rafmacalaba/datause-encoder-data huggingface dataset:
- 26 -

Although the approach presented in this paper can be improved in many ways -- household surveys rather than aggregate data are necessary to design country-specific policy interventions-- several policy recommendations are straightforward. First, the positive effect of education on savings is enhanced by the reduction in the costs of education. The unit costs of education may be reduced in many countries by exploiting economies of scale and developing adequate incentives for greater cost-consciousness among consumers and providers. Many education systems may also upgrade their internal efficiency. Second, the focus on primary education should be encouraged, specifically in less developing countries, because the empirical results indicate that the positive long-run effect associated with primary education is twice as large as that estimated for secondary and tertiary education. Latin America's traditional neglect; of primary education contrasts sharply with the policy of Asian countries.. Finally, it is important to increase the coverage of education, not only for equity, but also for efficiency reasons as the savings rate would increase and, thus, the growth rate. Indeed, how much a child learns is also influenced by the nature of the learning environment as supported by the role played by externalities and the initial level of education in the relationship between education and savings..
77 Figure A9: Distribution of Food Expenditure Shares <!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Food’s share in household expenditures<br>2<br>1.5<br>1<br>Density<br>.5<br>0<br><!-- End of picture text --> _Notes_ : The figure shows kernel density estimates for the share of total household expenditure that is spent on food. A unit of observation is a household. The data comes from the 2019 edition of the HIES. The density is estimated using an Epanechnikov kernel with optimal bandwidth (0.0213) and household-specific statistical weights provided by the HIES.

Figure A10: Association Between Food Share and Income <!-- Start of picture text --> −4 −2 0 2 4<br>Log of household income (residualized)<br>.5<br>0<br>(residualized)<br>Food’s share in household expenditure<br>−.5<br><!-- End of picture text --> _Notes_ : The figure plots a residualized version of the log of household income against a residualized version of the share of total household expenditure that is spent on food. A unit of observation is a household. The data comes from the 2019 edition of the HIES. The residualized version of each variable is obtained by extracting the residual from a regression of that variable on a set of district dummy variables, with the regression estimated by Weighted Least Squares (WLS) using the statistical weights provided by the HIES. A red regression line is overlaid on the graph representing the regression (also estimated by WLS) of residualized food share on residualized log household income.
3 key driver behind rural poverty reduction, but some of those who migrate to urban areas remain poor. Urban poverty therefore accounts for a growing share of global poverty: Ravallion et al. (2007) estimate that the urban share of total extreme poverty rose from 19% in 1993 to 25% in 2002. In some regions, like Latin America (76.2%); Eastern Europe and Central Asia (63.5%) and Middle-East and North Africa (55.8%), urban poverty is already dominant.

Poverty is expected to continue to urbanize, in the sense that the share of the total number of poor who live in urban areas is expected to continue to grow (with some exceptions, notably in Eastern Europe). Some expect that the urban share of $1-a-day ($2-a-day) poverty may reach 40% (51%) around 2030. Urban poverty is also thought to be accompanied by a different set of characteristics and challenges, including health and sanitation problems in urban slums; unemployment, and a greater incidence of violent crime. In response, strategies to fight urban poverty – and its specific peculiarities – are growing in importance, both at the national and at the international level.

Yet urban poverty is far from a homogeneous phenomenon, even within a single country. It is often remarked that poverty is spatially heterogeneous. Usually this is stated with reference to a marked rural-urban dichotomy in measured poverty. But there is also considerable spatial heterogeneity _among_ urban areas, and one important dimension of that heterogeneity is across city sizes. In Brazil, for instance, while most anecdotal discussion of urban poverty focuses on the sprawling slums of Rio de Janeiro or São Paulo, over 50% of the country’s urban poor live in cities with fewer than 50,000 inhabitants. Only around 10% live in cities with populations greater than a million. In Kazakhstan, the incidence of poverty in smaller towns is six times larger than in Almaty. And there are large differences in access to local public goods and services too: in Morocco, average access to sewerage is over 80% in cities greater than a million, but less than 50% in the smallest towns.

A greater understanding of how poverty – both in terms of incomes or consumption expenditures and in terms of access to public services – varies across different types of cities should help inform the discussion of appropriate poverty reduction strategies in most countries. Yet, the evidence base needed for this disaggregated analysis is seldom available, since household surveys – on which most
Table 14: Worker-level BOS wage premium, 2016-2020. Differences across ownership types.

|||Dependent|variable: _l_|_n_(_wifjt_)||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Public|0.413**<br>(0.192)|0.342**<br>(0.160)|0.369**<br>(0.162)|0.043***<br>(0.014)|0.0439***<br>(0.0148)|
|Mixed|0.600***|0.291***|0.393***|0.047***|0.0460***|
||(0.108)|(0.049)|(0.035)|(0.017)|(0.0167)|
|Participated|0.099|0.023|0.018|0.0004|0.00968|
||(0.099)|(0.036)|(0.028)|(0.010)|(0.00958)|
|Age|0.290***<br>(0.004)|0.286***<br>(0.003)|0.252***<br>(0.003)|0.770***<br>(0.018)|0.911***<br>(0.0261)|
|Gender|-0.223***<br>(0.003)|-0.157***<br>(0.001)|-0.140***<br>(0.001)|||
|Primary school|0.086***<br>(0.004)|0.093***<br>(0.003)|0.038***<br>(0.003)|||
|Middle school|0.165***<br>(0.005)|0.160***<br>(0.004)|0.083***<br>(0.003)|||
|High school|0.297***<br>(0.006)|0.254***<br>(0.004)|0.160***<br>(0.003)|||
|College or higher|1.082***<br>(0.010)|0.903***<br>(0.007)|0.600***<br>(0.006)|||
|Tenure|0.085***|0.072***|0.064***|0.026***|0.0278***|
||(0.001)|(0.0006)|(0.0006)|(0.0005)|(0.000656)|
|Temporary|0.018<br>(0.012)|0.282***<br>(0.024)|0.164***<br>(0.018)|0.050***<br>(0.008)|0.0509***<br>(0.00785)|
|Firm size|0.043***|0.043***|0.045***|0.024***|0.0190***|
||(0.002)|(0.001)|(0.001)|(0.0006)|(0.000741)|
|Year||Yes|Yes|Yes|Yes|
|Sector||Yes|Yes|Yes|Yes|
|Occupation|||Yes|||
|Region|||Yes|||
|Worker||||Yes|Yes|
|Observations|147943807|147943807|147943807|137063393|109720650|
|R-squared|0.413|0.490|0.563|0.910|0.916|

_Note:_ This table reports worker-level regressions where the dependent variable is the logarithm of the hourly wage. Firm size is measured as employment. The inclusion of fixed effects for year, sector (4-digit CNAE code), occupation (2-digit CBO code), region, and worker is indicated at the bottom of the Table. Data from RAIS, 2016-2020. Robust standard errors clustered at the firm level in parentheses. Significance levels: * 10%, ** 5%, *** 1%.

26
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<!-- Start of picture text --> Figure 4. Share of agriculture in GDP and total exports<br>(Percent)<br>90<br>80 Sub-Saharan Africa<br>Other regions<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70<br>Share of agriculture in GDP<br>Sources: World Development Indicators ; and author's estimates .<br>Figure 5. Sub-Saharan Africa: Share of agricultural output in GDP, 2003-05<br>(Percent)<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Source: World Development Indicators.<br>BotswanaSouth AfricaSeychellesCongoEquatorial GuineaMauritiusCape VerdeAngolaGabonNamibiaSao Tome & PrincipeSwaziland LesothoSenegalZimbabweZambiaMozambiqueEritreaChadCote d'IvoireNigeriaMauritaniaGuineaKenyaMadagascarBurkina FasoBeninGambiaUgandaSudanMalawiBurundiMaliGhanaNigerCameroonTogoRwandaCongo, Dem. Rep.TanzaniaSierra LeoneEthiopiaCentral African Rep.ComorosGuinea-BissauLiberia<br>Share of agricultural raw materials in total exports<br><!-- End of picture text --> What does Africa produce in agriculture? Table 2 indicates some major agricultural items that African countries are producing. On a regional aggregate basis Africa mainly supplies traditional staple crops, such as cereal, cassava, sugar, maize, yams and rice. Besides grains and root crops, cocoa and coffee, meat (beef, veal and chicken) and fruits (bananas, oranges and papayas) are also important. Dairy (cow and goat milk) is increasingly gaining in importance as an agricultural commodity in the region.

From the exports point of view, there are five important commodities produced in Africa: (i) coffee, cocoa and tea, (ii) cotton-related commodities, (iii) livestock products, (iv) tobacco
- 39 -

billion (11% of total assets). Thus the exposure of the banking sector to the government securities market is at least as large off-balance sheet as on-balance sheet. The importance of the govermment debt market to the banking sector is also reflected in the observation that 25% and 22% of total interest income came from investment in government securities in 1996 and 1997 respectively.

|**Turkey: Perfor**|**Tabl**<br>**mance**<br>**Ratio**<br>**(perce**|**e 15**<br>**s of the**<br>**ntages**|**Comm**<br>**)**|**ercial**<br>**Ba**|**nks**||||
|---|---|---|---|---|---|---|---|---|
||**1990**|**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|
|**Capital Adequacy**|||||||||
|capital base/risk-weighted assets|-|-|-|11.0|9.5|13.0|12.2|11.9|
|**Asst Quality**|||||||||
|non-performing loans/loans|4.2|4.9|3.2|2.9|3.4|2.3|1.9|2.2|
|**Uquidity**|||||||||
|liquid assets/total assets|34.6|37.3|40.1|43.2|40.9|38.4|37.8|34.4|
|liquid assets/(deposits+ non-deposit funds)|45.3|49.4|51.7|54.7|50.7|48.1|45.3|41.9|
|FX liquid assets/Fx liabilities|48.0|48.9|53.2|54.3|56.0|48.4|47.9|43.1|
|**Profinability**|||||||||
|net income/average total assets|2.6|2.0|2.3|2.9|1.6|3.0|3.1|2.6|
|net income/average shareholders' equity|34.7|27.6|36.8|47.4|24.3|47.0|50.9|43.8|

Source: The Banks Association of Turkey and Central Bank of Turkey.

The volatility of inflation has also induced lenders to include in interest rates a premium to cover their uncertainty which together with other factors, such as the government demand for funds, has resulted in very high real interest rates, which is already analyzed in more detail in section IV. The high domestic interest rates, real exchange rate appreciation, and tax distortions have induced banks to borrow abroad to finance both domestic loans and positions in the government paper market. Some of the exchange rate risk on the loans is reduced by lending foreign exchange but the banks do have foreign exchange risk to the extent they use foreign borrowing to finance their purchases of government paper. In using the government securities for overnight repos, the banks also expose themselves to interest rate risk.

Net Foreign Asset Position of Commercial Banks Turkish banks' liabilities to nonresidents has grown from being negligible in early 1980s to 13% of total liabilities in 1993, and 12% in 1997. Though the foreign liabilities of the Deposit Money Banks grew extremely fast in imost of the years in the 1990s, these banks maintained positive foreign asset positions until 1997. This is in sharp contrast with the major capital importer countries such as Thailand, Indonesia, Mexico and Chile where the banking sector had large negative foreign asset positions in the 1 990s. The situation in Turkey changed in 1997, however, when the net foreign asset position of the banking sector turned negative for the first time.
||**Figure 1: Urb**<br>**Challenges**|**anization Issues – N**<br>**Instruments**|**ew, Resurrected or**<br>**Analytical**<br>**and Planning**<br>**Tools**|**Forgotten (contin**<br>**Data and**<br>**Benchmarks**|**ued)**<br>**Institutions**|**Regions/**<br>**Countries/**<br>**Cities**|
|---|---|---|---|---|---|---|
|**Old Issues Revived**|Urbanization as an<br>opportunity (as well as<br>problem)|Slum Upgrading<br>and Sites and<br>Services Projects|||Ministerial<br>coordination|Latin America|
||Slums|Capital subsidies<br>for urban services<br>for the poor|||Decentralization||
||Scaling up|Special<br>assessment<br>fees/land<br>readjustment for<br>infrastructure<br>finance|||||
||Urban land management|Sub‐national<br>borrowing|||||
||Urban housing finance|Urban/Municipal<br>Development<br>Funds|||||
||Investment climate||||||
|**Old Issues**<br>**Forgotten**|Employment and labor<br>markets||Cost‐benefit<br>analysis||||
||Managing urban<br>transport/automobiles||||||
||Small and medium city<br>development||||||

6
Table 4: Fiscal (pro)cyclicality of expenditure, whole sample (2000-2016): Impacts of the level playing field, natural resources, and other factors

|Dependent variable: Cyclicalityof realgeneral|(1)<br> governmentprimaryspe|(2)<br>nding|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|Cyclicality of real GDP|0.496***<br>(0.174)|1.557***<br>(0.453)|-0.159**<br>(0.073)|-0.154**<br>(0.075)|-0.159**<br>(0.073)|0.385**<br>(0.191)|0.393*<br>(0.232)|1.143**<br>(0.459)|0.473*<br>(0.268)|0.473*<br>(0.268)|
|**Levelplaying field**|||||||||||
|Crgdp*power.distribution<br>Crgdp*resource.distribution<br>Crgdp*ethnicandrel.stability|0.364<br>(0.326)|-2.291***<br>(0.624)|-0.416***<br>12||||||||
||||(0.5)||||||||
|**Natural resources**|||||||||||
|Crgdp*resource.dependant (bd)||||0.610**<br>(0.243)|||||||
|Crgdp*resource.rich (bd)|||||0.605**<br>(0.240)||||||
|Crgdp*resource dependant and rich||||||0.610**<br>0244|||||
|||||||(.)|||||
|**Other factors**|||||||||||
|Crgdp*elections|||||||0.161**<br>(0.066)||||
|Crgdp*debt.ratio||||||||0.001<br>(0.003)|||
|Crgdp*rigidity|||||||||-0.037**<br>(0.015)||
|Crgdp*fiscal space||||||||||-0.000<br>(0.000)|
|Constant|-269.523|-270.481|482.975|-330.074|-292.797|-298.574|-413.932|-434.781|537.497|-540.189|
||(502.504)|(436.305)|(807.001)|(367.332)|(328.276)|(327.165)|(355.390)|(283.039)|(376.005)|(420.066)|
|Adjusted R-squared|0.113|0.094|0.185|0.078|0.078|0.078|0.069|0.065|0.004|0.066|
|<br>Rmse|5,615|5,672|5,610|5,497|5,498|5,497|5,525|5,593|824|5,632|
|Joint significance (p-value)|0.00|0.001|0.000|0.029|0.029|0.029|0.004|0.009|0.015|0.000|
|CD test (p-value)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|<br>Order of integration|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|
|Cluster|country|country|country|country|country|country|country|country|country|country|
|Covariates|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,639|1,639|1,569|1,954|1,954|1,954|1,954|1,913|871|1,886|
|# countries|112|112|99|116|116|116|116|115|54|113|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.1 in appendix for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

14
<!-- Start of picture text --> Baseline WMS vs Anexo K Endline WMS vs Anexo K Changes: Anexo K vs WMS<br>—8 Correlatione = 0.26 NaQo Correlation @e= 0.34 4Qos Correlation = -0.06<br>Se oe” ee * < e 3 Oras<br>fo os ee S 2 Cr)<br>o* 7 & 4 we 9 &<br>as = 5 c F<br>. & 2<br>1 15 2 25 3 3.5 15 2 25 3 35 4 Ss) -1 0 1 2<br>2013 WMS Score 2016 WMS Score Change in WMS 2013-16<br>Baseline MOPS vs Anexo K Endline MOPS vs Anexo K Changes: Anexo K vs MOPS<br>v<br>3 Correlation = 0.23 x Correlation = 0.34 < Correlation = 0.22<br>5 34° 3 *e @on 2 e onee So pe £8x g e“2°<br>8 e gsie <I e416<br><.oe ode ©§<2 © oma ne coa geo le a % “e's<br>oe Oo ee w= e ee Ds<br>N & f=]<8 oOCc * e<br>0 2 4 6 8 N 2 4 6 8 ponyO -.2 0 2 4 6<br>2012 MOPS (recall) 2017 MOPS Change in MOPS 2012-17<br>Baseline MOPS vs WMS Endline MOPS vs WMS Changes: WMS vs MOPS<br>© Correlation = 0.08 3) Correlation = 0.27 o Correlation = 0.07<br>Asfour9 «6N oye Oo2°]9Ox _« sm feate === be- -2° a3* Ses See 2 —<br>Bo] ogg rete<br>[3 ae ee e%e nt:a efot ee e ® o e e eve<br>R a S<br>0 2 A 6 8 2 4 6 8 -.2 0 2 4 6<br>2012 MOPS (recall) 2017 MOPS Change in MOPS 2012-17<br><!-- End of picture text -->
# **Small Area Estimation of Poverty under Structural Change**<sup>1</sup>

Simon Lange,<sup>2</sup> Utz Johann Pape,<sup>3</sup> Peter P ̈utz<sup>4</sup>

**Keywords** : Poverty, Population estimates, Censuses, Small Area Estimation

**JEL classification** : D63, I32, R12

> 1Authors in alphabetical order. Findings, interpretations and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent. The authors would like to thank Pierella Paci and Nobuo Yoshida for valuable comments on earlier drafts.

> 2World Bank, Poverty and Equity Global Practice, Africa.

> 3World Bank, Poverty and Equity Global Practice, Africa. Corresponding author. E-mail: upape@worldbank.org.

> 4Economics Department, University of G ̈ottingen.
# **Table 12: Targeting of livelihoods program in the three states**

|**Panel (a): Government define**<br>|**d caste categories**|||||
|---|---|---|---|---|---|
|**Bihar**||**Odisha**<br>||**Tamil Na**|**du**<br>|
||JEEViKA household||TRIPTI household||PVP household|
|ST|-0.170|ST|-0.186**|ST|-0.052|
||(0.089)||(0.063)||(0.101)|
|OBC|-0.084***|OBC|-0.009|MBC|-0.101**|
||(0.023)||(0.038)||(0.031)|
|EBC|-0.083*|||BC|-0.028|
||(0.036)||||(0.025)|
|Muslim|-0.173***|Muslim|-0.204*|||
||(0.042)||(0.095)|||
|FC|-0.307***|FC|-0.054|||
||(0.040)||(0.039)|||
|Some Schooling|-0.018<br>(0.021)|Some Schooling|0.044<br>(0.033)|Some Schooling|0.013<br>(0.024)|
|Female headed household|0.066**<br>(0.022)|Female headed household|0.132<br>(0.089)|Female headed household|0.046<br>(0.031)|
|Per capita expenditure|0.119<br>(0.103)|Per capita expenditure|-0.026*<br>(0.010)|Per capita expenditure|-0.006<br>(0.007)|
|Per capita expenditure<br>squared|-0.037<br>(0.054)|Per capita expenditure<br>squared|0.001***<br>(0.000)|Per capita expenditure<br>squared|0.000<br>(0.000)|
|Land|-0.019***<br>(0.005)|Land|0.001<br>(0.013)|Land|0.001<br>(0.001)|
|Observations|4187|Observations|1298|Observations|1629|
|Adjusted R-squared|0.129|Adjusted R-squared|0.194|Adjusted R-squared|0.195|

Notes: (1) Source: Author's calculations based on data collected by Social Observatory, World Bank and Government of Odisha. (2) Non-SC jatis are the omitted group. (3) Each column represents a separate regression wherein an `empowerment indicator’ is regressed on variables that are reported as well as additional controls: age, age squared, marital status of the woman, age at marriage, and a dummy variable for some schooling of the female respondent; a dummy variable for whether it is female-headed household, per-capital expenditure, per-capita expenditure squared, landholdings, education of the household head, number of members in the household and panchayat level fixed-effects. The female employment is run for all female adults in the sample (individual level). (4) We report robust standard errors in the brackets. (5) We report the level of significance: * p value < .05 , ** p value < .01 and *** p value < .001.

39
# Table A7. Distribution of Manufactruring Establishments by Region and Employment Size, Thailand

<!-- Start of picture text --> Regfon Fire Size (U of Employese)<br><!-- End of picture text --> <!-- Start of picture text --> Frequwncy<br>Percent<br>Roi Pet<br>Col Pct 1-19 120-49 150-99 1100-199 1200-49 1500-999 11000 & I<br>I I I 1 I lover I Total<br>---------..... ........ _. ________+__...................... %........+_+.._._... ......... +_ __<br>Bangkok 24 39 1 36 19' 191 3' 6 14<br>8.00 13.00 1 12.00 6.33 6.33 1.00 2.00 48.67<br>16.4/. 261 I 24.66 13.01 13.01i 2.05 4.11<br>g n.n 3 52.70 g 52.94 I 35.19 40.43 18.75 75.00<br>Netropolitan 3 17 26 22 23 1 11 1 2 104<br>I 2.881 1.00 I 16.35 5.67 ' I 25.00 8.67 21.15 7.33 I 22.12 1 10.581 7.67 1 3.67 0.67 1.921 34.67<br>I 9.09W 22.97 38.24 40.74 ".94 1 68.75 25.00<br>_ ___+__-+---................ ........ ---------....... -. ....... . - ....... -------<br>North 1 6 18 2 0. 0 S1<br>i2.060 6.100 2.060 433 1.67 067 000 i6.67<br>12.00 36.00 12.00 26.00 10.00 I 4.00 I 0.0<br>18.12 24.32 8.82 24.07 10.64 1 12.50 I 0.00<br>~~~~~---- ........... ........................ . .... +_++++ . .. __ ........ ...<br>Total 33 74 68 54 47 16 a 300<br>11.00 24.67 22.67 18.00 15.67 5.33 2.67 100.00<br><!-- End of picture text -->
Turning now to the access to reliable electric power, table 3 shows a significant relationship between measures of performance and the reliability of electricity provision. The variable available in the Enterprise Surveys for this sector measures the number of days with power outages over the previous year. A particular caveat applies to this sector: faulty energy provision from the public grid does not necessarily imply that the firm had access interruptions, because producing electricity in-house using a private generator is a means of circumventing the deficiencies of electricity provision from the public grid. Given the significant scale economies in electricity production, it is clearly not a cost-effective way of providing the firm with energy, but generators can dampen the link between the reliability of power provision and firm performance. This is why we want to control in our regressions for the use of firm-owned generators.

The estimated coefficient on our outage measure is negative, and significant at the 5% level using the semi-parametrically estimated firm productivities (and negative but not significant using least squares). This means that firms in regions with more frequent power outages are less productive than others. We also find the coefficient on the share of firms that own a generator to be significant and positive. This is consistent with the interpretation that firms that are able to generate their own electricity are benefiting from the fact that the continuity of their production is decoupled from the continuity of local energy provision. In fact, our data display a strong and positive relationship between the decision to acquire a generator and the degree to which firms perceive electricity supply problematic. As shown in the appendix, a firm’s perception of the difficulties in accessing reliable energy is a highly significant predictor of the firm’s odds of owning a generator. This is true both at the level of firms and for regional averages.

7