rafmacalaba/data-use-mentions-extended huggingface dataset:
First, it, stands to reason that the more attractive enterprises were privatized first, thus the core growth rate of firms with private ownership and thus employment likely exceeded that of firms remaining in public hands. Second, employment in pnrvatized enterprises was reduced to a much greater extent compared to publicly owned firms, to the degree that the firing decisions were skill based, the remaining employees in private sector firms have higher average skills compared to public sector employees.<sup>9</sup> The different rates of employment reduction suggest that the chance of future unemployment, and thus income uncertainty, was also larger for employees remaining in publicly owned enterprises. If so, both the consumption smoothing and the precautionary savings model would imply lower savings rates for private sector employees.

Households deriving their main income from self-employment or from wages indeed save less compared to the reference group (households receiving public

> **s** A negative wealth elasticity of savings would also be at odds with much of the existing empirical literature [Avery and Kennickell (1991) or Bosworth, Burtless and Sabelbaus (1991), :.nter alia]. 9 Differences between occupation groups are of course notoriously difficult to interpret, as occupation choice itself is endogenous [Skinner (1988) and Carroll (1994)]. In this respect, however, the transition data are arguably less problematic, as it seems reasonable to assume that few households selected their pretransition employment with a view towards a possible collapse of the socialist system, furthermore, employment choice was highly restricted under the central planning system.
|Storms|8.8059|60.0757*|0.1052***|
|---|---|---|---|
|_annual number of events_|_(56.2382)_|_(33.4079)_|_(0.0392)_|
|Extreme rain|-0.0738|0.0437**|-0.0001|
|_annual number of events_|_(0.0605)_|_(0.0213)_|_(0.0001)_|
|N|20359|13734|23333|
|R<sup>2</sup>|0.38|0.12|0.19|

Estimated with weighted OLS, normalised survey stratification weights applied. Standard errors in parenthesis, clustered on region. Significance levels: *** 0.01, ** 0.05, * 0.10.

2.3. Firms’ exposure to infrastructure disruptions vs. direct exposure to natural hazards Based on Rentschler et al. (2019), this section investigates whether direct exposure to natural hazards influences firms’ experience of infrastructure disruptions. In 13 African cities, no such link is found between exposure to flood hazards and unreliable infrastructure.

Especially developing countries tend to perform poorly in terms of the reliability and efficiency of their public infrastructure. Infrastructure users tend to experience frequent disruptions, such as power outages, water supply interruptions, or frequently occurring problems with transport networks. These disruptions can have a range of causes, ranging from sub-standard and malfunctioning equipment, poor management, lacking investment and maintenance, as well as natural stresses and disasters.

To analyze how natural shocks cause infrastructure disruptions, this analysis combines firm-level microdata and natural hazard exposure data. Enterprise Survey observations are used for 13 major African cities. Flood hazard data is drawn from the highest-quality flood map available in each city. Using the nonrandomized ES locations, each firm’s
% age 15+) [w2]|Global Findex|Inclusion|Firms|Savings|
|Used an account at a financial institution for<br>business purposes (% age15+) [w1]|Global Findex|Inclusion|Firms|Payments|
|Bank Z score|GFDD|Stability|Resilience indicators|Capital|
|Bank capital to total assets (%)|GFDD|Stability|Resilience indicators|Capital|
|Bank credit to bank deposits (%)|GFDD|Stability|Resilience indicators|Liquidity|
|Liquid assets to deposits & ST funding (%)|GFDD|Stability|Resilience indicators|Liquidity|
|Bank NPLs to gross loans (%)|GFDD|Stability|Resilience indicators|NPLs|
|Provisions to NPLs (%)|GFDD|Stability|Resilience indicators|NPLs|
|Standard deviation of the bank lending rate<br>growth|GFDD<br>|Stability|Volatility measures|Credit volatility|
|Standard deviation of the outstanding loans|Financial Access<br>Survey|Stability|Volatility measures|Credit volatility|
|Standard deviation of the bank deposit rate<br>growth|GFDD|Stability|Volatility measures|Deposit volatility|
|Standard deviation of the outstanding<br>deposits|Financial Access<br>Survey|Stability|Volatility measures|Deposit volatility|
|Cumulative loss in income relative to a<br>precrisis trend|Laeven and Valen|Stability|Crisis outcomes|Milder/No crisis|
|Direct fiscal outlays due to financial sector<br>rescue packages|Laeven and Valen|Stability|Crisis outcomes|Milder/No crisis|
|Peak level of NPLs|Laeven and Valen|Stability|Crisis outcomes|Milder/No crisis|

22
# **Institutional Obstacles to Doing Business** Region-by-Region Results from a Worldwide Survey of the Private Sector'

Aymo Brunetti, Gregory Kisunko and Beatrice Weder

- This survey was conducted for the WDR 1997. We thank Ajay Chhibber for his support. We also want to thank Dan Atchison and Michael Geller for outstanding editorial assistance.

A companion background paper to this one explores the links between indicators of institutional uncertainty derived from this data set and cross-country differences in economic performance. See Brunetti, Kisunko and Weder (1997).
. To address this problem, we ran a series of regression equations for which the dependent variable is always real per capita expenditure, and the independent variables include geographic and household variables, as well as dummy variables for each quarter (with 2007:Q1 being the reference period). This allows us to net out the effects of the recession (or at least time) per se, after controlling for other influences – such as education or family composition – that might change over time. Each observation represents a household included in a socioeconomic survey between 2007 and 2010.

Losers and Winners in Recession: Thailand 2008-09 12
Indeed, there are good reasons to expect that commodity prices are relatively volatile. One is that commodities, by definition, are goods that retain their qualities over time, which allows economic agents to use them as financial assets. This might be the case, for example, of gold and other commodities whose prices tend to rise amidst global financial uncertainty. Caballero et al. (2008), for example, argued that the volatility of commodity prices could be due to the lack of a global safe asset (besides the U.S. Treasury bills). An earlier literature argued that commodity price volatility was fueled by stockpiling policies to secure access to food or fuel during times of relative scarcity (Deaton and Laroque 1992). These mechanisms add price volatility because of unavoidable asymmetric stockpiling rules; that is, the stockpile of commodities cannot be negative. Yet another potential explanation is the lumpiness of exploration investments in mining, which results in inelastic supply in the short run (Deaton and Laroque 2003). Finally, more traditional economic analysis of the effects of random demand shocks on homogeneous (i.e., commodities) and differentiated goods (i.e., manufactured products) also suggests that the resulting price volatility of the latter would tend to be lower as producers of differentiated products could maximize profits by reducing supply in response to negative demand shocks.

However, there are also good reasons to expect a higher volatility of differentiated manufactured goods. Product innovation and differentiation itself might contribute to price volatility by producing frequent shifts in residual demand for existing varieties. Indeed, the trade literature has acknowledged the wide dispersion in unit values of within narrowly defined product categories in the United States import data at the 10-digit level of the Harmonized System (HS) (Schott 2004). Also, the demand for differentiated products might be more unstable with respect to household and aggregate income shocks than that for basic 3
80 percent (CBO, 2006). Under this scenario, it estimated that demand for healthcare services would rise by 15 percent, and that no major consumption changes would occur in other sectors, such as utilities, professional activities, financial and ICT services, public administration and education.

# 3. Data Sources

The primary data source used throughout the paper is the European Labor Force Survey (EU-LFS), which is constructed by harmonizing nationally representative labor force surveys in all of the 27 member countries of the European Union, in addition to Norway, Switzerland and the United Kingdom. The total sample size ranges between 4.5 million and 5.2 million observations, depending on the year. The dataset annually compiles detailed information on labor-market outcomes of the working-age population and their demographic characteristics, such as gender, age, and educational attainment. We focus on the survey results from 2018, prior to the COVID19 pandemic as well as from 2020, the first year of the pandemic.

The dataset provides information on whether the individuals were born in the country in which they currently reside. We do not observe the specific country of birth or citizenship status of the migrant workers, but we see nine world regions. These are (i) the high-income EU-15 countries 6 OECD (2021).

8
These examples allude to the fact that statistical capacity varies by country characteristics, particularly by country income levels. Specifically, using the World Bank’s poverty database, we plot the number of poverty estimates against a country’s income (consumption) level (as measured in household surveys) over the past 40 years in Figure 1. The fitted line for the regression of these data points on country income is positive and strongly statistically significant, suggesting that countries with higher incomes more frequently implement household surveys. Indeed, a 10 percent increase in a country’s average income is associated with almost two-thirds (i.e., 0.67) as many surveys. This gap of survey data is consistent with the observation that poorer countries, especially in Sub-Saharan Africa, tend to have weaker statistical capacity (Devarajan, 2013; Jerven, 2013; Sandefur and Glassman, 2015; <mark>Dargent</mark> _<mark>et al.</mark>_ <mark>, 2018)</mark> .

The international community has long placed much attention on assessing and improving country statistical capacity—particularly for (poorer) countries with weaker capacity—to better guide international support. The Statistical Capacity Index (SCI) was a tool developed by the World Bank in 2004 to assess global improvements in country statistical capacity (World Bank, 2020). Most recently, it was replaced by the Statistical Performance Indicators and Index (SPI), which offers a clearer conceptual framework and broader country coverage (Dang _et al._ , 2023). Other global assessment tools that have been employed for evaluating country statistical capacity include the Open Data Inventory index (ODIN) (Open Data Watch, 2022), the Global Data Barometer index (GDB) and its predecessor the Open Data Barometer index (ODB) (Global Data Barometer, 2022). Regional assessment tools (e.g., the Ibrahim Index of African Governance Statistical Capacity (IIAG)) and self-assessment tools were also employed
) be the return to education, and let _re_ ( _·_ ) be the return to experience. While in school, where _rs_ ( _sit_ ) is the return to schooling level _sit_ . When not in school, where _re_ ( _sit, pit_ ) is the return to experience, which may depend on schooling _sit_ and on potential experience _pit ≡ ait −_ max _{aw, sit}_ .

Finally, GDP per person _pcGDPt_ equals total earnings divided by the population: We model human capital as earnings potential. Returns are based on earnings data.<sup>14</sup>

> 14 It could be that _rs_ and _re_ reflect different amounts of human capital. Alternatively, it could be that agents earn the same quantity of raw human capital in different countries but it is rewarded differently. Either interpretation of equations (1) and (2) is consistent with our quantitative exercises.
0_|_100.0_|
||||_URBAN_||||
|||**1985/86**|||**1992/93**||
|**Age Group**|**P0**|**P,**|**P2**|**P0**|**P,**|**P2**|
|16 to 25|1.2|_0.5_|0.4|2.0|0.0|0.6|
|26 to 35|17.6|6.1|9.6|13.1|10.1|10.9|
|36to45|29.9|35.8|28.8|31.9|31.9|33.1|
|**46**to 55|28.6|24.5|32.2|28.4|27.9|25.9|
|56 to 65|14.0|20.9|20.4|15.3|17.8|16.7|
|66 and Over|8.8|11.7|8.6|9.2|11.4|12.5|
|_All Urban_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|
|Source: NCS, 1|985/86 and 19|92/92.|||||

Table 4.18 shows the incidence of poverty by gender of head of household. Taking the rural areas first, in 1985 the headcount for male and female headed households was 50.5 percent and 42.1 percent respectively. In 1992 the values were 37.9 percent and 21.8 percent for male and female headed households respectively. There was a decline of 12.6 percentage
our panel sample, we can match firms with their banks<sup>10</sup> to identify the demand and supply on the SME financing market more accurately.

We also estimate the regressions using repeated cross-sections—to maximize the number of observations (countries and firms) and, thus, the statistical power for estimating the average crosscountry effect of Basel III implementation. However, the increased statistical power comes at the cost of not comparing identical firms before and after the treatment. We do not take the first difference when estimating the regressions for repeated cross-sections and include country- (and firm-) level fixed effects. Thanks to such broadening of the sample, our regressions for repeated cross-sections of firms contain more than 20,000 observations.

Basel III implementation may be nonrandom (Beck and Rojas-Suarez 2019) and our regressions may be susceptible to sample selection issues. To that end, we construct an inverse Mills ratio using a logit model that explains the probability that a country implements Basel III. We reestimate our baseline regression and the regression with heterogeneous impacts by firm and banking system characteristics, including the inverse Mills ratio to control for possible selection bias. The regression results are reported and discussed in the next section.

# **7. Regression Results: Basel III Implementation and SMEs’ Access to Finance**

This section reports and discusses our regression results. First, we investigate whether some quantitative factors could help explain the nonrandom implementation of Basel III in EMDEs, as highlighted by Beck and Rojas-Suarez (2019) and help us address possible selection bias in our main regressions.

Second, we present the baseline results for the repeated cross-sections and panel of firms estimated using the ordered logit model and adjusting for possible selection bias. For the panel of firms, we present the results with the matched bank-firm data as well as with bank systems characteristics.<sup>11</sup>

> 10 Not all firms report the name of the bank that granted credit to the firm.

> 11 In the latter case
3|56.4|<br>239.7|

1 Average monthly wage at the district level, based on the 2008 Agriculture Sample Survey. In case no data are available in a given district, the closest district data is used as a proxy.

# **IV. MAIN ESTIMATION RESULTS**

26. To estimate the translog cost function, both SUR and SFA are performed. The results are found broadly consistent with economic theory ( **Table 3** ). The implied price elasticities of production factor demand calculated based on Equation (6) range from -0.5 to -1.0 ( **Table 4** ). The elasticity of labor demand is estimated at about -0.6, the largest in absolute terms, implying that a small change in wages would result in a large change in employment. This is relatively large compared with the literature (e.g., Lichter et al. 2014) and may reflect the fact that the level of industrialization remains low in Mozambique and the demand for low-skilled labor is normally more elastic than the demand for high-skilled labor. The elasticity of electricity demand is estimated at -0.3, which is consistent with the literature, though it is statistically insignificant.<sup>6</sup>

> 6 The price elasticity of manufacturing energy demand is estimated at -0.28 to -0.49 in absolute terms in the United States (Anderson, 1981). As per Pindyck and Rotemberg (1983), the elasticity of U.S. manufacturing is - 0.36 in the short run, -0.58 in the medium run, and -0.99 in the long run. Energy demand elasticities also differ across industries (Denny _et al_ ., 1981). The short-run elasticity varies from -0.61 in the paper industry to nearly zero in the tobacco, metal fabricating, and machinery electrical industries. The long-run energy elasticities are also different between -
much smaller in percentage terms for welfare (6 percent) than it is for the other two variables (18 or 19 percent). On the other hand, the di¤erences can be striking. For example, Canada, which is the second only to the US in GDP and third judged by consumption, is third from the bottom in our welfare ranking. Canada, which leads Spain by 20 percent or more in terms of consumption and GDP per capita, trails Spain by about 10 percent in our welfare comparison. Indeed, Spain is last within our group of countries in terms of the conventional metrics of consumption and GDP, but ranks fourth in welfare terms, trailing only the US, UK and France. For the other countries, the welfare measure is not so kind. Japan trails the US by only 26 percent in GDP per capita, but double that— 52 percent— in terms of welfare. Similarly, Italy has more than 60 percent of the per-capita GDP of the US, but only about one-third the welfare level. On the other hand, France trails the US by 40 percent in consumption per capita, but by only half that amount in terms of a welfare. Thus, our measure clearly provides new information on welfare di¤erences among countries.

# 6 Relationship to the Literature

Measuring welfare change over time and di¤erences across countries using observable national income accounts data has been a long-standing challenge for economists. We note here the similarities and di¤erences between our approach and ones that have been taken before. We also suggest ways in which our results might be useful in other ...elds of economics, where the same question arises in di¤erent contexts.

Nordhaus and Tobin (1972) originated one approach, which is to take a snapshot of the economy’s ‡ow output at a point in time and then go “beyond GDP,” by adjusting GDP in various ways to make it a better ‡ow measure of welfare. Nordhaus and Tobin found
While limitations of early studies were borne from the measurement of infrastructure, the methodology adopted, and the empirical relationship established, these concerns were addressed with time as the literature evolved.

The key concern in the estimation techniques as well as in the use of production functions to represent the relationship in earlier studies was **_spurious correlations_** , where variables exhibit similar correlations even though they are unrelated. For example, Aschauer's (1989a) original aggregate time-series estimates suggested public capital has a large and positive impact on firms’ output and productivity (a 1% increase in public to private capital stocks yields a 0.39% increase in total factor productivity), though these estimates are likely too large to be credible due to the flawed methodology. That paper investigated output per unit of capital and total factor productivity, where the independent variables were government services. The structure of the data resulted in aggregate time-series data that are subject to common trends, thus yielding spurious correlation. Realizing these flaws, follow-up studies addressed the aforementioned concerns and improved the methodology used (Table 1). This resulted in scholars abandoning the use of the production function approach and instead adopting the use of first differences (e.g., Hulten & Schwab 1991; Evans & Karras 1994), which addresses the identification problem arising from spurious correlation. Targeting spurious correlation, Hulten and Schwab (1991) applied firstdifferencing methods to remove potential common trends. Similar to fixed-effects methods, firstdifferencing controls unobserved time-invariant region-specific fixed effects. Sturm and De Haan (1995) bring up another reason for estimating the model in first differences: nonstationarity of the time-series data used in the early work. Recently, this strand of the literature included updated and further refined empirical techniques. For example, Calderon et al. (2015) used pooled mean group estimators, which unrestrict short-run parameter heterogeneity across countries while imposing (testable) restriction of long-run parameter homogeneity.

Other key concerns raised toward the early literature were **_endogeneity and reverse causality_** , which
productivity across almost all the sectors. As highlighted by McMilan _et al_ . (2014), inter-sectoral productivity gaps are a feature of underdevelopment, and the data confirms that Guinea does not depart from that reality.

**Figure 1.** Guinea: Labor productivity (millions of GNF) across sectors and over time

<!-- Start of picture text --> Economy-wide<br>Government services<br>Community, social and personnel services<br>Finance, insurance, real estate and services to business<br>Transport and communication<br>Wholesale and retail trade, hotel and restaurants<br>Construction<br>Public utilities<br>Manufacturing<br>Mining and quarrying<br>Agriculture, hunting, forestry and fishing<br>0 30 60 90 120 150 180 210 240<br>2015 2006<br><!-- End of picture text --> Source: Authors’ calculations based on data from the INS.

Another interesting stylized fact about Guinea’s labor market, is the concentration of workers in lower-productivity sectors. While agriculture is the least productive sector, it concentrates about 67 percent of workers, making agriculture the largest employer sector in Guinea. On the other hand, mining is the most productive sector, but one of the least employer sectors in Guinea. Thus, there is a negative correlation between sectoral productivity and sectoral share of total employment.

Overall, the data highlight a dual economy in Guinea, where agriculture, the least productive sector co-exists with non-agriculture sectors that are relatively more productive. This is not a bad thing, if labor could flow from agriculture to more productive sectors of the economy. The question is whether such flow of labor happens in the Guinean economy. This is what we analyze in the next section.

11
from labor market exclusion such as unemployment or inactivity, the binary status employed/unemployed or active/inactive cannot be used to estimate individual deprivation. All these considerations motivated our search for a labor deprivation measure.

# **5.2 Relative deprivation in labor space**

In this section, we shift from relative deprivation in the income space to relative deprivation in the labor space. In doing so, we also turn from the household dimension to the individual dimension of relative deprivation. This will allow us to be more precise on assumptions related to the relevant reference group for individuals and for inter-personal comparisons.

Table 5 reports descriptive statistics of labor market outcomes as well as the characteristics of individuals used in the estimations of relative labor deprivation in the paper. The statistics is limited to those individuals who can feasibly be in the labor market, i.e., to working age individuals who are not enrolled in school. In general, employment levels are very low in the sample with only 40 percent of the working age population being employed. Among young people aged 15-29, employment levels are considerably lower than the working age population as a whole, with only one in three people being 14