rafmacalaba/datause-encoder-data huggingface dataset:
19

# **Table 3. Bank Performance and Regulation in 2011, Survey IV**

|`------------------------------------`<br>`------------------------------------`|`--------------`<br>`Bank`<br>`Developmen`<br>`--------------`|`-------------`<br>`Interest`<br>`t Margin`<br>`-------------`|`--------------`<br>`Overhead`<br>`Costs`<br>`--------------`|`---------------------`<br>`Nonperforming`<br> <br>`Loans`<br>`---------------------`|
|---|---|---|---|---|
|<br>`Capital Regulatory Index`|<br>`-0.156**`<br>`(0.0669)`|<br>`0.00445`<br>`(0.00268)`|<br>`0.00459`<br>`(0.00490)`|<br>`0.717`<br>`(0.430)`|
|`Private Monitoring Index`|`0.131**`<br>`(0.0630)`|`-0.0131**`<br>`(0.00531)`|`-0.0123`<br>`(0.00873)`|`0.0971`<br>`(0.390)`|
|`Official Supervisory Power Index`|<br>`-0.0375`<br>`(0.0361)`|`-0.00173`<br>`(0.00289)`|`-0.00180`<br>`(0.00509)`|`0.0512`<br>`(0.272)`|
|`Entry Requirements Index`|`0.111`<br>`(0.198)`|`0.00570`<br>`(0.00772)`|`0.00281`<br>`(0.0163)`|`0.752`<br>`(1.723)`|
|`Restrictions on Activities Index`<br>`------------------------------------`|<br>`-0.0347`<br>`(0.0503)`<br>`--------------`|`0.00124`<br>`(0.00211)`<br>`-------------`|`-0.00272`<br>`(0.00376)`<br>`--------------`|`-0.425`<br>`(0.352)`<br>`-------------------------`|
|<br>`Observations`<br> <br>`------------------------------------`|<br>`55`<br>`--------------`|<br>`60`<br>`-------------`|<br>`59`<br>`--------------`|<br>`50`<br>`-------------------------`|

``` Notes: This table presents four regressions, where each column reports the findings from an OLS regression. The dependent variables, measured in 2011, are: Bank Development equals claims on the private sector by deposit money banks as a share of GDP; Interest Margin equals net interest income divided by total bank assets; Overhead Costs equals total bank overhead costs as a share of total bank assets; and, Nonperforming Loans equals officially reported nonperforming loans as a share of total banks assets. The regulatory indexes are measured in 2011: Capital Regulatory Index measures the extent and stringency of regulatory requirements; Private Monitoring Index measures the degree to which regulatory policies encourage and facilitate private sector monitoring of banks; Official Supervisory Power Index measures the extent to which official supervisory agencies have the authority to take specific actions to prevent and correct problems; Entry Restrictions Index measures the number of specific legal requirements for obtaining a banking license; and Restrictions on Activities Index measures regulatory restriction on the degree to which banks can engage in securities, insurance, and real estate activities plus restrictions on banks owning and controlling nonfinancial firms. Though unreported, the regressions also control for the legal origin of each country by including three dummy variables for Common, French civil, and German civil law countries, as well as a constant term. Heteroskedasticity-consistent standard errors are in parentheses under the estimated coefficients. To designate statistically significant benchmarks, *** indicates significance at the 0.01 level, ** at the 0.05 level, and * at the 0.10 level.
```
about half as large as the contemporaneous effect. Point estimates suggest that this lagged negative effect of increased rainfall on city lights in the base case is again offset in moist countries and those where cities are less likely to have an industrial base, but these effects are statistically weak. Longer lags are not precisely estimated.

In Table 7, we consider two other forms of heterogeneity. First, we consider whether effects differ for cities that are likely to be served by hydro power. Our concern is that lights could be affected directly by electricity availability and pricing, which could be affected by climate directly, independently of climate effects on income. However, because most towns are served by national grids with uniform pricing, we do not actually expect differential effects. In columns 1 and 2, we interact rainfall with an indicator for whether the nearest power plant to a city is hydro or not. This has little effect. In columns 3 and 4, we interact rainfall with an indicator for cities with an estimated initial population over 50,000. We expect these to be especially likely to have an industrial base. The base effect is stronger for these cities, but precision is poor.

# **7. Occupational choice within rural areas**

Migration, whether temporary or permanent, is not the only possible response to adverse climate fluctuations or long term changes in the rural sector. Drier growing conditions will lower the returns to farming and farmers may stop working or switch to non-farm activities. In this section, we find evidence of both, with differential patterns by gender. These possible responses must be seen in the overall context of climate change in rural economies. As noted above, if farm incomes drop, there will be less money in the rural economy in general, so alternative work opportunities may be scarce, muting the expected benefit of switching to a non-farm occupation. We note we looked only at responses in the rural sector. Our data do not provide industry information to analyze shifts between services and manufacturing in the urban sector (which in themselves may be second order effects) nor do they provide relevant migration information.

## **7.1 Data and specifications**

We test whether changes in climate have an impact on employment by sector within rural areas using individual-level data from the Demographic and Health Surveys (DHS, Macro International) for 18 African countries, all but two of which are in our urbanization dataset (Annex 3). DHS use a two-stage sampling design, first randomly selecting enumeration areas in a country and then surveying a cluster of about 30 randomly selected households in each. The surveys oversample female household members since one of the primary purposes is to collect data on fertility and reproductive health. We compile DHS 23
|0000000009|3,854,101|0.39|309%|99%|2%|49|
|---|---|---|---|---|---|---|
|0000000010|3,356,433|0.45|415%|100%|0%|26|
|0000000011|2,919,223|0.45||94%|0%|34|
|0000000012|2,344,306|0.41|51%|91%|0%|25|
|0000000013|2,227,170|0.42|690%|71%|0%|10|
|0000000014|2,197,989|0.37||100%|0%|19|
|0000000015|2,161,430|0.42|102%|92%|0%|19|

_Note_ : Where the growth rate is not mentioned, it means that the importer is new.

**<u>Table 9: Characteristics of brokers identified as risky</u>** — **<u>rice</u>**

|Declarant number|Declared value<br>(US$)|Declared price<br>per kg|Growth rate|Share of rice<br>imports|No. of import<br>operations|
|---|---|---|---|---|---|
|006-AAA|5,817,736|0.42|441%|74%|43|
|007-AAA|599,298|5.30||4%|2|
|008-AAA|110,093|0.42||100%|1|
|009-AAA|66,204|0.61|44%|3%|5|

_Note_ : Where the growth rate is not mentioned, this means that the broker is new.

# **<u>B. Evidence of Undervaluation: The Case of Suits</u>**<sup>**35**</sup>

As previously mentioned, the trade gap related to clothing (HS2 chapters: 61 and 62) is huge (US$83.17 million), and estimated losses are significant (US$15.75 million). Since the ratio of weights is nearly equivalent to unity (1.02), imports, in volume, seem to be truthfully declared. The very low ratio of values<sup>36</sup> suggests a widespread undervaluation.

In practice, it is difficult to accurately assess the value of clothing because of differences in manufacturing quality, _inter alia_ . However, as indicated below, discrepancies can come to light when the scale of undervaluation is very significant.

For suits, the analysis of import declarations<sup>37</sup> suggests a sizeable undervaluation. The average value declared is US$0.94 per suit. A simple calculation shows that customs collects only US$0.41 per imported suit. Table 10 below lists the importers whose declared unit value is particularly low.

> 35 HS6 digit codes: 610310, 620311, 620312 and 620319.

> 36 It should be borne in mind that there may be errors in the number of units declared, which would therefore distort the figures for certain importers.

> 37 Declarations where the weight and quantity imported are identical have not been taken into account.

16
AC _= EApc.qc_ (4) It is possible to show that a first-order approximation of the change in money metric utility resulting from a change in the price of a commodity can be given by<sup>2 6</sup> _AMMU = Y -AC_ _(5)_ This makes intuitive sense: an increase (say) in the price of a good which is both produced and consumed will increase income and also increase the cost of achieving the original level of consumption - the difference between these is therefore an approximation to the welfare change.

Note that we can combine equations (1), (2), (3) and (4) to write equation (6): <!-- Start of picture text --> M=BS° Ao EBS' APk +ZBSw+ m _ __ B SC<br>110 k+ fi p 1<br><!-- End of picture text --> where _BSjo_ indicates the budget (or income) share of output revenue in total income, _BSj'_ is the budget share of input costs, _BSjW_ is the income share of net factor income from factorfJ and _BSjC_ is the budget share of good] in consumption. Thus the first-order percentage change in net income can approximated by the budget shares of income and expenditure on each item times the percentage changes in prices experienced.<sup>2 7</sup>

> **26** See Chen and Ravallion (2002) for an exposition of the theory.

> **27** See Minot and Goletti (2000) Appendix 2 for a full derivation.

36
# **VI. Differences across regions and countries**

Using the Mean Group (MG) estimator (Pesaran and Smith, 1995) we estimate the individual country coefficients of the cyclicality of the real GDP on the cyclicality of primary spending for the entire sample. Equation (2) is adapted for the MG estimator: Cycl. rggpx�� �α� �β�Cycl. rgdp�� �ρixX�� �ε�� (3) To facilitate comparison across regions, we first compute the MG coefficient for the entire sample and then estimate the same model for each region. That is, we compute the procyclicality coefficient for the 112 countries, similar to Table 1, but relaxing the fixed effects constraint.<sup>15</sup> The disadvantage is the limited degrees of freedom when applying this estimator because the methodology consists of estimating OLS regressions at the country level.<sup>16</sup> Table 7 summarizes the results for the entire sample and the different regions. For the overall sample, the effect is positive, indicating the procyclicality effect. Across different regions, the effect is larger in SSA, followed by LAC, and then ECA. The effect is not significant for the overall MENA region.

**Table 7: MG Estimator (2000-2016)**

||(1)<br>Whole<br>sample|(2)<br>SSA|(1)<br>LAC|(2)<br>MENA|(1)<br>ECA|(2)<br>SEAP|
|---|---|---|---|---|---|---|
|Cyclicality of real|||||||
|GDP|0.164***|0.289***|0.229**|-0.042|0.188***|0.167***|
||(0.049)|(0.099)|(0.109)|(0.120)|(0.059)|(0.150)|
|Observations|1,954|653|357|221|440|283|
|# countries|116|39|21|13|26|17|

Notes. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Table D.1 allows comparing the different determinants of procyclicality across regions. SSA and LAC, the more procyclical regions, show the lowest credit to private sector ratios. SSA has the highest perception of corruption. Also, it is the region with the highest degree of ethnic and religious fragmentation. Also, SSA has the most unequal distribution of resources, after MENA. All the conditioning factors point at SSA as the one with the highest procyclicality bias.

> 15 This is equivalent to relaxing the common know-how (common intercept for each country), and of common technology (common coefficient for each country) which are critical when using macro data.

> 16 When balanced, we have 17 observations per country (2000-2016), and six coefficients to be estimated, excluding the intercept. The **xtmg** command by Markus Eberhardt (https://sites.google.com/site/medevecon/code) is considered to regress the MG estimator on STATA software.

22
improving the stability of the financial sector (Cook and McKay, 2015). Similarly, a study from South America found that mobile call data could be used to accurately predict creditworthiness (Björkegren and Grissen, 2020). In Germany, credit-scoring models based off a user’s digital footprint outperformed those based solely on credit scores (Berg et al., 2020)

# **5. The importance of consumer protection for enabling safe and beneficial digital payment adoption**

Throughout this paper, we have highlighted the benefits of digitalizing payments for both senders and recipients. It is necessary to also acknowledge, however, the risks that come with advanced digitalization.

The marketplace for retail financial services can be challenging to navigate, especially for financially inexperienced adults. Although millions of people have been able to take advantage of digital financial services and mobile money, using them safely and in a way that delivers benefits requires both financial and digital skills, which many low-income adults do not have.

Atop these challenges lie several consumer risks that can be exacerbated by the speed and rapid change inherent to digital financial services. Responses to Global Findex 2021 survey questions reveal some of them. For example, the survey asked respondents about their ability to use their account without help. In Sub-Saharan Africa, 31 percent of mobile money account holders could not. Women were more likely to say they could not. That is consistent with a government-toperson (G2P) mobile payments study showing that women were significantly more likely than men to send a representative to cash out government transfer payments (Glynn-Broderick and Koechlein 2021). Women are also more likely to report that their transfer was spent by another family member and to say they had to pay a fee or a tip to an agent to get their money when, officially, there should have been no charge. A nationally representative survey in Côte d’Ivoire similarly found that women are less likely than men to understand the financial products offered through their phones and more likely to lose money to scams (CGAP 2022).

Further Global Findex questions about fees likewise show that in some economies, a high percentage of adults receiving digital wage payments directly into a mobile money account paid unexpected fees to do so. While it is not clear whether these fees were illicit or that the recipient did not understand the fee schedule of their account, they point to ways in which financially 15
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47
# FDI and the Skill Premium: Evidence from Emerging Economies

Marcio Cruz<sup>_†_</sup> Gaurav Nayyar<sup>_‡_</sup> Gerhard Toews<sup>_§_</sup> Pierre-Louis V ́ezina<sup>_¶_</sup> JEL CODES: F21, F23, Q32, Q33 Key Words: FDI, inequality, skill premium

> _∗_ We are grateful to seminar participants at the Oxford Centre for the Analysis of Resource-Rich Economies (OxCarre) brownbag for their comments. This paper was supported by the Knowledge for Change Partnership multi-donor trust fund.

> _†_ World Bank and UFPR, Email: marciocruz@worldbank.org.

> _‡_ World Bank, Email: gnayyar@worldbank.org.

> _§_ New Economic School. Email: gtoews@nes.ru.

> _¶_ Dept of Political Economy and Dept of International Development, King’s College London. Email: pierre-louis.vezina@kcl.ac.uk.
11 -a

# FIGURE 1

<!-- Start of picture text --> COTE d'lVOIRE, SENEGAL & BENIN<br>7-CC^O lendsng #o banks m % M2<br>60<br>40-<br>30<br>10<br>107010711072107310741075lG7810771078107010601981 1821083iS841015M IQ87198m<br>a am + C' GM<br><!-- End of picture text --> <!-- Start of picture text --> THREE SMALL ECONOMIES<br>70-E;CO lending to banks o M2<br>6 _<br>so<br>40-<br>30<br>10<br>10701971107a1910731e741075107610771078107010a10819 a 21G310S4181510W10671091<br>0 w10 + NrR v TOO<br><!-- End of picture text -->
_Plasmodium falciparum_ malaria for 2007. These estimates are then compared with those derived under existing surveillance-based approaches to arrive at the final data used in the malaria mapping (Hay et al., 2009).

<u>(http://www.map.ox.ac.uk/media/maps/pdf/mean/World_mean.pdf, accessed 2011)</u> Malaria maps generally separate the malaria endemicity into three broad categories by _Plasmodium falciparum_ parasite rate (PfPR), a commonly reported index of malaria transmission intensity: PfPR < 5% as _low_ endemicity, PfPR 5%-40% as _medium/intermediate_ endemicity, and PfPR > 40% as _high_ endemicity. In the _low_ endemicity category, there may not be much use of DDT as other measures of vector control can be generally adequate to eliminate malaria. In the _medium/intermediate_ endemicity range one can expect to see the use of Insecticide Treated Nets (ITN)/Long-lasting insecticide treated nets (LLIN) along with targeted IRS to interrupt malaria transmission. In the _high_ endemicity range, however, DDT is likely to have widespread use in IRS.

In this study, global mapping techniques were used to estimate population exposed to DDT. This involves combining spatial information of endemicity areas and administrative areas and then summarizing the estimates of population by the combined areas.<sup>15</sup> First, the malaria endemicity maps were overlaid on global population maps from Landscan 2005<sup>16</sup> (Dobson, 2000) and country-level population exposure in the three endemicity areas were then computed. Figure 1 presents country level estimates of the population exposure to DDT that is expressed by Malaria endemicity categories in 71 countries known for malaria prevalence. Table 3 summarizes the vulnerable population estimates for each category of malaria intensity by presenting the top-10 malaria cases based on total exposed population.

# **[Insert Figure 1]**

> 15 Due to the spatial reference of the data and the number of observations in the combined data, the use of Geographic Information Systems functions from ESRI ArcGIS (v 9.3.1) were used and automated in the python (v 2.5) language.

> 16 This product was made utilizing the LandScan (2005)TM High Resolution global Population Data Set copyrighted by UT-Battelle, LLC, operator of Oak Ridge National Laboratory under Contract No. DEAC05-00OR22725 with the United States Department of Energy.

9
Pakistan, Simler (2010) who examines Uganda,<sup>4</sup> and Ivanic and Martin (2008) who simulate price increases in nine low-income countries.<sup>5</sup> In these studies, the magnitude of the impact of the crisis varies greatly across households and countries, depending on many characteristics including the degree of price transmission, dependence on food imports, whether staple foods are traded internationally, and overall levels of productivity.<sup>6</sup> But the general finding is similar – national poverty rates increase, with urban areas on average suffering larger increases.

From these studies though, it is difficult to disentangle the extent to which the simulated estimates result from actual changes in household behavior and wellbeing, or from the simulation assumptions. For example, Woden _et al._ (2008) assume that increases in food prices directly produce an equivalent reduction in total consumption. That is, the household is assumed to have no behavioral response to the price increase. In this case, the magnitude of the poverty reduction is determined by the shape of the distribution of consumption and the assumed reduction in total consumption.

The need to simulate welfare effects is driven by a lack of comparable pre- and postcrisis data. Jensen and Miller (2008) improve upon the simulation studies by using a panel of 1,300 urban poor households in two Chinese provinces to measure the nutritional effects of the crisis. The data in their analysis though are from April and December, 2006, and largely do not reflect the stark increase in the price of staples during the crisis.<sup>7</sup> They find a limited nutritional impact of the crisis, as measured by calorie intake, which they suggest is due to government policies that stabilized grain prices and household substitution towards cheaper foods.

The analysis in this paper is similar to Jensen and Miller (2008) in that the estimates are based on measured (not simulated) differences in consumption behavior. While we do

> While urban poverty rates increase more than rural poverty rates in most countries, they find evidence of the reverse in three countries: Ghana, Senegal, and Liberia.

> 4 Simler simulates changes in both the prevalence and depth of poverty, and finds increases in both. He argues that this result is due to the large increases in the price of all staple goods and the fact that most poor households, even in rural areas, are net buyers of food.

> 5 In their study, poverty increased in seven of the nine countries studied -- Bolivia, Cambodia, Madagascar, Malawi, Nicaragua, Pakistan, Peru, Vietnam, Zambia.

> 6 The simulations also produce variation based on whether households are net buyers or sellers of food, whether they own land, and whether the household head is a woman (FAO, 2008).

> 7 While their data track the start of the crisis, the province-average increase in the price of rice was just 4 percent between April and December of 2006. In contrast, between June, 2006 and June, 2008, the China CPI indicates that the price of grains more than doubled.

3