rafmacalaba/datause-encoder-data huggingface dataset:
Kraay, A. 2004. “When is Growth Pro‐Poor? Evidence from a Panel of Countries.” _World Bank._ Kwon, H. and Yi, I. 2009. “Economic Development and Poverty Reduction in Korea: Governing Multifunctional Institutions.” _Development and Change,_ 40(4): 769‐792.

Legislative Council of Hong Kong. Undated. “Information Note: Poverty Combating Strategy in South Korea.” Research and Library Services Division, IN34/04‐05.

Leipziger, D. and Thomas, V. 1993. _Lessons of East Asia._ Washington, DC: World Bank.

<mark>Levy, S. and Walton. M. 2009.</mark> _<mark>No Growth without Equity? Inequality, Interests, and Competition in Mexico.</mark>_ <mark>New York: Palgrave Macmillan.</mark> <mark>Lipton, M. 2014. “Farming, Fertility, Food: Why Has Bangladesh Done Better than Expected? Can It Continue to Do So?” 1st Bangladesh Economic Forum Paper, Dhaka.</mark> <mark>Liu, Y. and Deininger, K. 2010. “Poverty Impacts of India's National Rural Employment Guarantee Scheme: Evidence from Andhra Pradesh.” Agricultural & Applied Economics Association 2010 AAEA,CAES, & WAEA Joint Annual Meeting Paper.</mark> <mark>Lobell, D.B. and Burke, M. 2009.</mark> _<mark>Climate change and food security: Adapting Agriculture to a Warmer World</mark>_ <mark>. New York: Springer Science & Business Media.</mark> Lopez‐Acevedo, G. 2008. “Mexico: Quality of Public Expenditure.” Note No. 4 – June 2008.

McCaig, B. and Pavcnik, N. 2013. “Moving out of Agriculture: Structural Change in Vietnam.” _National Bureau of Economic Research,_ Working Paper No. 19616.

McNamara, R. 1973. Speech by World Bank Group President Robert S. McNamara: “Address to the Board of Governors.” Merrick, T. 2002. “Population and Poverty: New Views on an Old Controversy.” _International Family Planning Perspectives,_ Vol. 28, No. 1, 41‐46.

Molini, V. and Paci. P. 2015. _Poverty Reduction in Ghana: Progress and Challenges._ Washington, DC: World Bank.

Mu, R. and van de Walle. D. 2009. “Rural Roads and Local Market Development in Vietnam.” _The Journal of Development Studies,_ Vol. 47, No. 5, 709‐734.

Musacchio, A. 2012. "Mexico's Financial Crisis of 1994‐1995." Harvard Business School Working Paper, No. 12–101.

Oks. D. 1992. “Stabilization and Growth Recovery in Mexico.” _World Bank,_ Policy Research Working Paper WPS 833.

Olinto, P. et al. 2013. “The State of the Poor: Where are the Poor, Where is Extreme Poverty Harder to End, and What is the Current Profile of the World’s Poor?”, _World Bank Economic Premise,_ Number 125.

Otoo, N. et al. 2014. “Universal Health Coverage for Inclusive and Sustainable Development: Country Summary Report for Ghana.” _Health, Nutrition and Population Global Practice,_ World Bank Group Report.

Parker, S. 2003. “The Oportunidades program in Mexico.” Shanghai Poverty Conference ‐ Scaling Up Poverty Reduction.

Perkins, D. et al. 2012. _Economics of Development_ , _7th ed_ . New York: W.W. Norton and Co.

Radelet, S. and Sachs, J. 1997. “Asia’s Reemergence.” _Foreign Affairs_ , Nov/Dec 1997, 44‐59.

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Figure 15 presents the sensitivity analysis for the number of <u>reinsurers sharing the same</u> amount <u>of</u> capital. It illustrates the oligopoly eect: when reinsurers are fewer, they deliver less capacity to the market and prices are higher during soft market, the impact of important <u>losses is however slightly</u> equivalent.

<!-- Start of picture text --> Losses Market pri ce<br>100 10<br>50 5<br>0 0 3.000<br>0 10 20 30 40 50 0 10 20 30 40 50 4.000<br>Year Year<br>5.000<br>Total external capital Market supply<br>6.000<br>20 1<br>7.000<br>0 0.5<br>−20 0<br>0 10 20 30 40 50 0 10 20 30 40 50<br>Figure 15: Sensitivity analysis for the number of reinsurers<br><!-- End of picture text --> Figure 16 corresponds to the sen ~~sitivit~~ y analysis for _κ_ . The lower _κ_ , the more capitalized the industry, and the lower the market prices.

<!-- Start of picture text --> Losses Market pri ce<br>100 10<br>50 5<br>0 0 0.010<br>0 10 20 30 40 50 0 10 20 30 40 50 0.033<br>Year Year<br>0.055<br>Total external capital Market supply<br>0.077<br>10 1<br>0.100<br>0 0.5<br>−10 0<br>0 10 20 30 40 50 0 10 20 30 40 50<br>Figure 16: Sensitivity analysis for κ<br><!-- End of picture text --> The Figure corresponding to the ~~sensi~~ tivity analysis for _πlim_ can be found in Section 7 (Figure 8) .

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There is a deeper issue with the choice of a welfare measure given that the underlying concept of living standard is multidimensional. Standard benefit incidence analysis ignores this fact to the extent that it translates all public spending, whether in cash or in-kind, into the equivalent monetary transfer and evaluates the implied changes in the distribution of income (van de Walle 1998). Yet, as noted in the introduction, public policy ultimately seeks to improve people’s lives. Thus, in the context of social expenditure for example, we are in fact more interested in knowing whether health status has improved as a result of public spending on health care, or whether spending in education leads to improved literacy and numeracy.

Hammer et al. (1995) recognize this issue in their assessment of the distributional effects of social sector expenditures in Malaysia. In particular, they note that spending on health and education is a productive investment in human capital. This observation led them to broaden their incidence analysis and seek to determine the extent to which the improvement in health and education status in Malaysia between 1974 and 1989 was a result of government policy. Using regression analysis<sup>28</sup> , they found that immunization and the provision of safe water had the strongest and most robust effects on health outcomes. They also concluded that public expenditure on primary education significantly improved outcomes.

# **3.2. Accounting for Behavioral Responses to Public Spending**

The interaction of demand and supply factors discussed above suggests that the way individuals react to an expenditure program is crucial in determining both the _welfare_ and the _distributional outcome_ of such a program. Ignoring behavioral responses to a spending program is bound to introduce some bias in the estimates of the

> 28 The analysis for health applies OLS, fixed and random effects models to a panel of state-level data for 1986-1989.  The dependent variables are infant and maternal mortality rates.  Explanatory variables include: (1) average state income, (2) immunization rate, (3) the proportion of the population with safe water, (4) the number of doctors per capita who are publicly assigned.  Educational outcomes are measured by schooling transition rates or repetition rates.  In the case of primary school, the dependent variable is the proportion of students in the last year of the primary level who continue on to the lowest grade of the secondary level.  The independent variable include: (1) income, (2) public expenditure per student, and (3) the percentage of state income accounted for by agricultural production.  According to the authors, this last variable acts as a proxy for the opportunity cost of students’ time.

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16 numbers presented here are therefore best seen as illustrative pending corroboration from alternative data sources.

# _Preamble_

The WHS collects information on out-of-pocket spending both in the individual and the household survey, and we construct alternative measures from both sources. The household is asked how much it spent in total on health services in the previous four weeks, and is then asked how much the household spent _in addition_ over the previous 12 months on inpatient care. We divide the latter by 48/4 to get an estimate of four-week additional inpatient spending, and add it to the fourweek total to get our household-level out-of-pocket spending figure. In the individual questionnaire, a randomly-selected household member is asked whether they were admitted to hospital in the previous five years, when, and how much they paid out-of-pocket. Those with no admission during the previous five years were asked about their out-of-pocket spending during their most recent outpatient visit in the previous 12 months. We focus on inpatient admissions in the last 12 months, and construct for each randomly-selected household member an estimate of their out-of-pocket spending in the last 12 months; given the routing of the questionnaire, this reflects either inpatient care or outpatient care but not both.

One merit of the household-based measure is that it captures the out-ofpocket spending of all household members. Another is that it captures spending associated with all contacts over the time period in question. A disadvantage of the
Figure 1: Trends in TFP level, Capital and Output per Worker (1970-97) <!-- Start of picture text --> 5 -<br>006<br>2-7<br>0<br>AI' '\O A~t' - %O RI iS P cP I<br>-- KIL -- Y/L -TFP<br>IoI<br><!-- End of picture text --> Figure 2: TFP growth (1 97 1-97) <!-- Start of picture text --> 0.06-<br>0.04 -<br>0.02 -<br>0 -<br>-0.02-<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0. 12<br>-0.14<br><!-- End of picture text --> 18
# **6. References**

Autoridad del Canal de Panamá (ACP). 2006a. ―Proposal for the Expansion of the Panama Canal: Third Set of Locks Project.‖ April 24. Available at - - <u>http://www.pancanal.com/eng/plan/documentos/propuesta/acp expansion proposal.pdf</u> Autoridad del Canal de Panamá (ACP). 2006b. ―Plan Maestro del Canal de Panamá.‖ June 7. Available at http://www.pancanal.com/esp/plan/documentos/plan/acp-plan- <u>maestro.pdf</u>

- Bourguignon and Pereira da Silva (2003), ―The Impact of Economic Policies on Poverty and Income distribution – Evaluation Techniques and Tools‖, World Bank, Washington D.C., Oxford University Press, New York..

Bourguignon, Bussolo and Pereira da Silva eds. (2008) ―The Impact Of Macroeconomic Policies On Poverty And Income Distribution - Macro-Micro Evaluation Techniques and Tools‖, World Bank, Washington D.C., Palgrave Macmillan, New York.

Bussolo, Lay, and van der Mensbrugghe (2008), ―Trade options for Latin America. A Poverty Assessment Using Macro-Micro Linkages‖, in Bourguignon, Bussolo and Pereira da Silva eds. ―The Impact Of Macroeconomic Policies On Poverty And Income Distribution - Macro-Micro Evaluation Techniques and Tools‖, World Bank, Washington D.C., Palgrave Macmillan, New York.

Chen, Shaohua, and Martin Ravallion. 2004. ―Welfare Impacts of China‘s Accession to the World Trade Organization.‖ World Bank Economic Review 18: 29–57.

- Duflo, Esther and Pande, Rohini. 2007. "Dams," The Quarterly Journal of Economics, MIT Press, vol. 122(2), pages 601-646, 05

- Ferreira, Francisco H. G. & Leite, Phillippe G., 2003. "Policy options for meeting the Millennium Development Goals in Brazil: can micro-simulations help?," Policy Research Working Paper Series 2975, The World Bank.

- Galliani, Sebastián. 2006. ―El Mercado de Trabajo en Panamá: Notas para su análisis.‖ Mimeo.

International Monetary Fund (IMF). 2007. ―Panama: Staff Report for the 2006 Article IV Consultation.‖ Washington, DC, January 5.

- International Monetary Fund (IMF). 2010. ―Panama: 2010 Article IV Consultation— Staff Report.‖ Washington, DC, October.

- Lokshin, Michael and Yemtsov, Ruslan 2005. "Has Rural Infrastructure Rehabilitation in Georgia Helped the Poor?," World Bank Economic Review, Oxford University Press, vol. 19(2), pages 311-333

- Ravallion, Martin and Shaohua Chen. 2003. ―Measuring pro-poor growth,‖ _Economics Letters_ 78 (1): 93-99.

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Table 2.2: Annual late of Growth in Agricultural Production and Population and Per Capita Availability of Cereals from Domestic Sources

|Country|Average Annual<br>1971<br>(per|Rate of<br>-1981<br>cent)|Growth|Average Annual<br>Population Growth<br>1972-1981|Averag<br>Capita Ava<br>of Ce<br>(to|e per<br>ilability<br>reals<br>ns)|
|---|---|---|---|---|---|---|
||Agricultural|<br>Pro|duction||||
||Productiona|Food|Cereals|<br>(percent)|1970-72|1979-81|
|Southeast Asia|||||||
|Indonesia|3.7|3.8|5.2|2.5|0.18|0.23|
|Malaysia|4.7|5.9|2.6|2.6|0.17|0.16|
|Philippines|4.9|4.9|4.6|2.6|0.18|0.22|
|Thailand|6.0|6.4|4.6|2.2|0.40|0.45|
|South**Asia**|||||||
|Bangladesh|2.9|3.0|3.4|2.9|0.22|0.24|
|Burma|3.4|3.4|6.0|2.3|0.29|0.39|
|India|2.9|3.1|2.8|2.1|0.20|0.21|
|Nepal|1.2|1.3|0.9|2.0|0.30|0.26|
|Pakistan|3.2|3.7|4.8|3.0|0.19|0.21|
|Sri Lanka|3.8|6.0|3.8|1.7|0.12|0.14|

aAgricultural GDP Source: Food and Agriculture Organization of the United Nations, Production Yearbook, vol. 35 (Rome, 1981).
per year _after inflation_ . For-profit commercial microfinance banks, in contrast, charged an average of just 22 percent per year. Consistent with the pattern of costs, NGOs as a group thus charge more than commercial microfinance banks.

<!-- Start of picture text --> Percent<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 1 2 3 4 5<br>Avg Loan Balance per borrower / GNI per capita for the poorest 20%<br>NGO NBFI Bank<br><!-- End of picture text -->

**Figure 3: Average Yield on gross portfolio (real), percent**

Source: Cull, et al. (2017). Notes: Observations: 446 NGOs, 380 non-bank financial institutions (NBFIs), and 82 microfinance banks. The figure is restricted to observations in which the ratio of the lender’s average loan size divided by the per capita gross national income at the country’s 20<sup>th</sup> percentile is under 5. Original, underlying data provided by Microfinance Information eXchange, Inc. (MIX).

Despite the high interest rates, and despite the finding that most firms earn positive accounting profits, only a minority earn _economic_ profit (which accounts fully for the opportunity costs of inputs). Accounting profit reflects an institution’s ability to cover its costs with its revenues. It is a helpful statistic, but it does not account for implicit grants and subsidies.

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**5.**

# **CONCLUSIONS AND RECOMMENDATIONS**

Testing Hypotheses: This study tested hypotheses about impact of regulatory reforms against Turkey's experiences. Results confirm the main hypothesis: regulatory reforms accelerated introduction of new technology which raised productivity and incomes. The study also confirms the three sub-hypotheses of Section 1: (1) appropriate technology was available in other countries; (2) the private sector introduced new technology when regulations allowed; and (3) new technology had a significant impact on productivity and incomes.

Both before and after 1980s reforms, most new technology in Turkey has come from foreign countries. This observation echoes a similar finding by Eduardo Venezian for Chile: "the overall most important innovations in Chilean agriculture post-1960 ... originate mainly abroad" (Venezian 1987, pp 107-108). While foreign research continues to provide most new technology, reforms changed channels for technology transfer and facilitated flow.

While this study confirms hypotheses for Turkey, some might argue that aspects of Turkish geography and economy favored strong response to reforms. Turkey shares latitudes with Western Europe and the US, which allows variety transfer. Entering the 1980s, hundreds of thousands of Turkish farmers had visited European countries. Much technology was already present in fertilizer and other domestic inputs industries. Turkey had hundreds of agricultural PhDs. Also, infrastructure such as roads, telephones, electricity, and ports was in place to handle increases in agricultural production. In short, people at all levels were ready to import and to adopt new agricultural technology when reforms allowed them to act.

On the other hand, these favorable conditions meant that Turkey had already closed some of the technology gap with West European and other developed countries. For a country that is poorer and less developed, response to reform would be measured from a lower base, so that a relatively weaker response in terms of flow of new technology could bring comparable percentage changes in input trade and output response.

Recommendations: To increase the flow of new technology to farmers in developing countries:

- donors and governments are encouraged to review input regulations to assess the extent to which they slow or block private sector introduction of new technology; these assessments can be attached to agricultural research projects, trade reform studies, inputs projects, etc;

- depending on the outcome of such assessments, donors and governments are encouraged to promote regulatory reforms as opportunities arise.

In Turkey after the 1980s reforms, most new agricultural technology comes from other countries through private inputs trade, technology licensing, investment, joint ventures, etc. Many developing countries continue to block these channels for new technology. It makes little sense to design and fund projects or programs to deliver new technology through public channels without first considering essentially costless regulatory reforms to un-block private sector channels. Once

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policies of providing infrastructure and/or subsidies, instead sending facilitators to villages to initiate participatory analysis of existing sanitation practices and the consequences and implications of such practices, thus generating demand for better sanitation services that the market can then respond to.

In Indonesia, the project’s programmatic approach consists of three main components:

# _Community-Led Total Sanitation (CLTS)_

This component focuses on stopping open defecation. It aims to trigger the desire for an opendefecation-free (ODF) community. It does this by raising collective awareness of the open defecation problem. Facilitators are sent to communities to initiate analysis and discussions of the sanitation situation. These discussions are held in public places and are open to all. They involve a “walk of shame,” during which the facilitator helps people analyze how fecal contamination spreads from the exposed excreta to their living environments and food and drinking water. A map of the village is drawn on the ground and villagers are asked to indicate where they live, where they defecate, and the routes they take there and back. It soon becomes apparent that everyone is ingesting small amounts of each other’s feces (to people’s horror and embarrassment). This inevitably leads to personal and collective decisions to be free of the hazard by becoming an ODF community. The villagers must forge their own plan for making this happen with only limited follow-up support and monitoring from the program. ODF status is verified by local government agencies. Communities achieving ODF status receive recognition and commendation from local and provincial governments.

# _Social Marketing of Sanitation_

This component focuses on increasing the availability of sanitation-related products (such as latrines or slabs) and services (such as pit emptying). It involves extensive consumer and market research that investigates the sanitation solutions that people desire, the options available to them in the market, and their attitudes and knowledge of sanitation issues. The component develops targeted communications campaigns and enhances the supply of a range of sanitation goods and services that are responsive to preferences and economic capacities of all consumer segments. It also involves partnering with the local private sector—for example, to train local artisans to meet the increased demand for specific products that results from CLTS facilitation sessions.

# _Strengthening the Enabling Environment_

This component aims to support the development of policies and institutional practices that facilitate scaling up, program effectiveness, and sustainability. These include national, state, and local government sanitation policies; sanitation program financing, implementation, and management practices; fiscal rewards for results consistent with policies; training and 4
production rates and growth rates during that period (Barrios et al., 2008). According to their estimates, the rainfall decline accounted for a 9 – 23% drop in per capita GDP in SSA relative to levels without such decline in rainfall.

A common drawback in previous studies that attempt to assess climate impacts empirically is the form of the precipitation data that is used. Since socioeconomic data is most widely available at the national level, climate data must also be aggregated to national spatial scales for use in analysis. Typically, a spatial average of rainfall and temperature over a given country’s national borders is used. This averaging introduces a systematic bias in the resulting rainfall and temperature data due to the smoothing effect that averaging causes. The calculation of a spatial average reduces the variability that is present across a country. As a result, it underestimates the climate challenges that countries face, especially for larger countries. The bias is worse for precipitation than for temperature as precipitation tends to be more variable in space.

While the bias may seem trivial, the way in which precipitation anomalies are expected to affect economic indicators amplified its significance. In typical economic systems that might be affected by rainfall, such as agricultural production, small deviations from normal amounts are likely to have minor or negligible impacts, while large deviations may have very large impacts. This nonlinear response makes the use of spatial averaged rainfall problematic. The calculation of a spatial average over a country means a small deviation below the normal rainfall can have the same value as results from a rainfall pattern of large deviation over a small part of the country and normal rainfall over other parts. An extreme example is the case where part of a country experiences drought while other parts receive normal or above normal rainfall. The resulting spatial average may show normal rainfall. In addition, when used in regression analyses, there’s an implicit assumption of symmetry in the effects of above normal and below normal rainfall. This assumption is not supported by the evidence of how such anomalies impact society. In addition, previous analyses use calendar year precipitation which is not appropriate in tropical climates where the rainy season occurs over the end of the year, splitting a single season between two 7