rafmacalaba/datause-encoder-data huggingface dataset:
|Notes|Mid-1970s|10 yrs|$150,000|Prime based|
|---|---|---|---|---|
|||7 yrs|$250,000||
|||5 yrs|$1,000,000||
|Floating rate|1980-1992|10-12 yrs|$25,0000, $5,000|Prime based|
|Floating rate|1993-99|10 yrs|$5,000|Prime based|
|Floatingrate|Since End 1999|10yrs|N/A|Libor based|

_Source: Bank of Israel_ Figure 2: Bond Sales by Type in Israel <!-- Start of picture text --> % of total<br>100<br>90<br>Notes<br>80<br>70<br>60<br>50<br>40<br>30<br>Floating Rate<br>20<br>Fixed Rate<br>10<br>0<br>1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br><!-- End of picture text --> _Source: Bank of Israel_ Currently, Israel uses proceeds from bond sales to diaspora Jewry to finance major public sector projects such as desalination, construction of housing, and communication infrastructure. The Ministry of Finance defines DCI’s annual borrowing policy in accordance with the government’s foreign exchange requirements. The Finance Ministry periodically sets interest rates and more recently other parameters on different types of DCI bonds to meet the annual borrowing target. Still, the Israeli government does not consider borrowings from diaspora Jewry as a market-based source of finance. Accordingly, it does not seek credit ratings on these bonds from rating agencies such as S&P and Moody’s.

6
**Table 14: Determinants of energy consumption (1999-2001, Non CRS, Pooled effects)**

|**Dependent**<br>**variable=ln(energy/output)**|**Four indust**|**ries**|**Pulp and**<br>**indu**|**Paper**<br>**stry**|**Cement ind**|**ustry**|**Iron and**<br>**indus**|**Steel**<br>**try**|**Aluminum**|**industry**|
|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|P-value|Coef.|P-value|Coef.|P-<br>Value|Coef.|P-<br>Value|Coef.|P-<br>Value|
|**Constant**|0.8884|0.029|2.0243|0.007|1.8058|0|0.1756|0.838|-0.255|0.788|
|**Ln(price of energy/price of**<br>**output)**|-0.4914|0|-0.483|0|-0.1520|0|-0.7942|0|-0.102|0.607|
|**Ln (technology development)**|-0.0106|0.053|0.029|0.02|-0.0036|0.363|0.0192|0.351|-0.006|0.734|
|**Ln(Value of industry output at**<br>**constant price)**|0.8081|0|0.7355|0|0.8541|0|0.9524|0|0.9447|0|
|**Ln(Price of energy/price of**<br>**output)*Ln(technology**<br>**development)**|-0.0067|0.142|0.0216|0.075|-0.0002|0.953|0.0194|0.303|-0.007|0.708|
|**Foreign capital intensity**|0.3797|0.329|-0.0268|0.976|-0.0762|0.815|6.9901|0.058|5.3497|0.002|
|**Foreign capital**<br>**intensity*Ln(technology**<br>**development)**|-0.0358|0.082|0.0027|0.958|-0.0062|0.733|-0.7559|0.041|0.0335|0.777|
|**Collectives**|-0.7103|0|0.1809|0.355|-0.4367|0|-1.4585|0|-0.11|0.679|
|**Foreign**|-0.6565|0.007|0.4506|0.21|-0.0881|0.706|-1.1067|0.075|-3.787|0|
|**Hong Kong SAR, China; Macao;**<br>**Taiwan, China**|-0.4755|0|0.213|0.258|0.0987|0.328|-0.5517|0.114|-1.022|0|
|**Shareholding**|-0.3146|0|0.194|0.169|-0.1830|0.002|-0.4197|0.064|-0.527|0.004|
|**Private**|-0.0772|0.735|(omitted)||-0.2379|0.047|(omitted)||(omitted)||
|**Other**|0.2081|0.734|(omitted)||0.0021|0.995|(omitted)||(omitted)||
|**North**|0.4830|0.044|-0.7866|0.003|-0.2625|0.32|-0.4807|0.179|-0.669|0.103|
|**Northeast**|1.2883|0|(omitted)||0.0837|0.755|(omitted)||(omitted)||
|**East**|0.6302|0.006|-0.6885|0.005|-0.4119|0.116|-0.4343|0.172|-1.262|0|
|**South**|0.5743|0.014|-0.2558|0.256|-0.4529|0.085|-0.7951|0.029|-0.915|0.013|
|**Southwest**|(omitted)||(omitted)||(omitted)||-0.6325|0.269|-1.153|0.005|
|**Year 2000**|0.055|0.468|-0.0471|0.691|0.0523|0.368|-0.0643|0.731|0.1043|0.521|
|**Year 2001**|0.066|0.385|-0.0102|0.931|0.0619|0.29|-0.1144|0.547|0.0992|0.551|
|**R**<sup>**2**</sup>**(obs.)**|0.6566 (770|)|0.7351 (14|6)|0.7348 (342|)|0.7118 (21|0)|0.883 (72)||

38
across the globe must undertake the transactions listed above at one point or another in the life and operation of the business.

This index can be criticized for being based on the self-reporting of illegal activities. Interviewees’ may fear the consequences of answering honestly, especially if they have themselves been directly involved in corrupt transactions. However, the questions asked puts the interviewee in the role of victim and not promoter of corruption. It would be very different to worry about receiving an honest answer when asking about how much and who a firm had to bribe to be granted a lucrative government contract than it is when asking the same firm to tell the interviewer if the firm was compelled to provide a bribe to get a service or license.

The index can also be criticized for its narrow focus on the bribery of officials delivering infrastructure services and licenses. The index does not measure corruption that may take place in largescale business transactions such as favorable deals on government contracts, the granting of government licenses or rights of use of public goods to insiders, rigged participation in public tenders, or lax enforcement of regulations or terms of government contracts because of a payoff. The index also does not measure corruption in situations where an economic transaction is not concerned, such as in the legal system where a court is asked to look the other way or to rule in favor of a party that paid a bribe. Lastly, the index also does not deal with political corruption; that is corruption associated with manipulated and non-transparent elections, the buying of legislative votes, or political nepotism. These kinds of corruption may involve both greater amounts of money and represent larger economic distortions than the common, petty corruption that our index measures.

# **3.2 GIFT estimates**

We estimate the GIFT for all six transactions taken together (Table 2). We also grouped transactions into two separate subsets, _infrastructure_ (electricity, water, telephone --- Table 3) and _licensing_ (import licenses, construction permits, operating licenses --- Table 4), and estimate the GIFT for each subset. Finally, we estimate the GIFT for each transaction separately in each country, but we warn that in many instances our confidence intervals become large (Tables A.1 to A.6). In all cases, we estimated the standard error of our estimate and its 95-percent confidence interval. In countries with few transactions, the confidence intervals can be substantial, making it hard to definitively rank several countries.

We first note that, pooling data from all 33 countries, a total of 9519 requests for licenses or infrastructure services were registered in the _Enterprise Surveys_ . Firms reported being asked for bribes in 933 instances. Thus, on average, firms in these countries are the target of bribery one out of 10 times they perform any of the six transactions included in the survey. Nevertheless, the difference in the incidence of
uniforms, winning the lottery is highly correlated with receipt of a uniform and thus serves as an effective instrumental variable (IV). Since schools in Kenya already use uniforms, we are not measuring the effect of a student uniform policy per se, but rather the impact of paying for the uniforms. Our results are thus less relevant to the debate over school uniforms in developed country contexts, which focuses on the impact of uniform policies on reducing aggressive behaviors.<sup>2</sup> The rest of the paper is organized as follows. Following a description of the context of the experiment in Section 2, Section 3 describes the data and the empirical strategy. Section 4 presents our results, and finally Section 5 discusses some policy implications of our results and concludes.

# **2. Context**

# <u>2.1 Costs of schooling</u>

In a range of countries, parents face many costs of education such as school fees and provision of uniforms. In Kenya, students were required to pay school fees to attend primary school through 2002. In January 2003, a new government policy eliminated fees and provided free textbooks and notebooks to schools, which led to dramatic increases in school participation. However, uniforms remained a major cost of schooling. Kenyan schools each have their own uniform.

Historically, students who did not pay their school fees or those who did not wear uniforms could be sent away from school. Whether they were sent away and for how long varied greatly at the discretion of the school’s headmaster. Students would often not pay full fees at the beginning of the year and would fulfill these obligations as the year progressed. Over the last 15 years, several prominent officials in the

> 2 Brunsma & Rockquemore (1998) examine cross sectional tenth-grade students from the United States, controlling for observable characteristics, and find no impact on behavioral problems or attendance. In other work, Brunsma (2006) uses similar methods to look at school climate, academic achievement, and attendance using national U.S. data for kindergarten, first grade, and eighth grade, and finds no significant impacts. Other work finds positive relationships between uniforms and school climate in two South Carolina middle schools (Murray 1997) and on academic achievement (but without controlling for confounding factors) (Bodine 2003). Recent work in the U.S. finds some evidence for positive impacts on student attendance and teacher retention (Gentile & Imberman 2012).

5
<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1/1/2020 2/1/2020 3/1/2020 4/1/2020 5/1/2020 6/1/2020 7/1/2020<br><!-- End of picture text -->
Note, however, that this sort of short-term jump (or fall) in inequality is not unique to ATG. WDI also sees a short term-jump caused by a single year outlier in 2001.

Of even greater concern may be the inability for these databases to tell a consistent story even about medium-term trends in Jamaica. The WDI data tells a story of declining inequality in the early 1990s followed by increasing inequality for the remainder of the decade. Data from WIID suggest almost exactly the opposite.

Finally, in Figure 5 we compare SWIID’s Gini coefficients for imputed market and net incomes with those obtained from CEQ, for a set of fourteen developing countries. Specifically, the figure shows the difference between the Gini for disposable (or net) incomes and the Gini for market incomes (for the same survey and country), as estimated both by CEQ and SWIID. Recall that CEQ calculates the impact of fiscal policy on inequality through a detailed fiscal incidence analysis, validated by local experts and a series of robustness checks.<sup>13</sup> One worrisome aspect of the comparison is that the discrepancies between CEQ and SWIID are not systematic: i.e., sometimes SWIID’s estimate of the redistributive impact is higher and sometimes lower than CEQ’s. In some cases, the discrepancies are quite large (e.g. Guatemala, and Indonesia). In one extreme case, Armenia, the redistributive effect is negative in SWIID (i.e., net income inequality is higher than market income inequality) while positive (as expected) in CEQ. Such significant discrepancies suggest that caution may be needed when interpreting the results of cross-country regression analysis based on the SWIID imputation-based data, such as Acemoglu et al. (2013) and Ostry et al. (2014).

# **4. Concluding Remarks**

Scholarly research and public debate on inequality are on the rise. Naturally, so is the demand for comparable cross-country inequality databases. The eight databases reviewed in the five articles that follow this Introduction contribute to meeting this growing demand. Some are older and wellestablished, others have arisen much more recently; but all are potentially useful public resources, and should in principle be welcomed. Their producers expend considerable time and resources assembling what are essentially public goods, and we owe them a debt of gratitude.

That said, the increasing number of such databases and the fact – documented in this Introduction – that they yield different results, both in levels and in trends, for a significant number of countries, suggest that it may be time to take a closer look at the objectives, inputs and procedures that shape these databases. The five articles that follow do just that for eight of the most frequently used crossnational databases. As readers will notice, each data set is its own special case, and has its own unique strengths and weaknesses. In closing this Introduction, we simply summarize six key messages, which

> 13 For details on the methodology see Lustig and Higgins (2013) available in <u>http://www.commitmentoequity.org.</u>

17
# The Rising Concentration of Foreign Direct Investment<sup>∗</sup>

Alexandros Ragoussis<sup>†</sup> Davide Rigo<sup>‡</sup> International Finance Corporation London School of Economics Gianluca Santoni<sup>§</sup> Centre d’Etudes<sup> ́</sup> Prospectives et d’Informations Internationales _Keywords_ **_—_** Foreign Direct Investment (FDI), Concentration, Development, Uncertainty _JEL_ **_—_** F63; L16; O24

> ∗ **Acknowledgments** : Jonathan Boulle, Matisse Gauthier, and Eduardo Antonio Jimenez Sandoval who provided analytical input at early stages of the project; as well as Chiara Criscuolo, Ralf Martin, Paolo Mauro and participants to seminars at the IFC and OECD for helpful suggestions. The study has been prepared as background to the IFC-World Bank White Paper for Japan’s Group of Seven (G7) Presidency, to inform discussions of finance ministers and central bank governors (FMCBG). The team gratefully acknowledges financial support from the World Bank Comprehensive Japan Trust Fund.

> †International Finance Corporation (IFC), World Bank Group; E-mail aragoussis@ifc.org, Tel. +1 202-258-5886. Corresponding Author.

> ‡London School of Economics and Political Science (LSE), Department of Geography and Environment; E-mail d.rigo@lse.ac.uk

> §Centre d’ ́Etudes Prospectives et d’Informations Internationales (CEPII); E-mail gianluca.santoni@cepii.fr
ways. It can produce its goods at home and directly export them to the other market. Alternatively, it can indirectly export its goods to the other market by assembling them in the low cost country, _China_ . Since _China_ is located in the vicinity of _East,_ the model provides an explanation for the negative correlation between export and import distance for _China_ ’s processing trade: the inputs that _China_ imports from nearby _East_ are processed into final goods and exported to the far-away _West_ ; conversely, the inputs that _China_ imports from the far-away _West_ are processed into final goods and exported to the nearby _East_ . Furthermore, the model allows us to develop a number of testable hypotheses relating trade costs to China’s processing trade patterns. First, China’s processing exports should be negatively affected by both an increase in import distance and an increase in export distance. Second, China’s processing exports to _East_ should be more sensitive to export distance and less sensitive to import distance than its processing exports to _West_ . The intuition underlying the model is the following. For _Eastern_ firms, the key distance factor that determines _China_ ’s attractiveness as a processing location is its vicinity to Eastern input suppliers, i.e. import distance. The larger is import distance, the less attractive _China_ becomes as a location for processing activities and therefore the less processed goods _China_ exports. Conversely, for _Western_ firms, the critical determinant of _China_ ’s attractiveness as a processing location is its proximity to the East Asian market, i.e. export distance. The larger is export distance, the less attractive _China_ becomes as a location for processing activities. Using China’s bilateral processing trade data, we find support for the theoretical predictions of the model. Specifically, our empirical analysis provides evidence that China’s processing exports are negatively affected by both import and export distance. Furthermore, it shows that processing exports to East Asian countries are more sensitive to export distance and less sensitive to import distance than processing exports to non-Asian OECD countries.

In Section 5, we take advantage of the panel structure of the processing trade data to investigate whether rising oil prices have rendered intra-GPN trade more sensitive to distance-related trade costs. We find evidence that China’s processing exports indeed have become more sensitive to both import and export distance in times of rising oil prices. We also find that processing exports are more sensitive to oil price movements than non-processing exports. Specifically, an increase in oil prices tends to reduce the 4
to measure the learning deprivation; and EMIS and Population Census or Household Surveys which are required to measure to schooling deprivation. To combine these two data sources, we use a nested measure, in which all out-of-school children are assumed to be learning deprived. As a consequence, we do not observe the joint distribution of learning and schooling.

In the unidimensional and univariate cases, poverty and deprivation are the same, since the identification of someone as poor depends on a single deprivation, and the choice of standard will automatically divide the population into poor and nonpoor.

But the multivariate context is more complicated, since a nested identification criterion determines learning poverty status. In that case, the first observation is whether a child is in school, and the second is whether he or she is learning by a certain threshold. If the first threshold is not satisfied, a child will be considered in learning poverty, regardless if he or she achieves the learning MPL. But if a child is in school, only if the second identification criterion is also satisfied will the child be considered in learning poverty. So, in the nested approach, first schooling is assessed, and then learning. If either of the identification criteria is not met, a child is considered in learning poverty.

# Aggregation

Aggregation in this exercise will implicitly follow the most straightforward procedure, a headcount rate. That is, it will use the number of children below either deprivation threshold divided by the total number of children in the age category. This ratio, learning poverty (LP), is extremely simple and clear for policy makers to interpret, given the observable nature of school enrollment and the use of an agreed common standard of proficiency defined in the context of the SDGs.

Learning poverty can also be expressed in terms of the equations below.

The aggregation step of this deprivation is the simple ratio of the number of students, _G,_ who satisfy the identification criterion for learning deprivation, divided by the total number of students _S._ The number of students who are deprived in this dimension is . 1 B �=qq≤OO = (eq. 2) qq 8
business training) as complements to the public works program.<sup>26</sup> Fourth, additional research using broader measures of women’s empowerment is also critical to better understand intra-household dynamics, such as the conditions under which improvements in women participants’ individual earnings improve household consumption and their decision-making power. Fifth, there is a need for more rigorous cost-effectiveness analysis. Only two studies in the literature offer detailed cost-effectiveness analysis (Bertrand et al., 2021 and Rosas et al., 2016). Future experiments in this area must pay close attention to collecting cost data to allow for a clear comparison between marginal benefits and marginal costs of the intervention. Lastly, while our review has not focused on general equilibrium effects, the value of public goods provided, or other social externalities from public works programs, are also first-order questions on which more evidence would be needed across settings.

> 26 <mark>In Côte d’Ivoire the basic public works program induced substantial savings, possibly facilitated by the payment of wages into bank accounts.  Some participants were also offered a complementary basic entrepreneurship training to facilitate the transition to self-employment activities after their exit from the program, and other participants were offered a wage employment sensitization training to help them prepare and apply for wage jobs. Post-program impacts on being employed, total earnings, and the number of hours worked were not statistically different across the different treatment arms. Overall, the changes in skills and practices were small in magnitude and did not generate earnings beyond those induced by the basic public works program, suggesting limited value-added of the complementary interventions. In contrast, in Urban DRC, participants that received a combined package of public works programs, incentivized savings, and hard skills training experienced large impacts on savings and investment, indicative of medium-run improvements. Thus, the results suggest that a multifaceted approach may be most effective in supporting livelihoods in Urban DRC, where participation in the public works program alone did not significantly improve economic welfare outcomes in the medium run.</mark>

31
# **50 Years of Urbanization in Africa: The Role of Climate Change**

# J. Vernon Henderson, Adam Storeygard, and Uwe Deichmann

# **1. Introduction**

Sub-Saharan Africa (hereafter Africa) is urbanizing quickly, with cities and towns growing at an annual rate of close to four percent over the last 20 years. Its urban population of 335 million now exceeds the total population of the United States. Nevertheless, almost two-thirds of Africa’s population still lives in rural areas. How urbanization evolves in Africa over the next decades will determine where people and jobs locate and where public services should be delivered. A longstanding debate in the global development literature about the relative importance of push versus pull factors in urbanization has focused recently on Africa. Papers have assessed the contribution of pull factors including structural transformation driven by human capital accumulation and trade shocks (e.g., Fay and Opal 2000; Henderson, Roberts and Storeygard 2013) and of resource rent windfalls spent in cities (Jedwab, 2011; Gollin, Jedwab and Vollrath 2013). Other papers examine push factors including civil wars (Fay and Opal 2000), poor rural infrastructure (Collier, Conway and Venables 2008), and our focus, climate variability and change (Barrios, Bertinelli and Strobl 2006).

This paper analyzes the consequences of climate variability and change for African urbanization and the transformation of the rural sector. Over the last 50 years much of Africa has experienced a decline in moisture availability. Figure 1 shows average moisture for different areas of Africa in the 1950s and 1960s, where moisture is measured by an index combining precipitation and potential evapotranspiration (which is itself a function of temperature). As explained later, a moisture level under 1 indicates that there is less rainfall available than would evaporate given prevailing temperature. This is a useful cut-off for defining arid areas.<sup>1</sup> As Figure 2 shows, much of the strongest (10-50%) decline in moisture over the subsequent forty years occurred in parts of Africa that were initially relatively dry (moisture under 0.65 or between 0.65 and 0.95 in Figure 1), increasing the vulnerability of these already vulnerable areas. This decline in moisture has surely affected agricultural productivity, and we show that declines in moisture push people to the urban sector in Africa.

In particular, we look at local, within-district urbanization for an unbalanced 50-year panel of 369 districts in 29 African countries. Typical intervals between censuses in the panel are 10-15 years. In arid countries, in our baseline specification, a one standard deviation increase in a district’s annualized

> 1 We use “arid” as shorthand for areas that also include dry-subhumid, semi-arid and hyper-arid climates (see UNEP 1992).

2