rafmacalaba/datause-encoder-data huggingface dataset:
- Kaushal, Neeraj (2014). “How Public Pension affects Elderly Labor Supply and Well-being: Evidence from India.” _World Development_ 56: 214-225.

- Keynes, John Maynard (1930) “Economic Possibilities for our Grandchildren”, pp. 358-73 in _Essays in Persuasion_ . New York: W.W. Norton and Co.

- Kraay, Aart and David McKenzie (2014) “Do Poverty Traps Exist? Assessing the Evidence”, _Journal of Economic Perspectives_ 28(3): 127-48.

- Kugler, Adriana D., and Ingrid Rojas (2018). “Do CCTs Improve Employment and Earnings in the Very Long-Term? Evidence from Mexico,” _NBER Working Paper_ No. 24248.

- Lam, David, Murray Leibbrandt, and Vimal Ranchhod (2006). “Labor Force Withdrawal of the Elderly in South Africa,” in _Aging in Sub-Saharan Africa: Recommendations for Furthering Research_ (Barney Cohen and Jane Menken, editors). The National Academies Press. Washington, DC.

- Lehmann, Christian, and Daniel Masterson (2014): “Emergency Economies: The Impact of Cash Assistance in Lebanon,” <u>unpublished manuscript, accessed on 2/22/2018.</u>

- Marinescu, Ioana (2018). “No Strings Attached: The Behavioral Effects of U.S. Unconditional Cash Transfer Programs.” _NBER Working Paper_ 24337.

- McKenzie, David (2017a) “How Effective Are Active Labor Market Policies in Developing Countries? A Critical Review of Recent Evidence”, _World Bank Research Observer_ , 32(2): 127-54

- McKenzie, David (2017b) “Identifying and Spurring High-Growth Entrepreneurship: Experimental Evidence from a Business Plan Competition”, _American Economic Review_ , 107(8): 2278-2307

- McKenzie, David, and Chris Woodruff (2008) “Experimental evidence on returns to capital and access to finance in Mexico”, _World Bank Economic Review_ 22 (3), 457–482.

- Mishra, Prachi (2007) “Emigration and wages in source countries: Evidence from Mexico”, _Journal of Development Economics_ 82: 180-99.

- Overseas Development Institute (2015): “Doing Cash Differently: How Cash Transfers Can Transform Humanitarian Aid,” <u>report</u> accessed on 2/22/2018.

- Parker, Susan W., and Petra E. Todd (2017). “Conditional Cash Transfers: The Case of Progresa/Oportunidades,” _Journal of Economic Literature_ 55(3): 866-915.

- Parker, Susan W., and Tom Vogl (2018). “Do Conditional Cash Transfers Improve Economic Outcomes in the Next Generation? Evidence from Mexico.” _NBER Working Paper_ 24303.

- Picchio, Matteo, Sigrid Suetens, and Jan van Ours (2017) “Labour Supply Effects of Winning a Lottery”, _The Economic Journal,_ forthcoming.

- Prifti, Ervin, Elisenda Estruch, Silvio Daidone, Benjamin Davis, Paul van Ufford, Stanfeld Michelo, Sudhanshu Handa, David Seidenfeld, and Gelson, Tembo (2017). “Learning About Labour Impacts of Cash Transfers in Zambia,” _Journal of African Economies_ 26(4): 433-442

24
Supplemental Table 7. Relationships between baseline receptive vocabulary and follow-up English receptive vocabulary among children of literate and illiterate caregivers

||e1|e2|e3|e4|e5|e6|
|---|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
||||Luo li|teracy|English|literacy|
|Sample (above/below median literacy):|Both|Both|Below|above|Below|above|
|Receptive vocabulary in Luo (_z-_score)|0.1812***|0.1788***|0.1412*|0.1925***|0.1511**|0.1932***|
||0.0463|0.046|0.083|0.0547|0.0732|0.0592|
||0.0001|0.0001|0.0915|0.0005|0.0411|0.0013|
|Receptive vocabulary in English (_z-_score)|0.2593***|0.2515***|0.3113***|0.2288***|0.3391***|0.2128***|
||0.0485|0.0488|0.0914|0.0562|0.0802|0.0594|
||<0.001|<0.001|0.0009|0.0001|<0.001|0.0004|
|Receptive vocabulary in Swahili (_z-_score)|0.1034**|0.0998**|0.0564|0.1316**|0.052|0.1336**|
||0.048|0.0481|0.0764|0.0613|0.0698|0.066|
||0.0318|0.0386|0.4623|0.0331|0.4576|0.0446|
|Caregiver literacy in Luo||0.0639|||||
|||0.0429|||||
|||0.1376|||||
|Caregiver literacy in English||–0.018|||||
|||0.0546|||||
|||0.7416|||||
|Constant|–0.0099|–0.1825**|–0.1697**|0.0801|–0.0986|0.0549|
||0.045|0.0776|0.0729|0.0568|0.0681|0.0594|
||0.8267|0.0193|0.0218|0.1605|0.1501|0.3569|
|_R-_squared|0.1821|0.1951|0.1833|0.1796|0.2149|0.1656|
|N. of cases|442|442|158|284|187|255|

Notes. Receptive English, Swahili, and Luo vocabulary scores are age-adjusted _z-_ scores measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.
# **3.3. Measuring the Quality of Local Governance and Decentralization**

The quality of local institutions and local governance is measured through various proxies. First, we constructed an objective measure of corruption based on the incidence of bribe payment in three steps. In the first step, we derived a binary indicator from answers to the question _”How often, (if ever, have you had to pay a bribe, give a gift, or do a favour to government officials in order to:...”_ , to which the respondents indicate whether they have bribe government officials in order to obtain public services such as school placement, medical services, official documents, household services and police help. In the second step, the binary indicators are then aggregated using polychoric correlation to create an indicator of the tendency of bribe payment at the individual level. In the third step, the individual estimates are averaged at the local community level. Unlike the individual perception of corruption, the incidence of bribe payment points to an objective exchange between authorities and residents. Second, we measure the quality of local governance by averaging the individual distrust in local government councillors at the regional level. Lastly, we corroborate the results with a corruption perception indicator, which also takes the regional average number of residents that perceive their local governments as being corrupt.

As per our third contribution to the literature, we introduce a measure of decentralization based on the legal assignment of tax-related decisions to local governments issued from a new dataset on tax and revenue assignment in developing and emerging economies.<sup>1</sup> The dataset provides comprehensive information on the vertical decision-structure over the tax system across government tiers. It was constructed through in-depth reviews of legal and policy documents, scientific and grey literature, as well as fiscal archives from the International Bureau of Fiscal Documentation (IBRD, Access: 2015-2017), which inform on the discretionary power of central and local governments over deciding on tax-related matters. Based on the legal and policy information and using a pre-defined matrix, the discretionary power of all government tiers is coded for each significant revenue instrument and across four decision components which refer to the legal ability of local and central authorities to _(i)_ introduce new instruments or altering existing ones, _(ii)_ define the base, _(iii)_ set the rates and _(iv)_ collect and administer the revenues.

For each decision component, a score is calculated by taking the ratio of subnational governments’ involvement across the number of identified revenue instruments. An overall index is then computed by taking the average of scores on the four decision components. This aggregated indicator reflects the overall discretionary power of subnational governments over the fiscal space or their level of taxing rights in each country. Given that natural resources extraction generates revenues for local authorities, either directly through extraction royalties or

> 1 See Vincent (2020).

11
**Figure 6: Change in Relative Employment by age group. Restricted to workers with**

<!-- Start of picture text --> college.<br><!-- End of picture text --> <!-- Start of picture text --> A. Age 23-44 B. Age 45-65<br>2002 2004 2006 2008 2010 2012 2002 2004 2006 2008 2010 2012<br>Year Year<br>Professionals Education Directors Supervisors Professionals Education Directors Supervisors<br>Admve Support Retail Rest Admve Support Retail Rest<br>Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2012. Relative employment refers<br>to the share in employment of that occupation out of total employment. Restricted to workers with college<br>education. Lines are smoothed by a moving average with a 0.4 weight to the current observation and 0.3 to the<br>previous and post observation.<br>Figure 7: Monthly Earnings by age group. Restricted to workers with college.<br>A. Age 23-44 B. Age 45-65<br>2002 2004 2006 2008 2010 2012 2002 2004 2006 2008 2010 2012<br>Year Year<br>Professionals Education Directors Supervisors Professionals Education Directors Supervisors<br>Admve Support Retail Rest Admve Support Retail Rest<br>30 30<br>25 25<br>20 20<br>15 15<br>% Relative Employment % Relative Employment10<br>10<br>5<br>5<br>25000 30000<br>20000 25000<br>15000 20000<br>Monthly Labor Earnings10000 Monthly Labor Earnings15000<br>5000 10000<br><!-- End of picture text --> Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2012. Relative employment refers to the share in employment of that occupation out of total employment. Restricted to workers with college education. Lines are smoothed by a moving average with a 0.4 weight to the current observation and 0.3 to the previous and post observation.

Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2012. Earnings are in constant Mexican Pesos 2014:1. Earnings refer to full-time workers (at least 30 weekly hours of work) with a valid wage. It includes workers with college education: salaried, self-employment and owners. Earnings below and above the 1 and 99 percentile (within gender and education group) are dropped. Lines are smoothed by a moving average with a 0.4 weight to the current observation and 0.3 to the previous and post observation.

# **4. Summary of the Results**

In sum, there is no evidence supporting the hypothesis that the decline in the wages of collegeeducated workers has been driven by what happened to younger workers. If the degraded tertiary 11
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE make temporary connections of power at night to avoid detection during the day. The remaining 15 percent of the system losses are due to technical issues. Distribution cables in Bangui are over 40 years old and are made of bare copper. The copper is a rather attractive commodity for looters, and aging cables have been an easy target for illegal connections.

Despite relatively high tariffs, ENERCA has not been able to recover its total cost of production. At 15 cents per kilowatt-hour (kWh), the CAR’s consumers pay higher prices than consumers in other African countries that generate power from hydro resources (figure 11). High prices are driven up further by the high costs often associated with the small scale of production, escalation of fuel prices, and regular breaks of the power infrastructure. Historically, ENERCA has only recovered 75 percent of its total cost of around 20 cents/kWh (table 9, figure 12). Underpricing has hindered ENERCA’s ability to undertake new investment and refurbish its dilapidated infrastructure. But in the medium term, as the structural problems facing the sector are addressed and new generation capacity comes on stream, long-run marginal costs would likely be on the order of 11 cents/kWh, or within range of existing tariffs (figure 12).

**Figure 11. Power prices, benchmarked against other Sub-Saharan African countries**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>U.S. cents per kWh<br>Liberia Chad Burkina Faso Cape Verde Senegal Mali Uganda Kenya Congo, Rep CAR Rwanda Bénin Niger Cote d'Ivoire Namibia Madagascar Cameroon Ghana Botswana Mozambique Lesotho Tanzania South Africa Ethiopia Zimbabwe Nigeria Malawi DRC Zambia<br><!-- End of picture text --> _Source:_ World Bank 2009f; AICD estimates. _Note:_ Average effective tariff estimates are based on prices paid by domestic and nondomestic customers, weighted by their contributions to total consumption.

27
firms of given characteristics to be randomly exposed while other similar firms remain unexposed to such interventions. This would allow comparison of outcomes before and after the interventions between the exposed and unexposed groups. Such an approach, known as the randomized controlled trial (RCT), has been applied less frequently in the IC space compared to some other fields of development partly because many IC reforms, including reforms of business laws and regulations, occur at the national level or economy-wide, and aim to affect the entire private sector in the country simultaneously.<sup>4</sup> Accordingly, the effects of investment climate tend to be measured through cross-country or cross-sectional empirical analysis, and sometimes through the use of cross-country panel data. Routinely in the evaluation of various programs or projects, a comparison of the situation before and after the reform is used to measure the success or failure of such reform intervention.

A well-known problem with this type of analysis is that it is difficult to infer about the causal effect of any particular IC reform on the outcome of interest, such as job creation. Various uncontrolled or unobserved firm, industry, location, country-specific conditions, and international circumstances, could confound the potential causal effect of any IC reform on job creation. IC reforms might also occur alongside other political reforms or political commitments to private sector development that could make it difficult to distinguish the effect of IC reforms from those other reforms or commitments. Sometimes, the reform intervention itself could be potentially endogenous to the firm’s decision-making process rather than being a purely exogenous change. While the use of cross-country or cross-sectional panel data is an improvement over simple cross-country or before-after comparison, the former still suffers from time-variant uncontrolled or unobserved factors. To overcome this problem, while an instrumental variable approach has been used in a number of studies, finding good instruments is not always that easy.

On the other hand, in many circumstances RCT can be built into a reform evaluation effort even when a reform is nationally implemented, for instance by randomly varying the degree of exposures of such reforms to the firms through encouragement design. Similarly, when resource

> 4 See Mckenzie (2010) for a detailed discussion on rigorous evaluation in Finance and Private Sector Development (FPD) space, which also argues that while the economy-wide nature of the PSD or IC reforms could be one reason for the dearth of rigorous evaluation in the PSD space, the mindset of the project designers and implementors also got a lot to do with such dearthness.

10
Table 2. Impact of diaspora on selection ( level and change in log high-skill/low-skill ratio) <!-- Start of picture text --> Log-skill ratio Log-skill ratio ∆LSR ∆LSR<br><!-- End of picture text -->

||Log-skill ratio<br>(OLS)|Log-skill ratio<br>(Heck)|∆LSR<br>(Heck)|∆LSR<br>(IV)|
|---|---|---|---|---|
|Lagged diasp|-0.171|-0.194|-0.212|-0.215|
||(16.19)***|(20.62)***|(17.62)***|(2.95)***|
|Col links|-0.042|-0.022|0.101|0.270|
||(0.62)|(0.32)|(1.67)*|(1.77)*|
|language|0.466|0.460|0.176|0.235|
||(9.38)***|(9.37)***|(4.17)***|(3.19)***|
|Log(dist)|0.096|0.090|0.086|0.019|
||(3.35)***|(3.40)***|(3.78)***|(0.30)|
|Schengen|0.502|0.519|0.390|0.414|
||(5.65)***|(6.26)***|(5.48)***|(6.08)***|
|Constant|-1.109|-0.734|-1.250|-0.481|
||(1.16)|(1.32)|(2.54)**|(0.63)|
|Mills|-|(-0.380)|(-0.10)|-|
||-|(6.86)***|(0.22)|-|
|F-stat First stage|-|-|-|30.07|
|Hansen J-test (p-value)|-|-|-|0.747|
|Observations|3486|5760|5760|3486|

<!-- Start of picture text --> (OLS) (Heck) (Heck) (IV)<br>Lagged diasp -0.171 -0.194 -0.212 -0.215<br>(16.19)*** (20.62)*** (17.62)*** (2.95)***<br>Col links -0.042 -0.022 0.101 0.270<br>(0.62) (0.32) (1.67)* (1.77)*<br>language 0.466 0.460 0.176 0.235<br>(9.38)*** (9.37)*** (4.17)*** (3.19)***<br>Log(dist) 0.096 0.090 0.086 0.019<br>(3.35)*** (3.40)*** (3.78)*** (0.30)<br>Schengen 0.502 0.519 0.390 0.414<br>(5.65)*** (6.26)*** (5.48)*** (6.08)***<br>Constant -1.109 -0.734 -1.250 -0.481<br><!-- End of picture text --> <!-- Start of picture text --> (1.16) (1.32) (2.54)** (0.63)<br>Mills - (-0.380) (-0.10) -<br>- -<br>(6.86)*** (0.22)<br>F-stat First stage - - - 30.07<br>Hansen J-test (p-value) - - - 0.747<br>Observations 3486 5760 5760 3486<br><!-- End of picture text --> Absolute values of robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Instrument sets for Mij in all columns include a dummy for bilateral guest-worker agreements and a proxy for diaspora size in 1960. In column (1) , the proxy is computed as ln(popi ∗ immstj/distij) ∗ Conf 1i.In column (2) , the proxy is computed as ln(popi ∗ immstj/distij) ∗ Conf 2i;in column (3), the proxy is computed as ln(popi ∗ immstj/distij) ∗ (conf 1i + Conf 2i).

17
A. Sanghi and D. Johnson (section 4.2.2).

**Chinese companies hire fewer female employees** Chinese companies hire few female employees: women only represent five percent of total employees for Chinese companies on average versus 29 percent for all companies in Kenya. Chinese companies in the services sector have 19 percent female employees compared to 31.8 percent female employees in the services sector overall. In manufacturing, women are only three percent of employees in Chinese companies, but they are 22.7 percent of full-time employees overall (SACE 2014; World Bank Enterprise Survey 2013). 15 percent of the workforce in private companies is female, but only two percent of the workforce is female in state-owned enterprises. Hiring more female employees has a direct impact on poverty because women spend more on health, education, and household durables. When women work, they also tend to increase household savings (Lawson _et al_ 2009).

### **3.2.5 Chinese firms face some competition from the informal sector**

93 percent of Chinese companies in the sample have registered their business. 24 percent of companies reported competition from the informal sector, and informal competition was more likely in services (35 percent) than in manufacturing (13 percent) (SACE 2014).

### **3.2.6 Chinese companies less likely to take credit line**

Chinese firms are less likely to obtain a loan or credit line. Only 26 percent of Chinese companies obtained a loan; 36 percent of all companies in Kenya had a loan. (World Bank Enterprise Survey 2013). Potential borrowers are dissuaded by unfavorable interest rates and complex application procedures. 51 percent of Chinese companies reported not needing a loan because they had sufficient capital (SACE 2014). State owned enterprises had financing from headquarters in China, and private companies accumulated savings and loans from family and friends. Manufacturing and construction companies take as much credit as other companies in Kenya, but only 16 percent of companies in the services sector have taken a loan or credit line. Larger companies tend to have better access to loans and credit. 95 percent of Chinese companies, however, have a local bank account and can access basic banking services.

# **4 Official Development Assistance from China**

## **4.1 Chinese aid is small compared to commercial activities**

Chinese aid to Sub-Saharan Africa was US $3.2 billion in 2013, and is quite small relative to its investment stock of US $32.35 billion in Africa; the small amount of aid supports China’s no handout policy (China Africa Research Initiative 2013). China is more of a business partner than a donor, but it does provide grants and other forms of aid (Wang and Bio-Tchane 2007). Chinese aid only started to become significant after 2002, and it became 1.23 percent of total loans and grants to Kenya in 2003 (Mwega 2009; Onjala 2008). China also accounted for 10 percent of loans and 20 percent of physical infrastructure assistance to Kenya in 2005 (UNDP 2005; Onjala 2008). Unfortunately, as we discuss in section 4.3, we do not have access to quality aid data.

30
<!-- Start of picture text --> Figure 4: Predicted Income in Guyana from Solow/MRW model and actual<br>income<br>6000<br>Predicted income from<br>sources of growth<br>5500 accounting with<br>productivity growth 2%<br>5000 -<br>4500 -<br>34000 -<br>3500<br>3000<br>0<br>2500<br>2000 S / _ ~~~~~~~~~~~ACUl 11 wcoESMl#c<br>1500<br>NC)-.,'', -l .C -. ' -.-- ' .q .''- "'q "') NC NC IC C C<br>1000 -1 i i I I I I I I I_ _ .<br>2500 4/ 0b _p - . -- c -- c<br><!-- End of picture text -->
**<u>Table 6: Estimates of  program effects on hired labor and family labor demand in agricultural production</u>**

|||**Total**|||**Pre harvest**|||**Harvest**||
|---|---|---|---|---|---|---|---|---|---|
||**Total **|**Male**|**Female**|**Total **|**Male**|**Female**|**Total **|**Male**|**Female**|
|||||**Panel A: Hi**|**red labor, e**|**ntire sample**||||
|MNREGS|-8.368**|-4.946|-1.588|-5.155*|-3.224|-1.330*|-3.061*|-1.293|-0.618|
|* post|(4.201)|(3.504)|(1.270)|(2.888)|(2.542)|(0.729)|(1.751)|(1.289)|(0.695)|
|Observations|6,468|6,468|6,468|6,468|6,468|6,468|6,468|6,468|6,468|
|R-squared|0.730|0.709|0.748|0.712|0.690|0.749|0.731|0.726|0.738|
|||||**Panel A1 :**|**Hired labor**|**by farm size**||||
|MNREGS *|-4.533|-1.743|-0.950|-3.181|-0.754|-1.269*|-2.070|-0.576|-0.149|
|post * small|(3.544)|(2.911)|(1.157)|(2.618)|(2.293)|(0.664)|(1.424)|(1.018)|(0.638)|
|MNREGS *|-8.774|-3.989|-2.817|-5.875|-3.825|-1.422|-2.735|-0.174|-1.224|
|post* med.|(6.900)|(5.840)|(2.015)|(5.210)|(4.659)|(1.187)|(2.443)|(1.631)|(1.107)|
|<br>MNREGS *|-6.388|-5.724|0.928|-4.433|-3.933|-0.560|-1.026|-1.180|0.739|
|post * large|(11.143)|(9.002)|(3.628)|(7.214)|(6.085)|(2.097)|(4.948)|(3.657)|(1.918)|
|Observations|6,468|6,468|6,468|6,468|6,468|6,468|6,468|6,468|6,468|
|R-squared|0.734|0.712|0.751|0.713|0.691|0.750|0.737|0.732|0.742|
|||||**Panel B:Fa**|**mily labor, e**|**ntire sample**||||
|MNREGS|18.091***|11.048**|5.943**|<br>6.162|<br>3.652|<br>1.156|<br>12.193***|7.443***|4.274***|
|* post|(6.965)|(4.939)|(2.806)|(5.011)|(3.817)|(1.846)|(3.104)|(2.075)|(1.404)|
|Observations|6,468|6,468|6,468|6,468|6,468|6,468|6,468|6,468|6,468|
|R-squared|0.674|0.661|0.724|0.662|0.649|0.711|0.689|0.668|0.744|
|||||**Panel B1:F**|**amily labor**|**by farm size**||||
|MNREGS *|18.450*|11.774|7.395*|9.141|5.263|2.963|8.848**|5.892**|3.209*|
|post * small|(10.129)|(7.613)|(3.924)|(7.648)|(6.373)|(2.617)|(4.444)|(2.940)|(1.901)|
|<br>MNREGS *|10.095|4.596|3.775|0.411|-0.710|0.327|10.261*|5.909|4.003|
|post* med.|(12.217)|(7.995)|(5.152)|(8.156)|(5.647)|(3.259)|(5.762)|(3.646)|(2.599)|
|<br>MNREGS *|<br>25.573*|<br>18.527*|<br>5.954|<br>8.299|<br>6.887|<br>-0.160|<br>17.882***|<br>11.670***|<br>5.412*|
|post * large|(14.245)|(9.933)|(5.765)|(10.291)|(7.454)|(3.873)|(6.078)|(4.238)|(2.789)|
|Observations|6,468|6,468|6,468|6,468|6,468|6,468|6,468|6,468|6,468|
|R-squared|0.675|0.662|0.724|0.663|0.649|0.711|0.690|0.669|0.745|

_Notes:_ Household and year fixed effects as well as controls included but not reported. Standard errors, clustered at household level, in parentheses; *** p<0.01, ** p<0.05, * p<0.1 20