rafmacalaba/datause-encoder-data huggingface dataset:
# **INTRODUCTION**

In the early 1980s, coffee and cocoa exports from the Dominican Republic accounted for about 30-35% of total exports. By 2000 this figure was closer to 5%. However, it is estimated that there are still about 80,000-100,00 Dominican coffee and cocoa producers, or about 35-40% of all Dominican farmers. In addition, there are tens of thousands of laborers and persons employed in linked economic activities. Furthermore, the majority of coffee and cocoa producers are smallscale producers and most coffee and cocoa producing areas are located in environmentally sensitive watersheds. Recent trends in international commodity markets have challenged the existence and sustainability of both the coffee and cocoa sectors, as have periodic hurricanes that have damaged coffee and cocoa plantations. Problems with marketing and financing arrangements in the coffee and cocoa sectors have tended to exacerbate these challenges, which are also associated with low yields and poor quality.

During 2002, the Commodity Risk Management Group (CRMG) of the Agricultural and Rural Development Department prepared a report about the Dominican Republic’s coffee and cocoa sectors. The focus of the report was on the appropriateness of piloting commodity price insurance for coffee and/or cocoa producers, and included a survey of producers (see World Bank, 2002a). After preparing the report, and attempting to pursue a pilot commodity price insurance scheme, it was realized that a broader set of issues should be investigated in the coffee and cocoa sectors of the Dominican Republic.

Since the Country Management Unit for the Dominican Republic was beginning to undertake an Agricultural Sector Strategy review, it was thought that a deeper investigation of the coffee and cocoa sectors, particularly in the context of the country’s overall macroeconomic and agricultural sector performance and policies, would be a useful contribution to the process of background analyses and policy dialogues.

Thus, this report is an initial attempt to provide some additional analyses and to stimulate policy dialogue for the coffee and cocoa sectors in the Dominican Republic. This report also points out that although considerable data and information are available, there are significant knowledge gaps. This is especially true if the objectives of the government are poverty reduction, environmental protection and maintenance of rural society, which requires that analyses and policy dialogues move beyond a commodity-specific approach to a broader focus on households, regions and environments where coffee and cocoa are currently being grown. While policies and programs can help bolster the economic viability of both commodity sectors, their ultimate sustainability depends on the mosaic of linkages between households and their economic, social and environmental context. And there are still important gaps to fully understanding and appreciating this complex mosaic, and for identifying the appropriate roles for Government, the private sector, international development agencies, and non-governmental organizations (NGOs).

7
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|Country|Data source|Implementing agency|Time period|Spatialunit<br>(count)|Plant count|
|---|---|---|---|---|---|
||Encuesta Nacional IndustrialAnual (Annual|Institute Nacional deEstadisticas||Municipalities||
|Chile|National Industrial Survey—ENLA)|(INE)|2001-2005|(208)|6.344|
||Encuesta Annual Manufacturera (Annual|Departamento Administrative||Municipalities||
|Colombia|Manufacturing Survey<br>—EAM)|NacionaldeEstadistica(DANE)|2000-2009|(267)|12,397|
||Survei Tahunan Perusahaan Industri|Badan Pusat Statistik (BPS-||Districts and||
|Indonesia|Pengolahan(Annual Manufacturing Survey)|Statistics Indonesia)|2000-2015|metros (291)|34,366|
|Ethiopia|LargeandMediumManufacturingSurvey|CentralStatisticalAgency(CSA)|=2000-2009|Towns(91)|3.465|

Notes: For each country, the statistical agency listed in the source column implements an annual survey of manufacturing establishments. In Chile, although we have access to the data from 1996-2007, we used only the period 2001-2005 because product categories changed in 2001 and the correspondence between the old and new product classification is not perfect, while for the year 2006-07, establishment location is missing.
urban population share is above 50 percent (circa 2000) are considered urban while the rest are classified as rural.

**Table 2. Municipality types: definitions and descriptive statistics**

||N|Avg.<br>growth|Std.<br>Dev.<br>Growth|Density|Comple<br>seconda|te<br>ry<br>Self-<br>employment<br>share|Wage<br>earners|
|---|---|---|---|---|---|---|---|
|**Mexico**||**2000**|**-15**|||**2000**||
|Urban-Metro (large)|41|0.003|0.017|1820|0.19|0.22|0.74|
|Urban-Metro (small)|72|0.012|0.018|735|0.16|0.24|0.71|
|Urban (large)|358|0.017|0.021|369|0.11|0.27|0.64|
|<br>Urban (small)|440|0.030|0.027|168|0.08|0.33|0.56|
|Rural (large)|587|0.030|0.029|70|0.05|0.35|0.49|
|Rural(small)|832|0.049|0.044|41|0.04|0.43|0.36|
|**Brazil**||**2000**|**-10**|||**2000**||
|Urban-Metro (large)|46|0.011|0.009|3438|0.25|0.24|0.75|
|Urban-Metro (small)|176|0.013|0.014|691|0.17|0.24|0.74|
|Urban (large)|434|0.016|0.015|139|0.16|0.31|0.65|
|<br>Urban (small)|842|0.018|0.019|50|0.13|0.31|0.64|
|Rural (large)|317|0.029|0.017|34|0.07|0.44|0.43|
|Rural(small)|225|0.028|0.024|31|0.08|0.44|0.42|
|**Colombia**||**2011**|**-19**|||||
|Metro|59|0.028|0.033|||||
|Urban (large)|168|0.020|0.045|||||
|<br>Urban (small)|150|0.016|0.051|||||
|Rural (large)|341|0.021|0.040|||||
|Rural(small)|404|0.028|0.055|||||
|**Peru**||**2000/03-**|**2017/19**|||**2000/03**||
|Urban|71|0.030|0.016||0.237|<br>0.502|0.398|
|Rural|115|0.046|0.021||0.127|<br>0.756|0.183|
|**Ecuador**||**2003/04-**|**2017/19**|||**2003/04**||
|Urban|69|0.024|0.025||0.125|<br>0.396|0.524|
|Rural|122|0.036|0.029||0.077|<br>0.529|0.385|
|**Chile**||**2000/03-**|**2015/17**|||**2000/03**||
|Urban|201|0.030|0.016||0.238|<br>0.213|0.691|
|Rural|98|0.036|0.014||0.117|<br>0.291|0.569|
|**Dominican Republic**||**2000/02-**|**2014/16**|||**2000/02**||
|Urban|51|0.007|0.020||0.107|<br>0.383|0.445|
|Rural|64|0.015|0.028||0.049|<br>0.530|0.297|
|**Honduras**||**2004/06-**|**2017/19**|||**2004/06**||
|Urban|34|0.005|0.030||0.089|<br>0.444|0.423|
|Rural|210|0.004|0.038||0.042|<br>0.597|0.278|
|**Costa Rica**||**2001/03-**|**2008/09**|||**2001/03**||
|Urban|30|0.019|0.022||0.142|<br>0.277|0.634|
|Rural|49|0.031|0.025||0.079|<br>0.315|0.591|

Table 2 displayes the number of different types of localities, obtained based on the classification in Table 1, along with their socioeconomic characteristics. In most countries, there are more rural than urban or metropolitan municipalities. Brazil and Chile are exceptions. In the former, there are more 11
Policy Research Working Paper 5014

# **Abstract**

This paper uses two recently completed surveys of individual entrepreneurs (farmers and microentrepreneurs) and registered enterprises (agricultural and nonagricultural) operating in Mexico’s rural sector to provide new evidence about the factors influencing the incidence of credit constraints and investment behavior. To measure the incidence of credit constraints, the authors use self-reported information on whether economic agents have a demand for loans, separating formal and informal markets. They define credit constraints as a situation where rural agents report an unsatisfied demand for loans (formal or informal), which originates from rural agents having projects that are too risky or from impediments hindering the ability of rural agents and lenders to reduce information asymmetries. The authors find that the self-reported demand for loans is low. Nevertheless, the incidence of credit constraints is pervasive, especially among individual entrepreneurs. The low use of loans has consequences for the amount of investments that occur in the rural economy, posing a major obstacle to Mexico’s convergence towards its NAFTA partners. The empirical analysis, which includes proxies of business prospects and creditworthiness, shows that improving the availability of loans to credit constrained agents would increase the number of agents making investments and their investment to capital ratios.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to evaluate access to credit. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at ilove@worldbank.org@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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79

# **Table A4a**

## **Dependent Variable: Growth Rate of Real GDP Per Worker Regressions with Economic Openness**

|**Years : 1960 - 2000 **|**Reg A4a.1 **|**Reg A4a.2 **|**Reg A4a.3**|**Reg A4a.4**|**Reg A4a.5 **|**Reg A4a.6 **|**Reg A4a.7 **|**Reg A4a.8**|**Reg A4a.9**|**Reg A4a.10 **|**Reg A4a.11**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**<br>**per worker**|**0.2119*****|**0.2042*****|**0.2127*****|**0.2118*****|**0.2001*****|**0.2141*****|**0.2035*****|**0.2105*****|**0.2091*****|**0.1972*****|**0.2143*****|
|**Initial GDP (1960)**|**-3.7103*** **|**-3.2636*** **|**-3.1095*****|**-2.9651*****|**-3.5286*** **|**-2.8308*** **|**-3.1840*** **|**-3.0670*****|**-2.9476*****|**-3.4596*****|**-2.7276*****|
|**Economic**<br>**Openness**||**1.0153****|**1.1014****|**0.8914***|**1.0327****|**1.1106**|**1.0534****|**1.1284****|**0.9147***|**1.0579****|**1.1446****|
|**Education Variables**||||||||||||
|**HC15**|**4.4661****|**3.0962**|**2.4333**|**2.0483**|**2.2036**|**3.5601**||||||
|**Sq of HC15**|**-1.0965****|**-0.7656**|**-0.6074**|**-0.4950**|**-0.6414**|**-0.9150**||||||
|**HC25**|||||||**2.6911**|**2.0693**|**1.6596**|**1.8891**|**3.2435**|
|**Sq of HC25**|||||||**-0.7097**|**-0.5551**|**-0.4286**|**-0.6078**|**-0.8872**|
|**Log (Educational**<br>**Quality)**|**0.3719**|**0.4252**|**0.5709**|**0.5377**|**0.5128**|**0.4228**|**0.4705**|**0.6198**|**0.5882**|**0.5478**|**0.4481**|
|**Innovation Variables**||||||||||||
|**Log (Patents)**|**0.1956*****|**0.1890*****|||**0.2106*****|**0.2091****|**0.1925****|||**0.2140*****|**0.2119****|
|**Log (Journals)**|||**0.2356*****|||||**0.2393*****||||
|**Royalties per**<br>**capita**||||**0.000004*****|||||**0.000004*****|||
|**ICT Variables**||||||||||||
|**Phones**|**0.0040*****|**0.0019**|**0.0014**|**0.0032****|||**0.0019**|**0.0014**|**0.0032****|||
|**Computers**|||||**0.0061****|||||**0.0063****||
|**Internet Users**||||||**0.0055**|||||**0.0058**|
|**Constant**|**-4.5304****|**-3.6029**|**-4.3760***|**-2.9169**|**-2.7535**|**-4.0324***|**-3.2739**|**-4.1202***|**-2.6418**|**-2.4924**|**-3.7067**|
|**R squared**<br>|**0.5912**<br>|**0.6084**<br>|**0.6457**<br>|**0.6240**<br>|**0.6528**<br>|**0.6082**<br>|**0.6039**<br>|**06426**<br>|**0.6199**<br>|**0.6506**<br>|**0.6041**<br>|
|**No. of Countries**<br>*, **, *** denotes significan<br>Robust standard errors in|**85**<br>ce at the 90, 95<br>parentheses ob|**81**<br>and 99 perce<br>tained using W|**81**<br>nt confidence l<br>hite's correcti|**80**<br>evel respectively<br>on for heterosced|**78**<br>asticity.|**81**|**80**|**80**|**79**|**77**|**80**|
13. Table 2 lists selected indicators per SDG goal that are deemed significant to the Egyptian narrative; further, a complementary analysis is provided within the note disseminating the current and projected performance of the indicators (with the exception of indicators related to SDG 13, Climate Action, and SDG 16, Peace, Justice and Strong Institutions, which have been excluded from this study due to a lack of data). For the selected indicators, the table summarizes data for Egypt: historical evolution; actual and expected values for a recent year; and the projected 2030 values. In Annex 1, the figures representing the selected indicators for SDGs 1-12, and 14, 15 and 17 present the data for Egypt in the context of the estimated cross-country relationship between each indicator and GNI per capita.

14. The data used in this note are based on national averages; with that being said, domestic regional disparities should be considered given the government’s focus on addressing the large discrepancies between the urban and rural poor, particularly with respect to those residing in Upper Egypt. Such a region-specific and more granular approach to collecting and interpreting the data will enable policy makers to devise better-targeted interventions to address the pockets of extreme poverty and deprivation in the country (Ministry of International Cooperation, n.d.).

**Table 1:** Egypt – Summary of data coverage for SDG indicators.

|**Category**|**Total**|**Missing**|**Available**|**Over**|**As**<br>**Expected**|**Under**|
|---|---|---|---|---|---|---|
|SDG Goal 01: No Poverty|30|15|15|4|2|9|
|SDG Goal 02: Zero Hunger|18|5|13|4|0|9|
|SDG Goal 03: Good Health and Well-Being|51|2|49|28|2|19|
|SDG Goal 04: Quality Education|92|50|42|13|10|19|
|SDG Goal 05: Gender Equality|32|15|17|6|9|2|
|SDG Goal 06: Clean Water and Sanitation|52|38|14|6|1|7|
|SDG Goal 07: Affordable and Clean Energy|5|0|5|3|0|2|
|SDG Goal 08: Decent Work and Economic Growth|127|89|38|15|4|19|
|SDG Goal 09: Industry, Innovation and Infrastructure|22|0|22|18|1|3|
|SDG Goal 10: Reduced Inequalities|5|4|1|1|0|0|
|SDG Goal 11: Sustainable Cities and Communities|12|4|8|2|0|6|
|SDG Goal 12: Responsible Consumption and<br>Production|10|0|10|1|0|9|
|SDG Goal 14: Life Below Water|3|0|3|2|1|0|
|SDG Goal 15: Life on Land|17|0|17|6|2|9|
|SDG Goal 17: Partnership of Goals|12|4|8|4|2|2|

7
relative importance of different dataset characteristics, such as spatial detail, temporal frequency and length of record, with respect to the intended analysis.

In an ideal setup, we would compare the objective truth of rainfall and data measured on the ground, with remote sensing EO sources, and simply determine which EO product best matches with the measured truth. However, as we allude to, it is not possible to measure the truth, through direct measurement or through gauge station data. This leaves us with our EO weather data. We discuss the particular EO sources which we use and their various interpolation methods in Section 3.1. It is this second-best and its outcomes for the measurement and potential mismeasurement of agricultural production and productivity that we explore in this paper.

With this said, we acknowledge that EO data are not intended for field-level analyses - that is, they are not explicitly designed to fix the problems identified. And so, we are pushing these data to the limits of precision. While this is justified due to lack of availability of higher resolution alternatives, we appreciate that spatial aggregation likely itself creates some messiness within the data.

# **3 Data**

To understand the possible sources of measurement error associated with integrating spatial, remotely sensed EO weather data with socioeconomic data, we combine publicly available EO weather data products with publicly available unit-record survey data that have been generated as part of the World Bank LSMS-ISA initiative and that are made available through the World Bank Microdata Library.

The publicly available data contains obfuscated GPS coordinates to protect the privacy of survey participants. Specifically, spatial anonymization in the LSMS-ISA is achieved by only releasing GPS coordinates for enumeration area (EA) clusters, not households (Blankespoor et al., 2021). Further, following a method pioneered by the Demographic and Health Surveys (DHS), the EA cluster coordinates are displaced. Urban clusters are displaced in any direction within a two kilometers radius while rural clusters are displaced in any direction within a five kilometers radius. Additionally, one percent of rural clusters are displaced up to ten kilometers.

In trying to assess measurement error in EO weather data, the displacement of coordinates coming from spatial anonymization obviously can have a confounding effect, since one would be unable to disentangle mismeasurement in the EO product from mismeasurement introduced by the mismatch between the true household location and the displaced EA cluster coordinates. However, for this analysis, members of the authorship group were granted access to the true household coordinates - not just the un-displaced EA cluster coordinates - and matched the EO data to the true household location data. Thus, the temperature and precipitation reported by each EO product is measured at the exact location of the household. While this gives our analysis an added degree of accuracy in that we can precisely match household point location to EO grid cell, Michler 6
# **Table C.3. Robustness check with alternative CSI (six items)**

|||CSI w|ith six item|s<br>|Limit the vari|ety of foods|
|---|---|---|---|---|---|---|
|||||Assistance=0<br>|||
||(1)|(2)|(3)|(4)|(5)|(6)|
|VARIABLES|||||||
|Conflict events|0.057***|0.048**|0.051***|0.042**|0.072***|0.063**|
||(0.020)|(0.019)|(0.016)|(0.018)|(0.022)|(0.025)|
|Observations|3,550|3,457|3,457|3,253|3,550|3,457|
|R-squared|0.013|0.029|0.040|0.040|0.011|0.040|
|Number of households|717|717|717|674|717|717|
|Controls|NO|YES|YES|YES|NO|YES|
|Year FE|YES|YES|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|
|Region-round FE|NO|NO|YES|YES|NO|YES|

_Note_ : Dependent variable is the standardized CSI, including five items as listed in table 2, and an additional (standardized) component “Limit the variety of foods” included in the index. In column 4, households that report having received assistance have been removed from the sample. Data used are from the six visits of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 xiii
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residents. This pattern suggests a number of possible mysteries: First, given the high and growing level of investments in Accra’s real estate, why is it the case that the housing conditions of the poor in Accra are considerably worse than those of the poor in a number of other African cities which have lower incomes, as shown both by Konadu-Agymang (1990) and our data? And second, is this pattern is similar to that observed in the U.S. cities, referred to as “Superstar Cities,” by Gyourko, Mayer, and Sinai (2006), in which the growth in the number of higher-income families in cities with inelastic housing supply results in these families outbidding lower-income families, driving up the price of the underlying land? Finally, are these mysteries related? That is, could Accra’s real estate boom lead to both deteriorating housing conditions for many, as well as have broader adverse effects on the economy?

While this last question is perhaps the most intractable, it is also the most important as the situation in Accra is by no means an isolated case. Rapid house price escalation has characterized many cities across the world besides Accra, and particularly cities in emerging economies that have achieved higher levels of growth, such as India, China, and the EU-accession countries among others.<sup>4</sup> This paper attempts to shed some light on these mysteries. It examines the effects policies have on house prices in Accra, showing how these factors adversely affect not only house prices but also the housing situation of the poor and the broader economy.

> 4 Konadu-Agyemang (1998) traces through the sharp increases in real house prices in Accra since 1980. House prices in India are similarly interesting in terms of external finance, in this case foreign direct investments. In 2005, foreigners were for the first time allowed to invest in Indian real estate. During the next year, housing prices in most major Indian cities skyrocketed, leading the Reserve Bank of India to undertake a study, by Joshi (2006), of whether there was a bubble in housing prices in the country. Of course there is an important issue of fungibility of funds that make it extremely difficult to infer how remittances are ultimately used. For instance, unlike most of the earlier literature, Chami et al. (2005) suggest that remittances may well have a negative effect on growth. We do not focus on such macrobalance questions. Our focus is on the distributional effects of relative price changes and the effects these changes can have on the ability to reallocate resources across space.

2