rafmacalaba/datause-encoder-data huggingface dataset:
Some caveats apply to the use of efficiency<sup>ranks for STATE banks in developing nations.These banks</sup> may have goals other than minimizing<sup>costs or maximizing profits (e.g.,"directedlending"),whichmay make</sup> their measured efficiency ranks inaccurate<sup>indicators of managerial quality.As well, these institutions often have</sup> unusual balance sheets. STATE banks<sup>in some cases operate with very little equity (their equity is effectively the</sup> nation's treasury). Profit efficiency<sup>measures forthesebanks mayalsobecompromisedinsomecasesby</sup> understated losses or overstated<sup>revenueson problemloans,as STATEbanks oftenrolloverorreschedule</sup> payments on such loans, rather than classifying<sup>them as nonperformingor losses(Hanson 2002).</sup> Similar concerns apply to the measured<sup>efficiency ranks of FOREIGNbanks in developing nations,as</sup> there may be cross-subsidies from the<sup>parent banking organizationin another nation.As well, FOREIGNbanks</sup> may be able to operate with very little<sup>financialcapital, in effect, using the capital of organization as a whole.17In</sup> some cases, FOREIGN banks may<sup>be set up to serveestablished corporatecustomers withoperationsin many</sup> nations, so that some of the related<sup>costs and revenues may be bookedin other nations.</sup>

# **4. Empirical models and results**

We first briefly show our empirical models.<sup>We then give the empiricalresults using SMALL banksas</sup> our definition of community banks<sup>for both developed and developing nations.We also report the findings when</sup> we also allow STATE and FOREIGN<sup>banksto actas community banksfor developingnations.As well, we</sup> briefly discuss some of the measured<sup>effects of the controlvariablesandreport additional robustnesschecks.</sup>

# **4.1. Empirical models**

For our first set of tests of the effects of<sup>the relative health of community banks on economicgrowth for</sup> developed nations, we run the reduced-form<sup>regressions:</sup> GDP GROWTH = a + Pi *SMALL<sup>SHARE + P</sup> 2<sup>*SMALL EFFRANK</sup> + 012*SMALL SHARE x EFF RANK + yi*MKTCAPGDP + y2*ECON FREEDOM + y3*COMMON LAW + **y4*** BANK<sup>CR3</sup> + Control variables for region and year. (2) 7 Our definition of FOREIGN banks<sup>includes both subsidiaries and branches of foreign banks, although we do not makea</sup> distinction in our empirical analysis. In<sup>some nations, subsidiariesand branches are subject to different capital requirements.</sup> 15
Policy ReseaRch WoRking PaPeR 4887

# **Abstract**

The spike in food prices between 2005 and the first half of 2008 has highlighted the vulnerabilities of poor consumers to higher prices of agricultural goods and generated calls for massive policy action. This paper provides a formal assessment of the direct and indirect impacts of higher prices on global poverty using a representative sample of 63 to 93 percent of the population of the developing world. To assess the direct effects, the paper uses domestic food consumer price data between January 2005 and December 2007—when the relative price of food rose by an average of 5.6 percent —to find that the implied increase in the extreme poverty headcount at the global level is 1.7 percentage points, with significant regional variation. To take the secondorder effects into account, the paper links household survey data with a global general equilibrium model, finding that a 5.5 percent increase in agricultural prices (due to rising demand for first-generation biofuels) could raise global poverty in 2010 by 0.6 percentage points at the extreme poverty line and 0.9 percentage points at the moderate poverty line. Poverty increases at the regional level vary substantially, with nearly all of the increase in extreme poverty occurring in South Asia and SubSaharan Africa.

This paper—a product of the Development Prospects Group, Development Economics—is part of a larger effort in the department to monitor the poverty and income distribution impacts of global economic trends and policies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmedvedev@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
> where the treatment amount offered to firms is used to instrument capital stock, _Ki,t_ .

Table 3 reports the estimated return to capital from estimating equation (2). Columns 1 through 4 repeat the specifications used in Table 2, using random effects, fixed effects, trimming the level of profits, and trimming changes in profits. Columns 5 through 8 adjust for potential outliers in capital stock, by additionally trimming the top and bottom 1 percent of initial capital stocks. After trimming profits we get real returns to capital in the 6.8-6.9 percent per month range, which increase to 7.3-7.4 percent once we trim outliers in initial capital stock.

The lower panel of Table 3 shows the coefficient on the treatment amount in the first stage regression for capital stock. The treatment amount is a strongly significant predictor of changes in capital stock (F-statistic always above 41), with each 1000 rupees of treatment increasing capital stock by 744-800 rupees. This explains why the return on capital is higher than the treatment effect, since the change in capital stock is less than the treatment amount.

# **6. Controlling for potential Treatment Spillovers**

The key condition for randomization to provide us with a valid estimate of the treatment effect and of returns to capital is the standard unit treatment value assumption, which requires that the potential outcomes for each firm are independent of its treatment status, and of the treatment status of any other firm (Angrist, Imbens and Rubin 1996). As Miguel and Kremer (2004) and Duflo, Glennerster and Kremer (2006) note, the presence of spillovers can cause this assumption to be violated, leading to biased estimates of the treatment effect. It is therefore important to test for spillover effects arising from our grants to firms.

We collected the GPS coordinates of each firm in our survey, taking advantage of improvements in precision and technology which allow location to be measured accurately to within 15 meters, 95 percent of the time (Gibson and McKenzie, 2007). This allows us to construct a measure of the number of treated firms in the same industry at any given point in time within a given radius of each firm. In our baseline survey, the 14
Policy Research Working Paper 9344

# **Abstract**

The expansionary fiscal contraction (EFC) hypothesis states that fiscal austerity can increase output or consumption when a country is under heavy debt burdens because it sends positive signal about the country’s solvency situation and long-term economic wellbeing. Empirical tests of this hypothesis have suffered from identification concerns due to data sources and empirical methodology. Using a sample of OECD countries between 1978 and 2014, this paper combines new IMF narrative data and the proxy structural Vector Auto-regression (SVAR) method to examine whether fiscal austerities can be expansionary when debt levels are high. Fiscal austerities are measured as 1) narrative fiscal shocks and 2) structural shocks from a proxy SVAR. Additionally, this paper uses a model-based approach to determine the cutoff debt level beyond which EFC is expected to be observed. This paper finds empirical evidence in support of the EFC hypothesis for OECD countries: results for output are driven by changes in tax rates and are robust to how one defines a high-debt regime and how one measures austerity.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at onie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Inequality Is Bad for Growth of the Poor (But Not for That of the Rich) Roy van der Weide and Branko Milanovic<sup>1</sup> Keywords: inequality, poor, rich, growth, United States JEL Classification: D31 Total number of words: Around 11,000.

> 1 Respectively, World Bank Research Department, and Graduate Center City University of New York. Corresponding author: rvanderweide@worldbank.org. A thank you goes to Bob Rijkers and Peter Lanjouw for providing useful comments. We also gratefully acknowledge financial support received from the World Bank’s Knowledge for Change Program (KCP II).
# **TABLE II. Correlations between Shocks and Maternal Mental Health Indexes (MHI) in 2002**

|**in 2002**|(1)|(2)|(3)|
|---|---|---|---|
|_Panel A. Descriptive statistics of Maternal MHI_|Mean|S.D.|N|
|MHI-1|-0.02|0.50|1095|
|MHI-2|-0.04|0.98|1095|
|MHI-3|-0.04|0.92|1095|
|_Panel B. Shocks experienced by mothers during pregnancy or_<br>_within thefirstyear after the child was born(in 2002)_|Mean|S.D.|N|
|Crop or livestock loss|0.03|0.16|1095|
|Death, severe illness, divorce|0.13|0.34|1095|
|Birth/new household member|0.06|0.24|1095|
|_Panel C. Correlations between shocks and Maternal MHI in_<br>_2002_|MHI-1|MHI-2|MHI-3|
|Crop or livestock loss|0.34***|0.70***|0.65***|
|Death, severe illness, divorce|0.15***|0.32***|0.29***|
|Birth/new household member|0.13**|0.27**|0.24**|

Table II present summary statistics (mean and standard deviation) of maternal mental health indexes and shocks experienced by mothers of our sample of analysis. These variables are available in the first round of the Peruvian Young Lives Survey (2002). Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. Mental health index 1 is the standardized average of the SRQ-20 items. Panel A presents statistics of mental health indexes. Mental health index 2 and 3 are standardized indexes estimated using principal components and factor analysis, respectively. Panel B presents the % of mothers reporting being exposed to any of the four shocks. Panel C shows correlations between the mental health indexes and shocks. *** and ** indicate statistical significance at 1% and 5%, respectively.

27
Similarly, in the allocation of entrepreneurship model, the stock of entrepreneurs in the economy is relatively constant, but the nature of their activities changes.

The motivation for entrepreneurs to operate in the formal versus informal sector is examined further in our empirical analysis. We find that the magnitude of differences reported in the datasets across countries is related to the institutional and environmental conditions for entrepreneurs. In terms of institutional differences, we find that the conditions related to registration, operation and closure of business are important; and in terms of environmental differences, we find significant affects of economic and political conditions. Overall, entrepreneurs in developed countries have greater ease and incentives to incorporate, both for the benefits of greater access to formal financing and labor contracts, as well as for tax and other purposes not related to business activities. We elaborate on this further in the comparative analysis section of this paper.

# **2. Data Description**

# **_2.1 GEM_**

The Global Entrepreneurship Monitor (GEM) project is unique in that while all countries collect official data on self- employment, the size distribution of firms, census data on all or most plants and firms, firm and plant entry, almost none of these registry sources are comparable across countries, even in developed countries. Official data sources differ in the way they define when an establishment enters a file and when it leaves, and how they handle self-employment makes cross-national comparisons almost impossible.<sup>3</sup> Therefore, one of the major strengths of the project is the application of uniform definitions and data collection across countries for international comparisons.

The intent of GEM is to systematically assess two things: the level of start-up activity or the prevalence of nascent firms and the prevalence of new or young firms that have survived the start-up phase. First, start-up activity (the “nascent” rate) is measured by the proportion of the adult population (18-64 years of age) in each country that is currently engaged in the process of creating a nascent business. Second, the proportion of adults in each country who are involved in operating a business that is less than 42

> 3  For a discussion of the GEM data see Reynolds, P. D. et al.,. “Global Entrepreneurship Monitor:  Data Collection Design and Implementation 1998-2003.” _Small Business Economics,_

3
_Figure 1: Evolution of the Average Technical Efficiency of Container Terminals in LAC_ <!-- Start of picture text --> 55 53<br> 50<br>Compound Annual 49<br> 50<br>Growth Rate = 3.5%<br> 45<br> 45<br> 41 41<br> 40 38 38<br> 36 35 35<br> 35<br> 30<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>Source: Own Calculations<br>Technical Efficiency, %<br><!-- End of picture text --> _Table 9: Average Technical Efficiency of Container Ports by sub-region_

||1999|2004|2009|
|---|---|---|---|
|Central America|30.8%|38.8%|38.7%|
|South America|37.2%|41.4%|43.9%|
|Caribbean|42.1%|42.0%|40.6%|
|Source: Own Calculations||||

# **7. Conclusions**

In an effort to assess port technical efficiency in Latin America and the Caribbean, we developed a Stochastic Frontier Analysis using a panel of 67 container ports for the period 1999–2009. The output variable in the production function is annual container throughput, whereas the input variables are total area, berth length, and number of cranes in container terminals. Our model also evaluates three other port productivity sources: (i) we consider ship-to-shore gantry cranes and mobile container cranes as separate variables, in order to account for the higher productivity of the former; (ii) we use a binary variable indicating ports that take advantage of cranes mounted on vessels for container handling; and (iii) we use a binary variable indicating ports whose main form of container traffic is transshipment. We also control for other exogenous effects such as terminal purpose, national trade flows, maritime connectivity and GDP. Moreover, following the Battese and Coelli (1995), in a onestep estimation we determine inefficiency as a linear function of independent variables, such as port ownership, terminal purpose, corruption and income. To our knowledge, this is the first estimation of technical efficiency using Stochastic Frontier Analysis in a large sample of ports in the LAC region.

The estimations indicate that the gains in productivity from the use of ship-to-shore gantry cranes are the largest among the inputs considered followed by berth length, which has the second largest elasticity. Moreover, the effect of the binary variables in the model is positive and significant, confirming the premise that ships’ cranes and transshipment traffic are significant sources of productivity in the region and that these variables improve the accuracy of parameter estimation. The inclusion of the control variables terminal purpose (either container or multi-purpose), country’s GDP, shipping liner connectivity and trade openness, also help explain output in the production function and disentangle technical efficiency.

In order to associate technical efficiency with its potential explanatory factors, we have incorporated a conditional mean structure to the model’s inefficiency term. The results revealed that technical efficiencies in our sample are significant and time-varying. First of all, the findings show that there are efficiency gains in specialized container terminals with respect to multi-purpose 20
# **Appendix G: Table G1: Sample Descriptive Characteristics by Gender**

|**Panel A: Entrepreneur Variables**|(1)<br>Male<br>Mean/SE|(2)<br>Female<br>Mean/SE|(3)<br>t-test Difference<br>(1)-(2)|
|---|---|---|---|
|Age of the respondent|37.48|38.49|-1.00***|
|Highest Education: University or college|0.59|0.62|-0.02***|
|Highest Education: More than secondary|0.20|0.21|-0.02***|
|Highest Education: Secondary|0.17|0.14|0.02***|
|Highest Education: Primary|0.03|0.02|0.01***|
|Having a long-term partner or spouse (Yes=1; No=0)|0.72|0.72|0.00|
|Children under 5 years live in my household (Yes=1; No=0)|0.25|0.22|0.03***|
|Children 6 to 18 years live in my household (Yes=1; No=0)|0.37|0.41|-0.04***|
|Dependents age 65+ live in this household (Yes=1; No=0)|0.18|0.15|0.03***|
|Household income per month USD (Winsor 0.05)|3062.30|3013.76|48.54|
|Main Income Earner in the household (Yes=1; No=0)|0.81|0.57|0.24***|
|Business has a line of credit or a loan|0.34|0.27|0.07***|
|Receiving any form of financial assistance COVID-19|0.18|0.22|-0.04***|
|**Panel B: Firm Level Variables**|(1)|(2)|(3)|
|Role: Both own and manage a business|0.47|0.49|-0.02***|
|Role: Manage a business|0.16|0.13|0.02***|
|At least one employee|0.75|0.60|0.16***|
|Firm Size = No Employees|0.23|0.38|-0.16***|
|Firm Size = Micro (1 to 4)<br>|0.38|0.37|0.01***|
|Firm Size = Small (5 to 49)|0.30|0.19|0.12***|
|Firm Size = Medium and large (50 and over)|0.08|0.05|0.03***|
|Annual Revenues 2019 in USD (Winsor 0.05)|127224.24|83004.73|44219.50***|
|**Panel C: Firm Sector**|(1)|(2)|(3)|
|Information and communication|0.10|0.05|0.05***|
|Agriculture, farming, forestry, or mining|0.04|0.02|0.02***|
|Transportation and logistics|0.03|0.01|0.02***|
|Hotels, cafes and restaurants|0.09|0.09|-0.00|
|Construction|0.08|0.02|0.05***|
|Manufacturing|0.05|0.03|0.02***|
|Services|0.28|0.33|-0.05***|
|Other Sector|0.12|0.17|-0.05***|
|Retail and Wholesale<br>Notes: N = 154,458. The number of observations for HH income and revenues ar<br>waves and had greater non-response than indicator variables.|0.20<br>e less because they we|0.27<br>re only collecte|-0.07***<br>d in certain survey|

Notes: N = 154,458. The number of observations for HH income and revenues are less because they were only collected in certain survey waves and had greater non-response than indicator variables. The value displayed in Column (3) presents the difference between columns (1) and (2) i.e. t-tests showing differences in the means across male and female entrepreneurs in the sample.

<mark>***, **, and * indicate significance at the 1, 5, and 10 percent critical level.</mark> 75
tested using the GDP per capita growth rate. We re-estimate our simulations for the study periods and country classifications incorporating GDP per capita growth information (the results are summarized in Figure A 8 in Appendix A). These new results are consistent with those reported in previous sections with similar magnitudes and directions. The main difference is that extreme poverty is reduced by a more significant proportion in 2030 when using GDP per capita growth rates (see Figure A 8, Panel A and B, Appendix A) compared to the outcomes obtained using consumption growth rates (see Figure 2 and Figure 3).

# **VII. CONCLUSIONS**

This research contributes to the discussion on future economic growth, poverty, inequality, and shared prosperity measures. Our analysis exploits two country typologies to show heterogeneity of predictions and uncertainty in poverty outcomes and income distribution conditions. The first typology splits the economies by the size of their natural resource sectors. The second country classification relies on the speed of the output per capita growth. Although our typologies are arbitrary, our results and robustness checks suggest that these studied country classifications matter significantly in poverty eradication and income distribution attainments, possibly capturing and indicating institutional quality. The primary finding of this paper shows a significant level of uncertainty in future poverty and income distribution achievements at the global level and by country classifications. One important caveat of the article is that our predictions are based on recent historical performances. This recorded history used in our simulations suggests that countries performing poorly—in terms of future poverty reduction and shared prosperity attainments—should emphasize efforts to implement policies that boost economic growth and provide social safety nets as a hedging mechanism, especially in the most impoverished and unprotected sectors of their societies.

We summarize the three main results of this study. The first significant insight is related to the predicted poverty headcount. The results indicate that despite the continuous decrease in relative and absolute poverty, alleviating extreme poverty below the 3 percent threshold by 2030 is very optimistic at the global level and involves considerable uncertainty. The simulations generated by combining the information from the 2000–2014 sample period with GDP per capita  growth rates which were used to complement the household survey mean income Page **28** of **45**