rafmacalaba/datause-encoder-data huggingface dataset:
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households in flood-affected communes by 1.8 percentage points. Another study by Bui _et al._ (2014) for the same country estimate that poverty would have decreased by 2.7 percentage points if households had not been exposed to natural disasters. Analyzing data from Mexico during 2000-2005, Rodriguez-Oreggia _et al._ (2013) highlight significant adverse effects of natural disasters on both human development and poverty, with an increase in poverty of between 1.5% and 3.7%. Notably, floods and droughts are the most devastating. Similar results are found in Argentina by González _et al._ (2022) where they find that a variety of natural disasters from 1970 to 2010 contributed to more multi-dimensional poverty.

On a global scale, combining household surveys from 24 Sub-Saharan African countries, Azzarri and Signorelli (2020) find that flood shocks result in a 35% decrease in total and food per-capita consumption and a 17-percentage point increase in extreme poverty. Azzarri and Signorelli (2020) also predict that increases in rainfall and temperature, and consequently the incidence of disasters across Western and Central Africa, could result in dramatic increases in extreme poverty rates by up to 30 percentage points. Analyzing a new global, subnational database on poverty across 134 countries, Dang _et al._ (2023a) find strong and statistically significant global effects of both higher and lower temperature on poverty, with a one-degree Celsius (i.e., 1<sup>◦</sup> C) annual increase in temperature causing poverty increases of 0.9, 1.8, and 2.3 percentage points, respectively, using the daily poverty lines of $1.90, $3.20, and $5.50 (which correspond to 9.1 percent, 9.0 percent, and 6.8 percent increases). But this study also finds smaller impacts in the longer term, suggesting household adaptation to gradual warmer temperature over time.

Other studies simulate the future effects of climate change and disasters on poverty. For example, using a CGE model to explore how future climate scenarios would affect economic growth and poverty in Zambia, Thurlow _et al._ (2012) find that even small changes in climate expected by 2025 can increase the absolute number of poor people by 32,000 over 10 years, 21
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- Chauvet, L., Collier, P., 2009. Elections and economic policy in developing countries. Economic Policy 24, 509–550.

- Clemens, M. A., Radelet, S., Bhavnani, R. R., Bazzi, S., 2012. Counting chickens when they hatch: Timing and the effects of aid on growth. The Economic Journal 122 (561), 590–617.

- Collier, P., Vicente, P. C., 2008. Votes and violence: Evidence from a field experiment in Nigeria. CSAE Working Paper Series 2008-16, University of Oxford.

- Collier, P., Vicente, P. C., 2012. Violence, bribery, and fraud: The political economy ofelections in SubSaharan Africa. Public Choice 153 (1-2), 117–147.

- Cruz, C., Keefer, P., 2013. The organization of political parties and the politics of bureaucratic reform. Policy Research Working Paper Series 6686, The World Bank, Washingotn D.C.

- de Renzio, P., 2009. Taking stock: What do PEFA assessments tell us about PFM systems across countries? ODI Working Papers 302.

- de Renzio, P., Andrews, M., Mills, Z., 2011. Does donor support to public financial management reforms in developing countries work? ODI Working Paper 329, London.

- Devarajan, S., Khemani, S., Walton, M., 2011. Civil society, public action and accountability in Africa. Policy Research Working Paper Series 5733, The World Bank.

- Djankov, S., Montalvo, J., Reynal-Querol, M., September 2008. The curse of aid. Journal of Economic Growth 13 (3), 169–194.

- Dreher, A., 2002. The development and implementation of IMF and World Bank conditionality. Hamburg Institute of International Economics.

- Easterly, W., 2005. What did structural adjustment adjust? The association of policies and growth with repeated IMF and World Bank adjustment loans. Journal of Development Economics 76, 1–22.

- Geddes, B., 1991. A game theoretic model of reform in Latin American democracies. The American Political Science Review 85 (2), pp. 371–392.

- Grindle, M. S., 2007. Going Local: Decentralization, Democratization, and the Promise of Good Governance. Princeton University Press.

- Harun, 2007. Obstacles to public sector accounting reform in Indonesia. Bulletin of Indonesian Economic Studies 43 (3), 365–376.

- Hesse, J. J., 2000. Rebuilding the State: Public Sector Reform in Central and Eastern Europe. Nomos, Sinzheim.

- IEG, 2008. Public Sector Reform: What Works and Why? An IEG evaluation of World Bank Support.

27
assess the reasons for low-take up. In addition to traditional issues of firms’ lack of working capital to fund the acquisition of the business development services upfront and other red tape issues, we noticed that one of the first questions in the application form was whether or not the firms were registered, although in theory this was not a criterion for selection into the program. Firms were also asked during application to attach their business registration certificate. This could naturally put-off a number of firms AVO After discussions with the project implementation team of another case, it was clear that the targeting was reaching fewer firms than initially anticipated, while a critical group of businesses in a lower turnover range were not being serviced because of the minimum turnover eligibility criterion. This led to a change in the criteria (reducing the turnover requirement from USD 60 thousand per year to USD 30 thousand and increasing the matching proportion by government) and a dramatic increase in applications (Figure 1).

c) _“Last mile” issues and red tape:_ Much of the emphasis in matching grant programs concerns the design of the eligibility criteria, and the bureaucratic procedures involved in setting up a project implementation unit to run the program. Far less attention is given to what Mullainathan (2009) terms the “last mile” problem – how do you actually get firms to take up a program offered? Marketing is part of the need, but there are a number of issues related to psychology and behavioral economics that are also important.

Marketing efforts varied across our seven projects. In order to ensure a sufficient number of applications and make firms aware of the program, in one country impact evaluation funding was used to supplement the project’s marketing budget and radio and television advertisements were produced and shown to get the word out to firms. In other countries the outreach was less intensive and slower – in one country there were only two branch offices for the program throughout the country, neither particularly noticeable, and the marketing materials produced were not visually very appealing.

Developing strong awareness campaigns can be a challenge. Despite one of the projects investing considerably in a large launch and communications campaign, a survey of 209 businesses conducted one year after the launch found that none of the sampled businesses had heard of the program. Of those contacted, 39% were interested, and ultimately only 6% were both interested and eligible to participate, highlighting the need for targeted awareness campaigns when eligibility criteria are restrictive (Figure 2).

22
stage), as well as accounting and legal services (back‐office stage). These sectors may be indicative of the broader restrictiveness of services within each production stage, as many more services fall under each stage as shown in Table A2 in Annex 1. However, the regulatory databases used for this analysis only include a subset of the entire range of services.

**Figure 4.4: Domestic regulatory restriction in services for Turkey and production stages (2014)**

<!-- Start of picture text --> Manufacturing Post-manufacturing<br>Gas 0.4 Rail 1.0<br>Electricity 0.4 Road 0.6<br>Egnineering 0.2 Airlines 0.3<br>Architecture 0.2 Retail 0.2<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Post-sales Back-office<br>Legal 0.9<br>Postal service 0.4 Accounting 0.8<br>Telecoms 0.0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br><!-- End of picture text --> Source: OECD PMR; authors’ calculations. The scale of the regulatory restriction index is 0‐1.

**Turkey shows a mix of entry and conduct barriers.** Rail transport services are completely restricted, affecting both firm entry and conduct. Air transport services mainly have restrictions related to public ownership, which affects the entry of foreign firms, but some conduct barriers also exist. The road transport sector has high entry barriers, while operations regulations are significantly lower. Gas and electricity services (manufacturing stage) have high conduct regulations, such as vertical integration policies, public ownership regulations, and uncompetitive market structures, whereas they are fairly open to firm entry. Architectural and engineering services (manufacturing stage) have high entry barriers while accounting and legal services (back‐office services) have both restrictive entry and conduct regulations.

# **_Trade barriers in services_**

**Services trade barriers are more prevalent in the post‐manufacturing and back‐office stages.** Regulatory policies can have an explicit discriminatory nature preventing entry from foreign services suppliers.<sup>14</sup> These

> 14 Although regulatory services barriers can be _de jure_ non‐discriminatory, some non‐discriminatory regulatory barriers may still have an effect on the operating costs of the foreign entrant, making them _de facto_ discriminatory

26
.23 Ordinal Logit for 1990. 1991. and 1992.

_Easei,=a+PIAPRFrL+-,( ABE 1) +,E AEMPL_ Table 13

||1|990||1991|[<br>|1992|
|---|---|---|---|---|---|---|
||Estimate|t stat|Estimate|Estat|Estimate|**t**s:t|
||_3 -5.060|-2.196|2.539|0.919|5.771|3.057|
|A:2|-6.998|0-4306|3.720|0.271|_-7.845_|-1.166|
||3|.839||1.290|8|.502|

'Ease" Ease of obtaining credit (scale= l to _5;_ L most difficult - 5 very easy); groups 0 and 1 were joined to avoid zero cells..

APRF : Profit margin for the whole year. ABB Real stock of bank borrowing at the end of the year. AEMPL: Employment at the end of the year.

When running the equation for 1990. the ease of gaining access to credit was inversely related to profitability, which highlights the bailout function of the financial system immediately after the demise of coinununism- But already in 1991 the clear negative link between access to credit and profits breaks down: the coefficient actually turns positive although it is not significantly different from zero. But in 1992. following the banking reforms implemented at the end of 1991 whereby the cornmercial banks were subject to the new governance scheme, the switch is clear: for 1992 profits are positively related to the ease of obtaining credit, and significantly so. An equally interesting result is the complete lack of significance of lagged bank loans: having had access before apparently does not guarantee continued access at all.
Table 4: Impact of Heat Exposure on Standardized Scores

||Standardize|d Score|
|---|---|---|
||Urban|Rural|
||(1)|(2)|
|T_>_33C|_−_0_._009<sup>_∗∗∗_</sup>|_−_0_._007<sup>_∗∗_</sup>|
||(0_._003)|(0_._003)|
|T 30-33C|_−_0_._007<sup>_∗∗∗_</sup>|_−_0_._0001|
||(0_._002)|(0_._002)|
|T 27-30C|_−_0_._002|0_._001|
||(0_._001)|(0_._001)|
|T 24-27C|_−_0_._004<sup>_∗∗∗_</sup>|_−_0_._0003|
||(0_._001)|(0_._001)|
|T 21-24C|_−_0_._003<sup>_∗∗∗_</sup>|_−_0_._001|
||(0_._001)|(0_._001)|
|T_<_18C|_−_0_._005<sup>_∗_</sup>|_−_0_._003<sup>_∗_</sup>|
||(0_._003)|(0_._002)|
|Outcome Mean:|0.09|-0.09|
|Female Control|X|X|
|**Fixed Effects**|||
|Year|X|X|
|School|X|X|
|Stream|X|X|
|Observations|1,232,230|900,405|
|R<sup>2</sup>|0.260|0.224|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variable is the standardized test scores of students taking the 12th grade exams in urban and rural areas. All regressors include the number of days in the temperature bin for the school-year cycle: June-May. All regressions control for the number of days in the precipitation bins. Errors are clustered at the school level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

23
**Table 1. Average nightlight data for Caribbean countries and territories (2000–12)**

|||||DN value|||||Average DN|Area (sq|Population density|GDP per capita,|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Country / Area|0|0-3|4-6|7-11|12-21|22-63|63|Average DN|(excluding 0)|km)|(per sq km, 2018)|PPP (2019 $)|
|Anguilla|0%|0%|2%|11%|50%|45%|0%|21.00|21.00|91|169.8|19,890|
|Antigua and Barbuda|11%|15%|13%|27%|27%|30%|0%|17.99|20.22|440|219|22,817|
|Aruba|0%|0%|7%|12%|16%|72%|7%|36.08|36.16|180|588|38,442|
|Bahamas, The|37%|52%|42%|10%|4%|10%|1%|7.21|11.47|13,880|39|37,266|
|Barbados|0%|0%|0%|9%|36%|61%|4%|30.90|30.90|430|667|16,287|
|Bermuda|0%|0%|1%|3%|11%|89%|0%|36.24|36.24|4,290|1184|59,474.60|
|Bonaire, Sint Eustatius,<br>and Saba|18%|23%|40%|29%|14%|11%|0%|8.93|10.84|328|79.9 (year 2020)|24,266 (year 2016)|
|British Virgin Islands|8%|17%|44%|17%|19%|21%|0%|12.25|13.26|150|199|48,511|
|Cayman Islands|1%|4%|26%|38%|19%|29%|1%|18.88|19.17|260|267|72,481|
|Cuba|44%|57%|43%|12%|3%|2%|0%|3.60|6.38|109,880|109|8,821|
|Curaçao|5%|8%|23%|27%|17%|37%|7%|23.58|24.95|444|361|25,572|
|Dominica|32%|48%|56%|11%|3%|1%|0%|3.47|5.10|750|96|12,659|
|Dominican Republic|33%|41%|42%|18%|7%|6%|0%|6.15|9.18|48,670|220|19,182|
|Grenada|0%|3%|45%|47%|16%|10%|0%|10.07|10.10|340|328|17,956|
|Guadeloupe|1%|4%|24%|26%|28%|31%|1%|17.69|17.88|1,705|245.8 (year 2020)|26,855 (year 2016)|
|Haiti|70%|71%|19%|9%|3%|3%|0%|2.72|9.12|27,750|404|1,801|
|Jamaica|5%|11%|46%|42%|14%|9%|1%|9.59|10.13|10,990|271|10,661|
|Martinique|0%|2%|11%|17%|26%|54%|1%|25.31|25.42|1,128|354 (year 2020)|30,056 (year 2016)|
|Montserrat|29%|41%|51%|27%|1%|0%|0%|3.81|5.37|103|52.4 (year 2020)|12,753|
|Puerto Rico|0%|1%|4%|18%|24%|61%|5%|30.69|30.83|8,870|360|34,948|
|Sint Maarten|0%|0%|0%|0%|0%|100%|2%|53.67|53.67|34|1,193|23,366|
|St. Barthélemy|5%|7%|1%|3%|26%|68%|0%|26.79|28.29|25|9,961 (year 2016)|21,514 (year 2014)|
|St. Kitts and Nevis|1%|5%|30%|46%|0%|15%|0%|12.58|12.70|260|202|27,449|
|St. Lucia|1%|6%|31%|39%|0%|16%|0%|12.76|12.94|620|298|16,089|
|St. Martin|1%|1%|0%|0%|3%|96%|2%|46.02|46.39|54|672|16,572 (year 2014)|
|St. Vincent and the<br>Grenadines|14%|23%|47%|32%|10%|7%|0%|7.28|8.44|453|283|12,983|
|Trinidad and Tobago|8%|11%|25%|25%|19%|33%|3%|19.58|21.27|5,130|271|27,261|
|Turks and Caicos Islands|31%|45%|38%|11%|9%|14%|0%|8.02|11.58|950|40|27,055|
|Virgin Islands, U.S.|1%|2%|11%|15%|17%|63%|1%|32.83|33.11|350|306|35,090 (year 2018)|

_Source:_ Based on estimates from the Food and Agriculture Organization (FAO), the World Bank the United Nations, the French National Institute for Statistics and Economic Studies and the Dutch Central Bureau of Statistics. _Note:_ DN value corresponds to the pixel converted into a digital number representing light intensity, with values ranging from 0 to 63.

7
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51 returns to zero (Griliches, 1977, 1979). Household fixed effects, therefore, **may** represent a lower bound on **the** estimates of **the** market productivity effects of schooling. It **has** not, to my knowledge, been examined how the restriction of the estimation **sample** to those residing with other **wage** earning family members distorts the comparison group **and** thus potentially biases the return **estimtes.** Private rates of return to schooling have **been** estimated for men and women from a 1986 survey of Indonesia that compare ordinary **estinates** of the wage function to those that include both community fixed-effects (proxy for school quality, etc.) and household fixed-effects (proxy for family background correlates) (Behrman and Deolalikar, 1988b). **Three** increments of ochooling are compared in Table 3: primary, general senior high school, and'university. Private rates of returns to schooling for women in all **six** ctmparisons **exceed** those for men, and **as** expected the estiuates that include community and household fixed-effv..ts are between 9 and 24 percent smaller than thise obtained from the simple regressions that include inter-family and inter-community variation. These estimates do not attempt to control for the potentially unrepresentative character of the sample of wage earners, nor are the two sets of estimates based on the same sample, since 16.percent of the wage earners included in the first set of estimates apparently did not reside in a household with another wage earner and is therefore excluded from the fixed-effect estimates (Behrman and Deolalikar, 1988b: Table 3). Differences in the private rates of return estimated for women and men in Indonesia change moderately as fixed effects are added for the community and household, with the difference decreasing at the primary school level and increasing at the university level.
||Age 7<br>Age 11|0.038<br>0.019||Above 0.01 and<br>below 0.05.<br>Above 0.1|Above 0.03<br>and below<br>0.15.<br>Above 0.3|Above 0.03 and<br>below 0.15.<br>Above 0.1|
|---|---|---|---|---|---|---|
||Years of education|0.013|0.005|0.012|0.050|0.012|
|**Furre Haaland (2013)**|Completed high school|0.003|0.001|0.003|0.011|0.004|
||Attended colleage|0.004|0.001|0.001|0.003|0.002|
||Loginc 29|0.005|0.002|0.001|0.003|0.002|
||Time at school|6.506|1.102|0.000|0.000|0.000|
|**Afridi et al. (2016)**|Enrollment|0.472|0.138|0.001|0.002|0.001|
||Gradeprogression|0.406|0.101|0.000|0.000|0.000|
||PIAT reading, year 1|-0.00069|0.00064|0.281|1.000|0.422|
|**J-Bd 2005**|PIAT reading, year 2|0.00064|0.00059|0.278|1.000|0.422|
|**amesurmuy, ;**<br>**weeks worked**|PIAT reading, year 3|0.00039|0.00047|0.407|1.000|0.488|
||PIAT math, year 1|-0.00117|0.00067|0.081|0.485|0.422|
||PIAT math, year 2|0.00034|0.00061|0.577|1.000|0.577|
||PIAT math, year 3|0.0006|0.00051|0.239|1.000|0.422|
||PPVT, year 1|0.03651|0.5222|0.944|1.000|0.944|
||PPVT, year 2|-0.05182|0.05137|0.313|1.000|0.580|
||PPVT, year 3|-0.00949|0.0469|0.840|1.000|0.944|
|**James-Burmudy, 2005;**|PIAT reading, year 1|-0.0526|0.02506|0.036|0.322|0.129|
|**hours worked**|PIAT reading, year 2|-0.02109|0.02131|0.322|1.000|0.580|
||PIAT reading, year 3|0.01945|0.0864|0.822|1.000|0.944|
||PIAT math, year 1|-0.07385|0.02595|0.004|0.040|0.036|
||PIAT math, year 2|0.01512|0.02275|0.506|1.000|0.759|
||PIAT math, year 3|0.04108|0.02026|0.043|0.383|0.129|
|**Aughinbaugh and**|Any risky behavior|1.018|0.546|0.062|0.685|0.294|
|**Gittelman (2003)**|Drank alcohol|0.629|0.246|0.011|0.116|0.116|
||Smoked cigarettes|0.184|0.269|0.494|1.000|0.764|

17