rafmacalaba/data-use-mentions-extended huggingface dataset:
Between 1993 and 2008, interpersonal inequality for Sub-Saharan Africa as a whole increased. For example, the Africa-wide Gini increased from 52% in 1993 to 56% in 2008. By contrast, Beegle et al. (2016) find no systematic increase of _within_ -country inequality in the region. Taken together, the country-decomposition of regional inequality shows that the rise in regional inequality was driven by increasing inequality between countries, i.e. an increasing dispersion in average consumption across countries. However, within-country differences continue to dominate the level of African inequality. The African growth incidence curve is upward-sloping, consistent with the increasing inequality. While mean African consumption grew at around 1% per year, living standards for the bottom third of the distribution stagnated, while the top 5% received some 40% of the total gains between 1993 and 2008 (with a growth rate of around 2% per year).

The paper is structured as follows. First, we explain the construction of our database, and then show summary statistics. Section IV presents results for the cross-sectional distribution and its inequality. Overall African inequality is decomposed into between- and within-country components in Section V. In Section VI, we study growth incidence curves, looking at both relative and absolute gains along the distribution. Section VII compares these results with what can be learnt from GDP per capita alone. We analyze relative movements over time of specific country groups in the regional distribution in Section VIII, and the final section concludes. The Appendix presents a number of robustness checks, especially the results with the 2005 PPPs. Throughout, we draw comparisons with regional results for East Asia and Pacific from a companion paper (Jirasavetakul and Lakner, forthcoming). In the early 1990s, average consumption per capita was comparable in the two regions or even slightly lower in East Asia. However, since then growth in East Asia has been much faster and, while regional inequality also increased, its growth appears less concentrated than in Africa.

# **II. Data Construction
/mark>_ <mark>97, no. 1: 88‐98.</mark>

- <mark>Gupta, Manash Ranjan. 1993. "Rural‐urban migation, informal sector and development policies A theoretical analysis."</mark> _<mark>Journal of Development Economics</mark>_ <mark>41, no. 1: 137‐151.</mark>

- <mark>Harris, John R., and Michael P. Todaro. 1970. "Migration, unemployment and development: a two‐sector analysis."</mark> _<mark>The American economic review</mark>_ <mark>: 126‐142.</mark>

- <mark>Henley, Andrew, G. Reza Arabsheibani, and Francisco G. Carneiro. 2009. "On defining and measuring the informal sector: Evidence from Brazil."</mark> _<mark>World Development</mark>_ <mark>37, no. 5: 992‐1003.</mark>

- <mark>Ihrig, Jane, and Karine S. Moe. 2004. "Lurking in the shadows: the informal sector and government policy."</mark> _<mark>Journal of Development Economics</mark>_ <mark>73, no. 2: 541‐557.</mark>

- <mark>ILO, latest available.</mark> _<mark>ILO Global Wage Database</mark>_ <mark>. Geneva: International Labour Office.</mark>

- <mark>ILO and WIEGO. 2012. “Statistical Update on employment in the informal economy.” ILO Department of Statistics.</mark>

- <mark>Johnson, Simon, Daniel Kaufmann, and Pablo Zoido‐Lobaton. 1998. "Regulatory discretion and the unofficial economy."</mark> _<mark>American Economic Review<
Given the constraints discussed above, generating evidence to guide policy makers on the overall issue of how to improve investment efficiency appears to deserve a high rank on the list of applied research priorities.

# **References**

Cameron, A. C. and D. L. Miller. (2015). “A Practitioner's Guide to Cluster-Robust Inference”, _Journal of Human Resources_ , Vol.50, No. 2, pp.317-373.

CEDLAS and The World Bank. (2014). “A Guide to SEDLAC Socio-economic database for Latin America and the Caribbean.” http://sedlac.econo.unlp.edu.ar/eng/methodology.php Costa, F., and F. Gerard. (2015). “Hysteresis and the Social Cost of Corrective Policies: Evidence From a Temporary Energy Saving Program”, mimeo.

Davis, L., and P. Gertler. (2015). “Contribution of air conditioning adoption to future energy use under global warming”. _Proceedings of the National Academy of Sciences USA_ 112(19): 5962–5967.

Dinkelman, T. (2011). “The effects of rural electrification on employment: New evidence from South Africa”, _American Economic Review_ , Vol. 101(7) pp. 3078-3108 Estache, A., and M. Fay. (2009). Current Debates on Infrastructure Policy. Comm. Growth Dev., World Bank Publication.

Fay, M. and T. Yepes. (2003). “Investing in Infrastructure: What is Needed from 2000 to 2010?” World Bank Policy Research Working Paper No. 3102.

Ferreira, F. H. G., Messina, J., Rigolini, J., López-Calva, L.-F., Lugo, M. A. and R. Vakis. (2013). _Economic Mobility and the Rise of the Latin American Middle Class_ . Washington, DC: World Bank.

Galiani, S., P.
probability _ψ_ , while a new entrepreneurial productivity is drawn with the complementary probability 1 _− ψ_ .

The remaining parameters to be calibrated are _α_ + _θ_ , _η_ , _ψ_ , _β_ and the collateral constraint _λ_ of the initial stationary equilibrium. To do so, we target the following moments in the US data: employment share of the top decile of establishments by size (number of employees), the top 5 percent earnings share, the annual exit rate of establishments, the real interest rate, and the ratio of external finance to total non-financial assets of the non-financial business sector.

||US Data|Model|Parameter|
|---|---|---|---|
|Top 10% Employment Share|0.69|0.69|_η_ = 5_._25|
|Top 5% Earnings Share|0.30|0.30|_α_+_θ_ = 0_._79|
|Establishment Exit Rate (Annual)|0.10|0.10|_ψ_ = 0_._97|
|Real Interest Rate (Annual)|0.04|0.04|_β_ = 0_._98|
|Credit Market Instruments to Non-Financial Assets|0.70|0.70|_λ_= 7_._5|

**Table 1:** Calibration

Table 1 shows the moments in the US data and their counterparts in the calibrated model. The decile of the largest establishments (in terms of employment) accounts for 69 percent of total employment in 2000. The earnings share of the top 5 percentiles is 30 percent in 1998. The annual establishment exit rate is 10 percent in the Business Dynamics Statistics from the US Census. We assume that the annual interest rate is 4 percent. Lastly, we target the ratio of credit market instruments to total non-financial fixed assets in the non-financial business sector of 0.7, a level attained one year before the 2008 financial crisis.

Although all parameters are jointly pinned down in the model equilibrium
There is a growing empirical literature documenting the relationship between indicators of various aspects of governance and economic outcomes.5 Using the six aggregate governance indicators described above, we contribute to this literature by providing new empirical evidence of a strong causal relationship from improved governance to better development outcomes. Following the approach of Hall and Jones (1999), we estimate a series of very parsimonious regressions of the log-level of per capita income on each of the six aggregate governance indicators in turn. We correct for reverse causation, omitted variable bias, and measurement error in the governance variables, by using the shares of the population speaking European languages as instruments. We find that govemance matters a great deal for economic outcomes. In particular, a one-standard deviation increase in any one of our governance indicators causes between a two-and-a-half and four-fold increase (decrease) in per capita incomes (infant mortality), and a 15 to 25 percent increase in literacy. Tests of overidentifying restrictions and summary statistics of the explanatory power of the firststage regressions provide support for the validity of this modelling strategy.

The remainder of this paper proceeds as follows. Section 2 briefly describes the governance database, with the details of each source of data and each governance measure relegated to a lengthy Appendix 1. Section 3 summarizes the components of each of the six govemance aggregates and explains the aggregation procedure. The details of the composition of each aggregate are presented in a series of tables in Appendix 2. Section 4 contains new empirical evidence on the relationship between governance and development outcomes.

This literature includes Mauro (1995) on the effects of corruption on economic growth and investment; Loayza (1996) on the determinants of the unofficial economy; Ades and DiTella (1996) on the causes and consequences of corruption; Knack and Keefer (1997) on the importance of institutions for economic growth; Tanzi and Davoodi (1997) on corruption and public investment; Wei (1997) on the effects of corruption on FDI; Rodrik (1997) on the role of instiitutions in the success
|.00291956|
|Rainfall - 3o Princ. Component|.01082346|.00425524|.0101329|.00634718|
|Employment rate in 1991|.50765127|.14583623|.55593884|.22256766|
|crit. fn. test of overid. restrictions|----|----|4.3194265|4.3194265|
|Dependent Variable:<br>Delta ln Employment|Spatial GMM<br>Est.<br>(Cutoff = 1)|Spatial GMM<br>SE<br>(Cutoff = 1)|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|Intercept|-.58264148|.21309273|-.55973566|.23194173|
|ln Wage in 1991|.17833635|.02288821|.17472738|.02582378|
|ln 5 to 15 over 15 to 55 yrs Ratio|<br>.32701012|.05974778|.32103962|.06498975|
|Proportion of Natives in 1991|.28581622|.07064752|.2838648|.08167097|
|Delta ln Market Potential|.22956501|.04807927|.22942847|.05732953|
|ln Teacher qualification in 1991|-.01069225|.00410323|-.01055971|.00482079|
|Homicides in 1991|-29.090509|16.986828|-34.329254|26.672443|
|Employment in Farming in 1991|-1.1970719|.28437662|-1.2222365|.31455466|
|Bank Dummy|-.00550414|.01287836|-.00412273|.01383933|
|ln Population in 1991|-.028508|.00898918|-.02745768|.01065978|
|Mean Temperature|-.00111862|.00469047|-.00119964|.00556795|
|Total Rainfall|.00003371|.00002314|.00003857|.00002848|
|Rainfall
are likely reassigned, with new demands on income-earning activities or household-related chores that may impose constraints on time use by secondary age youth, which can affect school attendance.<sup>19</sup> The marginal effect on school enrollment of being in a single-parent household is negative in every country and significant in most cases.

If single-parenthood is associated with migration, the value of the marginal effect for this variable should be added to the positive effect of remittances to obtain a more complete sense of the overall effect. When doing so, we find that the positive effect of remittances on enrollment is canceled out in Costa Rica and Guatemala (although marginally). In the other countries where both effects can be measured, the positive effect prevails. These positive correlations, however, have not held up in research for El Salvador that attempts to control for a more complete set of factors that may affect both remittances and school participation through various techniques (Propensity Score Matching, Instrumental Variables, etc.) and by using panel data (Acosta 2011a; Acosta 2011b). In these papers, the author finds that both the gender of the migrant and the gender of the remaining youth matters – for example, while male migration does not significantly affect enrollment, female migration reduces it even though women send more remittances, potentially in line with the discussion of reassignment of household duties discussed above.

> 18 Indigenous group status is also self-reported, which may affect how these correlations are interpreted. 19 Evidently, there are other reasons why a household can be of single parent, but due to the high migration rates in some CA counties it is likely that an important source is precisely the exit of one parent that seeks employment in another region or country.

15
.. .. · • eo_,utable Geaeral lqu1libri .. Models for Trade Policy Allalyaia in De~elopiag Countries: A Saney

# 1. Introduction

• · The use of computable general equilibrium (CGE) models for policy analysis has become widespread for both developed and developing economies. Por developed economies, with a few notable exceptions, applications have had a .tcroeconomic focus with the analysis concentrating on the estimation of the welfare impact of alternative tax structures or energy policies. _JJ_ In de•elopina countries, CGE models have been used for a wider range of issues, r&llliaa froa JHdiua- to lonr-tera ucroeconomic policy. analysis to the more traditional .tcroeconomic issues analyzed in developed countries as well. _lJ_ There are a nuaber of reasons for this wider range of applications, including the fact that notwithstanding the use of econometric models in the framework of LlNl, developing countries are usually not well suited for economywide policy analysis relying on econometric techniques. This is so for several reasons. Firat, and foremost, reliable time-series data for sufficiently long ct .. periods are usually not readily available. Second, when available, the data are often not appropriate for standard econometric analysis without considerable further preparation to remove inconsistencies. Last but not least, sigaificant changes in policy regimes often take place calling for different structural models, thereby reducing the time span available for hypothesis testing with a selected model.

CG! models have found much use for mediu.- to long-term policy analysis in developing countries. The policy applications have ranged from lona-run issues like the the impact of alternative development strategies on arowth and resource allocation or policy towards exhaustible resources, to .ediu.-run issues such aa rural-urban migration, labor markets and employment, ' - ··· . '.
of 0.288. This point estimate is about 73% as large as the estimate when considering only insured crops. Given that in the average municipality 76% of the land is devoted to insured crops, this result suggests that the increase in total land area is coming almost exclusively from insured crops. The results using alternative specifications, reported in columns 2 and 3, are similar in magnitude, although less precisely estimated. Two potential mechanisms can explain these results: learning about insurance policies and credit constraints. In the first mechanism, farmers either do not trust that payments will be made in case of a weather shock or there is some ambiguity about how the policies work. When they receive a payment following a weather shock the ambiguity and/or uncertainty is resolved, and given the promise of insurance coverage they now find it optimal to expand the area under cultivation with insured crops. This mechanism is consistent with the result that the area expansion comes mostly from insured crops. The second alternative is that after a bad harvest farmers do not receive sufficient income to cover the cost of inputs for the following growing season, and the presence of credit constraints means they cannot borrow funds to optimally invest in inputs. The inflow of cash from insurance payments eases this credit constraint. While farmers with such a constraint would face a tradeoff between yield on a given plot and total area sowed, their decision to put inputs toward sowing a larger land area is reasonable given that any additional area they sowed would be insured, but they would not receive a larger payment for plots with higher expected yields. In interviews carried out in Mexico, farmers have said that the payments of 1,500 pesos/ha provided by the insurance do not fully cover the cost of inputs for the average plot of that size. Lastly, these two mechanisms are by no means mutually exclusive such that the observed impacts reflect a combination of the two effects.

Next, we turn to economic outcomes as measured in the household income and expenditure surveys. Panel
## **_3.3. Industry Growth Rates_**

Our dependent variable is the average annual growth rate of value added. We use the data obtained by RZ from the _Industrial Statistics Yearbook_ database put together by the United Nations Statistical Division (1993). We also use a decomposition of the industry growth rate. Specifically, we consider the growth in the number of establishments and the average size of establishments. **11** Table 3 lists descriptive statistics and correlations between the three different dependent variables. We note that the growth rate in the number of establishments is negatively correlated with the growth rate in the average size of firms. This suggests that industries grow either due to the entry of new firms or due to the expansion of existing firms, but not both.

# **4. The Results**

### **_4. 1 Financial Development and Industry Growth_**

We first present the results of regressions of industry growth rates on the interaction of financial development and external dependence. Our regressions differ from those presented by RZ to the extent that (i) our measures of financial development capture both the effects of financial intermediary and stock market development, and (ii) we include the indicators of financial sector development in logs instead of levels to allow for the nonlinearity in the relationship between financial development and growth illustrated by Levine, Loayza, and Beck (2000). To make our results comparable to those in RZ, we include the "Differential in real

> 11 There are no cross-country data available on firms. An establishment is defmed as a "unit which engages, under a single ownership or control, in one, or predominantly one, kind of activity at a single location." The growth in the number of establishments (average size of establishment) is defined as the log difference of the number of

18
12 decisions. In 1997 antidumping initiations declined relative to 1996 but remained high compared to the early 1990s.

Table 2: Antidumping Activity in Latin America - Initiations, 1989-97

|Country|1989|1990|1991|1992|1 1993|1994|1995|1996|1997|
|---|---|---|---|---|---|---|---|---|---|
|Argentina|||l||||6|42|18|
|Brazil|2||2|9|4|30|12|1|19|
|Chile|||||||2|4|2|
|Colombia||||||4|1|5<br>|1|
|Guatemala|||||_|||1||
|Mexico|17|9|14|25|24|23|18|3|5|
|Peru|||||=||4|**4**|**3**|
|Venezuela|||||||1|**5**||
|LAC Total|19|9|16|34|28|57|44|65|48|
|World Total|118|102|179|234|245|247|160|149|190|
|(Rest of World)|99|**93**|**160**|**200**|**217**|**190**|**116**|**84**|**142**|
|LAC %**of World**|**16%**|**8%**|**10%**|**15%**|**11
.125|-0.325|-0.062|
|Lostjob|-1.602***|-1.092*|-1.701***|
|Lost farm|-0.516|-0.659|-0.332|
|Lost livestock|-0.682***|-0.036|-1.066***|
|Lost assets|-0.200|0.049|-0.273|
|Affected bythe war|-0.058|-0.168|-0.035|
|Experienced violence|-0.240***|-0.078|-0.292***|

Notes: The household level controls include log per capita consumption expenditure, gender, gender of household head, average years of education in the household, ethnic groups and religious groups; *** implies significant at 1%, ** implies significant at 5% and * implies significant at 10%.

# **VI. Conclusion**

The relationship between education and war in Côte d‟Ivoire is complex. While anecdotal evidence from various reports and studies suggests that education has been a clear victim of war, 24
Watson hypotheses on a restricted sample of Rauch-differentiated products where he proxies search costs by the number of potential suppliers and reliability by income levels.

Evidence on trade flows from other countries largely confirmed these early findings. The determinants of export duration were explored by Nitsch (2009) using Cox regressions on a ten-year panel of German imports at the HS8 level. He found that gravity variables (distance, exporter GDP, common language, common border, etc.) influenced the duration of trade flows pretty much the same way they influenced trade volumes. Interestingly, he found that the short duration of trade flows held even when flows below 10’000 euros were excluded. Fugazza and Molina (2009) extended the exploration to a nine-year panel of HS6 bilateral trade flows between 96 countries using, as regressors, gravity variables and time required for export procedures (based on the World Bank’s _Doing Business_ surveys) as proxies for fixed costs. Besides usual findings on the effect of gravity variables and income levels, they also found that fixed costs reduced survival.<sup>2</sup> A similar exercise was carried out on Asian trade flows by Obashi (2010) with largely convergent results. In particular, the 2-to-3 year median survival seems to hold across all samples studied. Obashi also found that vertical trade relationships (involving the sale of semi-finished product) have hazard rates one-third lower than those involving the sale of final goods, and that they are less sensitive to trade costs (e.g. distance or exchange-rate fluctuations).

A smaller number of recent papers have made use of the growing availability of firm-level datasets to shed new light on the determinants of export survival. For instance, G ̈ _o_ rg et al. (2008) tested the implications of the heterogeneous-firm model of Bernard et al. (2006) on a rich panel of 2,043 Hungarian firms spanning the transition from centrally-planned to market economy (1992-2003). Their data contained firm characteristics and exports at the firm-HS6 level. They found large
from the second round ( ) can be directly used to predict consumption in the first round as follows (4) Equation 4 allows us to compute lower bound estimates of movements in and out of poverty. For example, the fraction of poor households in the first round who escaped poverty in the second time is given by (5) Since we are not drawing from the empirical distribution of estimated errors, we do not need to repeat the procedure _R_ times as in the upper bound approach. In fact, this last approach provides a clean under-estimate of true mobility since we are using household-specific error terms (from the second round in this example). In other words, because mobility is estimated across two survey rounds in which the same disturbance term applies to both consumption measures, the lower-bound measure of mobility has been “purged” of classical measurement error and thereby provides a lower-bound estimated of “true” mobility.

# **3. Data and harmonizing the approach across countries**

In order to validate the technique, we use three panel datasets for Chile, Nicaragua, and Peru. True panel estimates of mobility from each country are compared with mobility estimates that result from applying the Lanjouw et al. (2011) approach. The three panel datasets we use are: (i) the 1996, 2001, and 2006 CASEN panel survey from Chile; (ii) the 1998, 2001, and 2005 National Household Survey on Living Standards Measurement (EMNV in Spanish) panel survey from Nicaragua; and (iii) the 2004-2006 and the 2008-2009 Peruvian National Household Survey (ENAHO in Spanish) panel surveys in Peru.

7
# **Figure 5. Cumulative share of total grants used by month**

(a) Cumulative share of total amount of cash grants withdrawn by month <!-- Start of picture text --> 0,90<br>0,70<br>0,50<br>0,30<br>0,10<br>-0,10<br><!-- End of picture text --> (b) Cumulative share of total amount of matching grants approved by month <!-- Start of picture text --> 1,0<br>0,8<br>0,6<br>0,4<br>0,2<br>0,0<br><!-- End of picture text --> _Source:_ Monitoring data.

We also plotted (unconditional) post-treatment cumulative distribution functions (CDFs) of investment by treatment group (Figure A.1, appendix). The graph shows that among those who invested, investment is substantially higher in the group of cash grant recipients compared to the two other groups. Their distribution is clearly to the right of the two other groups. The distributions of the matching grant recipients and the control group do not show a noticeable difference.

We do not find any systematic difference in the effect size within cash beneficiaries, whether we look at gender, the sector of activity, initial capital stock, or the size of the support requested (results not presented in Table 3). Yet, this also has to do with the fact that we are somewhat limited in power with the sample size at hand. Some of the differences across groups might simply be too small to be detectable.

17