rafmacalaba/datause-encoder-data huggingface dataset:
Development Projects in Different Sectors.”<sup>32</sup> As he put it: “having learnt in fairly rapid succession about a wide variety of projects, I became alerted to the characteristic advantages or handicaps under which power projects, say, proceed as compared to irrigation projects.”<sup>33</sup>

**_Table 1. Hirschman’s Journey and Project Loans He Examined_**

|El Salvador|July 20-August 1, 1964|Electric power|
|---|---|---|
|Ecuador|August 3-20, 1964|Roads in Guayas Province|
|Peru|August 21-Sept 11, 1964|San Lorenzo irrigation|
|Uruguay|Sept 20-Oct 7, 1964|Pasture improvement|
|West Pakistan|Oct 28-Nov 8, 1964|Karnaphuli paper mill|
|India|Nov 9-Dec 16, 1964|Damodar Valley development|
|East Pakistan|Dec 17-Jan 3, 1965|Karnaphuli paper mill|
|Thailand|Jan 4-28, 1965|Chao Phya irrigation|
|India|Jan 29-Feb 25, 1965|Selected ICICI-financed industries in Mysore|
|Italy|Apr 26-May 28, 1965|Irrigation in Southern Italy|
|Uganda|May 29-June 18, 1965|Electric power transmission and distribution|
|Ethiopia|June 21-July 9, 1965|Telecommunications|
|Nigeria|July 10-Aug 12, 1965|Railways modernization and Bornu extension|

_Source:_ Albert O. Hirschman, “A Study of Selected World Bank Projects – Some Interim Observations”, August 1965, World Bank Hirschman Folders Vol. 1.

_Note:_ Italy was added to the list only as a second thought, after the advice of several World Bank’s officials who maintained that the Bank’s loans to the Italian Cassa per il Mezzogiorno – the Italian agency for the development of Southern Italy – would be very relevant to Hirschman’s study; see Douglas J. Fontein to Prof. Gabriele Pescatore, April 9, 1965, World Bank Hirschman Folders Vol. 1.

The principal aim of Hirschman’s interim observations was clearly methodological. Far from addressing questions such as the economic return of the Bank’s loans, or the traditional distinctions of, say, infrastructure vs. agricultural and industrial projects, or human vs. physical capital, Hirschman emphasized the need for a change in perspective. What really affects projects is their degree of uncertainty: “the element of the unknown, the uncertain and the unexpected which deflects projects from the originally chartered course is considerable in

> 32 Albert O. Hirschman, “A Study of Selected World Bank Projects – Some Interim Observations,” August 1965, World Bank Hirschman Folders Vol. 1.

> 33 Albert O. Hirschman, “A Study of Selected World Bank Projects – Some Interim Observations,” August 1965, World Bank Hirschman Folders Vol. 1.

16
10 The indexes are computed using data on the domestic production and consumption sides of the region’s national commodity markets, and the measures account for all forms of border and domestic price distortion in each country for the commodity market of interest, as well as incorporating import-competing and exportable countries into the measure. In the absence of elasticity estimates, we make simplifying assumptions analogous to those made for national indexes. Croser, Lloyd and Anderson (2010) demonstrate that these assumptions have a minimal impact on the estimated series when constructing indexes for global markets.

# **Distortions to Agricultural Incentives database**

This study makes use of the World Bank’s Distortions to Agricultural Incentives database (Anderson and Valenzuela 2008). The database came out of a global research project seeking to improve the understanding of agricultural policy interventions and reforms in Asia, Europe’s transition economies, Latin America and the Caribbean as well as Africa. The database contains annual estimates of nominal rates of assistance (NRA) (positive or negative) for key farm products in 75 countries that together account for between 90 and 96 percent of the world’s population, farmers, agricultural GDP, and total GDP. There are 21 African countries in the database.

We concentrate on the sample of 19 Sub-Saharan African countries listed in the introduction, but exclude relatively affluent Egypt and South Africa which together account for between one-third and one-fifth of Africa gross value of production at undistorted prices over the period under analysis. For the 19 African focus countries, the database contains around 6000 consistent estimates of annual NRAs to the agricultural sector and the same number of CTEs between 1955 and 2005. Country coverage up to 1960 is much less than from 1961, so the series of estimates presented in this paper begins in that latter year.

The estimates of NRA and CTE in the database are at the commodity level and cover a subset of 41 agricultural products in Africa. These so-called covered products account for around 70 percent of total agricultural production over the period studied. The data identifies each year the extent to which each commodity in each country is considered an importable, exportable or nontradable, a status that
Programme (OFSP/HABP) has larger effects than access to the PSNP alone. Joint access increases livestock holdings by 0.99 TLU (Berhane, et al., 2014). The SCT in Malawi seems to perform somewhat better than the PSNP in Ethiopia – though at a much smaller scale.<sup>5</sup> For the one-year pilot of the SCT, Boone et al. (2013) and Covarrubias et al. (2012) attest that the program promotes investment in agricultural assets, including crop implements and livestock.<sup>6</sup> While the studies in Ethiopia and Malawi only looked at the effects when households were still benefiting from the program, Stoeffler et al. (2016) assessed some longer term implications, i.e. if asset levels can be sustained beyond the program. Their study of a pilot income transfer program in Niger shows that 18 months after the end of the transfer the program still induced a sustained increase in livestock of 0.4 TLU on average. The authors argue that participation in savings groups, which was promoted by the program, plays an important role in facilitating the asset accumulation observed (Stoeffler, et al., 2016). Synthesizing the evidence from Ethiopia, Malawi and Niger suggests that poverty-based income transfers can have a positive effect on asset accumulation. Yet, the magnitude and sustainability of these effects depend on the size of the transfer and the duration. Furthermore, complementary programs encouraging savings and (agricultural) investments seem to increase the magnitude and sustainability of the effects.

In the studies mentioned above and in most of the literature on income transfers, the household is the unit of analysis and the majority of studies consider the household as a uniform entity, taking little account of differences in household types, and the different needs and capacities of household members. This stands in contrast to the literature on collective and nonunitary models of household behavior and an increasing body of work that argues that intrahousehold dynamics and gender, i.e. differences in men’s and women’s productive and reproductive roles, responsibilities and opportunities, have been neglected in the debate and that these greatly influence the results of income transfers and related asset accumulation (Guilbert and Pierotti, 2016, IEG, 2014, Johnson et al, 2016, Luttrell and Moser, 2004). Counter to the conventional wisdom, a systematic review of safety nets conducted by the World Bank’s 3
**8 Appendix 3: Additional Heterogeneity**

Table C1: Results Pooled: Earnings and Work - Heterogeneity by wealth

||(1)<br>Earned Income<br>Last 7 Days|(2)<br>Main Job Earnings<br>Last 7 Days (USD)|(3)<br>Main Job Hours Worked<br>Last 7 days|
|---|---|---|---|
|Offered Training|0.04<br>(0.03)|3.61<br>(3.95)|2.63<sup>_∗_</sup><br>(1.54)|
|Offered * High Ubudehe|0.03|4.67|1.59|
||(0.04)|(5.75)|(2.11)|
|Control Mean: High Ubudehe|0.72|34.95|29.79|
|Control Mean: Low Ubudehe|0.65|34.86|25.46|
|Observations|2,597|2,597|2,597|
|p-value high Ubudehe|0.02|0.04|0.00|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level. Earnings are top winsorized at the ninety-ninth percentile; we replace earnings with 0 for participants who do not work.

Table C2: Results Pooled: Work Categories - Heterogeneity by wealth

||(1)<br>Employee|(2)<br>Apprentice/Intern|(3)<br>Employer|(4)<br>Self-Employed|
|---|---|---|---|---|
|Offered Training|-0.00|0.04|-0.01|0.00|
||(0.03)|(0.03)|(0.01)|(0.02)|
|Offered * High Ubudehe|0.07|-0.05|0.02|0.01|
||(0.05)|(0.04)|(0.01)|(0.03)|
|Control Mean: High Ubudehe|0.40|0.21|0.04|0.19|
|Control Mean: Low Ubudehe|0.32|0.23|0.03|0.17|
|Observations|2,597|2,597|2,597|2,597|
|p-value high Ubudehe|0.03|0.53|0.43|0.74|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

43
**Table 2.9a  Sectoral composition of creative content sector in Singapore, 1986-2001**

||Establishments|Employment|Operating Receipts|Value Added|Value Added/ worker|
|---|---|---|---|---|---|
|**1986**|||S$million|S$million|S$’000|
|IT Development|44|265|29.4|7.6|28.7|
|Published activities (excl software & mm works)|330|13,227|991.9|403.9|30.5|
|Motion picture and video production/distribution,<br>Radio and television activities|191|191|198.9<br>|108.4|567.5|
|Broadcasting|na|na|115.8<sup>1</sup>|na|na|
|Dramatic arts, music and other arts activities|153|409|20.7|6.2|15.2|
|_Total creative content services_|_718_|_14,092_|_1,356.7_|_526.1_|_37.3_|
|**1990**||||||
|IT Development|184|2,102|222.6|61.5|29.3|
|Published activities (excl software & mm works)|343|16,097|1,869.4|724.7|45.0|
|Motion picture and video production/distribution,<br>Radio and television activities|297|4,291|356.9<br>|208.0|48.5|
|Broadcasting|na|na|196.0<sup>2</sup>|na|na|
|Dramatic arts, music and other arts activities|134|366|27.0|6.1|16.7|
|_Total creative content services_|_958_|_22,856_|_2,671.9_|_1,000.4_|_43.8_|
|**2001**||||||
|IT Development|1,106|14,282|3,077.5|1,134.9|79.5|
|Published activities (incl software & mm works)|757|4,293|683.9|186.3|43.4|
|Motion picture and video production/distribution,<br>Radio and television activities|334|4,988|910.4|180.9|36.3|
|Broadcasting|13|1,591|631.6|65.3|41.0|
|Dramatic arts, music and other arts activities|363|1,334|95.9|22.7|17.0|
|_Total creative content services_|_2,573_|_26,488_|_5,399.4_|_1,590.2_|_60.0_|

1 Figure is for Singapore Broadcasting Corporation for 1985 2 Figure is for Singapore Broadcasting Corporation for year ended 1990 _Note_ : The creative content sector includes the following industries: IT development; publishing; motion picture production, distribution & allied services; radio & television services, theatrical producers, entertainment IT development has been included in both the KIBS sector and the creative content sector

# _Sources_ :

Singapore Department of Statistics (DOS). (Various years). _Economic Surveys Series: Business Services and Real Estate_ . Singapore: DOS. Singapore Department of Statistics (DOS). (Various years) _Economic Surveys Series: Community, Social & Personal Services._ Singapore: DOS. Data obtained from DOS 19
social network. That discount rates vary with wealth and income has been confirmed experimentally for landless workers and farmers in South India (Pender 1992).

The educational and informational status of different interest groups affects their ability to evaluate the impact of any given policy or program on their welfare. With low levels of education, it is difficult to detect misinformation (in the form of ideology or "obfuscation") that may be spread by other interest groups. High levels of education, informational advantages, and the ability to appeal to popular ideologies, by contrast, can greatly enhance a group's ability to appropriate resources for itself. Clearly observable and distinguishable group characteristics, in addition to furthering collective action, can facilitate discrimination-using characteristics such as cultural identity, class, gender, race, or skin-color as proxies for economically relevant traits such as skills. Historically, discrimination has been used to create and maintain privileges for certain races and cultures, and to hide the real economic costs of existing privileges.21 The continuing importance of discrimination is confirmed by studies that demonstrate the existence of systematic wage differentials that are not related to relevant performancecharacteristics, such as schooling or individual ability. Psacharopoulos and Patrinos (1995), for example, document such differentials for Brazilians of African origin (who make up much of the poor rural population of the northeast) and for Indian groups in several Latin American countries (which are over-represented in poor peasant populations). Observable wage differentials may, of course, be compounded by the fact that certain groups are denied equal access to economic opportunities, such as education, markets, or credit, a factor that could-through unequal access to education and its returns-lead to persistence of such differences across generations. Rural panel data from South India, for example, show that higher caste status and male gender are associated with better access to education, even

> 21 Roback (1990) illustrates that in many cases racial differences that had fallen into oblivion were resurrected to legitimize privileges that had no objective justification. Akerlof (1985) shows that sorting on the basis of group characteristics can be dynamically stable, even if the key characteristics are completely unrelated to individuals' relevant performanceattributes.

26
_5_

**Table 1. Latin American GDP Growth Rates (in** %)

|||GDP Growt|h|Slowdown<br>3|
|---|---|---|---|---|
||1997|1998|19992|1999-1997|
|Argentina|8.1|3.9|-4.0|-12.1|
|<br>**Brazil**|3.6|0.2|0.8|-2.8|
|**Chile**|7.6|3.4|-1.0|-8.6|
|**Colombia**|3.0|0.6|-4.5|-7.5|
|**Ecuador**|3.4|0.4|-7.3|-10.7|
|**Mexico**|6.8|4.9|3.6|-3.2|
|**Peru**|6.9|0.7|2.7|-4.2|
|**Venezuela**|5.9|-0.4|-7.0|-12.9|
|**Weighted Average'**|**5.4**|**1.8**|**-0.2**|**-5.7**|

**'** Weighted by 1997 GDP.

2 Forecasted for 1999 based on third quarter data 3Difference between 1999 and 1997 growth rates The idea that common external factors are fundamental explanatory variables of macroeconomic outcomes in developing countries was revisited by Calvo et. al. (1993) who elucidated the resumption of international capital flows to Latin America in the early nineties based on the role of external factors. More recently, business cycle models have been extended to capture the role of external factors (Mendoza, 1995). However, the empirical literature has mixed results about the relative importance of domestic and external factors as sources of output volatility. On one hand, Hausman-Gavin (1995) conclude that domestic factors are the main cause of output volatility, and so do Hoffinaister and Roldos (1997) and Ahmed (1999)4. On the other hand, Joyce and Kamas (1997) show that the relative importance of each factor varies with the country. All these papers, except Ahmed's, have in common a sample period that covers, at the most, until 1994.

The prevailing external environment during 1998 and 1999 was very unfavorable for the region, as depicted by the evolution of regional terms of trade (Fig. 2) and the cost and availability of external financing<sup>5</sup> (Fig. 3 and 3A). Additionally, some countries,

> 4 Kamin-Babson show that the probability of currency crises in Latin America is determined fundamentally by internal factors. This result can be interpreted in favor of the domestic determinants of output volatility, given the association between currency crises and output growth.

> ' As a proxy for the cost of financing we include besides the real interest rate in the US, the non-Latin Brady bond sovereign spreads assuming it is completely exogenous to Latin America. In the following section we allow for endogeneity of the spreads. As a proxy for exogenous capital flows, to describe the availability of external financing, we use the current account of industrialized countries. In the last section we discuss the inclusion of both price and quantity variables of this market.
|Household has a migrant network (0/1)|[0.297]<br>0.010|[0.195]<br>0.008|[0.420]<br>0.005|[0.118]<br>0.007|
|---|---|---|---|---|
||[0.532]|[0.669]|[0.799]|[0.774]|
|Rain - EA level CoV of Dec-Jan rainfall from<br>1983/84 - 2012/13|-0.886***<br>[0.006]||0.229<br>[0.526]||
|Average 12-month total rainfall (mm)|-0.000|0.000|0.000|0.000|
||[0.500]|[0.721]|[0.227]|[0.741]|
|Annual Mean Temperature (∞C * 10)|0.000|0.000|-0.000|-0.002|
||[0.838]|[0.909]|[0.839]|[0.419]|
|HH Distance in (KMs) to Nearest Road|-0.002|-0.004|-0.002|0.000|
||[0.326]|[0.263]|[0.589]|[0.948]|
|HH Distance in (KMs) to Nearest Population Center<br>with +20,000|-0.000|-0.001|-0.000|0.001|
||[0.953]|[0.542]|[0.851]|[0.574]|
|Value of daily_ganyu_wage salary in the EA|-0.000|-0.000|0.000|0.000**|
||[0.465]|[0.282]|[0.301]|[0.023]|
|Price of fertilizer in the EA|-0.000+|-0.000|-0.000|-0.000*|
||[0.106]|[0.233]|[0.638]|[0.090]|
|Price of maize grains in the EA|-0.000|-0.000|0.000|0.000*|
||[0.449]|[0.457]|[0.299]|[0.070]|
|EA neighbor’s wage employment participation|0.002***|0.003***|0.001*|0.002**|
||[0.001]|[0.003]|[0.088]|[0.026]|
|EA neighbor’s self-employment participation|0.001***|0.001**|0.004***|0.004***|
||[0.010]|[0.016]|[0.000]|[0.000]|
|Time dummy (year 2010=1)|-0.002|-0.004|-0.008|-0.022|
||[0.929]|[0.888]|[0.787]|[0.363]|
|District dummies (27 -1 dummies)|Included|Included|Included|Included|
|Time average of explanatory variables|Included||Included||
|Constant||-0.856<br>[0.276]||0.805<br>[0.369]|
|Number of observations|5,286|5,286|5,286|5,286|
|Number of households||2,751||2,751|

Source: Generated by authors using LSMS data Note: ***, **, *, and + indicate that the corresponding regression APE is statistically significant at the 1%, 5%, 10%, and 15% levels, respectively. The CRE model is estimated using GEE approach with cluster robust standard errors. The linear FE results are presented for comparison. Pvalues between brackets.

32
A. Sanghi and D. Johnson <!-- Start of picture text --> 20<br>15<br>10<br>5<br>1980 1985 1990 1995 2000 2005 2010 2014<br>Year<br>Savings (% of GDP)<br><!-- End of picture text --> Figure 16: Kenya’s Gross domestic savings has fallen sharply since 1993. Trend in red (1980-2014) Source: World Development Indictors World Bank 2015

# **3.1 Chinese FDI in Sub-Saharan Africa**

China’s FDI consists of many medium to short-term loans with a focus on extractive industries such as oil, mining, gas, minerals and natural resources (Wang and Bio-Tchane 2007). Some loans are repaid with future exports from natural resources, especially in countries with poor credit ratings. Such resource-backed loans can be extended to Ghana, for example, for a hydropowered dam, and the loan is repaid with exports of cocoa beans (Br ̈autigam, Gallagher 2013). Other loans go to support multinational or state-owned companies in accessing markets, or to support foreign firms in buying Chinese goods. In 2004, the Chinese Development Bank loaned the major telecommunications firm Huawei US $10 billion for overseas expansion, and Nigeria took US $200 million in loans to buy Huawei equipment (Executive Research Associates 2009). In 2012, Huawei was awarded a tender to build a national fibre-optic network in Kenya worth US $60.1 million, a deal financed the China EXIM Bank (ICT Authority Kenya 2015). The China EXIM Bank also gives cheap capital to state-owned firms to bid for large infrastructure projects. Their stateowned status allows them to report profits at longer intervals, instead of quarterly as most firms are required. Other foreign firms with shorter time horizons and a higher profit requirement face a unique challenge when competing for contracts in Sub-Saharan Africa.

## **3.1.1 Chinese FDI in Sub-Sharan Africa is relatively small**

In 2011, Chinese FDI stock in Sub-Saharan Africa was US $20.1 billion, or 3.2 percent of the total FDI stock of US $629 billion in Africa. China’s relative focus on Sub-Saharan Africa is large —US $26 billion in Sub-Saharan Africa compared to US $22 billion in the United States in 2013 —but its share of investment is still small (Chen, Dollar, and Tang 2015). Unlike in oil-exporting countries, Chinese firms are interested in coffee and manufacturing in Kenya because the returns are higher than in oil exploration. In particular, the communications and automotive equipment manufacturing sectors (OEM) attract a large share of Chinese investment (figure 23a) (Financial Times Ltd 2015). Figure 17 shows the investment flows from China between 2003 and 2012. Its 20
- 16 -

<!-- Start of picture text --> Figure 6. Monthly precipitation by district<br>June 2002 June 2003 June 2004<br>Monthly Monthly Monthly<br>precipitation precipitation precipitation<br>(mm) (mm) (mm)<br>4.5 - 10 3.6 - 10 - 10<br>10 - 25 10 - 25 10 - 25<br>25 - 40 25 - 40 25 - 40<br>40 - 55 40 - 55 40 - 55<br>55 - 121 55 - 95 55 - 161<br>No data<br>for Mat District<br>. . .<br>Source: Author’s drawing based on weather data from IEWE, Albania.<br><!-- End of picture text --> From the longer time perspective, the climate will be changing. The following analysis of this paper will remain static to the extent that the data are collected during the fairly short period of time and no dynamic consumer response, such as home appliance purchasing, is assumed. However, it is still indicative to see the impacts of climatic conditions by evaluating the estimated demand function with respect to expected climate change. In Albania, average temperature increased around 1 °C in the past 30 years, while precipitation declined especially during winter and spring (Figure 7). In the future, temperature is projected to increase 1.2–1.8 °C by 2050 and 2.1–3.6 °C by 2100, and more droughts and less precipitation are expected (ESMAP, 2009). Therefore, on one hand the electricity demand may continue increasing unless significant improvements in energy efficiency of the economy take place. On the other hand, the country’s water resources may become scarcer, intensifying the country’s climate vulnerability. If more droughts happen, as in 2002 and 2007, the country will be faced with significant supply shortage, which would lead to either massive power outages or costly power imports from abroad. Hence, energy efficiency and electricity demand management will remain a critical policy issue for Albania.