rafmacalaba/datause-encoder-data huggingface dataset:
years of schooling than their non-poor counterparts. Children should not have to work, but an estimated 250 million children are working worldwide. Child labor is one of the most devastating consequences of persistent poverty. Data reveal that 24 percent of children in Bahia worked in 1999 down from 29 percent in 1992 (Table 6.3). Bahia is in line with the region as a whole but around 35 percent above the national average. However, the majority may be both working and studying. Vital to achieving progress against harmful child labor are: (i) effective efforts to reduce poverty generally; and (ii) the economic and social policies, programs, and results that are the underpinning for success in poverty reduction. But these broad measures, while important, take time and are not sufficient by themselves. Additional actions focused specifically on child labor per se are also needed such as an expansion of PETI (see below).

|**Table 6.3: Child labor,**|**1999**|
|---|---|
|**Brazil**|**16.6**|
|**Northeast**|**24.5**|
|Maranhão|35.8|
|Piauí|31.0|
|Ceará|24.6|
|Rio Grande do Norte|13.3|
|Pernambuco|22.1|
|**Bahia**|**24.0**|

_Source:_ Data from http://www2.ibge.gov.br.

# **EDUCATION**

Education is key to poverty reduction. Increased educational attainment can improve the livelihoods of the poor and reduce the likelihood of becoming poor, as shown above. More education is also a key factor in obtaining a higher income. Furthermore, education is associated with fertility: the more education a woman attains, the lower her fertility rate and, therefore, the lower the dependency ratio and the lower the likelihood of falling into poverty. Therefore a clear message is that the _Bahianos_ need to be brought up the educational ladder to escape poverty.

Furthermore, human capital – education and health - is an important complement to labor, boosting its productivity and potential for income generation. Each year of schooling yields an increase in hourly earnings. Disparities in education are key causes of poverty and inequality. Education is also a crucial elevator for the poor to lift themselves out of poverty (see Sections 5 and 7). Higher educational attainment for a household head significantly reduces the probability of being poor.

Bahia has made impressive progress in education and in 2002 more than 97 percent of children and youth aged 7-14 are in school. Since the number of those under age 14 is not growing (see Section 2), Bahia has been presented with an excellent opportunity to increase access of the poor above 4<sup>th</sup> grade and improve the quality 30
<!-- Start of picture text --> Figure 2: MSME clients make up an increasing<br>share of banks’ loan accounts<br>Number of lending accounts and share<br>of lending accounts issued to MSME<br>140 45%<br>40%<br>120<br>35%<br>100<br>30%<br>80 25%<br>60 20%<br>15%<br>40<br>10%<br>20<br>5%<br>0 0%<br>% of Accounts are for MSMEs<br>Number of Account of Total Credit<br>Number of Account of Total MSMEs Credit<br>Number of lending accounts (in millions) Jan-12 Dec-12 Nov-13 Oct-14 Sep-15 Aug-16 Jul-17 Jun-18 May-19 Apr-20 Mar-21 Feb-22 % of lending accounts for MSME<br><!-- End of picture text --> Figure 3: Lending volumes have increased over time but not the share of lending to MSME <!-- Start of picture text --> Lending volumes and share<br>of lending to MSME<br> 7 Mio 30%<br> 6 Mio 25%<br> 5 Mio<br>20%<br> 4 Mio<br>15%<br> 3 Mio<br>10%<br> 2 Mio<br> 1 Mio 5%<br> 0 0%<br> % of Loans for MSME<br>Total Loans of Commercial Banks<br>Total MSME Loans of Commercial Banks<br>Total lending (in IDR Billion) % of Loans for MSME<br>Jan-12 Dec-12 Nov-13 Oct-14 Sep-15 Aug-16 Jul-17 Jun-18 May-19 Apr-20 Mar-21 Feb-22<br><!-- End of picture text --> Source: BI Source: OJK Improving access to finance for MSMEs is part of the target outcomes under the economic transformation pillar of Indonesia’s Medium-term Development Plan 2020-2024. Indonesia’s central bank, Bank Indonesia, mandates banks to lend to the MSME sector through a minimum “Macroprudential Inclusive Financing Ratio (RPIM)” which stipulates that, as of June 2024, 30% of bank lending portfolios should be dedicated to MSMEs. In addition to regulations, the Government of Indonesia has developed programs to support financial inclusion of MSMEs, which receive approximately 90% of the budget allocated to innovation and business support instruments (World Bank 2021). Most of this funding is channeled through the KUR program.

# 2.2 Description of the KUR program

In 2007, the Government of Indonesia launched the “People’s Business Loan” (Kredit Usaha Rakyat, KUR) program as a flagship public program to enhance MSMEs’ access to finance. KUR subsidizes credit for working capital and/or investments for individuals, business entities, and/or business groups that are productive and feasible but do not have sufficient collateral (Permenko Regulation No. 8 of 2019). Governed by an inter-ministerial Policy Committee led by the Coordinating Ministry for Economic Affairs (CMEA), the program began as a partial credit guarantee aimed to enhance bank lending to MSMEs by addressing the higher perceived credit risks associated with lending to small and previously unbanked firms. In 2015, the program was re-designed with the addition of interest rate subsidies to make credit more affordable for

**8**
AILYTICAL miNt OF TNiRTY NT m SiIES

||OF SO|pRmicr<br>TYRE AM<br>I|CHARACTE<br>ISTIM OF<br>SOIE<br>IMINYElRI|cOEM<br>RRTE<br>R- UCALaIAT<br>PERIOD<br>A<br>AALYSIS<br>ES PERIOD|-------------------------<br>IO RI<br>TAI<br>S<br>_INIITAL_<br>mRULB|INDEPSEN<br>V<br>-----------------------<br>OC .<br>I<br>A<br>EUCONNIC<br>POLICY<br>WIALES|T<br>MIALES<br>--------<br>WO<br>RIALES<br> <br>TEPIINIG<br>WMCTION<br>UOA FATSBLE|soCIO<br>POLITICA<br>S|L<br>BWIS|
|---|---|---|---|---|---|---|---|---|---|
|KLAE<br>(O9MP|_N_<br>_ar<br>gruth<br>pmrfoice<br>b<br>thersidal<br>of<br>a groh-rate<br>fumotion<br>estid<br>an<br>bais solely of<br>the factors<br>_Indo_rnt of<br>.cm ic policy<br>_ud_maltmte the<br>lmpt of<br>m~beruhi<br>of<br>fron<br>rn by<br>oxoprh<br>the<br>rmidals of the<br>"**catrim**<br>With ths<br>of<br>other**o**<br>**"itri**|CDP<br>(Wrold<br>BE*)<br>p|54-61 DCA,<br>dtharig<br>an<br>aitblbLe<br>dots|Av.mutL<br>rate_ow_<br>20**>_s**<br>(1962-81)<br>9yms<br>(1962-70)<br>rd 12ya<br>(1970-81)|For 12-81<br>- initiaL per<br>c"ite or<br>r midel<br>of<br>initial<br>jusenIte<br>mirtaLity<br>rat (sti-<br>stdan<br>bsis of pr<br>cqA toGN)<br>- inital<br>pqpuLation<br>+<br>-<br>tural<br>popiuation<br>rowth raft<br>- ta<br>of<br>trad trad||||0.40<R-qr < 0.55<br>The<br>r<br>-of<br>thef<br>r<br>(mhatadan<br>bsis of<br>reuidals)<br>asrsto be<br>cle<br>to tht of<br>the oteOc<br>id better then<br>that ofthe<br>other African ca izs|

<!-- Start of picture text --> StU* the role of 0l 73 DC. 11 ars ad Sitlnt Export growt Active R-q r 0.53<br>(195) h fort In (Wrd 8 yvrs vwrile for rate population (1960-70)<br>roth; plify so*) Av. growth ltowl o gr-oth rat<br>the pinwl rate cwtris + R-sqr a 0.51<br>stis hr. 190-70 - Intm (197r)<br>- mnlarumt df 1910-77 rate<br>the lwe U deOf<br>- 1PcCil uite tet<br>arilys of to -<br>the sitUation heteo-<br>of the ltw wedticity<br>noom risk<br>cantria<br>- Oxcison of<br>the iquct of<br>wort wh<br>r Nd<br>after 19<br>...............................................................................................................................................................<br><!-- End of picture text -->
<!-- Start of picture text --> 2021 2022<br>Activity 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9<br>Advertisements/online<br>application Y<br>Baseline survey v<br>Public lotteries v<br>Boot-camp v v<br>Follow-up 1 vovey<br>Follow-up 2 v v<br>Follow-up 3 voeoveey<br><!-- End of picture text -->
Table 9: Effects of SPEI shocks by Pre-colonial mode of subsistence

||_Dependent variable:_||
|---|---|---|
||current employment||
|cutofs=2|(1)<br>(2)|(3)|
|Dry shock (SPEI _< −_1_._5)|-0.00454<br>-0.0949***|0.0379**|
||(0.0155)<br>(0.0186)|(0.0153)|
|... _×_ Dummy_agri|-0.0655**<br>(0.0281)||
|... _×_ Dummy_fsh_hunt_gat|0.152***<br>(0.0231)||
|... _×_ Dummy_pastoral||-0.128***<br>(0.0238)|
|Wet shock (SPEI _>_ 1.5)|0.0259<br>-0.00693<br>(0.0308)<br>(0.0304)|0.0382<br>(0.0282)|
|... _×_ Dummy_agri|0.0956*<br>(0.0503)||
|... _×_ Dummy_fsh_hunt_gat|0.0853*<br>(0.0460)||
|... _×_ Dummy_pastoral||0.0217<br>(0.0556)|
|Controls|Yes<br>Yes|Yes|
|Full set of FEs|Yes<br>Yes|Yes|
|Observations|570,873<br>570,873|570,873|
|R<sup>2</sup>|0.234<br>0.235|0.235|

_∗ ∗∗ ∗∗∗_ **_Note:_ p** _<_ **0.1; p** _<_ **0.05; p** _<_ **0.01** . This table reports results on how the impact of wet and dry shocks varies with the historical mode of subsistence of the pre-colonial ethnic group that resided in a region. Standard errors clustered at the grid cell-month-year level are reported in parenthesis.

36
27. **Zheng, M, Fu, Q and Wang, XH.** Comparative Study on Structural Changes in Income Disparities in Urban Households in Chongqing Municipality, Shanghai Municipality and Sichuan Province. _Journal of Reform and Strategy._ 2008, Vol. 5.

28. **Tang, S, et al.** Tackling the challenges to health equity in China,. _Lancet._ 2008, Vol. Oct 20.

29. **Feng XL, Shi G, Wang Y, Xu L, Luo H, Shen J, et al. .** An impact evaluation of the safe motherhood program in China. _Health Economics ._ 2010, Vol. 19(S1).

30. **World Bank.** Reforming China's Rural Health System. . Washington, DC : World Bank, 2009.

31. **Ying, YZ.** _Benefit for Women and Children in Rural Cooperative Medical Scheme._ Beijing : UNICEF Report, 2009.

32. **Ma, J and Zhao, M.** Improve the Design of Medical Financial Assistance Programme and Build a Harmonious Society – Implementation of MFA in Shanghai. _China Health Economics._ 2009, Vol. Nov.

33. **Yu Baorong, Meng Qingyue, Tang Shenlan, Lennart Bogg, .** Health Service Utilization by Rural Residents in Shandong and Ningxia Provinces . _China Health Economics._ 2008, Vol. May.

34. **Singer Babiarz, Kimberly et al.** New evidence on the impact of China's New Rural cooperative Medical Scheme and its implications for rural primary healthcare:multivariate diference‐in‐difference analysis. BMJ, 2010. 341:c5617.

35. **UNDP and China Institute for Reform and Development.** _China Human Development Report._ Beijing : s.n., 2008.

36. **Chengdu Development and Reform Commission, Chengdu Bureau of Statistics.** _Chengdu Social Development Report 2008._ Chengdu : s.n.

37. **Gansu Provincial Bureau of Statistics.** _Gansu Social Progress Report._ Lanzhou : s.n., 2008.

38. **Guo, Y. et al.** Tracking China’s health reform. _Lancet._ March 27, 2010 , Vol. 375 .

39. _Sub‐provincial Intergovernmental Fiscal Transfers, presentation delivered at 2006 Annual China Fiscal Reform Forum._ **Liu, MD.** s.l. : UNDP Conference Volume., 2007.

40. **National Audit Office.** _National Audit Report on Central Budget Implementation and Uses of Other Financial Resources._ Beijing : National Audit Office, 2010.

41. **Hanson et al.** Expanding access to priority health interventions: a framework for understanding the constraints to scaling‐up. _Journal of International Devleopment._ 2003, Vol. 15, 1‐14.

42. **Travis, P. et al.** Overcoming health‐systems constraints to achieve the Millennium Development Goals. _Lancet._ 2004, Vol. 364, 9437.

43. **World Bank .** _Making Services Work for Poor People._ Washington DC : World Development Report, 2004. 44. **World Health Organization.** _Commission on Social Determinants of Health._ 2008. http://www.who.int/social_determinants/thecommission/finalreport/en/index.html.

12
Learning from Developing Country Power Market Experiences: The Case of Peru <!-- Start of picture text --> MW<br> 14,000 100%<br>90%<br> 12,000<br>80%<br> 10,000 70%<br>60%<br> 8,000<br>50%<br> 6,000<br>40%<br> 4,000 30%<br>20%<br> 2,000<br>10%<br> - 0%<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>System peak demand Effective capacity Reserve margin<br><!-- End of picture text --> Figure 5-4 System adequacy in Peru: peak demand, installed capacity and reserve margin.

<!-- Start of picture text --> Source: COES<br><!-- End of picture text --> Generation capacity additions have been driven primarily by gas-fired power plants, which have increased by 3.7 GW, from 661 MW in 2005 to 4,384 MW in 2017 (see Figure 5-5). Over the same period, capacity additions have also been significant in hydro-powered (1.9 GW increase) and oil-fired plants (1.7 GW increase).

<!-- Start of picture text --> MW<br> 14,000<br> 12,000<br> 10,000 4,948 4,883<br> 8,000 3,927<br>3,413<br>3,275<br> 6,000 3,244<br>4,362 4,384<br> 4,000<br>2,993 2,997 2,947 2,938 2,948 3,200 3,213 3,544 4,251 4,251<br> 2,000<br>661 977 1,516 1,553 2,154 2,661 2,806 3,461 2,923 2,699<br>991 991 773 699 756 673 564 631 1,237 1,142 1,528<br> -<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Coal Oil Gas Hydro Renewables<br><!-- End of picture text --> Figure 5-5 Evolution of technology-wise operational generation capacity in Peru.

Source: COES 20
two different surveys where individuals are asked to rank mutually exclusive alternatives and are therefore faced with explicit policy trade‐offs.<sup>6</sup> Third, we account for participation in the political process as a mediating factor in the relationship between ageing and expenditure preferences. Larcinese (2007) provides compelling evidence that voter turnout affects redistributive preferences overall, but the possible effects of ageing on spending preferences remains an empirical question.

# **3. Data and Empirical Analysis**

# **3.1. The Data Set and Descriptive Statistics**

This paper uses data from the Life in Transition Survey II (LITS II), collected by the European Bank for Reconstruction and Development and the World Bank in late 2010. Almost 39,000 respondents in 34 countries were asked questions about their preferences over government policy, their subjective well‐ being and their reactions to economic and political change. The Survey covers countries in Eastern Europe and the former Soviet Union, as well as in Western Europe. Table A1 in the Appendix lists the countries included in the Survey and the number of observations in each country.

We measure individual preferences over policy on the basis of responses to the following question: “ _In your opinion, which of these fields should be the first and second priorities for extra government spending? Education, Health care, Housing, Pensions, Assisting the Poor, Environment (including water quality), Public infrastructure, Other.”_ We focus on the policy areas that have been selected as the first or second‐highest priority for government intervention.

Preliminary analysis of the data shows how preferences over policy change with age and across countries. First, health care is the top policy priority area for LITS II respondents, and education, pensions and assistance to the poor are also listed as important priorities (Table 1). These four policy categories will therefore be the focus of our analysis. Second, support for additional government spending on education is high, but it falls with age, where the converse is true for pensions (Figure 1). Third, support for assisting the poor is consistently higher among countries in the Balkans than in the other regions covered in LITS II. Finally, increasing government expenditures for environmental purposes receives the least support in every region, although it is slightly higher among countries in Western Europe.

> 6 This is a significant improvement over the existing literature though of course it still does not provide a list of all possible spending programs, including defense.

7
data are shown to capture macroeconomic performance (GDP), even in low- and middle-income countries (Narita and Yin, 2018). Google search data and relevant queries are shown to realistically capture actual unemployment trends (e.g., Pavlicek and Kristoufek, 2015). Others have demonstrated the potential of Google search indices to measure actual consumption spending, sometimes outperforming common survey-based indicators (e.g., Vosen et al., 2011). Some recent studies have already validated some of the search queries we are employing in our study, those related to air transport and tourism services (e.g., Rivera, 2016; Havranek and Zeynalov, 2019). Table B.1 in Appendix B provides a list of selected studies that use Google search data to investigate various topics.

Nonetheless, we conduct alternative validation exercises to show that Google search data are indeed strong predictor of consumer demand for the services that we study in this paper. Because high-frequency data are not available for all services and countries covered in this study, we focus on countries and services for which relevant data are publicly available. The first dataset is the daily number of passengers screened at the Transportation Security Administration (TSA) checkpoints in the U.S. from March to May 2019 and the same period in 2020.<sup>8</sup> Figure 3 compares the number of passengers arriving in the U.S. with the Google search index for the term “airport” for these months. The top panel presents the daily volume of passengers and Google search intensity, and the bottom panel shows the growth rate of the number of passengers and Google search intensity. From visual inspection, the Google search index strongly traces out both the actual number of passengers and the variability in the volume of passengers for both years. We also run some linear regressions (Table B.2 in Appendix B) and find that the Google search indices associated with our search terms, “flight” and “airport,” explain over 90 percent of the variations in the daily number of travelers.

> 8 - <u>https://www.tsa.gov/coronavirus/passenger throughput</u>

12
**Figure 15: Carbon markets, 2005-2010.**

<!-- Start of picture text --> Carbon market transactions<br>U.S. dollars, billions<br>140 EU ETS allowances<br>Other allowances<br>120<br>Primary CDM<br>100 Seconardy CDM<br>Other Offsets<br>80<br>60<br>40<br>20<br>0<br>2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source State and Trends of the Carbon Market 2011 (World Bank, 2011) 69
|Other controls (as above)|Yes|Yes|Yes|Yes|Yes|
|---|---|---|---|---|---|
|F-statistic|988***|183***|159***|129***|131***|
|Number of observations|20,295|20,295|20,295|20,295|20,295|

Huber-White robust standard errors and clustered on the country in brackets. Significance is denoted by *** (1%), ** (5%), * (10%).

52
<!-- Start of picture text --> Urban<br>(a) 1972-1984 (N=3,148) (b) 1985–1994 (N=3,346) (c) 1995–2006 (N=1,941)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Rural<br>(d) 1972-1984 (N=5,631) (e) 1985–1994 (N=6,707) (f) 1995–2006 (N=3,857)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> Figure 5: Predicted probability of having a boy and probability of no birth yet from time of marriage for women with 1 to 7 years of education by quarter. Predictions based on age 17 at marriage. Left column shows results prior to sex selection available, middle column before sex selection illegal and right column after sex selection illegal. N indicates the number of women in the relevant group in the underlying samples.

before 1985, who also represent the biggest group (Figure 4d). For these women the predicted sex ratio is aligned almost perfectly with the expected sex ratio. For the other groups there is more volatility in the predicted percentage boys, but nowhere is it statistically significantly larger 27
**Figure C. Proporition of women by regional background and education in Turkey (bold line South-Eastern women SE, dot line North-Western women NW)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1935 1940 1945 1950 1955 1960 1965 1970 1975 1980<br>Less than primary completed SE Primary completed SE Secondary completed SE<br>University completed SE Less than primary completed NW Primary completed NW<br>Secondary completed NW University completed NW<br><!-- End of picture text --> Source: Census ( i-pums) 2000

**figure D. Proportion of women by activity sector in Turkey**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>agriculture<br>40 industry<br>30 services<br>20<br>10<br>0<br><!-- End of picture text --> Source: World Bank World Development Indicators (2014) 27
# Table 7 Tests of hyxxtheses

|||**Modelwith**<br>**primary**<br>**education**|**Modelwith**<br>**secondary**<br>**education**|**Modelwith**<br>**total**<br>**aducation**|**modelwith**<br>**both levels**<br>**of education**|
|---|---|---|---|---|---|
|**Hypothesis**|**(1)**|||||
|**D.F.**||**4**|**4**|**4**|**a**|
|F Value||**9.42**|**9.33**|**9.32**|**6.60**|
|Prob>F||0.0001|0.0001|0.0001|0.0001|
|**Hypothesis**|**(2)**|||||
|<br>**D.F.**||**S**|**S**|**5**|**10**|
|**F Value**||**7.67**|**14.20**|**7.76**|**5.86**|
|Prob>F||**0.0001**|0.0001|0.0001|0.0001|
|Hypothesis|**(3)**|||||
|D.F.||8|8|8||
|F Value||6.34|**5.82**|**5.73**|6.11|
|Prob>F||**0.0001**|0.0001|0.0001|0.0001|
|Hypothesis|**(4)**|||||
|D.F.||5|5|5|5|
|F Value||6.67|7.46|6.15|**5.S9**|
|Prob > F||0.0001|0.0001|0.0001|**0.0001**|
|**Hypothesis**|(5)|||||
|<br>D.F.||I|I|1|1|
|F Value||**2.26**|**0.72**|**1.43**|**1.28**|
|Prob>F||0.133|0.395|0.233|0.259|

Note: D F. are degrees of freedom of the constraint. Hypothesis (I) Educational effects are the same for all regions.

Hypothesis (2) Educational effects are zero.

Hypothesis (3) Growth trends are independent of region.

Hypothesis (4) Growth trends are the same between pre-1980 and post-1980.

Hypothesis (5) Constant returns to scale in capital, labor and land.

19