rafmacalaba/datause-encoder-data huggingface dataset:
# **B.3 Global Consumption Database**

A limitation of our core sample of 31 countries is that it tends to be geographically clustered and in does not contain countries in Asia. To obtain an idea on particular whether our results might be relevant for all low and middle income countries, we compare food expenditure with that of the Global Consumption Database (GCD). The GCD is the most data source on consumer in comprehensive spending patterns developing countries to date, by assembling all available representative household expenditure surveys across countries. In particular, it includes most countries in Asia. The dataset is curated by the World Bank: aggregatef consumption statistics and further details on sources and methodology are available at http://datatopics.worldbank.org/ consumption/.

We obtained access to the Global Consumption Database microdata, in order to comfood in our core of 31 countries to the 79 low and middle pare expenditure sample income countries available in the GCD. From our sample of 31 countries, 21 countries with the GCD and have the exact same as an source. overlap usually survey original With this enhanced dataset, which represents 51% of the world population,<sup>49</sup> we measure food consumption as a share of total consumption and the slope of the food Engel curve. First we note that for the 21 countries we curve overlapping find Engel slopes within 5% of the GDC estimate. Second we compare food expenditure patterns in our core sample 31 countries to the 58 countries which only appears in the GCD. We find remarkably similar food expenditure shares and food Engel curve slopes, as a function of development, which supports that our core sample informality measures could be informative to the entire population located in developing countries, with the caveat that informality patterns might still differ geographically.

> 49We exclude rich countries by design, but a few populous countries such as China, Egypt and Iran are not part of the GCD. This explains the lion’s share of the missing population in the GCD sample.

79
health and environmental conditions, or participation in the social life of the community). This calls for a multidimensional analysis of well-being that merges a quantitative approach with a qualitative one (Kanbur 2003). The UNDP’s Human Development Index is an attempt to combine monetary and non monetary dimensions (education and health), but the debate on the suitable ways to combine the income/consumption poverty approach with the deprivation and social exclusion approach is still ongoing (see Kakwani and Silber 2008, for the methodological debate; and Boarini and d’Ercole 2006, for a survey of the practices in OECD countries). Finally, there are cases in which the estimated measures of welfare change are not consistent with the public opinion discontent about the reforms (Di Tella _et al_ 2008; Lora, 2008; Carrera _et al_ 2004). And there is a long lasting debate on how to exploit both “objective” and “subjective” information (see Eroglu 2006, and the references therein).

Within this framework, the affordability indices can be seen as welfare indices which combine the standard poverty approach with a deprivation assessment. They can be used to provide information on the direction and magnitude of changes in consumer welfare due to changes in tariffs, increased enforcement of utility contracts, and legalization of illegal connections. An _ex ante_ affordability analysis may help to identify the target population of the mitigation actions which may accompany price changes (as in kind or cash benefits, or the introduction of social tariffs) as well as groups who will oppose the innovation. An _ex post_ analysis in the same direction may help to better understand the causes of the discontent.

In what follows, we first present a survey of the measures usually adopted to evaluate affordability in public utility sectors - mainly relying on Miniaci _et al_ . (2008a). In so doing, we explain why the standard headcount and poverty gap indices - based on actual and/or potential household consumption - are not satisfactory in describing the affordability issue. Moreover, we present new measures based on residual income and we discuss how to take into account investments in infrastructure which, in turn, may be needed to connect more customers and improve service quality.

10
Kee, Hiau Looi, and Alessandro Nicita. 2016. “Short‐Term Impact of Brexit on United Kingdom Export of Goods.” Work in Progress. The World Bank.

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<u>http://www.ecb.europa.eu/press/key/date/1999/html/sp990630.en.html.</u>

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- WTO, World Trade Organization. 2011. “World Trade Report 2011‐ The WTO and Preferential Trade Agreements: From Co‐Existence to Coherence.” Working Paper id:4335.

22
# **Figure 1: Domestic and External Debt.**

<!-- Start of picture text --> Domestic and External Debt ( as % of GDP ) Domestic Debt (as % of GDP) External Debt (as % of GDP)<br>HIPC and Non HIPC HIPC and Non HIPC<br>1995 2000 2005 2010<br>Year 1995 2000 year 2005 2010 1995 2000 year 2005 2010<br>Domestic Debt External Debt HIPC NON HIPC HIPC NON HIPC<br>Domestic Debt (as % of Total Public Debt) External Debt (as % of Total Public Debt) Domestic Debt (as % of GDP)<br>HIPC and Non HIPC HIPC and Non HIPC<br>Eritrea<br>Eritrea<br>Eritrea Eritrea Eritrea<br>Eritrea Eritrea Eritrea Eritrea Eritrea Eritrea Eritrea Eritrea<br>Eritrea<br>Eritrea<br>1995 2000 2005 2010 1995 2000 2005 2010<br>year year<br>HIPC NON HIPC HIPC NON HIPC Year<br>1996199719981999200020012002200320042005200620072008200920102011<br>80<br>20 100<br>60<br>80<br>40 15<br>60<br>Debt (as % of GDP)<br>20<br>Domestic debt (as % of GDP) External debt (as % of GDP) 40<br>10<br>0<br>20<br>50 90 150<br>40 80<br>100<br>30 70<br>50<br>20 60<br>Domestic Debt (as % of GDP)<br>Domestic Debt (as % of Total Public Debt) External Debt (as % of Total Public Debt)<br>10 50 0<br><!-- End of picture text --> _Source: our elaboration on the LIC domestic public debt dataset._

# **4.2. Financial cost and burden**

A main concern about domestic debt relates to its financial cost and burden relative to external debt. For the _Debt Structure Sample_ in 1996-2011, Figure 2 displays implicit interest rates as proxies of borrowing _cost_ . The nominal implicit interest rate is calculated as the interest payments in the current year divided by the average debt stock in the current and preceding year.<sup>15</sup> For the domestic debt, we calculate the real implicit interest rate by subtracting the GDP deflator inflation from the nominal rate. For the external debt, we add the average depreciation rate of the local currency against the US dollar and SDR in order to capture losses (or gains) resulting from exchange rate fluctuations in the presence of foreign currency-denominated external debt. On average, the cost of external borrowing never exceeded 4 percent per annum and has been always much cheaper than the nominal cost of domestic borrowing, even including the currency depreciation losses. The domestic nominal implicit interest rate, however, declined significantly from 18 percent per annum in 1996 to 8 percent in 2011. On average, the real cost of domestic borrowing is also lower than in the past and quite often the real implicit interest rates are negative

> 15 Our choice of using the average debt stock as denominator is justified by the large share of short-term liabilities in the domestic debt that accrue interests the same year in which they are issued. Other studies use the current debt stock as denominator (Christensen, 2005) or the previous debt stock (Arnone and Presbitero, 2010).

11
**Table 2: Panel data estimations for the relationship between ninis and homicide rates, including interactions**

|||**Homicide r**|**ate INEGI(1995-20**|**13)**||
|---|---|---|---|---|---|
|Independent variables|(1)|(2)|(3)|(4)|(5)|
|Share of ninis||||||
|All ninis|0.050|0.128|0.110|0.0781|0.0999|
||(0.27)|(0.27)|(0.26)|(0.25)|(0.26)|
|Male ninis|-0.66|-0.666|-0.765|-0.587|-0.632|
||(0.48)|(0.48)|(0.47)|(0.45)|(0.46)|
|Males ninis aged 19 to 24|<br>0.63 *|0.271|-0.034|0.0869|0.0859|
||(0.34)|(0.35)|(0.35)|(0.34)|(0.337)|
|% Male ninis aged 19 to 24,interacted with:||||||
|Dummy for period 2007-2013||1.209 ***|1.206 ***|0.918 ***|1.007 ***|
|||(0.34)|(0.33)|(0.32)|(0.33)|
|Dummy for border state|||2.296 ***|1.124 **|1.366 ***|
||||(0.41)|(0.45)|(0.51)|
|(Period)*(Border)||||1.328 ***|0.526|
|||||(0.23)|(0.85)|
|Controls||||||
|Dummy for period 2007-2013|7.761 **|-4.317|-4.830|-4.880|-5.706|
||(3.80)|(5.037)|(4.91)|(4.77)|(4.84)|
|(Period)*(Border)|||||8.396|
||||||(8.52)|
|Share of the population aged 15 to 24|0.079|0.079|-0.173|0.075|0.0656|
||(0.57)|(0.56)|(0.55)|(0.54)|(0.54)|
|Log of hourly labor income|-10.13 ***|-6.831 *|-1.833|1.257|1.740|
||(3.62)|(3.69)|(3.71)|(3.64)|(3.68)|
|Unemployment rate, aged 35 to 44|<br>58.25|42.23|31.80|24.77|25.84|
||(40.47)|(40.28)|(39.27)|(38.18)|(38.19)|
|Gini (per capita labor income)|12.22|11.47|-2.274|-12.22|-12.45|
||(16.02)|(15.85)|(15.63)|(15.28)|(15.28)|
|Share of informal workers|<br>-13.42|-11.38|-19.04|-17.59|-17.72|
||(18.14)|(17.95)|(17.54)|(17.04)|(17.04)|
|Share of employment in manufacture|<br>-33.32|-35.04|-12.76|-4.812|-1.974|
||(22.83)|(22.59)|(22.36)|(21.77)|(21.96)|
|High school dropout rate|<br>0.412|0.45 *|0.568 **|0.639 **|0.63 **|
||(0.270)|(0.27)|(0.261)|(0.25)|(0.25)|
|Constant|25.59|20.57|19.00|10.67|9.046|
|Notes: Standard errors in parentheses, *** p<0.01, **|(18.25)<br><0.05, * p<0.1; all|(18.11)<br>estimations include|(17.63)<br>State level fixed effe|(17.19)<br>cts and yearly dum|(17.27)<br>mies. Informal|

<u>Notes: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1; all estimations include State level fixed effects and yearly dummies. Informal</u> workers are defined as those who work for a firm that does not have accounting records and does not contribute to social security.

22
# **1 Introduction**

Formal credit institutions are reluctant to lend to households without credit history and verifiable steady employment. These two characteristics are inherent to people employed in the informal sector, which includes any economic activity that is not taxed or monitored by the government. This is a concern in developing countries, where the fraction of people working in an informal occupation is considerably large. In 2002, for example, 55% of Mexicans belonged to the informal sector. This does not imply that usage of credit among these people is low. On the contrary, households that cannot obtain credit from banks are very active borrowers with alternative suppliers. They rely heavily on loans from relatives and friends, and on more expensive credit suppliers such as pawn shops and moneylenders. According to the Mexican Family Life Survey 2002, of all informal households in 2002, 3.7% of them used pawn shops or moneylenders’ credit and 13.8% obtained loans from friends or relatives.

The goal of this paper is to analyze the effects of expanding access to credit on households’ decisions and welfare. To do this, I examine the opening of Banco Azteca, the first bank in Mexico that targeted households employed in the informal sector. Using panel data at the household level, I first analyze households’ saving and consumption patterns before and after the entrance of this bank. I then develop and estimate a dynamic model of household choices in which Banco Azteca selects the municipalities for branch openings. This is, to my knowledge, the first structural attempt at jointly modeling household choices and the location decisions of banks to measure the impact of access to credit.

There are several advantages of using this structural approach. First, since the entry of Azteca in a given municipality is unlikely to be exogenous, estimating its impact on households’ welfare is difficult. The model deals with this issue by endogenizing the location decisions of the bank. Second, the estimated model allows to quantify the impact of the expansion of credit caused by the entrance of Banco Azteca. Third, the model can be used to evaluate alternative policies aimed at extending access to credit. In this paper, I evaluate the proposed regulation of capping interest rates of formal credit institutions.

In October 2002, Banco Azteca opened more than 800 branches inside all Elektra stores that existed in the country. Some months later, various Azteca branches began relocating independently of the Elektra stores. At the time, these branches accounted for 15% of the supply of bank branches in Mexico. This new bank eliminated the proof of income requirement and hence allowed all Mexicans 2
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE disproportionate share of degradation. The incentives for coastal countries to maintain hinterland road corridors do not seem to be very strong, since their economies are typically concentrated along the coast, making the up-country segments regional public goods (table 4).

**Table 4. Road condition along transit corridors in ECOWAS where Benin participates**

||Per|centage in con|dition|Prnt|Perc|entage in traffic|band|
|---|---|---|---|---|---|---|---|
|||||eceage||||
|**Corridors**|Good|Fair|Poor|paved|<300|300–1000|>1000|
|**Gateways to the sea**||||||||
|**Cotonou–Niamey**|**49.5**|**7.9**|**42.6**|**98.5**|**1.5**|**26.4**|**70.0**|
|Benin|38.1|2.2|59.7|97.8|2.2|15.8|81.5|
|Niger|77.7|22.3|0.0|100.0|0.0|52.8|41.4|
|**Intra-regional corridors**||||||||
|**Abidjan–Lagos**|**50.7**|**28.0**|**20.6**|**98.8**|**0.0**|**0.4**|**43.6**|
|Benin|26.9|0.0|68.0|92.4|0.0|2.5|90.3|
|Côte d'Ivoire|0.0|100.0|0.0|100.0|0.0|0.0|100.0|
|Ghana|75.0|14.5|10.5|100.0|-|-|-|
|Nigeria|50.0|50.0|0.0|100.0|0.0|0.0|100.0|
|Togo|0.0|0.0|100.0|100.0|0.0|0.0|100.0|
|_Source_: AICD 2010.||||||||

Aggravating the problem of relatively poor road network quality in Benin is the large share of its primary network that is underengineered. About one-third of Benin’s network technically justifies paving or new construction, as more than 300 vehicles per day use the roads, particularly in highly populated coastal areas. Network underengineering is one of the reasons transport is considered a major business constraint in Benin (table 3).

Road financing is a major challenge in Benin due to its relatively high dependence on general taxation. At an estimated $0.05 cents per liter<sup>2</sup> , Benin’s fuel levy is among the lowest in Sub-Saharan Africa (figure 7), sufficient to finance total routine maintenance needs but not periodic maintenance, which is only 60 percent covered. The lack of appropriate periodic maintenance has further aggravated the problem of poor quality of roads. With less than one-fourth of its revenues coming from road user charges, the Road Fund in Benin is one of the few in West Africa where road user charges amount to less than 50 percent of total revenues. Public funding will likely continue to be needed despite implementation of road user charges, in particular for secondary and tertiary networks. In fact, 97 percent of the Road Fund’s resources are dedicated to the primary network. The goal is to increase road user charges and gradually phase out direct treasury transfers or external funding.

> 2 The fuel levy in Benin is based on the price and type of fuel. The levy is 10 percent of the diesel price and 7.2 percent of the gasoline price.

12
Farmer-to-Farmer Extension Make?” _International Journal of Agricultural Sustainability_ 16 (3): 310–25. https://doi.org/10.1080/14735903.2018.1472411.

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40
# **Table 2a. The Effect of Informality on Poverty, controlling for GDP per capita**

|**_Dependent variable: Poverty Headc_**<br>|**_ount inde_**<br><br>O|**_x, latest ye_**<br><br>LS|**_ar_**<br>||IV|||
|---|---|---|---|---|---|---|---|
|[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|
|Initial GDP per capita<br>-0.1331***|-0.1028***|-0.0995***|-0.0656**|-0.1129***|-0.0800***|-0.0796|-0.0346|
|(2000 US$, 1985, in logs)<br>6.18|4.07|3.02|2.33|3.48|3.10|1.26|0.94|
|Schneider Shadow Economy<br>0.0067**||||0.0104*||||
|(% of GDP)<br>2.34||||1.71||||
|Heritage Foundation Informal Market|0.0841**||||0.1229*|||
|(ranging 1-5: higher, more informality)|2.38||||1.89|||
|Self Employment||0.0004||||-0.0017||
|(% of total employment)||0.22||||0.24||
|Non-contributor to Pension Scheme|||0.0031**||||0.0051**|
|(% of labor force)|||2.34||||2.08|
|Constant<br>0.8607***|0.6053**|0.8476***|0.4127|0.5717|0.3001|0.7636|0.0436|
|4.54|2.30|3.48|1.55|1.46|0.83|1.09|0.11|
|No. of observations<br>51|51|34|46|41|42|30|38|
|R-squared<br>0.51|0.42|0.34|0.35|-|-|-|-|
|Hansen J Statistic (P-value)<br>-|-|-|-|0.47|0.33|0.11|0.69|

Notes:

1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients.

2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively.

3. For IV regressions [5] to [8],

- Endogenous variable: each of four informality measures.

- Instruments: four determinants of informality (Law and order; Business regulatory freedom; Average Years of Secondary Schooling; Sociodemographic factors).

4. See Appendix 1 for definitions and sources of variables.

Source: Authors’ estimation 19
# **4. Data and Variables**

To test these hypotheses, we create a unique database using a number of data sources. Our primary data source is the February 2002 edition of the Morningstar database for each U.S. mutual fund with a stated objective of investing primarily in emerging market equities.<sup>13</sup> All of the sample funds are primarily equity funds with more than 90 percent of their investment in equities. We include three classifications of U.S. mutual funds: Diversified Emerging Markets, Pacific/Asia excluding Japan, and Latin America. We exclude Diversified Pacific/Asia mutual funds because the majority of their investments are in countries that are not considered emerging markets, such as, Japan, Hong Kong and Singapore. Similarly, we exclude European funds because of their emphasis on developed Western European markets. In the Pacific/Asia excluding Japan sample of funds, we exclude 11 funds that invest 90 percent or more of their assets in 3 or less countries. We also exclude multiple classes of the same emerging market mutual fund. Certain funds have multiple classes that have identical portfolio holdings but different fee structures. For example, one fund may have an exit fee but the other does not. Finally, since our analysis focuses on active portfolio allocation decisions of U.S. mutual funds, we also exclude exchange-traded funds and funds that explicitly follow passive indexing strategies. Our final sample consisted of 111 funds consisting of the three categories; Diversified Emerging (73 funds), Latin America (14 funds), and Asia (24 funds). The country level data is taken from various sources including Datastream, IMF and La Porta et al. The data for individual firms is obtained from Worldscope, Datastream, Bloomberg and hand collected data from 20-F filings.

> 13 Consistent with the MSCI Emerging Markets Free Index, we define the following 30 countries as emerging markets: Argentina, Brazil, Chile, China, Colombia, Czech Republic, Egypt, Ghana, Greece, Hungary, India, Israel, Jordan, Malaysia, Mexico, Morocco, Pakistan, Peru, Philippines, Poland, Russia, Slovakia, South Africa, South Korea, Sri Lanka, Taiwan, Thailand, Turkey, Venezuela and Zimbabwe.

14
Figure 5: Expected change in import-weighted tariffs on digital goods after implementation of the AfCFTA by 2030 (trade between AfCFTA country members) <!-- Start of picture text --> Central African Rep.<br>Cameroon<br>Gambia<br>Senegal<br>Nigeria<br>Mali<br>Ethiopia<br>Congo<br>Ghana<br>Burkina Faso<br>Comoros<br>Togo<br>Mauritania<br>Kenya<br>Niger<br>Uganda<br>Ivory Coast<br>Burundi<br>Benin<br>Cape Verde<br>Madagascar<br>Africa average<br>Angola<br>Rwanda<br>Tunisia<br>Egypt<br>Tanzania<br>Malawi<br>Morocco<br>South Africa<br>Zimbabwe<br>Mozambique<br>Zambia<br>Botswana<br>Swaziland<br>Namibia<br>Lesotho<br>Seychelles<br>Sierra Leone<br>Mauritius<br>0 5 10 15<br>Expected tariff by 2030 Current tariff<br><!-- End of picture text --> Notes: Tariffs by country are an import-weighted average by product. Considered exporting country members of the AfCFTA only. Africa average is a simple average of these country-level tariffs. Authors’ calculations based on UNCTAD and COMTRADE.

22
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