rafmacalaba/data-use-mentions-extended huggingface dataset:
, Dabla-Norris, & Khalid, 2020)

> 19 Only 23 percent of Mexican aged 23-25 have a college degree according to OECD data.

> 20 The percent of households which access to computers in 2019 was 44.3 and 44.2 in 2020. Access to internet 56.4 and 60 percent. (ENDUTIH 2020)

8
Box 1

# Colombia: The Regulation of Professional Services

In a recent study, the World Bank conducted a survey among Colombian professional service providers to assess the main barriers to trade they face. The study identified recognition of accreditations and licensing requirements as the main factors affecting their establishment abroad.

Colombian firms indicated in 30 percent of cases that the lack of title recognition or accreditation of professional service suppliers was a serious obstacle. Micro, small, and medium-sized enterprises considered these barriers to be moderate. Architecture service providers were found to face the most restrictions in terms of lack of recognition and residency requirements.

The regulation of licensing requirement and the speed of the process for granting licenses were considered to be serious barriers for 24 percent of the respondents. Regulations on technical standards were considered moderate or small obstacles in 56 percent of cases. For accounting and legal services providers these barriers were perceived to be important or serious, while for engineering and architecture services they were found to be mostly moderate or small barriers.

The survey also identified other performance and growth challenges that companies face, both those with a branch overseas and those considering setting up one. The greatest performance and growth obstacles faced in foreign markets for these companies were found to be the regulation for foreign suppliers, work permits, visas, residence and regulations related to license granting procedures.

Source: Trade in Professional Services: Demand and supply analysis of the Colombian market, World Bank, (2016).

Trade costs can also stem from the need for compliance with diverse regulatory requirements in multiple jurisdictions within the same political entity (for instance in countries with federal systems of governance where specific services are regulated at the sub-national level) or at the regional level. For example, in the EU context, Mustilli and Pelkmans (2013) find that despite significant services liberalization within the Single European Market, a high level of regulatory heterogeneity prevails among countries in several sectors, notably in professional services. Regulatory heterogeneity can thus alter the fixed costs of entry within segmented markets, and empirical evidence
children born between 1957 to 1967. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

68
potential internal instruments. The use of too many instruments produces more statistically significant results, but increases the risk of ―over fitting‖ the model. This trade-off is addressed by restricting the number of instruments as proposed by Roodman (2006); i.e., one instrument is created for each variable and lag distance rather than for each time period, variable, and lag distance. Lastly, the time series depth does not provide a sensible number of observations for conducting panel unit root tests (Baltagi, 2005). Nevertheless, the system GMM estimation performs well even in the presence of near random walk processes (Stata Press, 2007).

In line with the elaborated analytical framework, the baseline growth equation takes the following dynamic specification: 1+ 2 _-_ + 1 ln + 2 + 3 + 4 + 5 + 6 + 7 * + 8 + _t_ + (1) where _i_ denotes the countries under investigation for which data is available during the observed period ( _i_ = 1, ..., 90), _t_ stands for the four-year non-overlapping intervals ( _t_ = 9 and refers to the non-overlapping intervals: 1973-1976; 1977-1980; ...; 2005-2008; however, as was already noted, transition countries are covered only in the 1997-2008 period), _gri,t_ is the average growth of real GDP per capita (measured in purchasing power parity terms in the Penn world tables 6.3), _fci,t_ represents alternative measures of a country’s reliance on foreign capital, and the _CG dummy_ represents an indicator variable for membership into three different EU-related groups (EU10, EU candidates and EU neighborhood). Also, _α1_ is the common intercept, _α2_ is the coefficient on the lagged dependent variable, _βi_ denotes the slope coefficients of the vector of explanatory variables (Table 1), _t_ stands for the period-specific effects (time dummies), and _vi,t_ is the stochastic disturbance term, assumed to be independently and identically distributed. The inclusion of the period effects is
(Agbor, 2012, Pattillo _et al_ ., 2008; Bagnai, 2010). However, a strand of the literature contends that the euro peg may render adjustment to external shocks difficult because a fixed exchange precludes a key policy instrument for redressing a protracted recession (IMF, 2016; World Bank, 1994, 2008).

The results in this paper are comparable with the findings of a recent IMF paper, though our analysis underscores the role of financial deepening more strongly. Using a benchmarking and cross-country regression approach, IMF (2018b) finds that high growth in the WAEMU region coincided with increased investment, improved macroeconomic stability, enhanced institutional quality, improved terms of trade, and productivity gains. It is important to note, however, that only the benchmarking and structural break analyses in Section 2 of this paper are directly comparable with the approach adopted in IMF (2018b). A notable difference in results is our finding of the strong growthfacilitating role played by financial deepening.

# **5. Model Accuracy and Robustness**

# **5.1 Model accuracy**

In general, the model predicts actual growth in WAEMU very well, although it under-predicts growth for some countries. Annex Figure 10 compares actual per capita growth with the predictions from the model. Predictions for Côte d’Ivoire are the least precise in absolute terms, although the model ‘error’ is modest proportionally. This perhaps suggests that our model may not be a good fit in explaining what happens in the aftermath of a deep recession such as Côte d’Ivoire’s during the 2000s. In general, prediction errors could arise from shortcomings of some of the model variables. For instance, institutional quality is proxied by the Polity IV index, which exhibits limited variability over the short-term. It may also be due to factors that remained unaccounted for by the model. Having said that, discrepancies for country-specific results derived from cross-country studies are not exceptional in the literature (see results for LAC countries in Araujo _et al_ . (
WPS3795 level of defaults (Miller 2003). Commercial credit bureau data have also been shown to have power in predicting firm failure beyond financial ratios and other descriptive information about the firm (Kallberg and Udell 2003).

There is considerable variation across countries in terms of the existence of credit bureaus, whether they are publicly- or privately-owned, and the coverage of available information (Miller 2003). Empirical evidence suggests a statistically important link between the existence of third-party information exchanges and credit availability (Pagano and Jappelli 1993, Padilla and Pagano 1997, Love and Mylenko 2003). Specifically, countries with stronger formal information sharing exhibit greater bank lending relative to GNP and country-level credit risk is negatively correlated with measures of formal information sharing (Jappelli and Pagano 2002).

Credit bureau information, where available, is used in conjunction with all of the lending technologies, but it is a driving component of one of the lending technologies, small business credit scoring. It is necessary to have a large database on SME loan performance and the variables used to predict that performance in order to estimate a credible credit scoring model. In the U.S., most banks use external vendors to create the scores, and these vendors rely on a strong information environment in which credit bureaus share consumer and business information and financial institutions share their loan performance data to estimate the model. Some large institutions are also able to generate proprietary models based on their own loan performance experience, but still need information from credit bureaus for the exogenous variables in their models. The strong information environment requirements may help explain why small business credit scoring appears to be used on a widespread basis only in a limited number of developed nations (Miller and Rojas 2004).<sup>13</sup> The evidence also indicates that access to historical credit information such as business credit bureaus is positively related to the presence of factoring (Klapper 2005). Under certain circumstances factoring can also work well even in weak domestic information environments if the receivables are from obligors located
>20</sup> We compute the correlation between the FAO data series on the numbers of tractors in use and our data on agricultural fixed capital (of which tractors are one component) for each country; the average of the country correlations is 0.43. The median correlation is 0.8, so for half of the countries in our sample, the data series are somewhat correlated. Only 30% of the sample have correlations greater than 0.9, while the correlation was actually negative for seven countries. In Figure 5, we plot the data on fixed capital in agriculture

> 19 As discussed, raw data needed to construct the livestock and treestock measures are available from the FAO for a larger number of countries.

> 20 In some studies, the data on tractors is converted into horsepower equivalents in an attempt to control for the quality differences in tractors (e.g. Craig, Pardey, and Roseboom (1997)).

10
(or even spouses)”.

# 4 Testing the predictive power of the models

Before investigating the e¤ect of the randomized capital grants on sample ...rms, we brie‡y examine the baseline data for evidence consistent with predictions made by the models presented in Section 2. The evidence we o¤er is non-experimental, but since we are not trying to draw causal inference, observational data remains useful.

The Ramsey model predicts, other things being equal, that more patient entrepreneurs have a higher steady state capital stock. More talented entrepreneurs should also have larger ...rms and thus larger capital stock. The time inconsistency model further predicts that entrepreneurs that are more hyperbolic in their discounting should also have a lower steady state capital stock.

We investigate these predictions using the baseline capital stock of all respondents. The results are shown on Table 2. Consistent with the predictions of the model, we ...nd some evidence that more able entrepreneurs have larger capital stocks. The regression in column 1 shows that years of schooling, results from a Raven test of non-verbal reasoning, and results from a forward digitspan test all have positive measured e¤ects, though only the digitspan score is statistically signi...cant.

The column 1 regression also includes measures of the owner’s short-term discount rate and an indication of hyperbolic attitudes. Hyperbolicity is proxied by answers to questions asked in the baseline survey involving discount rates over the two di¤erent time horizons, and patience is measured by whether a respondent has a discount rate above or below the sample median. Firm owners were asked hypothetical questions to elicit the amounts that would leave them indi¤erent between an amount today, and 100 cedis in one month; and between an amount in ...ve months and 100 cedis in six months. From this we construct two proxies: an indicator variable of whether the respondent has a discount rate above the median for the one month versus today comparison (the median person would take 90
carried home by the migrant, though it is likely that some portion of this is repatriated unofficially in cash. In addition, some of the door-to-door services may be unofficial or overlooked in national accounts. So even with the household data, it is difficult to get a good estimate of informal flows.

# _Other Surveys_

To expand regional variation, we also compile data from various other reports and surveys that ask questions about channel of transmission. The advantage of these surveys is that they have a large sample of remittance receivers and ask more detailed questions. The disadvantage is that it is not clear to what extent they are nationally representative.

Table 9 shows estimates from these surveys, as well as the ones above for comparison purposes. There is a lot of variation in the channel of transmission. Data from Uganda imply that a large share of remittances enter the country informally. This is consistent with the results on Sub-Saharan Africa from our aggregate estimation, which support high informal flows to the region. In addition, data from France show that 70 percent of remittances to Mali and Senegal move in informal Channels (ECFIN 2004). The Dominican Republic and Guatemala and tend to have small informal sectors.

# **VIII.  Conclusions**

Globally, our estimates suggest that informal remittances may equal about 35-75 percent of official flows. In countries with well-functioning financial systems, large, regular users tend to send remittances through formal channels. This acts to put a bound on the size of the informal sector. On the other hand, in some countries, and especially those 27
In the default model, we decompose total investment into private and public investment (these have identical effects in the LTGM-NR, but have different determinants). Private investment as a percentage of GDP is assumed to remain at approximately 17.7%, which is the historical average between 2004 and 2019 according to data from the Central Bank of Peru. Public investment is set at 4.7% using data from the IMFFAD, which represents the average of the last 10 years of available data.

Total factor productivity growth of the non-resource sector is generated using the LTGM-TFP extension (Kim and Loayza 2019) assuming that the determinants of TFP - innovation, education, market efficiency, infrastructure, and institutions – increase at their historical rate over the past decade.<sup>9</sup> This results in an almost-constant path of TFP growth over the simulation period of 0.5%-0.6%, which is similar to the historical average over 2004-19.

The human capital growth path for the baseline is generated using the LTGM-HC extension, assuming that the current quality and quantity schooling and health of today’s children persists throughout the simulation period. This generates human capital growth of 0.3% in 2022 and declines to about 0.1% in 2050. We assume the average expected years of schooling in the future is the same as that of today’s children, 12.7 years, taken from the World Bank Human Capital Index. Because today’s new workers are better educated than older workers moving to retirement, the average human capital of the workforce grows over time – despite no increase in the schooling of children. The rate of human capital growth slows over time due to rising average education quantity of the workforce, which reduces the educational gap between new workers and incumbents. Human capital also includes the quality of education, as well as health. The health of the population is measured by adult survival rates (ASRs), which is the probability that a 15-year-old will reach
the region have been somewhat reluctant to undertake sweeping reforms. Furthermore, India maintains a vehement opposition to the inclusion of textiles and apparel under SAPTA, although tariff rate quotas were allowed to a limited extent under the India–Sri Lanka bilateral trade agreement.

In sum, divergence and concentration data on the part of Bangladesh, Nepal, and Sri Lanka, and to some extent Pakistan, do not auger well for a rapid increase in regional trade. The prospects of increased regional exports, contingent upon the degree to which dynamic exports are incorporated in the regional export mix, do not appear to be the case in South Asia. The divergence can be discerned from the underlying shift of South Asia’s trade, which has undergone a greater orientation toward unskilled and semi-skilled manufacturing products for which the SACs appear to compete with each other in third markets.

Furthermore, the heavy bias demonstrated by the concentration of exports may complicate prospects for increasing regional trade. Except for India, most countries in the region export a narrow range of goods, dominated by textiles and apparel. Reforms that have helped reduce the general level of protection have nevertheless retained a relatively high level of protection for textiles, apparel, and certain manufacturing products. Furthermore, SAARC members other than India have the capacity to export a limited range of products. Unless the export base undergoes a substantial diversification that complements the imports of 26
of the phone surveys. These wage-related data are available for two rounds after the outbreak of the war (December 2020 and January 2021), allowing us to assess labor market participation effects of the war in the immediate aftermath of the outbreak of the war. Second, there was a slight change in the reference period across rounds. In the August 2019 face-to-face survey collected the labor market participation information of household members in the last 7 days preceding the survey. But the reference period and framing of the questions were slightly changed during the phone surveys. Specifically, when the phone survey started in April 2020, respondents were asked to report on whether household members were able to perform farming and non-farm activities since the outbreak of the COVID14
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The impact of the unreliability of the power supply in Pakistan is also confirmed by data obtained between 2013 and 2015 from the World Bank’s Enterprise Survey (2013) as shown in Table 7.<sup>28</sup>

**Table 7: Comparative Performance of Pakistan on power supply reliability**

||**Pakistan**|**South Asia**|**All**|
|---|---|---|---|
|Firms experiencing electrical outages (percent)|81|66|59|
|Number of outages in a typical month|75|24|6|
|Average duration of outage (hours)|17|5.3|4.5|
|Average losses as percent of annual sales|34|11|5|
|Percent of firms owning or sharing a generator|65|45|34|
|Average percent of electricity from generator if available|41|24|21|
|Firms identifying electricity as a major constraint<br>(percent)|75|46|31|

Source: World Bank Enterprise Surveys.

Putting all these facts together it appears that, although Pakistan was able to attract a modest amount of private investment into the generation sector, this was inadequate given the rate of growth of demand. The costs of the supply unreliability were high. With such an evident gap between existing supply and demand there were clearly important factors discouraging the entry of more IPPs. The GENCOs were not able to finance much expansion given the poor performance of the sector, while the magnitude of “circular debt” and the inability of some IPPS to be paid fully or on time, which in 2011 had led some to threaten to call in sovereign guarantees, is likely to have played an important role in deterring the entry of more IPPs. The persistence of different governments in continuing to set the actual tariffs below those calculated by the regulator to consumers so as to limit
# **Poverty and flood exposure.**

By combining flood exposure estimates with survey-based sub-national data on poverty, we further estimate flood exposure among the world’s poor. Our estimates show that 132 million people living in extreme poverty (living on less than $1.90 per day) are directly exposed to flood risk; of these, 72.5 million (or 55 percent) are in Sub-Saharan Africa (figure 16). Approximately two out of ten people exposed to flood risk globally are living in extreme poverty.

When poverty is defined using less extreme (i.e. higher) thresholds, the number of flood-exposed poor people increases significantly. Around 343 million are estimated to live in high-risk flood zones while living on less than $3.20 a day. The number increases to 587 million when considering incomes below $5.50 a day. Increasing the poverty threshold from $1.90 to $5.50 doubles the number of poor people exposed to floods in Sub-Saharan Africa from 73 million to 147 million. However, in South Asia the number of the poor and flood-exposed increases seven-fold from 41 million to 289 million; in East Asia the increase is nine-fold from 9 million to 80 million. but increases the poor people exposed in SAR by a factor of 6.55 to 286.8 million people. In other words, four out of every ten people exposed to flood risk globally are living on less than $5.50 a day.

||**Poverty t**<br>**$1.90**|**hreshold (consumptio**<br>**$3.20**|**n per day)**<br>**$5.50**|
|---|---|---|---|
|Number of poor (millions)|741.8|1,812.0|2,986.6|