rafmacalaba/datause-encoder-data huggingface dataset:
Table 7. Effects of Guerrilla Control on Agricultural Productivity

|_Panel A: Crop Produc_|_tion in 2005 (_|_1,000 Tons)_|||
|---|---|---|---|---|
||Subsisten|ce crops|Cash|crops|
||Bean|Maize|Coffee|Sugarcane|
||(1)|(2)|(3)|(4)|
|Guerrilla control|-0.00167|-0.0110|-0.00540|-1.829***|
||(0.00161)|(0.0324)|(0.00789)|(0.529)|
|Observations|3,652|3,652|3,652|3,652|
|Dependent mean|0.102|1.914|0.458|15.460|
|_Panel B: Share of Har_|_vest in 2005 (_|_Ha)_|||
|Guerrilla control|-0.0112***<br>(0.00356)|-0.0310***<br>(0.0109)|-0.0202***<br>(0.00753)|-0.00357<br>(0.00230)|
|Observations|3,651|3,651|3,651|3,651|
|Dependent mean|0.0360|0.113|0.0780|0.0260|
|_Panel C: Actual Crop_|_Yield in 2005_|_(Tons/Ha)_|||
|Guerrilla control|-0.00471***|-0.0161***|-0.00622**|-1.078***|
||(0.00126)|(0.00586)|(0.00242)|(0.241)|
|Observations|3,566|3,550|3,649|3,649|
|Dependent mean|0.400|2.254|0.835|61.220|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|

_Notes:_ The table presents the results of estimating equation (1) for outcomes related to agriculture. Panel A shows results using as dependent variable each crop’s production in 1,000 tons. Panel B uses as dependent variable the share of harvested land of each crop from the total area of each census tract. Panel C uses the actual yield of each crop, which is measured as the total production over the total of cultivated land for each crop. The unit of observation in all columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

# _Inequality and cooperatives_

As shown in the previous section, guerrilla-controlled areas were more likely to experience land fragmentation and remain divided today. Yet, even though these areas are poorer, there may be less income inequality. We explore this notion by evaluating reported income from household surveys and the wealth index from the demographic census.

Table D.18 presents results for different measures of inequality. We find that while individuals have less income in former guerrilla areas (Columns 1 and 2), there is no evidence of lower in36
made by the US Small Business Administration to data on the universe of employers in the US economy to show that credit constraints impeded growth of small businesses prior to receiving the SBA loans.

In the following section, we focus on the weaknesses in financial and legal systems that constrain small firms’ access to finance in developing countries.

# **III. Access to finance and institutional constraints**

# **_A. Transaction Costs and Interest Rates_**

One of the main problems in delivering credit to SMEs is the high transaction costs of processing, monitoring, and enforcing small loans, which increase breakeven interest rates for these loans. Evaluating an individual loan request entails fixed costs such as legal services, brick-and-mortar branch installations over computer system, regulatory costs, costs of payment and settlement systems, etc. that are at least partially independent of the amount of the size of the loan. As noted by Beck and de la Torre (2007) these fixed transaction costs drive a wedge between funding costs of financial institutions and the lending rate they charge borrowers.

Furthermore, the high dependence on relationship between lenders and borrowers in SME lending (Berger and Udell, 1998, 2006) only increases the lending costs for SMEs.

The higher costs of lending to SMEs and the greater risks involved are often reflected in higher interest rates and fees for SMEs relative to larger firms, particularly in developing countries. Beck, Demirgüç-Kunt, and Martínez Pería (2008) show that the annual fee to maintain a checking account varies widely exceeding 20% of GDP/capita in countries like Malawi, Sierra Leone, and Uganda.

11
Industrial Zone stretching from Sendai to Shiogama.

This grand plan failed to materialize in the intensified war period. The number of factories which located in Miyagi prefecture was ten before 1930, rose to six between 1931 and 1940, and jumped to 29 between 1941 and 1945. There were three types of factories located in Sendai City: rubber production factories established along with the Tohoku Promotion measure, arsenals and munitions plants, and factories built on the seeds planted by Tohoku University. However, unfortunately, much of the infrastructure created was hit by a massive air-raid in July 1945, just before the end of the war in August, which destroyed the central part of Sendai, where the research institutions and government agencies were located (Sato 1963, CHCCS 1954b, Fujii & Okuda 2002、 CCMH 1968).

# 3 Tohoku University in the second catching up process

## 3-1 “The Vannevar Bush of Japan”

When Karl T. Compton (the former MIT President), who was then the Director of Scientific and Technical Advisory Section (U.S. Army Forces, Pacific, GHQ), visited Japan to conduct the Scientific Intelligence Survey in September 1945, just a month after the end of the war, he said, “Our first business with a Japanese scientist was to locate Hidetsugu Yagi, the Vannevar Bush of Japan, a former student at Harvard” (Compton 1945: 102). This was presumably because Yagi was the 23
# Structural and Behavioral Barriers to Improving Development Outcomes: The Case of Maternal CVre in Haiti<sup>∗</sup>

Emilie Perge, Jimena Llopis Abella, and Anna Fruttero<sup>†</sup> JEL: D91, I12, I15, I18.

Keywords: Maternal health, behavioral biases, multilevel model, mixed methods, Haiti.

## **1 Introduction**

With 480 deaths per 100,000 live births in 2017, Haiti, was far from achieving the Sustainable Development Goal of having fewer than 70 maternal deaths per 100,000 live births by 2030 (UNICEF, 2022). In the Latin America and Caribbean region the average maternal mortality rate was 74 deaths per 100,000 live births. Pregnant women die as a result of complications such as preeclampsia, eclampsia, severe bleeding, and infections.

While some intrapartum complications cannot be reliably predicted or prevented, most of them can be successfully detected and treated with prompt diagnosis and care (Say et al., 2014). Adequate professional care before, during, and after childbirth has been proven to reduce death rates (UNICEF, 2014). Thus, all women should access antenatal care (ANC)

> ∗Acknowledgments: The authors thank the two peer reviewers, Jorge Luis Casta ̃neda and Nicolas Collin, for their detailed comments; Ondine Berland for excellent research assistance; Donald Antoine, Jamesson Vamblain, Mayerline Antoine, Manouchka Justin, Louise Estavien, Tania Mathurin and Fleurimonde Charles Joseph for their help collecting qualitative data in Haiti; Ingrid Dallmann for her help with the SPA data; and Lauren Manning and Chiara Broccolini for support with editing. All findings, interpretations, and errors belong to the authors.

> †Perge is affiliated with UN Sustainable Development Solutions Network, Llopis Abella is affiliated with Save the Children, and Fruttero is affiliated with the World Bank Group. Correspondence: Emilie Perge (emilie.perge@unsdsn.org), Jimena Llopis Abella (jimena.llopis@savethechildren.org and Anna Fruttero (afruttero@worldbank.org)).
majority of world population, summarized in the Penn World Tables. If you aggregate all of the industrial countries and all of the developing countries for which there are data back to 1960, you find that in general rich country growth rates have declined while growth of the developing world has accelerated (figure 6). In particular, in the 1960s growth of OECD countries was about twice as fast as that of developing countries. The rich country growth has since gradually decelerated from about 4 percent per capita in the 1960s to 1.7 percent in the 1990s. The latter figure is close to the long-term historical growth rate of the OECD countries; the rapid growth in the 1960s was still to some extent a rebound from the destruction of World War Two as well as a payoff to economic integration among the rich countries. Subsequently, growth has returned to the long-term historical trend level.

Figure 6. Growth rate of per capita GDP <!-- Start of picture text --> Percent<br>Industrialized<br>economies<br>4<br>Developing<br>3 economies<br>2<br>1<br>0<br>1960s 1970s 1980s 1990s<br><!-- End of picture text --> In the 1960s and continuing into the 1970s the growth rate of developing countries in the aggregate was well below that of rich countries, a paradox whose origin has been long debated in the economics profession. The slower growth of backward economies is a paradox because the 13
individual, how long the person spent there and the length of time in the destination. I make two assertions:

1. The likelihood a person, _pt_ , is infected is based on the fraction of the month spent in the origin district j, _Tjp_ , and the monthly incidence rate in j, _xjt_

2. The contribution of an imported case to a new location is calculated as a fraction of time spent in the destination location i, _Tip_ The contribution of person _pt_ , who enters i from j at time t, to imported cases is calculated based on the incidence of the origin district and the length of time spent in origin district j and destination health post catchment area i. Only up to 15 days in the origin and up to 15 days in the destination are considered since the human incubation period is 15 days. Therefore, _Tip_ is the proportion of 15 days _pt_ spends in _i_ after entering _i_ and _Tjp_ is the proportion of the month up to 15 days that _pt_ spent in _j_ with monthly malaria incidence _xjt_ in month _t_ .<sup>19</sup> Due to the complicated nature of the imported incidence variable, I break down what explains the variation in this variable. Imported incidence combines information on travelers, the incidence where they are coming from, the timing in the destination and the origin, and the population in the destination. Based on a partial _R_<sup>2</sup> of .44 the month explains a large portion of the variation, while .33 of the variation is explained by the health post catchment area. Jointly, they explain about half of the variation ( _R_<sup>2</sup> of 0.56), implying that the other half of the variation in the variable of interest is coming from a combination of the month and the location receiving imported cases. This shows how the identication comes from the unique combination of detailed data on travelers and the incidence in the origin. The matrix _Zit_ includes zero, one and two lags of rainfall, which capture both the agricultural seasons that could inuence movement and changes in malaria incidence due to

> 19I use the detailed knowledge of the timing from the mobile phone data to factor in how many of the 15 days were in month t and how many in month _t −_ 1 and use the incidence both in month _xjt_ and _xjt−_ 1 to determine the probability the person is infected.

16
- Boughton, James, “Commodity and Manufactures Prices in the Long Run,” May 1991. International Monetary Found Working Paper, 91/47,.

- Bravo-Ortega, Claudio, “Does Geography Matters for the Big Push. Why Scandinavia and not Latin America?,” 1999. Mimeo Berkeley.

- Caselli, Francesco, Gerald Esquivel, and Lefort Fernando, “Reopening the Convergence Debate: A new Look at Cross Country Growth Empirics,” _Journal of Economic Growth_ , 1996, _1_ (3), 363–89.

- Chenery, Hollis B. and Moche Syrquin, _Patterns of Development. 1950-1970_ , London: Oxford University Press, 1975.

- Davis, Graham, “Learning to Love the Dutch Disease: Evidence from the Mineral Economies,” _World Development_ , 1995, _23_ (10), 1765–79.

- Easterly, William, Pritchett Kremer, Michael Lant, and Lawrence Summers, “Good Policy or Good Luck? Country Growth Performance and Temporary Shocks,” _Journal of Monetary Economics_ , December 1993, _32_ (3), 459–83.

- Edwards, Sebastian, “Openness, Productivity and Growth: What do we really know?,” March 1997. National Bureau of Economic Research (Cambridge, MA) Working Paper No 5978.

- Farzin, Y.H, “Optimal saving policy for exhaustible resource economies,” _Journal of Development Economics_ , February 1999, _58_ (1), 149–84.

- Feenstra, Robert, Dorsati Madani, Tzu-Han Yang, and Chi-Yuan Liang, “Testing Endogenous Growth in South Korea and Taiwan,” May 1997. National Bureau of Economic Research (Cambridge, MA) Working Paper No. 6028.

- Gylfason, Thorvaldur, “Natural resources, Education and Economic Development,” _European Economic Review_ , May 2001, _45_ (4-6), 847–59.

- and Gylfi Zoega, “Natural Resources and Economic Growth: The Role of

- Investment,” January 2002. Paper presented at the Central Bank of Chile and World Bank Conference on Economic Growth and Natural Resources. Santiago, Chile.

28
Note that the estimation of the long-run coefficients aand f6are not affected **by** the endogeneity of _X_ That is, aand f6are the same functions of the coefficients _ony-, Xt, and Xt-i,_ taken either from **(2.10)** or, the observationally equivalent, **(2.6).** In order to appreciate more clearly the long- and short-run relationships embedded in the ARDL model _for_ **_y,_** let's express **(2.10)** in the form of an error correction model **(ECM):** where the expression in brackets is the error-correction term and **_(1-b)_** is the speed of adjustment. Note that, as in the case of the long run coefficients, the short-run coefficients (that is, the coefficients on the error-correction term and on the change in **X)** are not affected **by** the endogeneity of _X._ In particular, the coefficient on At is equal to the coefficient on _Xt_ in **(2.10)** (or the observationally equivalent **(2.6))** and, thus, can be estimated **by** simply running an **OLS** regression on (2.10).

# **_Multi-country estimation_**

Our empirical samples below are characterized **by** time-series (T) and cross-section dimensions **(N)** of roughly similar magnitude. In such conditions, there a number of alternative methods for multi-country estimation, which allow for different degrees of parameter heterogeneity across countries. At one extreme, the fully heterogeneous-coefficient model imposes no cross-country parameter restrictions and can **be** estimated on a country-by-country basis **--** provided the time-series dimension of the data is sufficiently large. When, in addition, the cross-country dimension is also large, the mean of long- and short-run coefficients across countries can be estimated consistently **by** the unweighted average of the individual country coefficients. This is the "mean group" **(MG)** estimator introduced **by** Pesaran, Smith, andlm **(1996).** At the other extreme, the fully homogeneous-coefficient model requires that all slope and intercept coefficients be equal across countries. This is the simple "pooled" estimator.

In between the two extremes, there is a variety of estimators. The "dynamic fixed effects" estimator restricts all slope coefficients to be equal across countries but allows for different country intercepts. The "pooled mean group" (PMG) estimator, introduced **by** Pesaran, Shin, and Smith **(1999),** restricts the long-run coefficients to be the same across countries but

**10**
the period of 2016-2030. The granular cost breakdowns of the LSMS-ISA database suggest that there is considerable variation in terms of aggregate costs for specific budget categories, inclusion/exclusion of line items in a given category, and unit costs for comparable line items. The factors underlying the cross-country variations include:

- 1) whether a given cost category/line item is absorbed through technical assistance outside the implementation budget (such as data analysis and dissemination, training and capacity building activities, and specialized equipment procurement outside the country),

- 2) survey duration, sample size and spatial distribution,

- 3) interview length and approach to respondent selection,

- 4) survey mode (computer-assisted personal interviewing (CAPI) versus pen-and-paper interviewing (PAPI) with field-based data entry and processing versus PAPI with centralized data entry and processing),

- 5) required field staff and their organization (mobile teams versus resident enumerators, need for specialized staff),

- 6) in-country salary and per diem structure for field-based and supervisory staff, and

- 7) costs for vehicle purchase versus rental, maintenance and repairs.

The second step in our analysis was to expand the country coverage and the regional representation of the survey implementation unit cost estimates, including as much as possible surveys that are used for estimating official poverty and inequality statistics. While the advantage of this database is that it includes actual implementation costs of household surveys that have been used for estimating official poverty statistics, its disadvantage is that the design and quality of surveys differ quite significantly across countries. To conduct a comparative review of the survey implementation budgets tied to the LSMS-ISA activities, we first created templates for aggregate and detailed implementation cost reporting for multi-topic household surveys to ensure to the extent possible cross-country consistency in accounting, both within and across budget categories. The templates, which are provided in the Appendix Tables A1 and A2, were in turn circulated among World Bank staff to facilitate their intelligence gathering from their NSO counterparts. The information that was received from the regional poverty economists was based on Appendix Table A1, with Table A2 serving as a reference point for the types of expenditures that could be included under the aggregate implementation cost categories in Table A1.

The final database of country-specific implementation costs per household (in USD 2014 prices) is presented in Table 1, based on the inputs received as of June 2016, spanning 18 countries from all six regions. Of the 18 countries, three are from South Asia, six from sub-Saharan Africa, two from Middle East and North Africa, five from Latin America and the Caribbean and one country each from Europe and Central Asia, and East Asia and Pacific. While nine out of 18 countries are 8
23

# **Figure 15: EU and Chinese Buyers’ Requirements –International Regulations and Standards (1=not important; 5=very important)***

<!-- Start of picture text --> EU<br>Formaldehyde emissions<br>CN 5<br>4 Phytosanitary<br>ISO standards<br>3 requirements<br>2<br>1<br>Product testing<br>GPP 0<br>requirements<br>Sustainability certification<br>Building codes<br>requirements<br>Legality certification<br>requirements<br><!-- End of picture text --> _Source:_ Terheggen’s fieldwork interviews _Note:_ * See Box 3 for details of standards

* See Box 3 for details of standards

# **_3.3 Changes in Market Destination of Gabon’s Timber Exports_**

From Gabon’s perspective, the evolution of the EU and the Chinese markets has had major consequences for the direction and nature of its timber exports. Beginning with the direction of exports, China (and India) have now become the dominant global importers of tropical hardwoods as EU economies have moved to the imports of processed woods and have become increasingly concerned about the sustainability of global hardwood reserves. Between 1990 and 2007, China’s share of global imports rose from 14 percent to 68 percent (and India from five percent to 17 percent), whilst the share of all OECD economies collapsed from 78 to 11 percent (Table 4). With 1990 as the base-year, in 2007 China’s imports of tropical hardwood had more than quadrupled in volume terms; in the same period, EU and wider OECD imports had fallen by more than 90 percent.

**Table 4:** **<u>Share of Global Tropical Log Importers for Selected Countries</u>**

|**1990**<br>||**1997**<br>||**200**<br>|**7**<br>|
|---|---|---|---|---|---|
|Japan|39.5%|Japan|32.3%|**China**|**68.2%**|
|Korea,Rep.|16.1%|**China**|**29.2%**|India|17.2%|
|**China**|**13.9%**|Korea,Rep.|6.5%|Japan|3.8%|
|Thailand|8.1%|India|5.5%|France|2.1%|
|India|4.7%|Thailand|4.5%|Thailand|1.3%|
|France|3.6%|France|3.7%|Spain|0.9%|
|Italy|2.9%|Philippines|3.7%|Korea,Rep.|0.8%|
|Portugal|2.1%|Norway|2.2%|Italy|0.8%|
|Spain|2.0%|Pakistan|1.9%|Turkey|0.7%|
|Germany|1.4%|Portugal|1.8%|Portugal|0.6%|
|**OECD **|**78.29%**|**OECD **|**53.23%**|**OECD **|**10.99%**|
|_Source:_<br>FAO|STAT Fores|tSTAT data, acce|ssed Novemb|er 2009.||
the amount of inputs they receive, and how the inputs are utilized. The eventual impacts of the program are therefore closely linked to the quality of the targeting process. Using two-wave panel data from Malawi, the goal of this study is to help improve the targeting of TFISPs by providing guidance for deciding on whether such programs should be targeted at poor or non-poor farmers.<sup>1</sup> The specific research question that this study seeks to answer is: _Do non-poor farmers use inorganic fertilizer more efficiently than poor farmers?_ Efficiency is measured as kilograms of maize obtained per kilogram of nitrogen applied, otherwise known as nitrogen use efficiency (NUE). The steps involved in the empirical approach used in answering this question will help clarify issues such as whether the poverty and food security goals of TFISPs can be achieved together by estimating if poor farmers obtain the same NUE as non-poor farmers, and estimating whether crowding out varies significantly across poor and non-poor farmers. By answering these questions, the study provides an empirical standpoint for the targeting discourse that has in the past been based mostly on anecdotal evidence.

The decision of whether a subsidy should be targeted at poor or non-poor farmers depends on three main factors. The first factor is the objective of the programs. Officially, TFISPs in SSA have two main objectives: 1) increasing smallholder agricultural productivity and food production, to improve household food security and national food sufficiency; and 2) reducing poverty by increasing the income levels of beneficiaries. Though both are admirable, these objectives potentially have different implications for targeting. The objective of ensuring household food security and national food sufficiency suggests that the programs should be targeted at productive 1 Targeting is made up of two processes: 1) identification of the targets, i.e., the category of people to be targeted, and 2) identification of the most appropriate method used in delivering the inputs to beneficiaries. The effectiveness of both processes are equally important in ensuring successful targeting. This study focusses on the first process, leaving the second process for future studies.

3