rafmacalaba/data-use-mentions-extended huggingface dataset:
about half of that of Bangladesh (7 percent versus 15 percent). While this highlights the growing attractiveness of Ethiopia as an investment destination, it may also highlight that Ethiopian employers in this sector have higher monopsony power compared to their counterparts in Bangladesh.

# **3.1 Robustness to use of alternative data sources**

The productivity figure used for Ethiopia are consistent with those obtained by an independent survey run in Addis Ababa at a similar point in time for the study of Abebe et al. (2017). I show this by comparing the distribution of sales for worker in the two A.7 A.7. surveys in Figure and by showing means and medians in Table Average sale for workers in Addis Ababa is remarkably similar across the two surveys (22,708 USD and 22,462 USD). Median sales are somewhat higher in the survey of Abebe et al. (2017), but the difference is not excessively large.

Finally, the figures reported in the previous section align well with the perception of foreign firm managers. I have carried out qualitative field in the Hawassa Industrial Park at several points during 2018. During these visits I had several personal conversations with firm managers about productivity and labor management. On average, firm managers believed that productivity in Ethiopia’s garment factories is between a third and a half of that of comparable factories producing similar products in India; and that wage costs are about one third of those in India. These figures confirm the aggregate A.1. statistics reported in Figure

# **4 Where does Ethiopia’s low productivity come from?**

**Fact 5.** _Capital stock, firm size and sectoral composition do not explain the low productivity of Ethiopian firms._

To study the factors that determine the low productivity of manufacturing firms in Ethiopia we run a regression model where we regress sales per worker on firm size, sector, and value of the capital stock. Specifically, we create dummies for quartiles of firm size
.3 percent had become employed as irregular workers or outside of the formal private sector. Even with the differences in the _Table 5-3 Jordan: Rates of Net Job Loss by Type of Job, Percentage Change in Employment Levels from March to December 2020_

|Employer Type|Net rate ofjob loss|
|---|---|
|PFPS core sectors|23.3 percent|
|PFPS other sectors|17.5 percent|
|Private sector non-PFPS|-4.2percent|
|Public sector|3.5 percent|
|Source: CMMHH.||

surveys’ scopes, sampling approaches, and the questions posed on employment, the overall magnitudes of estimated jobs lost among formal private sector workers are remarkably similar. This provides support for our general approach to projecting aggregate job losses by linking firm-level modeled estimates and labor force surveys.<sup>42</sup> (Detailed results on “PFPS” workers from the CMMHH are reported in Appendix 2 in the longer version of this paper.) Since PFPS jobs represented only 42 percent of private sector wage jobs and a minority of all jobs in Jordan pre COVID, we use the CMMHH survey to examine whether the levels of job loss have differed across broad

> 41 The modeled incremental job losses between rounds were 9.3 percent, with 2.3 percent due to permanent firm closures.

42 In the WBES, permanent workers are defined as those working fulltime in the formal private sector. In the LMPS, permanent workers are defined as private sector wage earners either reported to be permanent workers or to be fulltime and regular workers. In the MENA monitor survey, due the limitation in questions, we use regular and formal wage earners in the private sector as a proxy of permanent workers. We compare the share of permanent workers in total employment using our definitions and the results are very similar in the three surveys.

28
<!-- Start of picture text --> .08<br>.06<br>.04<br>.02<br>0<br>10 15 20 25 30 35 40 45<br>Mother's age when child is born/miscarriage<br>Source: IFLS 1 (1993). Note: dashed lines indicate 1st and 99th percentiles.<br>Density<br><!-- End of picture text -->

# **Appendix Figure 8. Distribution of mother’s age at childbirth or when miscarriage occurs**

Note: Data is taken from IFLS 1 (1993). It shows the distribution of mother’s ages at childbirth or when miscarriage occurs. Pregnancies pertain to any child order. Dashed lines indicate 1<sup>st</sup> and 99<sup>th</sup> percentiles at age 14 and 39, respectively. To avoid complications in teen pregnancies we limit our sample to mothers who were at least 19 in at least two rounds of IFLS. The upper boundary, age 45, was selected such that if the age-eligible kid is 6, then the woman has to be 39 or younger at childbirth.

67
0.206***|0.207***|0.206***|0.207***|
||(0.054)|(0.053)|(0.054)|(0.053)|
|F Statistic (excl. instrument)|14.38|15.11|14.38|15.11|
|**Panel C: Reduced Form**|||||
|Imputed Outfows|-0.576***<br>(0.123)|-0.583***<br>(0.124)|-0.309***<br>(0.052)|-0.314***<br>(0.051)|
|Observations|1,648|1,648|1,648|1,648|
|Dep. Var. Mean 2006|31.359|31.359|10.188|10.188|
|Municipality & Year FE|✓|✓|✓|✓|
|Controls _×_ Time Trends||✓||✓|

_Notes:_ The table illustrates the estimates of specifications (1), (3), and (2). Panel A presents the OLS estimates of equation (2). Panel B represent the 2SLS estimates of equations (3) and (2). Panel C presents the reduced-form estimates of the outcomes in the instrumental variable. Odd-numbered columns exclude baseline controls and even-numbered columns include baseline municipal characteristics interacted with year fixed effects. The F-statistic is the Kleibergen-Paap Wald F-statistic. Controls in the baseline are interacted with time trends and include urban coverage, water bodies, and tree-cover loss for 2001; night-light density for 1992; and political repression records for 2004. Robust standard errors clustered at the municipality level are in parentheses. *** _p <_ 0.01, ** _p
# **4.2 Robustness Checks**

The next step is to check the robustness of these findings to alternative specifications and data sources. Table 8, in the annex, reproduces the baseline results using ICAC data for yields and production, instead of FAO data. All the parameters of interest are of similar signs and magnitude. More importantly, we now turn to discussing potential serial correlation, reverse causality and endogeneity.

Bertrand _et al._ (2004) showed that serial correlation causes difference-in-difference standard errors to understate the standard deviation of the estimated treatment effects thus leading to overestimation of _t_ -statistics and significance levels. To check that our results do not suffer from such bias, we follow their strategy of “ignoring time series information”. We start by regressing log ( _Yit_ ) on country and year fixed effects as well as our additional vectors of covariates ( _C_ ). We then obtain the effect of the market structure variables and their standard errors from a second OLS regression on the residuals, which now form a two-period panel (with pre-reform being characterized by _Monopoly_ , the default category, and post-reform corresponding to either _Post Reform_ or _Regulation_ , _Low competition_ and _Strong competition_ ). We estimate the two following equations: _Log(Yit) = β0 +β1 Cit + γt + ci + εit (2)_ _Residuals it_ = _β0 + β1 Iit + εit_ where _Residuals_ are the residuals from equation (2). The results of the second regressions are displayed in Tables 4 (yield) and 5 (production). Both the significance and the signs of the institutional variables (the post-reform variable as well the disaggregated market structure indices) are very much in line with those found with the baseline specification. Two differences, however, are worth noting. First, whereas yields are still found to be higher in liberalized markets of ESA and non-Francophone WCA, this effect is of lower magnitude and it is not significant anymore in moderately competitive markets. Second, whereas production is still found to be higher in
Besides differences in the shared prosperity indicators per se, using a spatially deflated welfare aggregate affects the ranking of people. This can change the distribution of the population in the bottom 40 percent across locations and their characteristics. As an example, we show that in ten of the sixteen regions in Kazakhstan, the share of the population from the bottom 40 percent varies substantially depending on the type of welfare indicator chosen.

In many countries, shared prosperity is measured by income. Income tends to be more volatile than consumption and income may include zero and negative numbers. Empirical tests based on EUSILC data and data for LAC countries show that the shares of households with negative and zero incomes rarely exceed 1 percent. Nevertheless, excluding negative and zero numbers affects the estimates on shared prosperity. Even though the difference between estimates does not exceed one percentage point for any of the countries for each test, the impact depends on the shares of households with negative/zero income, its distribution across years and the magnitude of negative values.

This paper conducted a series of robustness tests of shared prosperity to different methodological assumptions. Further systematic analysis of such tests such as sensitivity to changes in reference periods, income versus consumption data, and revisions of microdata can be useful.

19
especially, **by** the steady declining foreign wealth **/** domestic wealth ratio, itself driven **by** rapid capital accumulation. This raises Chile's demand for foreign assets, bringing their equilibrium position close to zero.

The case of Mexico, portrayed in Figure _4.5,_ does not display such clear cycles. Both risk and return remain relatively steady, with the latter experiencing a sharp decline in **1977-81** that accounts for the marked rise in the equilibrium ratio. In the late 1980s, a rise in the risk index and a decline in the return index both tend to bring up the equilibrium series. Unfortunately, asset data for Mexico stop in 1994, when our estimates show a widening gap between the actual and equilibrium nfa **/** wealth ratios.

In Korea (Figure 4.6), large gaps between the actual and equilibritun series do not arise (with the exception of the year **1981).** The initial part of the sample period is characterized **by** high values of the return index, which declines steadily since the early 1980s. Risk declines too, but in a less pronounced manner, and rapid capital accumulation brings down the foreign domestic wealth ratio and raises the demand for foreign assets, bringing the equilibrium asset position close to zero **by** the end of the sample period.

The distinguishing feature of Singapore (Figure 4.7) is that it displays a very large and persistent gap between the equilibrium and actual values of the foreign asset **/** wealth ratio. Our parameter estimates yield a very slow speed of adjustment, which accounts for the persistence of the divergence Figures 4.8-4.11 portray industrial countries. In general, the gaps between actual and equilibrium nfa **/** wealth ratios are of a smaller order of magnitude than in developing countries. In Germany (Figures 4.8-4.9) the results suggest that
Business study. We also use an index of unit labor costs (ULC) by the OECD that proxies for a country’s cost competitiveness (base year = 2005). It measures the average cost of labor per unit of output and is calculated as the ratio of total labor costs to real output.

_Education and skills_ are represented on the education side by the expected years of _schooling_ (Barro and Lee 2013), which gives a quantitative measure of the education system. As qualitative measure, we use an index of _educational quality_ provided by the WEF on the basis of their Executive Opinion Survey. Regarding skills, we look at the share of workers with a secondary degree, or higher, in the total workforce (WDI) and the WEF’s index for on-the-job _training_ , which is equally a qualitative survey-based indicator.

_Innovation and product standards_ are represented on the quality side by the stock of ISO certifications related to quality management (ISO 9001, 13485, 16949, and 22000) per capita. Innovation is measured by _R&D_ intensity (WDI), and the WEF’s survey-based _Innovation_ and _Technology adoption_ indices—the former looks at private and public innovation capacities and spending while the latter measures countryand firm-level availability of new technologies.

_Labor standards_ cover basic rights including the WDI’s share of children between 7 to 14 years old in employment, _child labor_ , share of _vulnerable employment_ , and the share of female workers in the total workforce, _female intensity_ . In addition, we cover the number of ILO’s _basic conventions_ (that is, fundamental and governance conventions) signed, complemented with different measures of wage inequality. We use the ILO’s _wage dispersion_ between the 9th and 5th wage decile, and the OECD’s _minimum wage_ relative to the median wage.

_Social standards_ deal with a country’s basic welfare system. We use ILO data on the share of _health_ expenditure in total government spending, the share of workers contributing to a _pension_ scheme, and the share of
_<mark>Homicide Studies</mark>_ <mark>, 3(3), pp.187-214.</mark> Eaton, J., S. Kortum, B. Neiman and J. Romalis. (2016). “Trade and the global recession.” _American Economic Review_ , 106(11), 3401-3438.

<mark>Ellsberg,M., H. A. Jansen, L. Heise, C. H.Watts, and C. Garcia-Moreno (2008). “Intimate partner violence and women’s physical and mental health in the WHO multi-country study on women’s health and domestic violence: an observational study.” The lancet 371 (9619): 1165–72</mark> <mark>Erten, B., and P. Keskin. (2018). “For Better or for Worse?: Education and the Prevalence of Domestic Violence in Turkey”.</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>, 10(1): 64-105.</mark> <mark>Farmer, A., and J. Tiefenthaler. (1997), “An Economic Analysis of Domestic Violence”,</mark> _<mark>Review of Social Economy</mark>_ <mark>, 55, issue 3, p. 337-358.</mark> <mark>Gagliarducci, S. and D. Paserman. 2012. “Gender Interactions within Hierarchies: Evidence from the Political Arena.” Review of Economic Studies 79(3): 1021-1052.</mark> <mark>Gangadharan, L., T. Jain, P. Maitra, and J. Vecci. (2016). “Social identity and governance: The behavioral response to female leaders.” European Economic Review, 90, 302-325.</mark> <mark>Gulesci, S., E. Meyersson, and S. Trommlerová
# _IX. CONCLUDING REMARKS_

The main contribution of this paper to the literature on the micro-economic effects of input-trade liberalization on firm performance is to investigate the efficiency gains from input tariff cuts on firms’ decision to source capital goods from abroad.

We motivate our empirical analysis with a simple theoretical model of heterogeneous firms that explains the channels through which changes on tariff on intermediate goods might affect firms’ decision to upgrade foreign technology in imported capital goods. Assuming that imported intermediate inputs and foreign-technology are complementary and fixed costs of technology upgrading, the model predicts a positive effect of reductions of tariff on intermediate goods on firms’ choice to adopt a foreigntechnology. The impact of input-trade liberalization is heterogeneous across firms depending on their initial productivity level.

Using Indian firm-level data and the trade liberalization episode at the early 1990s, we test the main implications of the model. Our findings demonstrate that the probability of importing capital goods is higher for firms producing in industries that have experienced greater cuts on tariff on intermediate goods. Looking at the heterogeneous effect of input-trade liberalization, we find that only those firms in the middle range of the productivity distribution have benefited from input tariff cuts as predicted by the model. These empirical findings are robust to alternative specifications that control for imported capital goods tariffs, other reforms, and industry and firm characteristics.

48
<!-- Start of picture text --> Routine Manual<br><!-- End of picture text --> <!-- Start of picture text --> 1,0<br>0,5<br>0,0<br>‐0,5<br>‐1,0<br>‐1,5<br>Russia Georgia Kyrgyzstan Armenia Albania Moldova<br>NSE SE<br><!-- End of picture text --> Source: Own calculations based on Household surveys This analysis of the task profile was complemented, in turn, with hypothesis tests to examine whether the evolution recorded by non-standard employment follows a specific behavior or only shows the general trends of the labor market. Tables with the details of the hypothesis tests for the countries of Eastern Europe and Central Asia are included in the Annex of the paper. The main conclusions of this analysis indicate that, in general terms, the profile of part-time and temporary employment has followed the same trends as standard employment, although in most cases with the specificity of presenting significantly more pronounced variations.

Figure 28: Variance in the task performed of part-time jobs. (Change from the late 90s) <!-- Start of picture text --> 2<br>1,5<br>1<br>0,5<br>0<br>‐0,5<br>‐1<br>‐1,5<br>‐2<br>Russia Georgia Kyrgyzstan Armenia Albania Moldova<br>NRCA NRCI RC RM NRM<br><!-- End of picture text --> Source: Own calculations based on Household surveys Note: NRCA refers to Non-Routine Cognitive Analytical, NRCI is Non-Routine Cognitive Interpersonal, RC is Routine Cognitive, RM is Routine Manual and NRM is Non-Routine Manual.

Page 38 of 51
**O**

0 r 0 m x X Q , s R

|Table B7. Informal Working Arrangements<br>from Informal Sector Surveys conducted by the author|<25 hours per<br>week<br>Taxicabs|245hours<br>per week<br>Taxicabs|Entire<br>Sample<br>Taxicabs|Entire<br>Sample<br>Kiosks|Entire<br>Sample<br>Stre<br>Markets|Entire<br>Sample<br>et Services &<br>Vendors|
|---|---|---|---|---|---|---|
|Number of Observations (actual number may vary depending on the<br>question)|99|101|308|245|137|80|
|Number of People Present (kiosks only)|||||||
|Mean|na|na|na|1.6|na|na|
|Hours open per week (kiosks only)|||||||
|Mean|na|na|na|86.1|na|na|
|Hours open on an average ...|||||||
|Weekday|na|na|na|12.9|na|na|
|Saturday|na|na|na|12.2|na|na|
|Sunday|na|na|na|11.8|na|na|
|Hours worked on an average day (markets and street informals)|||||||
|Mean|na|na|na|na|7.9|7.2|
|Days worked in an average week (markets and street informals)<br>Mean|na|na|na|na|5.4|6.4|
|Months of the year worked (markets and street informals
**Table A12. Associations between SCP Quality Determinants and Labor Market Outcomes** **_Using Program-Level Data for all Survey Countries_**

|_Ddt ribl_|_Formal employment_|_Log Wages_|
|---|---|---|
|_epenen vaae:_|(1)|(2)|
|_Infrastructure_|||
|Has enough equipment or tools for practice|0.0702**|0.0200|
||(0.0352)|(0.0167)|
|_Training and curriculum_|||
|Teaches numerical competencies|0.133***|0.0561***|
||(0.0394)|(0.0189)|
|Offers remediation classes during the program|0.0525*|0.0275*|
||(0.0317)|(0.0146)|
|Offer non-class-based remediation||-0.0257*<br>|
|||(0.0152)|
|Employment outcomes or employers' requests are very|||
|important to update program|0.0678||
||<br>(0.0425)||
|HEI perception of labor market is very important to update|<br>||
|program|0.0400|0.0395**|
||(0.0356)|(0.0179)|
|Enrollment trends are very important to update program||-0.0169<br>(0.0130)|
|Offers credits for longer degrees||<br>0.0615***<br>(0.0207)|
|_Link with productive sector_|||
|Collect data on employment or employers' satisfaction more|||
|than once per year|0.0483*||
||(0.0292)||
|Industry has agreements to train faculty|-0.0488||
|
what is driving this growth; and finally what, if anything, can be done to prevent the risk of flooding from being realized in the form of the frequent devastation of cities and towns worldwide.

Disaster statistics appear to show flood events are becoming more frequent. At the outset we can state that studies based on reported disasters suggest that there has been considerable shift in the pattern and intensity of flood events, resulting in increased hazard for the growing world urban population. Figure 1 shows the statistic for water related hazards over the last three decades. Floods are seen to be increasing faster than other impacts. Floods are also growing faster than non-climate related hazards. Projected future increases in hazard due to climate change are predicted to impact differently in different regions, but an increase in flood hazard is a common future expectation.

**Figure 1: Trends in reported water-related disasters (after Adikari and Yoshitani 2009)**

<!-- Start of picture text --> Drought Epidemic Flood Mass Movement Wet Storm<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>1981-1983 1984-1986 1987-1989 1990-1992 1993-1995 1996-1998 1999-2001 2002-2004 2005-2007 2008-2010<br>Number of Disasters<br><!-- End of picture text --> Increased hazard is thus compounded by the global trend in urban population, as more people, infrastructure and resources will be affected by each event. Half the world’s population currently lives in urban areas, with two-thirds of this in low- to middle-income nations (IFRC 2010). Urban population is growing at a much higher rate than the world rural population at 2.1%, especially in the developing world, with rates of 3.3% in the Middle East and Africa, and 2.7% in Asia-Pacific. It is expected that it will reach 6.2 billion by 2050 as indicated by the United Nations in 2009, and is projected
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