rafmacalaba/data-use-mentions-extended huggingface dataset:
average the total value and volume of cash usage. Using firm-level transaction data, Agarwal et al. (2019) and Bounie and Camara (2020) investigate the causal impact of digital payment technologies on merchant sales. They find that merchants with the latest digital payment technology significantly increase sales, especially for small merchants and new entrepreneurs. My paper provides novel insight on the real effects of digitization. In particular, the results highlight the positive impact of contactless payment technology, relative to other payment means, on promoting business growth, especially in the time of a global pandemic where barrier gestures is required. My findings suggest that having a new technology (e.g., contactless payment) continues to drive retail traffic and attract new customers (Agarwal et al., 2019). There is little literature that examines the relationship between a merchant’s acceptance of new payment technology and its sales. My paper also contributes to the literature on technology innovation acceptance and diffusion and its impact on various economic areas (see among others Doms et al., 1997; Greenwood et al., 1997; Galor and Tsiddon, 1997; Caselli and 3
|Economic projections|Economic forecasts are from Global Economic<br>Prospects (GEP) June 2021 (for 2020) and January 2022<br>(for 2021).|Economic forecasts were from Macro Poverty Outlook<br>(MPO) October 2020.|
|---|---|---|

A key update in the latest simulations results is that with the school closure data from UNESCO, we no longer assume that closures are uniform across regions or countries, an assumption made in the previous simulation version due to limited data availability on school closures. As shown in Table 2, there are wide differences across regions in the share of the school system that was closed. Taking the intermediate scenario, in which partial closures are assumed to affect 85% of the student population, 70% of the school system was closed in South Asia, compared to 57% in Middle East and North Africa and 34% in Europe and Central Asia.

**Table 2. Share of the school system that is closed by scenario**

||Optimistic|Intermediate|Pessimistic|
|---|---|---|---|
|**Global**|42.9%|45.4%|49.2%|
|**Global(Part 2)**|45.1%|47.6%|51.4%|
|**By Region**||||
|East Asia and Pacific|33.8%|35.9%|39.1%|
|Europe and Central Asia|31.7%|33.7%|36.8%|
|Latin America and Caribbean|67.0%|71.0%|77.1%|
|Middle East and North Africa|53.6%|56.7%|61.2%|
|North America|56.5%|62.
food and non-food poverty lines, equals kwacha 37,001.68.<sup>5</sup> The ELL approach combines the household survey with the Malawi 2008 Population and Housing Census (henceforward referred to as the census). The census provides individual and household level information on household composition, education, employment, dwelling characteristics and asset ownership. These data are used to obtain multiple imputed values for household consumption expenditures for all households in the census, which are then aggregated to obtain estimates of poverty at the small area level. Alternatively, the SEM approach model combines the household survey with remote sensing data, such as night time lights, urban footprints, major roads, population density, vegetation, surface temperature, rainfall. It uses these data to obtain multiple imputed values for village level (i.e. enumeration area) poverty rates which too can be aggregated to the small area level to facilitate a comparison between the two approaches. The following sub-sections provide further details on the respective databases used.

|In 2010|% population|% urban|
|---|---|---|
|Malawi|100|15.2|
|North|13.1|14.3|
|Central|42.6|15.4|
|South|44.3|15.2|

Table 1: Population in 2010

# **3.1 Household expenditure survey**

The Third Integrated Household Survey (IHS3) was conducted between March 2010 and March 2011. Its target universe consists of the households and individuals in all districts, except for the Likoma district. A total of 12,271 households were interviewed (with 668 replacements), see Table 2. The sample is designed to be representative at Statistical Office and World Bank (2012). For a more general discussion on spatial (and temporal) price deflation and real consumption measurement, see e.g. Gibson et al. (2017) and van Veelen and van der Weide
|Past 1 year|1. Vehicle rental for<br>personal use: car,<br>moto/vélo etc.<br>2. Domestic and foreign<br>air transport<br>3. Purchase of personal<br>transportation (car,<br>moto,bicycle,etc.)|1. Bicycle<br>2. Motors, Pick-ups<br>3. Motorcycles|
|---|---|---|

# **Annex 2. Detailed Information on Transport Expenditures in LSMS Surveys (18 Countries in LSMS-tagged Collection)**

|**Survey Question**|**Afghanistan**|**Armenia**|**Benin**|**Bhutan**|**Burundi**|**Cameroon**|
|---|---|---|---|---|---|---|
|**_Non-food Expendi_**|**_tures during..._**||||||
|Past 7 days|||||||
|Past 1 month|1. Transportation fare -<br>bus and taxis<br>2. Fuel for car/motor bike<br>(do not include business<br>vehicles)|||1. Bus/ taxi fare &<br>other transport fare<br>2. Potter, pony<br>charges, etc.<br>3. Air fare<br>4. Rail/train fare<br>5. Transport (car,<br>motorcycle, etc.)<br>exclude power tiller,<br>tractor<br>6. Fuel and lubricants<br>for personal transport<br>(petrol, diesel, mobil,<br>etc.)<br>7. Spare parts and<br>accessories
Pakistan and Nepal but lags behind China and India. This suggests that the country has some room to expand its investment share of GDP. However, as can be seen from the graph below, the deficit in investment share with respect to India is not due to low public investment in Bangladesh. Average public investment as a share of GDP during 2011 – 2014 outpaced that of India by more than 2.5 percentage points. The targets from the 7<sup>th</sup> Five Year Plan are used to chart a reasonable path of investment. Specifically, I assume that the aggregate investment share of GDP increases from the present levels to 34.4 percent of GDP by year 2020 and remains at that level beyond 2020. This is based on the plan targets on public investment which rises from 6.5 <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>Bangladesh China* India** Nepal** Pakistan Sri Lanka<br>Aggregate Private Public<br>Source: World Development Indicators<br>*Aggregate average of 2011 ‐ 2014, **All measures average of 2011 ‐ 2014<br>Investment Share of GDP (%)<br><!-- End of picture text --> percent of GDP in 2016 to 7.8 percent of GDP by year 2020. This implies that private investment as a share of GDP expands by approximately 3 percentage points to reach 26.6 percent by 2020. I assume that both public and private investment continue to remain at the same level beyond 2020.

With regards to investment, there is a concern that public investment is not as efficient as private investment. This means that a percentage point expansion in public investment will deliver lower growth compared to a percentage point expansion in private investment. On the other hand, it is possible to generate additional growth by increasing the efficiency of public investment
profile landowners within each cluster and across countries as a function of their individual and household attributes. Our methods facilitate a more convenient and data-driven, as opposed to subjective, profiling of landowners based on their self-reported rights (the latter of which often involves, for example, picking one or two rights or decision-making roles to reflect nuances of ownership).

On the whole, the findings show a high degree of consistency across Ethiopia, Malawi, and Tanzania. Despite self-reporting themselves as owners, survey respondents often do not have full rights and decision-making power over their land parcels. There are significant gender gaps in land rights among landowners, in favor of men. Using MCA, we calculate a measure of variance that distinguishes landowners based on the composition of self-reported rights and control over land. Among different land rights and control variables across countries, transfer rights consistently contribute most to this variance. Hierarchical clustering further demonstrates that landowners can effectively be clustered into three categories: (1) owners with mostly exclusive transfer rights, (2) owners with mostly joint transfer rights, and (3) owners who claimed to not have transfer rights or only a limited number of them. Women are over-represented in the cluster of landowners without or with limited transfer rights, and in moving from the cluster with mostly joint transfer rights to

> 7 Please visit <u>www.worldbank.org/lsmsplus</u> for more information on the LSMS+ program. LSMS+ supported surveys over the period of 2016-2021 did collect self-reported, intra-household information on individuals’ ownership of and rights to a broad range of physical and financial assets, beyond land.

> 8 Compared to the work of Slavchevska et al. (2021), who used household survey data on land ownership and rights as reported by most knowledgeable household members.

4
macro-financial shock such as depreciation of local currency or a stress in the banking sector.

This section simulates the results of a macro-financial shock. The Systematic Banking Crises data set of Laeven and Valencia (2018) identifies 104 banking crisis episodes among the countries included in the PPI data set, out of which 13 also involved both sovereign debt and currency crises. In the episodes of banking, debt and currency crises, the maximum yearly deviation in depreciation rate from its long-run average ranged between 15.1 and 116 percentage points with an average of 48.3 percentage points.<sup>21</sup> The simulation assumes a 48.3 percentage-point depreciation shock,

> 21 The statistics exclude two episodes in Ecuador because the adjusted currency conversion factor remained stable, even though the official rate of depreciation was 56.8 and 77.2 percent in the banking crises of early 1980s and the late 1990s, respectively.

25
# **1 Introduction**

This paper provides the first evidence on returns to private equity investment in emerging markets using consistent data for a long period of time. Specifically, we analyze the cash flows associated with every equity investment made by the International Finance Corporation (IFC), a member of the World Bank Group, which invests across 130 emerging markets including the poorest and most neglected by private investors. Founded in 1956 with a mandate to “further economic development by encouraging the growth of productive private enterprise in member countries, _particularly in less developed areas_ ,” the IFC’s charter includes the goal of achieving market rate returns in markets “where sufficient private capital is not available on reasonable terms.” The portfolio is free of sampling problems, such as survivorship bias, and is much more diversified across countries than either foreign direct investment (FDI) inflows or the MSCI Emerging Market (MSCI EM) index of publicly-listed equities, both of which are highly concentrated on larger emerging market economies like China and Brazil. This unique dataset enables us to address long-standing questions relevant to international finance and development economics.

First, what is the performance of a diversified investment strategy focused on emerging markets? There is broad divergence in priors over whether this strategy can achieve returns comparable to those in advanced economies. Because we observe the timing of cash flows, we are able to measure returns in terms of a public market equivalent (PME) that accounts for both the absolute level of return and correlation of return with a global risk factor, as in the capital asset pricing model (Kaplan and Schoar 2005, Sorensen and Jagannathan 2015, Korteweg and Nagel 2016). Benchmarking the IFC’s complete equity investment portfolio to the S&P 500 index (available for our entire sample period of six decades), we calculate that the complete portfolio has obtained a PME of 1.15, indicating that it has returned 15 percent more over its life than an equivalently timed investment in the public index.

Given that these data
A measure of an Equity Adjusted Coverage Rate (EACR) in bank account usage informs on the coverage of bank account use with an adjustment on the equity of coverage. Using the FINDEX database, 57.6 percent of individuals (aged 15+) in Turkey had a bank account (coverage rate) in 2011. However, when adjusted for equity, the EACR rate is 44.0 percent. Ranked against all countries, Turkey ranks in the top half of countries in terms of bank account use coverage (See Figure 22 for graph of all 145 countries in the FINDEX database). In 2014, Turkey’s coverage rate decreases slightly but the EACR actually increases to 48.3 because of the falling level of dissimilarity. On the other hand three of the four benchmark countries experience significance increases in their coverage rates and substantial falls in their dissimilarity scores. Countries such as Belarus and Brazil are able to make significant advances in three years and move higher than Turkey in terms of their EACR rates in bank account ownership.

Compared to countries with a similar coverage of bank account usage, Turkey has a higher dissimilarity index (Figure 8) in 2011. The dissimilarity index can be interpreted as the difference in bank account coverage rates across groups. For example, if the bank account coverage rate is equal across age, gender, education, and income groups, then the dissimilarity index would be equal to zero. The dissimilarity index shows that in 2011, there is a higher level of inequality in economic opportunity in Turkey compared to benchmark countries in terms of bank account ownership across age, gender, education, and income groups. Between 2011 and 2014, Turkey is able to make a significant improvement in its dissimilarity index indicating that the inequality in bank account ownership is decreasing in turkey even if the overall level of ownership slightly decreases.

Turkey’s dissimilarity in account ownership is driven by gender (Figure 8). A Shapley decomposition measures which factors contributes to the dissimilarity index in Bank Account ownership. The Shapley
2010, and 2015. The 2005 and 2015 data have been used because they match more closely the end-point years of the trade data. (See Enamorado et al. [2016] for a detailed description of the data.) For labor market and migration indicators, tabulations from the 2000 and 2010 Census of Population and Housing and the 2015 Population Count are used to obtain labor market indicators, as well as information on the demographic characteristics of the population at the municipal level.<sup>5</sup> Given that publicly available data from the Mexican Institute of Statistics do not allow for more than two cross-tabulations, the census microdata samples of the Integrated Public Use Microdata Series are used to estimate labor market indicators on more detailed groups (Ruggles et al. 2007). Because the 2005 data do not include detailed information on labor market outcomes, 2000 data are used instead as the initial year for these indicators. The analysis also relies on the absolute index of migratory intensity between Mexico and the United States in 2000–10 to capture emigration and immigration with respect to the United States, as well as the remittances received in Mexico from that country (CONAPO 2014). The analysis uses the exports from developing to developed countries at the 2-digit level from the BACI international trade database to construct the instrumental variable (Gaulier and Zignago 2010).<sup>6</sup> Low- and middle-income countries are defined as developing economies, and highincome countries are defined as developed, following the World Bank’s income classification.

# **3.2. Descriptive Statistics**

Mexico has experienced a substantial increase in exports since the 1990s (figure 2). Exports of goods and services increased from US$96.7 billion to US$480.0 billion in 1990–2018. As a share of GDP, the rise was from 18.7 to 39.2 percent. This analysis focuses on 2004–14 because the municipal-level export data set only covers those years. While this
# **Table 1:** Data transparency and country sovereign spreads: **Basic Statistics, 1995-2018**

_(Annual data)_

||**1**|**995-2008**|**Non-SSA**|**2**|**009-2018**|**Non-SSA**|**2**|**014-2018**|**Non-SSA**|
|---|---|---|---|---|---|---|---|---|---|
||**EMDEs**|**SSA**|**EMDEs**|**EMDEs**|**SSA**|**EMDEs**|**EMDEs**|**SSA**|**EMDEs**|
|Sovereign Spreads (in bps)|561|999|504|418|506|402|413|561|380|
|Sovereign Bond Return Index|295|327|294|443|282|466|416|266|439|
|Statistical Capacity Index (SCI)|73.7|62.6|76.3|75.3|63.1|78.1|75.4|63.0|78.3|
|- Methodology Indicator|64.1|47.5|68.0|67.2|50.2|71.2|68.2|50.8|72.3|
|- Source Data Indicator|84.9|81.6|85.6|85.3|82.1|86.1|84.7|80.9|85.6|
|- Periodicity and Timeliness Indicator|72.2|58.7|75.4|85.3|82.
overcrowding and insecure residential status” (KNBS 2012, 4−5).<sup>12</sup> Table 1 reports household and housing characteristics by per capita consumption quartiles and informal status of their neighborhoods (see Table A1 for detailed summary statistics). Table A2 in Appendix A reports summary statistics for rental units in the city.

# **3-2. Methods**

_Job accessibility at the city level_ We use an urban accessibility metric to measure job accessibility in Nairobi (Avner and Lall 2016; Quirós and Mehndiratta 2015; Stewart and Zegras 2016). The model counts the number of reachable opportunities within a travel time isochrone. The most commonly used accessibility indicator is the share of opportunities that can be reached within a given time threshold. This normative choice has been recognized as meaningful (Bertaud 2014), although necessarily imperfect. The accessibility metrics, the job accessibility index (JAI), can be described by the following: where JAI� is the accessibility indicator for location i, O� represents the number of opportunities in location j, t�� is the travel time between location i and location j and t is a time threshold. �t�� �t�<sup>�</sup> in the numerator is a test that takes the value of either 0 or 1. In the latter case, opportunities in location _j_ can be reached and are added to the total of reachable opportunities. In the other case they are assigned the value 0. When averaged over the whole urban area ����JAI, localized accessibilities are weighted by population n�, to account for the fact that higher population densities are generally found in areas that benefit from good accessibility.

We also calculate the JAI for different groups of households based on their neighborhood status (informal or formal) and consumption quartiles. It is not clear a priori who benefits from better job accessibility between poor and non-poor households. Richer households may indeed choose to live at a further distance from the CBD
activities. In the absence of an adequate regulatory framnework and supervision the ability of domestic banks to mobilize funds will be strongly underrnined by a lack of depositors' confidence. This will drift funds abroad and generally away from viable domestic investment opportunities. On the other hand, under inefficient property rights and an underdeveloped system of title registration, realizing liquidity from collateral can be quite costly. This has a potential of undermining financial institutions' ability to lend to the extent they could, had they faced better property rights legislation and enforcement. Exploring the link between quality of institutions and financial depth, La Porta _et al._ (1997) summarize: "To the extent that better legal protections enable the financiers to offer entrepreneurs money at better terms, ... countries with better legal protection should have more external finance in the form of both higher valued and broader capital markets".

A growing body of empirical literature has tried to incorporate various indicators of institutional quality, civil liberties and the legal framework into conventional growth equations. Kormendi and Meguire (1985), and Scully (1988) find a strong effect of the Gastil civil liberties index on rates of investment and economic growth. A World Bank study by Brunetti, Kisunko and Weder (1997) finds that the differences between institutional frameworks (as perceived by private sector agents) in Transition countries may account for differences in countries' relative economic performance. La Porta _et al._ (1997) find strong evidence that the legal environment (measured by an indicator of Rule of Law) and institutional quality have a significant effect on the size of capital markets for their sample of 49 countries.

It is rather simple to make a point about an even stronger link between institutional quality factors and _industrial_ growth. Bearing in mind the nature of industrial technological process, few would disagree that the industrial sector on average requires _5_
. Approximately 89 percent of households in the in-kind and cash villages were eligible to receive transfers (and received them).

In addition to the randomization of transfer modality, the experiment also assigned all the cash villages and a randomly selected half of the in-kind villages to receive health, nutrition, and hygiene classes, which were designed to promote healthy eating and food preparation practices. In practice, however, few transfer recipients report attending classes and, importantly, administrators confirmed that the conditionality of transfers on class attendance was never enforced - that is, no household was denied transfers for not attending classes.<sup>8</sup> Fur-

> 6Villages were not incorporated in _Oportunidades_ if they did not have health facilities and/or secondary schools in close enough proximity, as needed to fulfill the conditionality of _Oportunidades_ transfers.

> 7The experiment was implemented in eight states: Campeche, Chiapas, Guerrero, Oaxaca, Quintana Roo, Tabasco, Veracruz, and Yucatan.

> 8Based on the household survey data, 76 percent of respondents attended a class in the in-kind villages assigned to receive classes and 69 percent attended a class in the in-kind villages assigned to not receive classes. In both cases, average attendance was roughly four classes over the course of the program. Furthermore, assignment to classes did not affect total food expenditure or the composition of food expenditure (results available from the authors).

6
We also estimate the labor supply curve of days worked per month. The supply curve is hypothesized to be a function of the logarithm of daily wages, age, and OSD status. Or, _In S = b, In W* + b2 0 + b3_ **_X_** _+ e_

- where, S: Average number of days worked per month in the past year.

- _W._ Daily wages earned.

- **_O:._** Severe OSD = 1. X: Vector of control variable such as age, gender, other income, and household characteristics.

Starred variables represent the fact that they are endogenous: we have<sup>a two-equation model with</sup> daily wages being endogenous. The wage equation is estimated<sup>using ordinary least squares and</sup> the predicted value of the wage rate obtained. This predicted value is then<sup>used to estimate the</sup> labor supply equation.

# **Results**

## **_Sociodemographic Characteristics of Study Participants_**

Data were collected from plantation records for a sample of 425 plantation workers.'<sup>0Of those</sup> sampled, 235 (55.3%) were permanent employees. The remaining (44.7%/O) were temporary (daily and contract) workers. 1 "Most sampled (82.1 %) were male, this being representative of the gender composition on the plantation in general. Table 1 disaggregates<sup>the sample in terms of</sup> employment type (i.e;, permanent or temporary), gender, and age.

- 10 It must be noted at the outset that the data are available for _currently employed_ workers only. Those<sup>plantation</sup> workers who are too incapacitated to work -- due to extremely severe manifestations of OSD,<sup>for instance --</sup> are not included in the