rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 6175

# **Abstract**

This paper introduces the Global Financial Development Database, an extensive dataset of financial system characteristics for 205 economies from 1960 to 2010. The database includes measures of (a) size of financial institutions and markets (financial depth), (b) degree to which individuals can and do use financial services (access), (c) efficiency of financial intermediaries and markets in intermediating resources and facilitating financial transactions (efficiency), and (d) stability of financial institutions and markets (stability). The authors document cross-country differences and time series trends.

This paper was prepared as part of work underlying the _Global Financial Development Report,_ cosponsored by the Financial and Private Sector Development Vice Presidency and the Development Economics Vice Presidency.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mcihak@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
internationally, and therefore accept a lower net profitability. Foreign banks have lower net profits in most developed countries, whereas they generally have higher net profits in developing countries.

Table 4 provides average accounting data for banks by different country groupings. Considering the breakdown by income, we see that foreign banks on average obtain lowest interest margins in high-income countries, and they achieve highest margins in lower income countries. At the same time, foreign banks achieve higher (lower) interest margins than domestic banks in low income and lower middle income (high middle income and high income) countries. Overhead expenses, taxes and net profitability of foreign banks in low-income countries similarly tend to be relatively high. Note that banks in low-income countries have higher overhead expenses than banks in high-income countries, despite lower wages in low-income countries. This probably reflects bank overstaffing and difficulties in evaluating loans in low-income countries. Interestingly, for all four income groups foreign banks have higher loan loss provisioning than domestic banks, despite the fact that foreign banks generally provide relatively little risky consumer credit. The reason may be that foreign banks are at an informational disadvantage in identifying good credit risks, or that they have more conservative reserving policies.

Turning to the breakdown by geographical region, we see that foreign banks achieve far better interest margins in Africa than the domestic banks. Generally, for domestic and foreign banks alike the achieved interest margins are highest in Latin America and in the transitional economies. In both cases, high overhead expenses seem to be the driving factor behind the high interest margins. Except in Africa, foreign banks have higher non-interest income to total asset ratios compared to domestic banks since they tend to engage in nonlending activities to a greater extent. Turning to taxes paid, the taxation of banking appears to be very high in the transitional economies, followed by Africa. Foreign banks pay lower taxes only in transitional and industrialized economies. Finally, only in Africa and in Latin America do foreign banks achieve higher net profitability than the domestic banks.

# 4. _Empirical
employment we detect in the household enterprise modules (4%-19%) can be explained by the use of a longer reference period rather than a different way of detecting employment.

Our results highlight some issues of under-detection of employment that are common in LMICs, but employment detection issues may vary across contexts. Our results may be more applicable to situations where women’s economic activities are carried out within the context of the home or the family farm and where a clear separation between domestic roles (in unpaid care work) and economic roles does not exist. Countries with a larger share of the population in non-wage work, living in rural areas, or engaged in agriculture may be particularly at risk for underdetection of employment. For instance, in Sudan, a country with a primarily rural population, disproportionately engaged in informal, non-wage, and agricultural work, the employment rate rises from 12% to 25% for women when moving from standard individual questions to measures that incorporate participation in household enterprises (Krafft et al. 2023).

# **_6.3 Implications for data collection and research_**

As noted in Section 2, efforts to improve the measurement of female participation in economic activity have a long history in LMICs such as Egypt. These efforts were often difficult to sustain and resulted in considerable instability in the measured rates. The 19<sup>th</sup> ICLS made substantial progress in distinguishing between the concept of employment (or work for pay or profit) and other kinds of work. Given the informality of much employment in LMICs (La Porta and Shleifer 2014), administrative data are not sufficient to assess employment rates, requiring reliance on household surveys. However, operationalizing these definitions in Labor Force Surveys has been fairly difficult and has been applied inconsistently across countries and even in the same country over time.

The main difficulty with measuring women’s employment has been with self-employment and unpaid family work that women undertake in the context of household enterprises and farms. Our work suggests that
**Figure 2. Evolution of foreign direct investment and telephone penetration**

<!-- Start of picture text --> 180 8<br>160 7<br>140<br>6<br>120<br>5<br>100<br>4<br>80<br>3<br>60<br>2<br>40<br>20 1<br>0 0<br>FDI % GDP EU2005 new members<br>FDI % GDP Turkey<br>Mobile and fixed phone subscriptions EU2005 new members<br>Mobile and fixed phone subscriptions Turkey<br>Source: authors' elaboration from UNCTAD, ICRG<br>Net FDI inflows to GDP<br>Fixed and mobile suscriptions per 100<br><!-- End of picture text --> A third opportunity for Turkey to increase its appeal to foreign investors would imply strengthening institutions. The cross-country econometric analysis presented in the previous section suggests that, over the past two decades, government stability and law and order have been key determinants of foreign direct investment in Eastern European countries. The International Country Risk Guide _government stability_ index, composed of measures of government unity, legislative strength and popular support, has been in Turkey higher than the regional average. This reflects the political stability the country has enjoyed under the _Adalet ve Kalkınma Partisi_ era, inaugurated in 2001, which may have arguably helped increasing FDI inflows.

On the other hand, however, Turkey has traditionally underperformed in terms of _law and order_ , understood as a product of the strength and impartiality of the legal system and the popular observance of the law. The _law and order_ index for Turkey declined starting in 2011, in a context of a series of controversial judiciary system reforms, which may have undermined the separation of power in the country. It could be argued that these institutional indexes cannot _per se_ reflect the complex and varied nature of institutions across the world (e.g.
Table 4 - Description and summary statistics of socio-economic variables included in the QUAIDS

|**Variable**|**Description**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|---|
|Age of the household head|Years|1,461|48.236|12.468|24|94|
|Female head|Dummy equal to 1 if the household head is<br>a female and 0 otherwise|1,461|0.190|0.392|0|1|
|Primary|Dummy equal to 1 if the household head<br>has a primary education and 0 otherwise|1,461|0.253|0.435|0|1|
|Tertiary|Dummy equal to 1 if the household head<br>has a higher education in college or<br>university and 0 otherwise|1,461|0.093|0.291|0|1|
|Wage|Dummy equal to 1 if the household head<br>earns a salary from a hired job and 0<br>otherwise|1,461|0.458|0.498|0|1|
|Farmer|Dummy equal to 1 if the household head is<br>self-employed in agriculture, aquaculture or<br>forestry and 0 otherwise if HH head|1,461|0.531|0.499|0|1|
|Self employed|Dummy equal to 1 if the household head is<br>self-employed but not in agriculture,<br>aquaculture or forestry and 0 otherwise|1,461|0.243|0.429|0|1|
|Household size|Logarithm of number of HH
For the additional data we obtain directly from household surveys, we replicate the approach followed by PovcalNet: First, we use the local CPI (from PovcalNet and World Development Indicators (WDI)) to deflate consumption to 2011 domestic prices, and (2) apply the 2011 PPP conversion factors for private consumption (also from WDI) to convert into 2011 PPP-adjusted USD. In the Appendix, we perform a robustness check of our main results using the 2005 PPP exchange rates.<sup>14</sup> It is appropriate to use consumption expenditure as the welfare indicator, as it can be measured more easily than income in low-income countries (Deaton and Zaidi, 2002), such as Sub-Saharan Africa. However, it is important to recognize that relative to income surveys, consumption surveys tend to show lower levels and slower increases in inequality. For example, this can be explained by a declining marginal propensity to consume, and difficulties in capturing expenditures on the types of items consumed at the top end of the distribution.<sup>15</sup> Therefore, if incomes at the top increase faster than in the rest of the distribution, consumption surveys would register this increase in inequality only to a limited extent.

# **III. Summary Statistics**

Table 1 shows the main summary statistics of our database of national household surveys. Our database includes around 25 surveys per benchmark year out of a total of 48 African countries. Most surveys lie within one year of their respective benchmark year. Given our interest in estimating the African distribution of consumption expenditure, it is important that the surveys included in our database cover as much of the region as possible. On average, we cover 80% of regional GDP, and at 72% a somewhat lower share of the regional population.<sup>16</sup> Given that richer countries are more likely to have a survey that can be included in our database, it is not surprising that the GDP coverage exceeds the population coverage. This
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for marginal conditional welfare dominance are satisfied. Part of the attraction of the concept of welfare dominance is that it allows at least some excise tax policies to rest on weak and reasonably nonrestrictive normative considerations. Using household budget data at least a partial welfare ordering of alternative excise taxes can be achieved. Besides being virtually value-free, the approach of searching for welfare dominance is to a large extent model free in that in its simpliest form no knowledge of consumer price behavior is required. More sophisticated application of the methodology would, however, have to consider the efficiency as well as the equity implications of changes in excise taxes and to take into account the reactions of consumers to tax reforms.

The welfare framework utilized in this paper is closely related to that found in the literature on optimum taxation. Ahmad and Stern (1984), for example, measure the marginal social cost of raising extra indirect tax revenue from different sources using explicit welfare weights and empirical estimates of consumer price elasticities. However, their objective is to describe the optimum commodity structure that would emerge from equalizing the marginal social cost of revenue from different sources. Our task of identifying welfare-improving tax reforms is less ambitious and requires significantly le!is information about the economy.

The other goal of this paper has been to apply the dominance approach to the C6te d'Ivoire and to extract some policy conclusions about the design of excise taxation in that country. What emerged from that effort was the clear dominance of telephones as a tax base and the near dominance of electricity consumption.. Further reflection on ET (electricity-telephone)

**Massml4I/Welfare Dominance** & **the** Design of Excise Taxation **in the** Cote D'Ivoire/WThirsk:dbI5/18/88
importables more expensive, and non‐tradables less expensive. Energy is an important input in the production of many final goods, and imports can be, in part, substituted by domestic varieties. The model results take into account these general equilibrium effects and, even if production of food tends to be energy intensive, the reduction of prices of other goods is expected to be larger (see last two rows of Table 1).

# _The distributive effects of the oil price shock_

The oil price shock will affect all households, but the final impact will likely be larger for some groups than others. In terms of per capita consumption, the poorest bottom 40 percent loses 6.2 percent of its welfare and the top 60 percent suffers a loss of 7.0 percent (see Table 3). This minor progressive impact, i.e. the fact that richer people experience welfare losses slightly larger than those for poorer people, amounts to a marginal change of the Gini coefficient, a standard indicator of inequality, which decreases from 41.06 to 40.86. Worryingly, poverty rates rise for all the usual poverty lines. For example, when measured at the low 2.5 $ a day line, the oil shock increases poverty by dragging about 100,000 additional people below that line. At higher poverty lines, such as 5 and 10 $ a day, 900,000 and 4.1 million additional people fall behind and poverty rates increase to almost 4 and 22 percent, respectively.

_Table 3: Welfare, poverty and distributional impact of the oil shock in the Russian Federation_

||Without Oil<br>Shock|With Oil<br>Shock|Change|
|---|---|---|---|
|Per capita Consumption|USD in consta|nt 2011 PPP|%|
|All Population|8,971|8,354|‐6.88|
|Bottom 40|3,619|3,395
(and related, age) especially important. Table 5 already suggests this since the percentage of large firms with complaints is less than that for the smallest firms, on average some eight-percentage points, but sometimes as much as 10 to 20 percentage points. Across a large sample of countries and controlling for other factors, it has also been found that size strongest affects access to credit (Beck, Demirgüç-Kunt, and Maksimovic, 2005). (This analysis used the World Bank Economic Survey data; the ICA-data are currently being analyzed to further determine what drives (lack of) firm access across countries.) For a specific country, Brazil, size was found to be more important than performance and other variables, suggesting quantitative limitations to credit access (Francisco and Kumar, 2004). The impact of size on credit was found to be greater for longer-term loans in case of Brazil (as well as for many other countries). As an aside, public financial institutions in Brazil are actually found more likely to lend to large firms, thus negating the idea that public banks necessary fill market gaps.

Size may, however, reflect not only profitability, financial and legal collateral but also political collateral. This is particularly so in developing countries where lending is often done on the basis of relationship and connections, often political. Indeed, in countries with well-developed financial systems size can be overcome. Many banks in developed countries lend to many small, single proprietary firms, sometimes without requiring collateral, financial statements, or other requirements. Thanks due to the spread of technological advances such as automated credit scoring, banks in developing countries are also becoming active in these forms of financing. And in the most developed financial markets, universal access to basic financial services, including some forms of credit, is essentially assured for households, as the data above showed.

Access can thus be based on opportunities and needs. Since in many countries it does not seem so, the question to ask is what constrains access in general and in some
dynamics of the ...scal adjustment process, as well as address the endogeneity of MTEF to ...scal performance. We use a Di¤erence Generalized Method of Moments (D-GMM) approach to estimate dynamic panel data models of ...scal performance. These models are designed for "small T large N " panels and, when correctly applied, generate valid internal instruments that hold the promise of overcoming identi...cation issues due to the absence of strong external instruments, a typical hurdle with country-level data.<sup>4</sup> When an MTEF is implemented well we should observe (i) spending that is limited by resource availability (...scal discipline), (ii) budget allocations that re‡ect spending pri-

> 3One way to circumvent econometric identi...cation issues has been to study similar institutions operating at sub-national levels of government. See Besley and Case (2003) for a review of the literature that has employed U.S. state-level data.

> 4The D-GMM approach was ...rst proposed by Holtz-Eakin, Newey, and Rosen (1988) and later developed by Arellano and Bond (1991). Recent re...nements include Windmeijer (2005) and Roodman (2009).

3
WPS3747 the difference between government revenue and spending; (ii) the external trade balance (in goods and non-factor services), which implicitly equates the supply and demand for foreign exchange; and (iii) savings-investment balance. We assume that savings are investment driven and adjust through flexible saving rates for firms, that foreign savings are in fixed supply with the demand for foreign exchange equated through an endogenous exchange rate, and that government income is also fixed with lost tariff revenue replaced through a variable tax rate on households. This closure is expected to be relatively “neutral” in terms of the distributive impact of the shocks implemented. An alternative tax replacement scheme will be also examined.

The original CGE model was constructed to reflect Indonesia’s economic pre-crisis situation and is based on a 1995 Social Accounting Matrix (SAM). In order to capture the postcrisis structural features of the Indonesian economy, the 1995 Indonesia SAM was updated using cross-entropy methods (see Robinson et al., 2001). The updating procedure imposes the following pieces of information from 2002: value-added by activity; the structure of imports and exports by commodity; and the data contained in a macro SAM. Table 2 summarizes the aggregate values of the resulting 2002 SAM.<sup>2</sup>

# **2.2  Specification of the Indonesia microsimulation model**

The microsimulation model is based on household and individual level data from the SKTIR survey for the year 1996 and simulates income generation mechanisms for 9,800 households corresponding to 42,400 individuals.<sup>3</sup> Four occupational choices are distinguished at the individual level: (i) inactivity, (ii) wage work, (iii) self employment, and (iv) multi-activity

> 2 The fully disaggregated SAM used has 39 activity accounts and 22 commodity accounts. Full detail is presented in Chapter Annex A.

> 3 The Special Survey on Saving and Household Investment (SKTIR) was integrated as a part of a module (submodule) of the SUSENAS
|
|Namibia|1|n.a.|8.4|—|1|n.a.|7.7|—|
|Niger|1|n.a.|12.2|—|1|n.a.|8.8|—|
|Nigeria|4|15 kVA|5|30|5|15<br>kVA|5|30|
|Rwanda|1|n.a.|17.2|—|1|n.a.|17.2|—|
|Senegal|2|TOU|14.4|44|2|TOU|11.8|45|
|South Africa|3/1|25 kVA|4|138|2|TOU|2.6|–31|
|Tanzania|1|n.a.|5.3|—|1|n.a.|4.9|—|
|Uganda|1|n.a.|21.8|—|1|n.a.|16.7|—|
|Zambia|1|n.a.|3.7|—|4|1200|2.2|–45|
|Zimbabwe|1|n.a.||—|1|n.a.||—|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Notes_ : For details see Annexes 3 and 4. n.a. Not applicable; — Not available; kVa = kilovolt-ampere.

13
5.5%|
|Term/conditon|Micro KUR|Small KUR|
|Loan amount<br>|Up to 50 million IDR<br> (US$3,500)|50 – 500 million IDR<br>(US$3,500 to US$35,000)|
|Loan duraton<br>|Max. 3 years (working capital)<br>Max. 5years(investment)<br>|Max. 4 years (working capital)<br>Max. 5years(investment)|
|Lifetme limit|No limit for the producton sector<sup>14</sup><br>200 million IDR (US$ 14,000) for the<br>non-producton sector|500 million IDR (US$ 35,000)<br>for both producton and non-<br>producton sectors|
|Collateral requirements|Principal collateral is the business or<br>object fnanced by KUR<br>Additonal collateral is not required<br>and without contract or agreement<sup>15</sup>|Principal collateral is the<br>business or object fnanced by<br>KUR<br>Additonal collateral in<br>accordance with the assessment<br>of the distributor|

> 9  Tax incentives play an important role in Indonesia’s policy mix to support businesses; however, the value of these incentives is not included in this calculation due to data constraints.

> 10  Data from the Indonesia Coordinating Ministry of Economic Affairs (CMEA), <u>available here.</u>

> 11  Data from Indonesia Financial Services Authority (OJK).

> 12  The five types of KUR loans include: (i) micro KUR loans; (ii) small KUR loans; (iii) KUR loans for migrant workers (TKI); (iv) ultra-micro KUR loans; (v) special KUR. Ultra-micro KUR
_Risk Preferences_ are assessed using a simple, non-incentivized risk game, detailed in the Annex 9.7. Similarly, _Altruism_ is measured by a hypothetical game in which the individual is given UGX 50,000<sup>12</sup> and has to decide how much of it to share with an anonymous individual.

To assess _cognitive ability_ , the survey uses a digit span memory test, in which an increasingly longer string of numbers is read to the respondent, who is in turn asked to repeat it. We use the number of digits the respondent can correctly recall as a proxy for cognitive ability.<sup>13</sup> . We complement this measure, following Dal Bo, Finan and Rossi (2013), with an indicator assessing whether the respondent chooses a dominated option in the risk game described above.

Tables 1 and 2 present descriptive statistics on the survey sample, highlighting differences in means between politicians (including non-elected contenders) and bureaucrats. Table 1 presents basic socio-demographics characteristics and confirms the intuition that politicians are very different from bureaucrats in basic observable characteristics. Politicians are much more likely to be females, to have been born in the same District where they currently work<sup>14</sup> and also to come from less wealthy households (they have larger families and hold less assets). The most striking distinction between bureaucrats and politicians is in educational attainment: whereas half of politicians have not completed college, 96 percent of bureaucrats did, including half with postgraduate degrees. These numbers are broadly consistent with Raffler’s (2017) data on subcounty politicians and officials in Uganda, and paint broadly the same scenario of policy environment: local politicians oversee project execution led by highly educated and centrally appointed bureaucrats.

Table 2 presents descriptive summaries of selection traits. Panel A reports, for each of the components on the Integrity module, the share of individuals who agree or strongly agree with each statement. For all components a larger share of politicians than bureaucrats agree with statements