rafmacalaba/datause-encoder-data huggingface dataset:
Fishing net income (by fish species) is also collected. These are provided separately for each of the two landing seasons in a year, high and low. We transform the total quantity per species in kilograms depending on the units reported and the form of packaging, which we use to value sold and unsold production. We net fishing income from intermediate input costs such as rented gears (e.g., mosquito nets, beach seine, long/hand line, gillnet, fish traps), rented boats/engines, fuel, oil, and maintenance, hired labor salaries and other in-kind payments.

Household annual capital income includes net interest income, pension income, rental income from nonagricultural land rental; apartment and/or house rental; shop and/or store rental; car and/or truck, other vehicle rental; capital gains (including sales) from real estate; nonagricultural asset sales; agricultural/fishing asset sales; and other income from inheritance, lottery, or gambling winnings. This information is available for the past 12 months. We include in capital income agricultural land rentals (per season) and income from renting fishing equipment (gears). As we describe in the next section, despite the level of detail, household capital income is negligible compared with other sources of income.

Finally, household annual net transfers are defined as income transfers/gifts received from rural areas/urban areas/other countries minus income transfers/gifts given in the past 12 months. Further, the value of received aid (e.g., free maize, other free food, food/cash-for-work programs such as Malawi Social Action Fund or Public Works Program) provided by social safety nets is added to transfers received. The survey also records remittances in cash received from children 15 years of age or older who no longer live in the household. Neither the Tanzania nor Uganda ISAs have a specific question on in-kind food transfers received by the household. The Malawi survey has such a question, but less than 20% of households report receiving in-kind food transfers in the past 12 months. Nevertheless, in the consumption questionnaire a much higher proportion of households report eating food gifts in the past 7 days. In this 7-day recall data, 62%, 41%, and 25% of households, respectively, in Malawi, Tanzania, and Uganda report consuming food gifts. We therefore include food gifts (deseasonalized and annualized) in our definition of disposable income. In Malawi, we can compare the contribution of this source of income with other reported transfers. Food gifts represent approximately 6% of total disposable income and dwarfs the 1% contribution of net transfers in Malawi.<sup>24</sup>

# **2.3 Wealth and Its Portfolio**

Our definition of wealth (i.e., net worth) includes land, housing, livestock, agricultural equipment and structures (e.g., tools and barns), fishing equipment, other durables (e.g, cars, furniture, and

> 24See further details on the computation of household income in Appendix Section A.3.

12
<!-- Start of picture text --> sata Short-run investment decisions « « Long-run investment decisions<br>it Tobins Q 25.090 WK<br>10.07 Real investment growth (y/y) [RHS} 20 — 1/K Steady State<br>-085<br>0.9 15<br>0.8 19-080<br>0.7 5 075<br>0.6 0<br>-070<br>0.5 5<br>0.4 -10 .065<br>98 86000 02 04 06 08 10 12 14 16 18 98 00 02 04 06 08 10 12 14 16 18<br><!-- End of picture text -->
Survey (HIECS), conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS), and which interviewed 47,095 households between July 1, 2004 and June 30, 2005. The sampling is stratified (by governorate), and clustered, with 40 households being interviewed in every sampled village or ward (the primary sampling units). The questionnaire collected basic socioeconomic data, including enough information to establish consumption-based measures of poverty.

Three changes were made to the survey to ensure that the data could be used for an impact evaluation. First, a module was added to the survey instrument that included questions about SFD activities and community organizations and initiatives. This supplementary module was administered to all of the 12,000 households surveyed in the fourth quarter of the HIECS survey (i.e. from April-June 2005) – each of the four rounds of the survey are nationally representative – as well as to all 10,000 sampled households living in areas with SFD interventions. This module gathered information on illiteracy and health programs, the extent of SFD interventions, and the providers and impact of microcredit.

Second, an additional 3,760 households in 94 primary sampling units (PSUs) were surveyed in areas where the SFD was active, in order to ensure enough coverage of areas in which the SFD operated; the sampling followed the HIECS protocol, and the questionnaire included the supplementary module, but omitted some of the irrelevant parts of the HIECS questionnaire. The intention was to ensure that strictly comparable survey data would be available for at least 80 treatment villages (PSUs) for each of the seven sub-sectors (education, health, potable water, wastewater, road maintenance, the environment, and microcredit) covered by SFD interventions.

The third modification was that a questionnaire on microcredit was administered to a randomly-selected sample drawn from all present borrowers under the SFD program, within PSUs in the HIECS master sample.

It should be noted that panel data were not available, so powerful techniques of impact evaluation such as double differencing were not feasible. This is a major, if inescapable, limitation of the current study.

The household-level data were supplemented with information from a community survey that was integrated with the HIECS survey, and provided information on the availability, accessibility, and quality of water and sewerage systems, health posts, and primary schools at the level of individual villages and wards (i.e. PSUs). It also sought to measure perceptions of the SFD interventions, and the extent of community participation in the projects. The community survey was also administered in the 94 areas _Impact Evaluation: Egyptian Social Fund for Development Version of June 29, 2009_ _Page 13 of 36_
it calls for a balanced approach that leverages both regional (South-South) and global (NorthSouth) trade markets for optimal economic transformation in Africa.

|**Table4.**Produc|tivityfrontier:<br>OL<br>|The role of im<br>S<br>|ported capital<br>2SLS(sec<br>|ond stage)<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||||**Panel A**||
|Total imported capital (log)<br>constant|0.193***<br>(0.017)<br>-4.382***<br>(0.258)|0.091***<br>(0.011)<br>-3.670***<br>(0.173)|0.082***<br>(0.016)<br>-3.788***<br>(0.206)|0.084***<br>(0.015)<br>-3.809***<br>(0.197)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|880|807|766|766|
|R-squared|0.31|0.84|0.84|0.84|
||||**Panel B**||
|Imported capital from North (log)|0.170***<br>(0.017)|0.061***<br>(0.014)|0.0871***<br>(0.014)|0.087***<br>(0.014)|
|constant|-4.075***<br>(0.255)|-3.310***<br>(0.201)|-3.8285***<br>(0.192)|-3.830***<br>(0.193)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|880|807|766|766|
|R-squared|0.30|0.84|0.84|0.84|
||||**Panel C**||
|Imported Capital from South (log)|0.151***<br>(0.018)|0.092***<br>(0.010)|0.066***<br>(0.015)|0.069***<br>(0.014)|
|constant|-3.522***|-3.521***|-3.489***|-3.514***|
||(0.236)|(0.147)|(0.175)|(0.169)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|880|807|766|766|
|R-squared|0.30|0.84|0.84|0.84|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.10 **_Note_** : The controls include initial industry structure, rule of law, human capital, inflation rate, mobile broadband, transport infrastructure, total natural resource rent (% GDP), and bank credit to private sector (% GDP).

23
**-4-**

Table 1: Nontfau **Share** of Total Employment: **hWd** 1961/81

||Rural Ar|eas Plus Rural|Towns|||
|---|---|---|---|---|---|
|Year|Rural<br>Areas|Rural<br>Townsa/|Total<br>RRT|Large<br>Urban bl|Total<br>Natlonal|
|||(Perce|nt of Total Full|-Time Workers)||
|1 961|1 8.0|79.4|22.8|96.2|27.7|
|1971|15.2|76.5|20.4|95.4|27.9|
|1981|18.9|77.4|24.3|95.8|33.3|

- a/ Rural towns are defined as urban areas under 100,000 in population. They are _settlements of betwen_ 5,000 and 100,000 people.

- b/ Large urban setlements are all those with population exceeding 100,000. Total urban figures reported in the censuses equal the sum of what have been partitioned here into rural towns and large urban settements.

**Scurce:** Population consus of 1961, 1971, and 1981. See India (1961a-d, 17a-b and 1981b). Raw data are reproduced in Appendix Table A.1.
# **Table 3.1:  Distribution of Country Probabilities of Project Success, by Region**

|**Region**|**Min**|**Median**|**Max**|
|---|---|---|---|
|Sub-Saharan Africa|0|64|100|
|MiddleEast, North Africa|0|71|100|
|South Asia|69|71|100|
|EastAsia,Pacific|33|76|100|
|Latin American, Caribbean|0|76|100|
|EastEurope, Central Asia|0|83|100|

# **4.  Environmental Resource Allocation by the World Bank**

The World Bank's Environment Department has recently completed an accounting of environmental lending and AAA in seven thematic categories: climate change, pollution management, land management, biodiversity, water resource management, environmental policies and institutions, and other environmental management. This exercise has drawn on recent changes in the Bank's accounting system, which now tracks the allocation of funds across both sectors (e.g., environment, infrastructure) and themes within sectors (e.g., climate change, pollution management). The new system identifies the environmental components of projects whose sectoral identification is nonenvironmental. For example, transport-related projects often include components that promote reduction of vehicular air pollution.

This paper draws on information for all World Bank projects approved since FY 1998, and all AAA since FY 2000. Using the appropriate thematic codes, we calculate total Bank lending and AAA by country and environmental theme. Our five environmental indicators and the institutional problem indicator are constructed to match the corresponding thematic categories in the project database. The seventh thematic 9
20 TABLE II.2 TYPES OF INVOLVEMENT PREFERRED BY VENTURE CAPITALISTS WHO WANT CLOSE OR FREQUENT INVOLVEMENT WITH THE MANAGEMENT TEAM OF THEIR PORTFOLIO COMPANIES ........................................................................................................

_ (Percent Distribution) .................................................................................

||T|ype of fta.||
|---|---|---|---|
|.................................................................................|S3iC<br>I|NDEPENDENT|<br>CORPORATE|
|Key Issues||||
|<br>Future financial<br>arrangement|94.9|93.9|86.4|
|Plamring devetopment|87.5|95.1|93.2|
|Marketing|35.3|63.4|50|
|Persomel<br>issues|25|73.2|56.8|
|Supplierrelationships|5.9|9.8|6.8|
|Day-to-day<br>operations|3.7|7.3|4.5|
|Others|8.8|12.2|6.8|

Source: Venture Capital and Irnovatior, Robert Premus. 1984.
TABLE **2** Mexico's **Trade**

|**(Millions of USD)**||||||||||
|---|---|---|---|---|---|---|---|---|---|
||**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**<br>|**1999**<br>|
|Total Trade|112,061.90|129,115.20|138,589.40|163,267.81|211,333.00<br>|219,675.00<br>|218,760.00<br>|253,624.00<br>|278,767.14<br>|
|Exports of goods and services|_51,459.50_|55,406.00|61,391.00|71,396.40|110,505.00<br>|113,568.00<br>|109,285.00<br>|122,956.00<br>|136,703.36<br>|
|Imports of goods and services|60,602.40|73,709.20|77,198.40|91,871.40|100,828.00|106,107.00|109,475.00|130,668.00|142,063.78|
|Trade Balance|-9,142.90|-18,303.20|-15,807.40|-20,475.00|9,677.00|7,461.00|-190.00|-7,712.01|-5,360.43|
|**(Annual percent growth)**|**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|
|Exports of goods and services|5.07|4.98|8.09|17.80|30.19|18.23|10.81|9.72|16.40<br>|
|Imports of goods and services|15.18|19.62|1.86|21.25|-15.04|22.88|22.80|14.20|13.30|
|**(Percent of GDP)**||||||||||
||**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**<br>|**1999**<br>|
|Total Trade|35.64|35.51|34.35|38.48|58.17|62.26|60.79|64.45|62.40<br>|
|Exports of goods and services|16.36|15.24|15.22|16.83|30.42|32.18|30.37|31.25|30.60|
|Imports of goods and services|19.27|20.27|19.13|21.65|27.75|30.07|30.42|33.21|31.80|
|SOURCE: World Bank LDB||||||||||

**TABLE 3 Mexico's Exports**

||**Total**<br>(USD rnillions)<br>|**Oil**<br>(USD millions)<br>|**Non-Oil**<br>(USD millions)<br>|**Ofl**<br>(percent)<br>|**Non-Oil**<br>(percent)<br>|
|---|---|---|---|---|---|
|1991|42,687.7<br>|8,166.4<br>|34,521.0<br>|19.13<br>|80.87<br>|
|1992<br>|46,195.5<br>|8,306.6<br>|37,889.0<br>|17.98<br>|82.02<br>|
|1993|51,886.0<br>|7,418.4<br>|44,467.4<br>|14.30<br>|85.70<br>|
|1994<br>|60,882.2<br>|7,445.1<br>|53,437.3<br>|12.23<br>|87.77<br>|
|1995<br>|79,541.6<br>|8,422.4<br>|71,119.0<br>|10.59<br>|89.41<br>|
|1996<br>|95,999.7<br>|11,653.7<br>|84,346.1<br>|12.14<br>|87.86<br>|
|1997<br>|110,431.3<br>|11,323.0<br>|99,108.2<br>|10.25<br>|89.75<br>|
|1998<br>|117,459.4<br>|7,134.3<br>|110,325.2<br>|6.07<br>|93.93<br>|
|1999|136,703.2|9,920.2|126,783.0|7.26|92.74|
|SOURCE: INEGI||||||

29
|Power Project(08)|
|---|
|HighwayProject(05)|
|HighwayProject(05)|
|Urban Development Project(03)|
|Urban Development Project(03)|
|Urban Development Project(03)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Technical Assistance Project(05)|
|Yogyakarta Rural Development Project|
|Yogyakarta Rural Development Project|
|Yogyakarta Rural Development Project|
|Yogyakarta Rural Development Project|
|Yogyakarta Rural Development Project|
|Irrigation Project(14)|
|Irrigation Project(14)|
|Irrigation Project(14)|
|Irrigation Project(14)|
|Irrigation Project(15)|
|Irrigation Project(15)|
|Irrigation Project(15)|
|Nucleus Estates and Smallholders Project(04)- NES Project(04)|
|Nucleus Estates and Smallholders Project(04)- NES Project(04)|
|Nucleus Estates and Smallholders Project(04)- NES Project(04)|
|Nucleus Estates and Smallholders Project(04)- NES Project(04)|
|Nucleus Estates and Smallholders Project(04)- NES Project(04)|
|Nucleus Estates and Smallholders Project(03)- NES Project(03)|
|Nucleus Estates and Smallholders Project(03)- NES Project(03)|
|Nucleus Estates and Smallholders Project(03)- NES Project(03)|
|Nucleus Estates and Smallholders Project(03)- NES Project(03)|
|Smallholder Rubber Project|
|Smallholder Rubber Project|
|Agricultural Extension Project(02)|
|Agricultural Extension Project(02)|
|Agricultural Extension Project(02)|
|National Agricultural Research Project|
|National Agricultural Research Project|
|National Agricultural Research Project|
|National Agricultural Research Project|
|National Agricultural Research Project|
|National Agricultural Research Project|
|Population Project(03)|
|Power(09)|
|Power(09)|
|Power(09)|
|Power(09)|
<!-- Start of picture text --> Develop Scope<br>"Review key policy documents and otherliterature (including existing<br>lEs and SARs}<br>*Consult with key stakeholders including policymakers<br>“ifthe purpose is informing policy, focus on systematic reviews<br>*ifliteratureislimited and the purpose istoinform research, gapmap<br>should also include primary studies<br>Search for Relevant Studies<br>*Search drawingon systematic review methods, dependingontime<br>and resources available aswell astheintended use ofthe gapmap<br>Assessfor Inclusion<br>“Screen studies against substantive and studydesien inclusion criteria<br>Data Extractionand Critical Appraisal<br>*Summarise induded studies usinga structured format<br>*Code studies accordinegto intervention/outcomes/status categories<br>“Critical appraisal of systematic reviews usingrecognised checklist<br>eens nenennene ners<br>Presentation and Analysis<br>“Populate gapmap with studiesin appropriate cells<br>*Time and resources permitting, produce a summaryreportwith<br>implicationsfor policyand research.<br>"a<br><!-- End of picture text -->
**Table 3. Investment rates and returns on fixed capital in eight countries**

||Investment rate|Average return on fixed<br>capital|
|---|---|---|
|Cameroon|0.11|1.36|
|Ghana|0.13|3.63|
|Kenya|0.11|1.82|
|Uganda|0.12|0.75|
|Zimbabwe|0.12|0.85|
|Belgium|0.13|0.18|
|France|0.11|0.12|
|Germany|0.12|0.16|
|UK|0.12|0.13|

Source: The data on all countries except Uganda were reported in Bigsten et al. (1999a). The Ugandan data were reported in Reinikka and Svensson (2001).

Using the same data set as in their 1999 study with Zambia added, Bigsten et al. (2005) examine more closely if investment is affected by irreversibility and fixed adjustment costs. In such models firms will not invest anything unless the marginal return on capital exceeds some threshold level, and so one would expect to see a significant share of zero investments in the data if the theory is right. Table 4, taken from Bigsten et al. (2005), summarizes the proportions of non-zero investments in this data set by country and firm size. With the exception of Zimbabwe, in all countries and size categories the proportion of positive investment is lower than 0.5. That is, the majority of the firms in these categories do not invest anything during a whole year. There is a weak positive relationship between firm size and the propensity to invest, although among the largest firms in Ghana only one in five firms invest in a representative year. In Zimbabwe investment activity is generally higher than in the other countries. With a third of the firms in the whole sample refraining from investing in a typical year, however, the proportions of zero investment is not negligible. The authors also show that investment is “lumpy”, i.e. whenever firms do invest they invest a lot. This suggests that fixed adjustment costs may be important. However when analyzing the decision to invest more formally, the evidence points to irreversibility as the main explanation of low investment in Africa. Reducing uncertainty, or improving the market for second hand fixed capital, is therefore likely to impact positively on investment.

28
**Table 5: Cost-effectiveness of remedial education program using community teacher salaries from other countries, holding all else constant**

|**Country**|**Type of teacher**|**Monthly community teacher**<br>**salary in base year currency**|**Base year**<br>**currency**|**Monthly community**<br>**teacher salary in 2011 USD**|**Additional SD**<br>**per $100**|
|---|---|---|---|---|---|
|Niger|Contract teacher|3,333.33|2003 CFA|6.91|4.30|
|Guinea|Community teacher|7,500.00|1998 GNF|8.04|4.01|
|_India (Balsakhi remedial_<br>_education program)_|Community teacher|7,500.00|2001 INR|13.24|3.07|
|Madagascar|Community teacher|12.83|2003 USD|15.42|2.79|
|Chad|Community teacher|10,000.00|2013 CFA|19.42|2.40|
|Benin|Contract teacher|25.00|2003 USD|30.05|1.74|
|Malawi|Community teacher|6,672.62|2011 KW|43.12|1.31|
|Nepal|Contract teacher|12,024.83|2010 NPR|168.33|0.38|

Source: Community and contract teacher salary data for Benin, Guinea, Madagascar, Nepal, and Niger are from a database extracted from World Bank Public Expenditure Reviews (World Bank 2014), those for Chad are from the Project Appraisal Document of the Education Sector Reform Project Phase II (World Bank 2013), those for Malawi from the baseline survey report of the impact evaluation of Online Distance Learning (World Bank, 2012), and those for India from J-PAL (2014). The exchange rate for India and average US inflation rates come from J-PAL (2014); average annual exchange rate data for all other countries are from OANDA (2014). Notes: SD means standard deviations. The reported monthly community teacher salaries presented in 2011 USD are calculated by converting the community teacher salaries in local currency in the various base years (the year the data were collected) to USD in the given base year, before inflating to 2011 USD using the inflation GDP deflator (annual %). Where teacher salaries are reported in USD as the base year currency, this reflects the unit of measure of the source data and does not imply that salaries are actually paid in <u>USD.</u> 21
22 Table 3 s Contributions of Different Factors to Growth In Agricultural Output. **Wage.** Rural Employment and Population 1970-80

|Variable|Acerejste|Anricultural|Rural Nonfarm|Agrlcultural|
|---|---|---|---|---|
||rerO<br>output|*elorsnt|*olo n0|roeIwsg2|
|Agricultural<br>output price|0.088.0.0|80.|0.027.|0.081|
|Real priceotfertilizer|-0.012*|0.012|0.001|0.006.|
|Road|0.021*|0.027|0.060|0.014*|
|Government Irrigation|0.004.|0.001|0.004|-0.002t|
|Priary<br>schools|0.0440|0.044|-0.186|-0.061*|
|Rural eloctritication|0.00.|-0.039.|0.070g|0.062.|
|Commercial**banks**|0.043*|-0.0687|<br>0.209|0.066.|
|Regulated market|0.1200|0.081.|-0.026|-0.018.|
|Growth explained by all factors|0.J50|0.083|0.J26|0.0U|
|Actual growth|0.179|0.10|0.847|-0.006|

Note: Asterisk refors to significance levol ot 10 percent or better on a two-tell test. re W tO significance level of 10 percent on a single-tall test
the world data in section 2.B. In this way, we extended the coverage of loan maturity and loan purpose to 107 and 103 countries, respectively.

# **B. Rest of the World Data**

Data for the rest of the world are obtained from Bankscope, a database that reports balance sheet and income statements for more than 30,000 global financial institutions. Information on loan maturity is provided for loans to banks and loans to customers. Customers include government agencies, public companies, households, and individuals. Maturity is reported in four categories: up to 3 months, 3–12 months, 1–5 years, and more than 5 years. Loan by purpose data are available across five categories: loans to customers for residential mortgage loans, other mortgage loans, other consumer loans, corporate and commercial loans, and other loans (which includes loans to governments and to financial companies). To make the Bankscope data comparable with data obtained for LAC, a combined maturity category for loans with up to 12 months maturity was created by summing the first two maturity categories. Similarly, residential and other mortgage loans were combined into a single mortgage series.

Data, on an annual basis, were downloaded from 2004 onward.<sup>15</sup> To construct countrylevel aggregates, we used banks’ consolidated statements when available, following Duprey and Lé (2016), which include loans provided by foreign subsidiaries. Each global financial institution has a different fiscal year. Thus, to construct an annual series, Bankscope assigns different fiscal years to different calendar years. There is no interpolation involved, but a consistent assignment of closing quarters or months is mapped to the calendar year, so that data are comparable year over year. There is some error that is introduced because of this bottom-up approach. However, the method used to construct the final data set is designed to reduce this error, and further comparisons with national data suggest that the error is small.

Across the 11 years of the Bankscope data (2004–2014) the panel of financial institution data is not strongly balanced. For both the loan maturity and the loan purpose data, there is a total of just over 210,000 observations (financial institution - years). Therefore, the average financial

> 15 The raw Bankscope data were downloaded in October 2015. Given the large size of the data, data were downloaded from 2004 onward. Similarly, a separate download script was written for the loan maturity and the loan sector database. The loan maturity file yielded a total of 33,593 global financial institutions’ statements, while the loan sector file also yielded a total of 33,593 global financial institutions’ statements.

10