rafmacalaba/data-use-mentions-extended huggingface dataset:
- **SME share of employment** : this variable measures the size of the SME sector in an economy, which should be reflected in overall SME demand for finance. The expected sign of this variable, is however, difficult to determine _a priori_ . On the one hand, a large SME sector can contribute to diversify banks’ loan composition and therefore be associated with more credit supply; on the other hand, a large SME sector may be prevalent in developing economies with relatively underdeveloped and constrained financial markets that limit access to finance by SMEs.

- **FCS** : this is a dummy variable that is meant to capture FCS countries, defined as those with an FSI above 90. To check the robustness of our results, we also employ the World Bank Group classification of FCS countries. As shown, SMEs in FCS countries have generally lower access and usage of financial services than SMEs in non-FCS countries. This dummy variable is therefore expected to be negatively correlated with the SME financial inclusion index.

Additional variables ( Zi,t ) included in the augmented model are as follows:

# _Macroeconomic environment_

- **Income level** : this is a categorical variable to capture the level of economic development of a country. It takes value of 0 if a country is classified as low income according to the World Bank Group classification; value of 1 if a country is listed as middle income, and value of 2 if a country is high income. SME financial inclusion is expected to improve as countries develop so this variable is expected to be positively correlated with our dependent variable. We do not include GDP per capita to reduce risks of collinearity with the other variables in the model.

- **Informality** : this variable proxies the degree of informality in an economy as measured by the estimated size of the shadow economy (as a share of official GDP). Economies with higher informality are likely to impose tighter constraints on SME financial inclusion due to opacity and traceability issues, hence the
economies of scale associated with the liberalization and restructuring of China’s automobile sector.

How do these simulated gains compare to the real effects of WTO accession? The difference between the average annual export growth rates before and after this historical event implies a phenomenally large WTO accession impact. It appears, as shown on Figure 3, that between the two 4-year periods before and after 2001 China tripled its annual real export growth rate. But, how much of this increase can be purely attributed to China’s WTO accession? It is difficult to answer this question because China’s export growth was influenced by many other factors unrelated to accession reforms. In addition, average growth rates vary substantially across different periods and across periods of different lengths, making it difficult to infer the magnitude of accession-related gains from historical export data. Figure 3 shows that export growth in the period immediately preceding WTO accession was half of what it was during the years leading to the Asian financial crisis. It averaged 13.1% in the period 1987-93 (Yang, 1996) and slightly more than that (13.5%) in the period 1993-1997 but then it dropped down to just 7.1% in the period 1998-2001.

**Figure 3 China’s export growth and East Asian GDP growth (%)**

<!-- Start of picture text --> 30 5<br>25<br>4<br>20<br>3<br>15<br>2<br>10<br>1<br>5<br>0 0<br>1992‐1997 1997‐2001 2001‐2005<br>Annual export growth Annual export growth to the US<br>Processing exports East Asia GDP Growth<br><!-- End of picture text --> Source: Exports growth rates, shown on the left-hand side, are computed based on export values obtained from China Customs Statistics and deflated using 1992 US Consumer Price Index, as
industrial composition allows for adjustment relative to changes in national output composition. For results in column 3, the instrument list readily passes the specification test. First stage regressions for the covariates have average partial _R_<sup>_2_</sup> ’s and _F_ ’s of respectively .52 and 2852, which are strong for differenced covariates. For differenced intercity-transport costs, we use the difference between 1995 and 1980 for 2000-1991; and the difference between 1980 and 1968 for 1991-1980.

We find that increases in rural population supply, market potential of goods, labor force quality improvements (measured by changes in educational attainment) increase the growth rate of city population. As a new effect, educational attainment in the base period increases city population growth rates afterwards, confirming spillover effects of knowledge accumulation. But as noted above, reductions in intercity-transport costs have a moderate effect on city population growth rate. A 10% decrease in intercity-transport costs increases city population growth by .9% over a decade. Initial city size has a negative coefficient, suggesting some conditional convergence in population growth across cities. Also, cities with high manufacturing ratios in the base period experience faster growth. We also find that once base period population and industrial composition are controlled for, state capitals are growing faster than other cities.

In Table 5-2, we introduce two additional local characteristics to the specification in Table 5-1, column 3. These are (1) ratio of public industry capital to total industry capital stock in 1980<sup>11</sup> and (2) base period homicide rates. The main difference between the GMM results in column 3, Table 5-1 and those from Table 5-2 is that the statistical significance for the change in market potential drops to 20 percent. Other results are

> 11 Total industry capital includes both public and private industry capital stocks. The capital stock data comes from Morandi and Reis (2004). Due to data limitation, we use capital stock in 1980, which is the most recent year available.
in Egypt. For non-model variables, the average varies widely, ranging from as low as 0.4 percent for computer usage in Brazil, to as high as 10,722.2 percent for working in “other sector” in Indonesia. Figure 5.1 presents a boxplot of the average deviations across all countries for model variables (panel A) and non-model variables (panel B). The spread of the deviation and the mean values of deviation are both lower for model variables compared to non-model variables. The mean values of these deviations for each country are reported in table 5.1.</mark> <mark>It is useful to check if the models perform better in countries that have higher Internet coverage data. For this, we present data on Internet coverage across the countries from various official sources (red dot markers in the figure) in the second y-axis and arrange the countries in the x- axis in ascending order of internet coverage. Internet coverage in 2019 is highest in Türkiye and Brazil, followed by Egypt, Indonesia, Sri Lanka, and Kenya in that order. For model variables, countries with higher Internet coverage seem to have lower median values of deviation on average. Türkiye, which has the lowest mean deviation for the model variables (as per table 5.1), has the highest Internet coverage. For non-model variables, evidently, there is no clear indication that the best performing models are in countries where Internet coverage is the highest. The median deviation for Kenya, the country that has the least Internet coverage, seems close to the median deviation for Türkiye, the country that has the highest Internet coverage. Collectively, these results indicate that even after reweighting using inverse probability weights, it is challenging to match the means of non-model variables of the online surveys with nationally representative surveys.</mark>

> 23 <mark>Online survey means statistically significantly different from GMD are indicated by yellow shaded cells in tables A.4.1 to A.4
In this context, the missing joint distribution, i.e., the value for c, does not contain much of the information that is relevant to perform cross-regions well-being comparisons. Most of the relevant information is available in the two separate surveys. By ignoring one of the separate survey, the classical approach wastes relevant information and yields suboptimal well-being comparisons. Shockingly, “making up” (overlap) data improves well-being comparisons. The reason is that the “made up” data allows using more “true” data, i.e., from both partial distributions.

More generally, these results illustrate a sometimes overlooked point: when dealing with data constraint, the objective is not to approximate the ideal indicator we could construct in the absence of constraint. Rather, the objective is to construct an indicator that makes better well-being comparisons than the comparisons made by the alternative indicators that are currently used. In other words, what matters the most is not whether the value of H<sup>ˆˆ</sup> is “close enough” to the value of H<sup>ˆ</sup> . The relevant question to ask is whether the well-being comparisons by H<sup>ˆˆ</sup> are better than the well-being comparisons by H<sup>ˆ</sup> M (and those by H<sup>ˆ</sup> OD).

The results presented in Table 2 are valid only for one context using one particular definition for H<sup>ˆ</sup> and one way of quantifying the deviations from H<sup>ˆ</sup> .<sup>48</sup> They call for more research to check that the proposed method can reliably improve on the classical “wasteful” approach. In particular, it would be interesting to investigate the performance of the proposed method for a multidimensional poverty measure constructed from the data available in a typical LSMS survey and a typical DHS or MICS survey. This performance can be assessed
countries NPL ratios are 2.3 percentage points lower compared to the pre-privatization ratios. Note that this is a reduction of around 42 percent of mean NPL ratio and thus is economically significant. Furthermore, the increase in number of employees after the privatization is subdued over time in high rule of law countries and these banks from high rule of law countries hold even less liquidity after privatization. Finally, in Panel B of Table 9, we focus on the financial development at the time of privatization and how it is related to privatization and various banking outcomes.

The results indicate that the post-privatization decrease in cost-to-income ratios and liquidity ratios over time is lower when the privatization occurs in a financially more developed country. Moreover, the banks which went through privatization in highly financially developed countries reduce their non-interest income generation much less and may even increase it over time, especially if the country has very high domestic credit to GDP (more than 100 percent). The results also suggest that the portfolio shift from interbank loans to corporate loans is mostly happening in lower financial development environments. Taken all together these results suggest that privatization offers significant benefits in lower financial development environments as well as implications for the optimal strategies of the privatizing banks.

# **4. Conclusions**

Building on a novel data set comprising 475 bank privatization events in 70 countries in emerging and advanced economies during the period from 1995 to 2017, this paper attempts to shed some light on recent privatization events and analyzes the relationship between privatization and banking outcomes. The first part of the paper offers some stylized facts. We find that bank privatizations increased sharply post-2000 and that China and India were the driving force behind the rise of privatizations post-2000. Following years of strong growth, privatization activity was more limited recently in other middle-income countries while privatizations in high-income countries remained broadly constant despite the wave of bank nationalizations in the aftermath of the GFC. Privatization proceeds were a relevant source of government revenues, including for many high-income countries. We also find that the average equity stake at
the UNCTAD database, and are defined as follows: “For associates and subsidiaries, FDI flows consist of the net sales of shares and loans (including non-cash acquisitions made against equipment, manufacturing rights, etc.) to the parent company plus the parent firm ́s share of the affiliate ́s reinvested earnings plus total net intra-company loans (short- and long-term) provided by the parent company. For branches, FDI flows consist of the increase in reinvested earnings plus the net increase in funds received from the foreign direct investor”.

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Individual characteristics include variables such as age, sex and education. Household characteristics include variables such as household size and composition, the living area of the house, the physical quality of the house (e.g., whether its roof or wall has good quality or whether the toilet is improved, such as a flush toilet), quality of drinking water, and household assets. These observed characteristics are commonly used as proxies for measuring household wealth.

For comparison and robustness testing, two estimation methods relying on different assumptions about the error terms are considered in Tables 2–6. Method 1 uses the normal linear regression model that assumes a normal distribution of the error terms, and Method 2 uses the empirical distribution of the error terms. Both methods include the random effects at the primary sampling unit. To ensure that consumption data are consistent, the poverty lines that are used to provide the imputation-based estimates are those in the base surveys. But we will also offer robustness checks when we use different poverty lines and alternative modelling techniques such as multiple imputation (MI) and machine learning.

Figure 1 presents a visual summary of the two types of poverty imputation that we implement. The first type of imputation is to employ the 2020/21 TZNPS as the base survey to impute into the different Tas to obtain poverty estimates over time (i.e., across-year imputation). While this offers our main estimation results, we will also provide robustness checks where we use the 2019/20 TZNPS as an alternative base survey. The second type of imputation is to use TA 1 as the base survey to impute into the other two Tas to obtain poverty estimates in the same year (i.e., withinyear imputation). Both types of imputation have implications for policy advice and survey design. While across-year imputation provides estimates on consistent poverty trends over time, withinyear imputation offers cost-saving and reduced logistic challenges where a small-scale consumption survey can be implemented instead of a full-scale consumption survey.

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# **Ahmedabad**

# **More but Different Government for “Slum**<sup>**2**</sup> **Free” and Livable Cities**

# **I. Methodology and Context**

The purpose of this work is (i) to gather basic information necessary to prepare a broad assessment of feasibility for immediate actions to improve key public services and environmental conditions for all households in low-cost informal housing; and (ii) to prepare an assessment of impediments to supply of housing across the income spectrum and recommendations for actions that will make that supply more responsive to increased demands as population and incomes grow. Such measures would reduce pressure on prices and make decent and dignified housing more widely affordable.

The objective is to mobilize support at the city, state and federal levels of government and within the local community for a program of short and medium term actions and investments based on a commonly shared and credible set of data. As such, the study focuses on working with reliable, available data to the extent possible rather than seeking to prepare a definitive research analysis that would take years to develop. .The focus of the study is its city-wide perspective—taking a comprehensive look at the entire market and infrastructure network and examining what it would take to provide minimum shelter standards for all. It does not pretend at this stage to present the most precise and detailed information on each topic—which must be left to the responsible agencies. We sought a level of accuracy that permits us to make a quick assessment of what a comprehensive program would entail. Should there be agreement on moving forward with the approach, full feasibility and design studies would be required.

We have chosen to illustrate the city level approach in Ahmedabad, because many parts of the envisaged program of investment and reform have already been successfully piloted here. For the purpose of data gathering and analysis, we have taken the Ahmedabad Municipal Corporation (AMC) as extended in 2006 to represent the
three regions: Lilongwe (the capital) in the Central region with a population of 674,448 (2008), Blantyre in the Southern region with a population of 661,256 (2008) , and Mzuzu in the Northern region with a population of 133,968 (2008) (Malawi NSO 2008).

Both approaches considered in this paper rely on the 2010-11 Malawi Third Integrated Household Survey (IHS3), which is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project (Malawi National Statistical Office (NSO) and World Bank, 2012). The survey contains high-quality data on household consumption expenditure that is used to measure poverty, defined as the percentage of individuals whose total annual household consumption per capita fall below the national poverty line. Household expenditure and the poverty line are expressed in Malawi kwacha using February/March 2010 prices.<sup>4</sup> The poverty line, the sum of the

> 4For details on the price deflator used in the Malawi IHS3, we refer the reader to Malawi National

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Figure 2: Percent of total sales on Chaldal by firm size <!-- Start of picture text --> < 0.25 0.25 to 0.5 0.5 to 0.75 > 0.75 < 0.25 0.25 to 0.5 0.5 to 0.75 > 0.75<br>Small (0-20 employees) Large (> 20 employees)<br>60<br>40<br>percent<br>20<br>0<br><!-- End of picture text --> As noted earlier, survey respondents were a mix of firm owners, managers and sales managers. These respondents are on average 35 years old, and mostly male. Among the respondents, 84 percent have at least a Bachelor’s degree. This is a relatively high number in the context of firms in Bangladesh, as discussed later in this paper.

# **2.4 Descriptive Statistics: Daraz Survey**

Data from the Daraz survey highlights some similarities but also important differences from the case of Chaldal.

In terms of geographic dispersion, 41.6 percent of the Daraz vendor sample is from Pakistan, 27.7 percent from Bangladesh, 21.8 percent from Sri Lanka, and 8.8 percent from Nepal. Compared to Chaldal, firms in the Daraz sample are on average young and small in size (table 2). The average age of responding firms is 3.4 years; 51.0 percent have been operational for two years or less, and 18.5 percent have been operational a year or less (figure A.1). Firms have on average 3.5 employees (in addition to the owner). Some 86.9 percent of firms in the sample have fewer than five employees, and 14.1 percent have no employees (figure 3).

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Table A1: Correlation between juvenile catch and measures of fishing productivity

||Dependen|t variable:|asinh(juv|enile catch)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|DistToCoast|0.0050|||0.0026|
||(0.0017)|||(0.0015)|
|TonsPerSet||0.0075<br>(0.0007)||0.0069<br>(0.0009)|
|TonsPerArea|||0.0138|0.0027|
||||(0.0036)|(0.0032)|
|Intercept|0.6375|0.3826|0.6975|0.3169|
||(0.0660)|(0.0524)|(0.0515)|(0.0556)|

All regressions have 34,272 observations. Dependent variable is inverse hyperbolic sine of millions of juveniles caught in a potential closure-treatment bin. Standard errors clustered at level of twoweek-of-sample by two-degree grid cell.

# **2 Treatment coefficients from estimating Equation 1 and variants of Equation 1**

Figure A2 displays the treatment coefficients from estimating Equation 1. The reader may interpret this figure in percentage terms by applying the transformation _exp_ ( _βst_ ) _−_ 1. My results exhibit the same pattern if I drop zero values and use a logarithmic transformation on the dependent variable instead of an inverse hyperbolic sine transformation (Figure A3). They also hold if I replace the dependent variable in Equation 1 with a binary indicator for positive juvenile catch (Figure A4) or if I replace treatment fraction with a binary indicator for positive treatment fraction (Figure A5).

My results are also robust to replacing the outcome variable with the inverse hyperbolic sine of tons of juveniles caught (Figure A6
What does the literature show for FLFP constraints in Morocco? Overall, the evidence is limited and out of date. Using the Moroccan Living Standards Measurement Study 19901991, Assaad and Zouari (2002) find that marriage does not affect FLFP, but the presence of children reduces it, particularly in terms of private-sector employment. Spierings and Smits (2007) study FLFP in four Arab countries (data from Morocco corresponds to the 2004 Demographic and Health Survey (DHS)) and find that higher education is the most important explanatory variable. Verme et al. (2016) conclude that the nature of economic growth in Morocco and gender norms interact to determine FLFP. In particular, they claim that women holding secondary education are unable to find jobs, which encourages marriage and deters female labor supply. Previously mentioned works do not explore how demographic characteristics affect FLFP for different periods of time. Also, they do not pay attention to the attributes of other members inside the household that could also affect women’s decisions towards work **_._** Other literature streams highlight the importance of traditional roles on FLFP. The values in a society—beliefs and motivations other than economic factors—can shape individual behavior and play a role in determining female labor supply. A key manifestation of these traits is the individual’s attitude toward work and its relationship with gender. In Chile, for example, Contreras and Plaza (2010) find that as more women internalize conservative cultural values, they participate less in the labor market, offsetting positive human capital accumulation effects. Identity—that is, the link between certain social categories and the expectations about them—also affects individuals’ decisions (Akerlof and Kranton, 2000); 5
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.3  Comparative costs of exporting goods by road and rail where both exist**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>road rail road rail<br>Abidjan-Ouagadougou Dakar-Bamako<br>Port Transport Border Administrative<br>landlocked capital(US$ per tonne)<br>Costs of mvoing imports from ports to<br><!-- End of picture text --> Source: Data collected from World Bank 2008, West Africa Trade Hub data, World Bank project documents, and AICD ports and railways database.

Prolonged dwell times at ports, delays in crossing borders, and customs clearance processes add significant amounts of time to moving imports along regional corridors from ports. There is significant variation across corridors in port efficiency, border crossing delays, and time required for customs, clearance, and technical control. The time required to move imports from ports to a landlocked country along the corridors takes anywhere from 400 hours (20 days) to 700 hours (48 days), close to double the times observed in southern Africa (figure 2.4a).

How that time is spent varies. Owing to long distances from landlocked capitals to ports, transport times contribute the lion‘s share to the total for importing freight, followed by expensive administrative processes (figure 2.4b)<sup>1</sup> . Other factors vary as well. For example, the Port of Lomé is far more efficient than the Port of Cotonou or Tema. Abidjan also records shorter delays at ports, giving the port a comparative advantage over its competitor ports (Teme Dakar and Lomé). Crossing West African borders has been described as a time-consuming ordeal. Previous estimates suggested that crossing Ghanaian borders took more than 20 hours (Teravaninthorn and Raballand 2008)