rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>Year<br>Seleka occupation CAR Synthetic CAR<br>10<br>5<br>0<br>-5<br>-10<br>1990 92 94 96 98 2000 02 04 06 08 10 12 14<br>Seleka Occupation<br>Year<br>Reduced Human Asset Index (0-100)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Gap Reduced Human Asset Index<br><!-- End of picture text --> _Source: The reduced HAI series are derived from Feindouno and Goujon (2019)_ (28) _, from 1990-2014. Note: In upper and middle charts, the blue (lighter in greyscale) line indicates actual light series, and the orange (darker in greyscale) line indicates synthetic light series. The grey bar indicates the beginning of the civil war up to the departure of Séléka rebel coalition from Bangui (from December 2012 to January 2014). In middle and lower charts, we normalize the gap between CAR and its synthetic control for 2012 and subsequent years, for consistency. In the lower chart, the dark continuous line indicates the gap between actual and synthetic GDP per capita series (normalized to 0 in 2012), the dark dash horizontal line indicates a null gap, and the two red vertical lines indicates the period of Séléka occupation in Bangui (2012, excluded, up to 2014, excluded)._ 22
# **Figure 3: Post-Reform Cost Recovery Levels A1 and A2 in 16 Case Studies (23 utilities)**

<!-- Start of picture text --> a) Average Financial Operating Cost Recovery b) Average Financial Operating and Limited<br>Level in 2010-17 (Country-level; A1; %) Capital Cost Recovery Level in 2010-17<br>(Country-level; A2; %)<br>Colombia Uganda<br>Egypt, Arab Rep. Pakistan<br>Peru Colombia<br>Pakistan 8 out of 16<br>Kenya ≥ 100 %<br>Uganda 11 out of 16<br>Ukraine<br>Tajikistan ≥ 100 %<br>Peru<br>Kenya<br>Tajikistan<br>Ukraine<br>Philippines<br>Philippines<br>Vietnam<br>Vietnam<br>Senegal India - Odisha 13 out of 16<br>≥ 80%<br>India - Odisha 14 out of 16 Egypt, Arab Rep.<br>Tanzania ≥ 80% Senegal<br>India - Andhra Pradesh Tanzania<br>India - Rajasthan India - Andhra Pradesh<br>Dominican Republic India - Rajasthan<br>0% 50% 100% 150% 200% 0% 50% 100% 150%<br><!-- End of picture text -->

## **c) Average Financial Operating Cost Recovery Level in 2010-17 (Utility-level; A1; %)**

**d) Average Financial Operating and Limited Capital Cost Recovery Level in 2010-17 (Utility-level; A2; %)**

<!-- Start of picture text --> Colombia Uganda<br>Egypt, Arab Rep. Colombia<br>Peru (Luz del Sur) Kenya<br>Peru (Hidrandina) Peru (Luz del Sur) 8 out of 22<br>Uganda Ukraine ≥ 100 %<br>Tajikistan 14 out of 23 Tajikistan<br>Kenya ≥ 100 % Philippines
To estimate these individual level regressions with just country fixed effects, we use a probit model where the dependent variable is account ownership, a binary variable, and a multinomial logit model to estimate the choice between formal and informal savings and credit. As a robustness check we also estimate ordered logit models for savings and credit with the (sequential) ordering: none, informal only, and formal savings or credit. All regressions account for stratification and clustering in the survey design.

# **5. Regression Results**

# **_5.1 Financial Inclusion and Individual Characteristics_**

Table 3 reports the results of regressing individual characteristics on our three financial inclusion indicators – account ownership, savings and credit – while controlling for country fixed effects. All models use our sample of up to 98 developing countries listed in Appendix 1. With regard to account ownership, the results confirm our earlier univariate analysis and show that women in developing countries are less likely to have an account than men, even after controlling for a host of individual characteristics. Gender affects account ownership also indirectly through gender differences in income, education, and employment status which we documented earlier in Table 1.<sup>18</sup> Next we consider the impact of individual characteristics on savings and credit. Because we estimate a multinomial logit model for these dependent variables, the interpretation of the coefficients differs from the ones reported in the account column where we used a probit model. Here, the coefficients represent log-odds ratios; that is, they show how the log of the odds of a certain outcome (informal or formal) compared to the omitted base category outcome (none)

> 18 See Allen et al. (2013) for a detailed discussion on how individual characteristics influence measures of financial inclusion.

19
**Figure 2: Kernel Regression of Program Receipt on the Sisben Score, by Place of Residence, Pooled across ENCVs 2010–13**

This result is not surprising considering how the Sisben is administered. The Sisben interviews are carried out by the municipalities. While the responses are tallied centrally and the formula for calculating the score is secret, the municipality administrations have a clear interest in raising the number of social program beneficiaries in their constituencies. Camacho and Conover (2011) have analyzed the problem clearly. They show that the municipal authorities time the Sisben interviews during the electoral cycle. More importantly for the present case, when the formula for an older version of Sisben became publicly known, the distribution of the scores started to develop a discontinuity exactly at the (then universal) cutoff level for social programs, bringing more applicants into the program. Even if the formula is secret, local authorities should be able to gauge the probability of falling below the eligibility threshold based on their own experience (the final Sisben score is observable to the household). The interviews can then be manipulated accordingly because how each variable affects the overall score should be clear.

In figure 3, we present the results of a similar exercise on household per capita income in the 2013 ENCV round. To avoid the long tail of high incomes, we have included only households with a per capita income of up to Col$1,000,000 a month (around US$400 at exchange rate values). This leaves 12
|123082|72848|49699|62.57|-1.969|2.775|
|PanelB: Village randomization (Sampl|e: Non-lotteryparticip|ants andControl|lotteryparticipan|ts)||||
|Hightreatment village|40,400.680|-2,065.567|4,475.503|-5,470.520|-4.740|-0.054|0.351|
||(47,646.148)|(7,087.364)|(4,494.362)|—-(4,290.873)|(3.586)|(0.117)|(0.961)|
|Observations|434|434|434|434|434|434|434|
|Lowtreatmentvillagemean|433344|125063|71692|52806|63.80|-1.943|2.655|

Notes: In Panel A, the sample is restricted to lottery participants. In Panel B, the sample is restricted to all control households and non-lottery participants. Standard errors are clustered by village and presented in parentheses. *, **, *** indicate significance at the 1, 5, and 10 percent respectively.

' The food consumption score (FCS) developed by the World Food Programme which measures both dietary diversity and food frequency which weights the frequency and nutritional value of different food groups consumed in the last 7 days. > The food security score (FSS) also constructed according to WFP guidelines that takes values from -1 to -4 depending on the severity of food shortages.

3 The inverse of the WFP coping strategies index (CSI) referred to as the resilience index which weights the frequency and severity of actions taken by households to cope with food shortages.
Trust

|Variables|Trust in national institutions|Trust in friends and family|
|---|---|---|
|Number of Observations|388,602|388,602|
|Number of Countries|134|134|

_Note_ : Models estimated using unweighted individual-level data from the 2014-17 Gallup World Poll. Standard errors are in parentheses and clustered by country and robust to heteroskedasticity. References categories are single or never married, elementary education, employed full-time, poorest quintile, rural area or farm, and the year 2014. Each model includes country and years fixed effects. * Significance at the 0.10 level. ** Significance at the 0.05 level. *** Significance at the 0.01 level.

Estimates show that food insecurity is strongly associated with both vertical and horizontal trust among the sample respondents. Trust is lower as the severity of food insecurity is higher. Food insecurity is associated with a decrease in trust in national institutions by 2.7 percentage points and a decrease in trust in friends and family by 11.7 percentage points. This is a sizable effect considering that the average probabilities of trusting national institutions and friends and family are roughly 53% and 80%, respectively. The effect of food insecurity on trust in friends and family is larger than for any other explanatory variable, including government corruption, poor infrastructure, or if the respondent has been robbed. Friends and family have more direct everyday interactions with people than the national government, or other national institutions such as the judiciary, which may explain the differences in magnitudes. These results imply that alleviating food insecurity would have a spillover effect of increasing both types of trust.

Our control variables are well-behaved. For example, being an immigrant is a significant factor for trust in friends and family but is statistically insignificant for trust in institutions. Living in a city, compared to that of a rural area, is a significant factor to trust in institutions but is statistically insignificant to trust in friends
# **Figure 2: SADC merchandise exports as a share of world exports and developing country exports, nominal and real values**

<!-- Start of picture text --> Share SADC in World exports and Developing country exports<br>10%<br>9%<br>8%<br>Nominal exports,<br>7%<br>share Developing<br>6%<br>Real exports, share<br>5% Developing<br>4% Nominal exports,<br>share World<br>3%<br>Real exports, share<br>2%<br>World<br>1%<br>0%<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> Source: Own calculations using data from World Bank World Development Indicators. See earlier figure for further details. The sample consists of 99 countries, 37 of which are from SSA (15 from SADC), 41 from other low and middle-income countries and 21 from high-income countries. Volume data for Namibia are not available, but the country is included in the figure based on current prices. Only countries for which data are available in each year are included in the sample to avoid changes in trade values arising from changes in the country composition.

However, trade values alone misrepresent the extent to which an economy under or over trades as they do not take into levels of GDP (Rodrik, 1997). Figure 3 therefore plots trends in the ratio of exports to GDP for various regions. There is substantial variation across SADC countries, but the data indicate that SADC is relatively open to trade compared to its international counterparts.<sup>2</sup> For example, in 2008, the world ratio of exports was about 20% while those for SACU and the rest of SADC were above 30%. The data also indicate that most SADC
ject to centralized policies and work in an environment with higher prevalence of incentives for performance. Principals report fewer problems with regard to teacher absenteeism, which squares with an explanation about a Confucian heritage culture.

**Schools** : Vietnam has a much lower level of economic development compared to the Dev7 countries, which is reflected in lower levels of educational attainment of parents and lower level of home possessions, including so called cultural possessions such as artwork and books. Also, comparatively more Vietnamese students go to school in villages and small towns, reflecting the national population distribution. Yet, two things are striking about schools - although schools have fewer computers compared to Dev7 countries, these computers are as likely as Dev7 countries to be connected to the internet. Also, indices regarding quality of school infrastructure and school educational resources are less deficient in Vietnam compared to Dev7, which is indicative of substantive investments in schools in the past few decades.

Overall, across these four domains of information, it seems likely that the PISA data set is able to detect significant cultural differences between Vietnam and Dev7 countries. There appears to be some influence of policy, looking at student achievement assessment and teacher incentives, and higher levels of centralized controls, but the effectiveness of such policies is also likely tied to cultural factors. Unlike the ‘World Values Survey’ the set of PISA instruments is not suited to clearly identify cultural differences, for instance through responses regarding beliefs, attitudes and practices defined specifically to discriminate between cultures. While mean differences provide interesting hints, they are essentially bi-variate correlations. In order to tell us more about the correlations, which ones are more important than others, and whether indeed some unobservable ‘Vietnamese culture’ variable may be a plausible explanation, we need to unravel the mystery further through a study of multi-variate correlations. We do this first by using the Fryer-Levitt approach.

# **3 Regression Approach I: Fryer-Levitt**

We are now ready to investigate the secret a bit further by deepening our analytical approach beyond
(US$100m in exports); a processor of fresh fruits and juices (US$33m in exports); and a number of timber companies (together accounting for up to US$200m in exports).

In contrast, Kenya’s EPZ program, often held up as an example of African success, looks rather anemic. Even including the single factory units, the program, which has been operating for nearly two decades, accounted for just over US$400m in exports in 2008 – just US$11 in exports per capita. The free zone programs in Nigeria and Senegal performed even worse, with Tanzania’s program also producing limited exports, but still in very early stages of development. As was the case with investment, what is notable in the African case is that while nominal exports from the African zones programs was extremely small (on average 10‐15 times smaller than the corresponding absolute and per capita exports in the non‐ African programs), their contribution to national exports was much more in line with international EPZ norms. In some countries – particularly Kenya, Nigeria, and Tanzania – the relative contribution of the EPZ program, however, remains limited.

> 15 According to trade statistics from UN COMTRADE, between 1996 (the first full year following the launch of the free zone program) and 2008, cocoa exports grew by an average of 10.7% annually, 57% of which is explained by quantity growth and the remainder by increasing prices of the commodity.

16
339–57.

- <u>Girdwood, S. and M. Leibbrandt. (2009). “Intergenerational Mobility: Analysis of the NIDS Wave</u> 1 Dataset”, National Income Dynamics Study Discussion Paper 15.

- <u>Güell, M., Pellizzari, M., Pica, G., and J. Rodríguez Mora (2018). “Correlating social mobility and</u> economic outcomes.” _The Economic Journal_ , 128: F353–F403.

- <u>Heckman, J., and S. Mosso (2014). “The Economics of Human Development and Social Mobility.”</u> _Annual Review of Economics_ , 6: 689–733.

- <u>Hertz, T., T. Jayasundera, P. Piraino, S. Selcuk, N. Smith, and A. Verashchagina (2007). “The</u> Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends.” -

- _B.E. Journal of Economic Analysis and Policy_ , 7(2): <u>https://doi.org/10.2202/1935 1682.1775.</u>

- <u>Joffé, G. (2009). “Morocco’s Reform Process: Wider Implications.”</u> _Mediterranean Politics_ , 14(2):151–164.

- <u>Lam, D. (1999). ‘Generating Extreme Inequality: Schooling, Earnings, and Intergenerational</u> Transmission of Human Capital in South Africa and Brazil’, Research Report.

- <u>Lee, H., and J. Lee (2020). “Patterns and determinants of intergenerational educational mobility:</u> Evidence across countries.” _Pacific Economic Review_ , 26: 70–90.

- <u>Maddison Project Database (2020). Available at:</u>

- https://www.rug
fraud data**

We merge these ASYCUDA data with additional data sets from the customs administration’s internal control systems. First, we combine these data with information on modifications made by the customs inspector during the customs clearance process to the inspection channel (upgrade in the channel the import declaration was initially allocated by the RMU, as discussed in Section 2), the import value and the import weight. Second, we obtained information on fraud records from the Legal Department ( _Service des Affaires Juridiques et du Contentieux_ ), namely which 13
2021). The combination of the previous financial crises, protests and Covid has put more than half of Lebanon’s population below the poverty line (World Bank, 2020). Similar to Jordan, Lebanon has restricted the sectors that can legally hire refugees. These are construction, agriculture, and environmental/cleaning services. Unemployment rates are considerably higher for refugees, particularly women (VASYR 2020). Moreover, the Covid crisis has contracted the construction industry, one of the few sectors where refugees could find legal employment (VASYR 2020).

With this overall deteriorating economic landscape, the presence of a large number of refugees in Lebanon contributes to an underlying fragility. In a poll conducted in 2019, Lebanese cited resource constraints related to public services and jobs as contributing to both intra-Lebanese and host-refugee tensions (UNDP and ARK, 2019). While tensions related to employment decreased after restrictions were put in place with regard to refugee employment, it is unclear how recent economic crises may have changed tensions, though there is widespread agreement that risks to social and civil unrest are growing due to this combination of crises (World Bank 2021).

# _Description of the programs:_

To address the risk that economic pressures could increase instability, Mercy Corps implemented the Fostering Resilience by Strengthening Abilities in Lebanon and the Access to Justice and Jobs in Jordan, funded by the Dutch Ministry of Foreign Affairs. This analysis also includes data from the _3Amaly_ program in Lebanon, funded by Global Affairs Canada. All three programs focus on increasing employment through skill building, targeting hosts and refugees who are largely 18-34 years old. These interventions are targeted in locations where a significant influx of refugees could affect labor markets. In Jordan, Mercy Corps 5
, the sophistication and quality of legal institutions varies somewhat across countries and there are cases in the RPED surveys where businesses have taken legal disputes to court.

> 3 The Global Competitiveness Report of the World Economic Forum, 2005 ranks the quality of SSA’s business environment at the bottom of the heap.  Its index of “Quality of National Business Environment,” which measures regulatory obstacles and legal obstacles to doing business, ranks the USA 2, India 32, and four countries in SSA we will be examining in this paper Kenya 63, Zambia 73, Zimbabwe 84, and Tanzania 87.

6
who did not finish primary school but are capable of reading or writing, or those who reported to have been in “Sishu.”9 Health related variables include the CES-D score, a score of cognition using questions from the Telephone Interview of Cognition Status (TICS), used by HRS and other surveys of the elderly, and dummies indicating whether one has poor general health, has any difficulties performing ADLs (activities of daily living) or IADLs (instrumental activities of daily living) and has been diagnosed with a major chronic disease. Following the HRS example, the CHARLS questionnaire asked respondents to assess their general health using a scale of: excellent, very good, good, fair, poor. Here, we look at whether a respondent reports poor health. ADL or IADL disability is defined as having difficulty in any of the ADL (including physical limitations) or IADL activities.

10 The cognition of the respondents comprises three questions about time orientation, one question about serial-7 subtraction from 10011 and one question concerning picture drawing. These are standard cognition questions from TICS (Smith et al. 2010). We differentiate people as those with full marks (11 points), those who scored 8, 9 or 10, and those with a score below 8. We choose 8 as the cutoff point because about one-third of the sample had scores below 8.

> 9 Sishu is a type of traditional private Chinese education that featured the Chinese classics before the 20th century. By 1949, study through sishu was already quite rare.

> 10Respondents are asked about today’s date (year, month and day), week and season.

> 11Respondents are asked to subtract 7 from 100, then another 7 from that and so on until the fifth 7.

11
- **_Experiment_** with different financial structures and approaches involving multiple World Bank institutions to better tailor them to client needs, especially in challenging contexts. IEG’s evaluation shows that projects with domestic investor participation, MDB involvement, and World Bank–IFC–MIGA collaboration have better PCM project outcomes than others, especially in the energy sector (World Bank, 2020). The World Bank’s IDA-IFC-MIGA Private Sector Window (PSW) is one innovative financial mechanism to support investment in IDA-only and Fragile and Conflict-affected Situations (FCS) countries. Since its inception in 2018-2019, its use has grown steadily, providing cumulative funding of $3.8 billion and demonstrating the viability of PCM approaches in the most challenging contexts.<sup>21</sup>

- **_Promote_** successful World Bank (and other MDB) efforts to mobilize private capital widely, while also learning from and being transparent about less successful efforts. Because the PCM efforts of MDBs cannot generate trillions of dollars of private sector investment on their own, demonstration effects could be crucial to enable the private sector to undertake such projects without MDB involvement. Promoting PCM experiences could also help generate greater academic interest in the topic, producing valuable research insights and rigorous tests for additionality. In this regard, the World Bank Private Sector Investment Lab, and its initial sectoral focus on renewable energy and energy infrastructure, is a welcome innovation for promoting interest in and housing knowledge on PCM.<sup>22</sup>

- **_Share_** relevant data and experiences with PCM. This, too, would help encourage more academic research on these topics. Again, the Private Sector Investment Lab would appear to be a natural repository for such data and knowledge. As noted above, because MDB participation in syndicated lending and public-private partnerships is well documented in global datasets, there is emerging empirical research in those areas. In contrast, to our knowledge, systematic information on MDB guarantees is not as readily available.

- **