rafmacalaba/data-use-mentions-extended huggingface dataset:
4 OFFICIAL DEVELOPMENT ASSISTANCE FROM CHINA

# **4.1.1 Kenya may rely more on Chinese aid because of volatile aid flows**

Chinese aid may continue to play a bigger role over time because Kenya’s foreign aid flows are volatile. The coefficient of variation of Kenya’s aid is 74.19 between 1960 to 2013; the coefficient of variation for Sub-Saharan Africa is 64.71 over the same period.<sup>11</sup> Chinese official development assistance is a small fraction of Kenya’s total aid flows. Figure 30 shows Kenya’s top sources of official development assistance in 2013.

<!-- Start of picture text --> 800<br>600 AfDF (African Dev.Fund)<br>EU Institutions<br>France<br>Germany<br>Global Fund<br>400<br>IDA<br>IMF (Concessional Trust Funds)<br>Japan<br>200 United Kingdom<br>United States<br>0<br>Country<br>Gross ODA (USD Millions)<br><!-- End of picture text --> Figure 30: US is a top donor for Kenya. Chinese finance does not meet OECD/DAC aid criteria (2013) Source: OECD DAC aid database 2015

**China is not a member of OECD donors** China is absent from the top ten donors because it is not a member of the OECD, so little of Chinese financing qualifies as official development assistance under the standard OECD definition (Br ̈autigam 2011). Figure 31 gives a sectoral distribution of aid to Kenya. Overall, aid to the health and population sector is the highest at US $574.3 million (31 percent) followed by economic infrastructure and services at US $407.5 million (22 percent).

> 11

where _μ_ is the average and _σ_ is the standard deviation.

31
# **7  Other Types of Programs**

This section describes other types of interventions that have been found to influence women‟s labor market outcomes.

## **7.1  Family Friendly Policies in Western Europe**

Most countries in Western Europe have relatively generous childcare and family leave policies in comparison with the rest of the world. Del Boca, Pasqua, Pronzato and Wetzels (2007) study the effects of family friendly social policies in European countries on the labor force participation, hourly wages and fertility of women aged 21-45. Their analysis is based on a random sample of households from 15 European Union member countries, drawn from the European Community Panel Survey. Their goal is to assess the impact of social policies on employment and fertility using a reduced form methodology that relates labor force participation and fertility decisions to a set of determinants of these decisions that include measures of countries‟ relative generosity in providing childcare services (for children age 0-3) and of countries‟ parental leave policies. They focus on the provision of childcare for young children, because there is a lot of variation across countries for the 0-3 age group. Del Boca et al. (2007) create indicators of childcare generosity by ranking countries according to their available programs and then grouping them into four generosity categories. A similar grouping is performed for parental leave. Denmark is the country with the most generous childcare programs for children age 0-3 and Italy is the country with the most generous parental leave policies. Spain is the least generous both in terms of childcare and parental leave.

Del Boca et al. (2007) then estimate a statistical model of the joint decisions to work and to have a child. The model is a bivariate probit model, with the outcomes modeled as a function of individual characteristics, such as education and age, household characteristics that include the husband‟s income, and the social policy generosity indicator variables. The study finds that the generosity indicators are highly predictive of fertility and employment outcomes and in some cases more
the bilateral level for selected banking centers. While the public dataset is not as comprehensive as the restricted one, it allows us to extend the sample period beyond 2010 and to show results for individual havens, which is prohibited under the confidentiality agreement governing the restricted data.

Among the 43 financial centers contributing to the Locational Banking Statistics, we classify 17 as havens and the remaining 26 as non-havens.<sup>3</sup> Havens generally have institutional characteristics that make them attractive places to hide funds: bank secrecy rules that ensure strict confidentiality and legal arrangements that facilitate asset protection by enabling investors to nominally transfer asset ownership to a third party while retaining full control (e.g. trusts or de facto anonymous shell corporations). Important havens in our dataset include Switzerland, Luxembourg, Cayman Islands, Bahamas, Hong Kong and Singapore.<sup>4</sup> We define _Havenit_ as deposits owned by country _i_ in the 17 havens in quarter _t_ , and similarly _Nonhavenit_ as deposits owned in one of the other financial centers. We exploit the sectoral breakdown in the BIS statistics to exclude interbank deposits from these measures.<sup>5</sup> The BIS statistics do not look through chains of ownership to the ultimate owners of deposits; our analysis does therefore not include accounts held through foreign shell corporations (Omartian, 2017), which is likely to reduce the estimated leakage. The dataset at our disposal spans the period 1977-2010, but we discard observations before 1990 because of a major data break in 1989.<sup>6</sup> Table 1 presents summary statistics on the deposit measures. Average haven deposits range from $4 million in Sao Tome and Principe to almost $200 million in Madagascar and generally constitute around one third of all foreign deposits. Quarterly growth rates in haven deposits, our main outcome variable, average 2.0%, which is significantly higher than the quarterly growth rate in non-haven deposits and GDP. The distribution of growth rates in haven deposits
climate performance of the countries in our survey.

**Table 5: Criteria for selecting and investment location according to surveyed EPZ firms, rankings by country (top 5 highlighted)**

|**Investment criteria**|**African zones**|**Non‐African**<br>**zones**|
|---|---|---|
|**Cost andquality of utilities**|1|3|
|**Access to transport infrastructure**|2|2|
|**Business regulatory environment**|3|5|
|**Tariffs, duties, and rules of origin**|4|8|
|**Level of corporate taxes**|5|6|
|**Access to highly skilled labor**|6|4|
|**Access to suppliers**|7|7|
|**Access to low cost labor**|8|1|
|**Availability / cost of land and buildings**|9|10|
|**Access to local and regional markets**|10|9|
|**Access to technology**|11|11|

Source: SEZ Investor Surveys Table 5 summarizes investors’ ranking of the relative importance of eleven criteria for selecting an investment location<sup>18</sup> . Among the African countries surveyed, there was remarkable similarity in the average respondent rankings, particularly among the top 3 factors. What is also striking is that the top 3 factors ranked by investors are issues over which the EPZ program and individual zone management should have at least a decent measure of control – the cost and quality of utilities, access to (efficient) transport, and the business regulatory environment. The other two factors rated as highly important by investors are tariffs and duties and the level of corporate taxes, the latter of which at least can also be controlled by zones
|1 _∼_ 1,000 per month on transportation and the remaining spent more than |1,000. Of users, both new and continuous, 55% did not spend any money on transportation, including buses, per month in the period after the scheme’s launch. The remaining 45% spent between |1 and |1,000 on transportation. Specifically, among new bus users (see Figure C2), 82% spent more than |1,000 per month on transportation before the scheme, but this percentage decreased dramatically to 1% after the scheme was implemented. This 81% difference is also statistically significant at a 1% level. Furthermore, while none of the new bus users reported any spending on transportation before the scheme (the |0 category in Figure C2), the proportion increased to

> 10The survey inquires about the monthly travel expenditures of all respondents after the launch of the _Pink Slip Program_ in Delhi. However, it only asks new bus users about their monthly travel expenditures before the program’s launch, that is, only for the new bus users the survey records expenditures before and after the scheme.

10
|
|HEI is a university|0.21|0.41|0|0|1|2,103|
|HEI is for profit|0.20|0.40|0|0|1|2,103|
|HEI age|37.84|30.80|1|32|481|2,094|
|Number of programs in the HEI|21.59|36.30|1|10|268|2,103|
|<br>HEI characteristics (PCA score)|0.08|1.16|-1.84|-0.21|9.57|2,094|
|**Panel C. Noise controls**|||||||
|Survey conducted during COVID|0.48|0.50|0|0|1|2,103|
|Number of attempts to complete the survey|8.36|3.16|1|9|17|2,103|
|Survey completed by phone|0.33|0.47|0|0|1|2,103|

_Sources_ : Own calculations using WBSCPS and administrative data.

_Notes_ : This table shows the descriptive statistics of the main variables used in the analysis (Table 1 presents an abridged version). An observation corresponds to a program. Dummy variables included in the list are those with means between 0.1 and 0.9. Statistics are weighted by WBSCPS sampling weights. Panel A refers to quality determinants, presented by category. Panel B refers to characteristics of the student body, program, and higher education institution (HEI); Panel C refers to survey noise controls. Total number of surveys completed is 2,103. Values in the “Obs.” column vary depending on the number of valid responses. * indicates the omitted category in the analysis. + indicates that the variable is conditional on another. For example, “
which farmers demonstrated each crop. These administrative data align remarkably well with our self-reported household survey data; 5% and 15% of non-demonstrators (defined at the household-crop level) report adoption of improved seed and cultivation, respectively, for promoted crops during the demonstration year, in contrast to 80% and 94% of demonstrators.

**Restriction to promoted crops** Our main analysis leverages data at the farmercrop-survey wave-level and restricts to what we refer to as “promoted crops”. A key choice made by extension agents for treatment villages is the selection of pulses for demonstration of improved seeds; as we do not expect impacts of demonstration on non-demonstrated pulses, pooling demonstrated and non-demonstrated crops together in analysis would meaningfully reduce power. However, no crops were demonstrated in control villages. We therefore identify comparison crops in control villages based on the observation that extension agents were instructed to select crops for demonstration packages based on suitability in their Union (Section 3.1); specifically, we select the crops that were demonstrated in the other sampled village in their Union (and therefore selected by the same extension agent) as comparison crops.<sup>15</sup> We refer to comparison crops in control villages, and demonstrated crops in treatment villages, as “promoted crops”, and non-promoted pulses as “placebo crops.”

The restriction to promoted crops would result in bias if promoted crops were endogenous to treatment assignment; for instance, extension agents could have been aware of the village’s treatment status (regular, shared, or decentralized) before selecting the demonstration package, and this information could have informed their choice of demonstrated crops.

We consider four tests for this bias, which we discuss and implement in Section 4.6. We compare, across treatment arms, the choice of promoted crops, baseline outcomes, and outcomes on placebo crops, and we estimate impacts controlling for crop-survey wave fixed effects. The results of these tests lead us to conclude that potential endogeneity of promoted crops to treatment assignment
,000 HS6 lines. Sub-Saharan African firms are less diversified on average in terms of both number of destinations and products. Differences in terms of diversification are particularly important in terms of number of products (total or averaged by firm).

# **3.2 Country-level variables**

Exchange rates vis-a-vis the U.S. dollar are yearly averages from the IMF’s International Financial Statistics (IFS) and are deflated by consumer price indices to obtain annual real exchange rates (RER). Exchange rates are all expressed in local currency units (LCU) per dollar in the IFS. Let _eot_ and _edt_ , respectively, be the origin and destination countries’ exchange rates in LCU per dollar in year _t_ , and _Pot_ and _Pdt_ their consumer price indices.<sup>17</sup> Let _eodt_ be the average

> 16Mineral and primary products are commonly disregarded due to large and sudden fluctuations in international prices and associated terms-of-trade shocks, arbitrarily driving the export performance.

> 17In our baseline estimations, we have dropped the top percentile of country-pairs in terms of variance of bilateral real exchange rates. Dropping these countries which display extreme price variations (generally countries with hyperinflation) limits measurement error and only drops 0.07 percent of total trade value.

9
C6te d'Ivoire is one of the few countries in Sub-Saharan Africa to have conducted household surveys over a ten year period between 1985 and 1995 that allow one to trace the evolution of poverty in urban and rural areas and among different socioeconomic groups. This ten year period spans an economic recession of major proportion and ends with the beginning of an economic recovery following the devaluation of the CFA franc in January 1994. Poverty increased substantially over the course of the recession, and did not diminish in the year following the devaluation in part because much of increase in export crop prices, especially cocoa, was taxed away.

Moreover, the impact of the recession and devaluation was not uniform. Between 1988 and 1993, urban poverty increased more rapidly than rural poverty, even though mean expenditure in the urban areas remained substantially above mean expenditure in rural areas throughout the period. And food crop farmers appear to have suffered more than export crop farmers. These trends persist after the devaluation.

However, an exploration of the poverty trends over time across regions and socioeconomic groups raises a number of questions about the pattern of change and about the reliability of the data or the methodology used to derive the poverty estimates. Secondary data cast some doubt on certain of the survey findings, suggesting that they may be more an artifact of the survey data than a reflection of real economic trends. These problems are not unique to C6te d'Ivoire; results from surveys in Uganda and to a lesser extent Ghana have also raised issues of comparability.' The ability to draw reliable conclusions from time series data is crucial for our understanding of the impact of policy reforms on poverty. Far more attention needs to be paid to the issues of comparability in designing surveys and analyzing data and also to the development of better regional and temporal price indices if reliable time series data on poverty is to be generated for sub-Saharan Africa.

# **_Economic Crisis and the Increase in Poverty_**

C6te d'lvoire's economic history is marked by a long economic boom
only positive and significant coefficient. Further, the previous pattern on prices and marginal cost showing Chile with the only significant and negative result continues to hold. Agglomeration continues to put downward pressure on mark-ups.

Cities may also attract a better quality of labor and the availability of local literacy data from Colombia and Indonesia offers a proxy for level of local human capital to potentially control for compositional effects (Table 2, panel d).<sup>15</sup> In Indonesia, the wage and TFPR elasticity estimates are lower, falling from .056 to .04 and .022 to .016 respectively, while the TFPQ elasticity using Foster et al. (2008) falls from - .011 to -.021, in both cases still insignificantly so. Price, mark-ups and marginal costs change marginally. By contrast, in Colombia, the wage elasticity increases slightly from .038 to .04 while TFPR rises from .003 to a now significant .005 suggesting that human capital in poor, traditional economies is highly heterogeneous (Chauvin et al 2017). Point estimates for the TFPQ elasticity fall from -.004 to -006, but in both cases insignificant.

> 15 In Chile and Ethiopia, we did not have access to local human capital by our spatial identifiers.

11
sources is available at no acquisition cost, but translating the raw data into usable outputs requires knowledge of Javascript, GIS technology, and a modular programming language (Python or R).

Overall, the evidence from Dhaka suggests that these three information sources should be viewed as complements rather than substitutes. Taken together, they can provide a powerful adjunct to traditional information systems that provided much more limited coverage while imposing replacement and maintenance costs that are difficult for many urban governments to sustain.

# **7. Summary and Conclusions**

This paper has assessed the potential for low-cost augmentation of ground-based air quality monitoring in Dhaka, Bangladesh, with information from more comprehensive global information sources. At present, systematic local monitoring data are confined to areas near Dhaka’s Darussalam (CAMS-3) station (Figure 1). In this exercise, we have assessed the potential contributions of three global information sources: the European Space Agency’s Sentinel-5P, NASA’s MODIS, and Google Traffic.

Our tests for Sentinel-5P (S5P) and MODIS use daily measures for the .01-degree cell surrounding the CAMS-3 monitoring station. We match the satellite measures with CAMS-3 observations during the satellites’ overpass times for Dhaka. Our tests for the CAMS-3 measure of fine 16
Toilet Safety|0.00|0.54|1.00|1,696,260|
|Urban|0.00|0.57|1.00|1,696,260|
|Panel C: Technolo|gy Access & Us|e|||
|Washing machine|0.00|0.20|0.00|1,696,260|
|Use of internet in the last 3 months|0.00|0.29|1.00|1,696,260|
|Panel D: So|cial Norms||||
|Female participatea in hhld decision making|1.00|0.77|1.00|1,696,260|

Sources: 2018 National Census of Guatemala. Notes: Table A1 in the paper provides the definition of each variable.

Table II.3 presents statistics for municipality and department level variables. Panel A reports statistics on policy-related variables, Panel B on social norms and Panel C on local labor market variables. Panel A shows that the number of public preprimary centers per 100 children aged 4 to 6 years old in each municipality is 1.5 in 2017. Preprimary education is mandatory in Guatemala and most preprimary centers are public (MINEDUC, 2021). However, net pre-primary attendance rate was only 52% in 2017 (MINEDUC, 2021). The availability of public education centers (free of charge) implies that fertility and work outside the home are not necessarily mutually exclusive for lower income families. The low net attendance rate may be reflecting low accessibility or low quality of public centers. The average per capita spending in education and health at the department level was US$ 201 and US$101 (2007 PPP), respectively. Guatemala is among the countries with 44
, IDA graduation also operates as a signal of creditworthiness for other lenders (bilateral lenders as well as the market), thereby permitting crowding in other lenders, which could further lower borrowing from the Bank. On the other hand, IDA graduation alleviates borrowing limits imposed on the IDA pool and countries face fewer restrictions on how much they can borrow. Finally, the creditworthiness signal could work in the opposite direction as well – observing the signal, bilateral donors may reduce aid amounts, thus increasing governments’ demand for Bank loans.<sup>4</sup> What is even less clear theoretically is how HD lending would respond to graduation vis-à-vis total lending.

In this work, we use the Bank’s portfolio data to understand and describe the patterns of total and HD lending at the World Bank. First, we describe the patterns of lending in IDA and IBRD countries. We then examine how total Bank lending and lending for HD sectors respond to countries’ transition from IDA to IBRD. To do this, we combine the Bank’s portfolio data with countries’ borrower status and analyze the effect of IDA graduation in a regression framework. Although, in principle, countries graduate from IDA status exogenously when they cross a prespecified income threshold, in practice, there are exceptions and additional factors that affect graduation eligibility, which makes it impossible to treat graduation as an exogenous event. As a second-best approach, we use fixed-effects regressions (country and year) to isolate the effect of graduation. Year fixed effects capture economic climate and

> 3 Interest rates for IBRD flexible loans with a variable spread were LIBOR + 0.99% as of October 1, 2018 (Group A countries, >18-20 years maturity). The USD LIBOR – 6 months average for 2018 was 2.487%. The interest rate on regular IDA loans was 3.125% with 38 years maturity, and 6 years grade period.

> 4 See Knack et al. (2014) for further discussion. The authors study how
Although weak and often not comparable, available data show that access of households to banking services varies greatly. In developing countries, many households do not have a bank account. Table 1 provides data on the degree to which households use a basic financial service provided by a formal financial institution, e.g., have a checking or savings bank account, across a number of countries.<sup>4</sup> It shows that usage in most of the OECD countries is nearly universal, with many percentages above 95% and with an average of 90%; in developing countries, though, usage is much less and the average is only 26%. The highest usage of financial services from formal financial institutions is 59% percent in Jamaica. Some other high numbers may not be representative of the whole country as they apply to the population of the capital city only (Mexico) or some specific cities or regions (China, Colombia, India) or urban areas (Brazil). For most of the other developing countries, use of a basic bank account does not exceed 30 percent, and in the lowest-income countries, use is less than 10% of households.

Individuals obtain financial services through other means, though, including through nonfinancial institutions, as the comparison for some Latin American countries shows (Table 2). The Microcredit data also show that there is often use of other forms of financial services, outside the banking system. As such, these numbers underestimate the degree of access to financial services, but they do show the large differences between developed and developing countries in terms of usage of financial services from formal financial institutions.

The next question then is who are then the unbanked households, and how do they differ, if at all, between developed and developing countries? To the extent we know, the

> 4 The data for Table 1 come from a variety of sources.  The main sources are households’ surveys, the LSMS (Living Standard Measurement Surveys). Here individual households
***|
|35-44|0.302|-0.177|-0.144|
||[0.0121]***|[0.0186]***|[0.0215]***|
|45-54|0.224|-0.200|-0.162|
||[0.0119]***|[0.0191]***|[0.0229]***|
|55-65|0.0747|-0.193|-0.139|
||[0.0122]***|[0.0213]***|[0.0292]***|
|Wage employee|0.204|0.109|0.0471|
||[0.00870]***|[0.0127]***|[0.0155]***|
|Constant|-1.045|-0.205|-0.297|
||[0.0264]***|[0.0186]***|[0.0240]***|
|Observations|138,954|24,085|22,088|
|R-squared|0.21|0.319|0.148|

Notes: All models include country fixed effects. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

# **4. Concluding Remarks**

This paper provides new evidence on which countries and types of workers have jobs that are less amenable to working from home. Using data from 53 countries on the types of tasks that each person does at work—as 18