|2.6|2.5|..|3.2|
|LICs|1.3|1.5|1.3|1.2|1.3|1.5|1.6|1.4|1.4|..|1.4|

Source: World Bank (SIMA) Domestic private investment has increased in importance, but in 2004 its share in total investment was still relatively small and only one third of private domestic investment was financed by bank loans (Table 3). Although domestic credit to the private sector has 9
7. Economic stabilization, including macro-economic reforms, public sector rationalization, private sector development, poverty alleviation, health and human services, etc. Although Bangladesh has made significant advances in macroeconomic reforms, much remains to be accomplished in the restructuring of public enterprises and providing incentives for growth of the private sector such as liberalization of the financial markets, clarity and consistency in industrial and labor policies, etc.<sup>8</sup> Poverty reduction objectives are intricately related to water policy since water is a major factor input in rural income generation.

8. Decentralization and shifting the point of control for development schemes, which will particularly assist in formulating plans for peoples' participation and involvement in managing water programs.

# _Establishing Priorities for Provision of Water Services_

The government has to examine water needs of various sectors pursuing their respective goals and determine priorities of demand based on the national objectives. Although at present agriculture consumes the major part of water resources in the country, the demand from municipalities and industry are rising. Presently municipal water supplies are mostly obtained from groundwater sources, but overexploitation has denuded aquifers and there are critical shortage of groundwater specially in large metropolitan areas like Dhaka. The country's industrial concentration is also around Dhaka and rising industrial demand for water is likely to become a problem in the near future. It is possible that the constrained water supply for domestic and industrial usage will require cutback of groundwater irrigation in many areas, There is no consolidated policy for this yet, but this has to be developed in the immediate future.

Preservation of natural fisheries must be weighed against irrigation development and flood control and drainage, as the latter has already affected river and open area capture fisheries in many places in Bangladesh.<sup>9</sup> Dams and diversions for irrigation also affect inland navigation and their respective importance have to be determined by policy makers. Similarly, provisions for environmental use of water has to be made to combat salinity and deforestation.

# _Water Policy_

Many countries in Asia, like India and Thailand, have published statements on national water policy.'<sup>0</sup> Others, like China, Philippines and Indonesia, have embedded water policies in legal codes. Bangladesh does not have an approved national water policy.

**S** See Bangladesh: From Stabilization to Growth, World Bank Country Study, Washington D.C. 1995. 9 See Bangladesh Water and Flood Management Strategy, FPCO, Dhaka, September 1995; Water Resource Management in Asia, by Herald Frederiksen, et al. Volume 1, World Bank Technical paper No. 212, Washington D.C. 1993.

> **'** See Water Resource Management in Asia, by Harold Frederiksen, et al. World Bank, ibid.

8