rafmacalaba/datause-encoder-data huggingface dataset:
ital, or sector. However, Ethiopian firms score particularly low on a measure of the quality of management. The most critical area is that related to the management of workers. In particular, the quality of selection, incentives and retention practices is lower than in all comparison countries. This suggests a need for reformed labor management practices. Further, given the scarcity of rigorous evidence on the merits of specific interventions, this highlights the need for more experimental work on labor management in Ethiopia.

What is particularly interesting is that the historical experience of other nations also points to the importance of developing the right labor institutions and practices to make industrialization possible and to distribute its gains. Scholars of both the industrial revolution in the UK (Thompson, 1967; Clark, 1994) and of mid 20th century industrialization in Asia and Latin America (Kohli, 2004) agree on this point. Research on early industrialization in Africa also points to labor management problems, in particular, retention (Elkan, 1971). Finally, newly available historical data has highlighted the important role of unions in securing a wider distribution of the gains from industrialization (Farber et al., 2018).

This paper is also related to and complements two recent studies by Gelb et al. (2016) and Gelb et al. (2017), which argue that high labor costs hold back industrialization in Africa. These studies identify Ethiopia as a country with relatively low labor costs. Our analysis further points out that this labor cost advantage is partly offset by low productivity.

The rest of this paper is organized as follows. Section 2 describes the data. I then present the comparison of productivity and input costs and discuss the determinants of Ethiopia’s low productivity in Section 3 and 4. Finally, in Section 5, I discuss evidence from recent studies of labor interventions. Section 6 concludes.

# **2 Data**

Most of the analysis is based on data from the _World Bank Enterprise Survey_ – an harmonised representative survey of firms covering about 139 countries in the world. For the analysis, I select five countries of interest: Ethiopia, Kenya, Bangladesh, India and Vietnam.<sup>1</sup> Kenya is a geographical neighbour of Ethiopia and a natural competitor within east Africa, with a key advantage coming from direct access to the sea. I then select three Asian countries, covering the range of the productivity distribution among

> 1I use the latest survey available for each country. For Ethiopia and Vietnam, this is the 2015 survey; for India this is the 2014 survey, and for Bangladesh and Kenya this is the 2012 survey.

3
WPS3987

# International Benchmarking of Infrastructure Performance in the Southern African Customs Union Countries

## by

## Željko Bogetić and Johannes W. Fedderke

Summary: The paper provides a first, systematic benchmarking of infrastructure performance in the SACU countries (South Africa, Botswana, Lesotho, Namibia, and Swaziland) in four major sectors––electricity, water and sanitation, information and communication technology, and transportation––against the relevant group of comparator countries using a new World Bank international data base with objective and perception-based indicators of infrastructure performance from over 200 countries. The analysis suggests important comparative gaps in all major infrastructure sectors, although performance varies widely across the SACU region. Infrastructure performance shortfalls are particularly acute in rural areas where most of the poor reside. The benchmarking is envisaged as a comparative input into deeper analyses of infrastructure performance, especially in the context of the ongoing scaling-up efforts (e.g., South Africa, Lesotho, Botswana).

## JEL classification: H54, H, R, O, O57

World Bank Policy Research Working Paper 3987, August 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Authors: Bogetić is a lead economist at the World Bank, Africa Region, Washington D.C. (Email: zbogetic@worldbank.org) and Fedderke is professor of economics at the University of Cape Town, School of Economics (Email: <u>jfedderk@commerce.uct.ac.za).</u>
**Table 9. Unconditional and conditional quantile regression estimates**

|||UQR|||QR||
|---|---|---|---|---|---|---|
||_25th_<br>_quantile_|_50th_<br>_quantile_|_75th_<br>_quantile_|_25th_<br>_quantile_|_50th_<br>_quantile_|_75th_<br>_quantile_|
|Log Real Value of Monthly Per|-0.242***|-0.433***|-0.619***|-0.317***|-0.457***|-0.573***|
|Capita Food Consumption|[0.0437]|[0.0447]|[0.0571]|[0.0259]|[0.0227]|[0.0263]|
|LogDailyPer Capita Calorie Intake|-0.0866***|-0.187***|-0.279***|-0.0933***|-0.169***|-0.262***|
||[0.0290]|[0.0268]|[0.0340]|[0.0181]|[0.0167]|[0.0173]|
|LogFood Consumption Score|-0.246***|-0.173***|-0.158***|-0.154***|-0.189***|-0.227***|
||[0.0508]|[0.0305]|[0.0319]|[0.0227]|[0.0163]|[0.0159]|
|See Table 6 notes.QR represents unc|onditionalquanti|le regression e|stimation.||||

**Table 10. UQR results for 10th and 90th quantiles**

||_10thquantile_|_90thquantile_|
|---|---|---|
|L Rl Vl f Mthl P Cit Fd Cti|-0.129**|-0.725***|
|og ea aue o ony er apa oo onsumpon|[0.0521]|[0.0762]|
|LogDailyPer Capita Calorie Intake|0.00402|-0.382***|
||[0.0444]|[0.0486]|
|LogFood Consumption Score|-0.248***|-0.183***|
||[0.0576]|[0.0331]|
|See Notes for Table 6.|||

32
Figure 6 <!-- Start of picture text --> Relationship Between Infant Mortality Rate and Health Expenditure, 1993<br>Infant mortality rate<br>100<br>*Oibet<br>80<br>*Ginghai O Yunnan<br>*Xinjiang<br>60 - *Guizhou Henan<br>* Jiangxi<br>uagi OHunan OSbchuan<br>40 - ONingxiaine<br>Shaanxi * -°<br>20 - Shanx# * Gunagdon<br>*Beijing Shandong<br>Tianjin<br>0 O _ ~ ~I I I II<br>0 200 400 600 800 1,000 1,200<br>Provincial health Expendiure (Yuan)<br><!-- End of picture text -->
Katmandu achieve a progressive distribution of subsidy benefits (Ω is 1.54), as do the subsidies in Bangalore (Ω is 2.14). Nonetheless, errors of exclusion of the subsidies are high: 72 percent of poor households do not receive this subsidy in Katmandu, and 61 percent do not receive it in Bangalore. The error of exclusion for the public tap subsidies is thus higher than that of the IBT in place in these cities (see Table 1), reflecting the fact that more poor households in the city have private taps than use public taps.

# **_Performance of connection subsidies_**

One overriding weakness of subsidies for consumption is that they exclude households that are not currently using the subsidized service. With the exception of the service-level targeted subsidies, this means that unconnected households are left out of the pool of subsidy beneficiaries. This “access handicap” severely compromises the targeting performance of consumption subsidies offered on standard connections. As only _unconnected_ households are the potential beneficiaries of connection subsidies, the problem that plagues consumption subsidies can work in favor of the targeting performance of connection subsidies.

Few cases of existing connection subsidies have been studied in sufficient detail to apply the conceptual framework used in this study. We therefore simulated connection subsidies using data on coverage levels among the poor and non-poor in all cases for which sufficient information was available. The first simulations were of universal connection subsidies and assumed that all unconnected households were both offered and accepted subsidized connections. The error of exclusion for these universal connection subsidies is solely a function of the connection rate in each country: more poor households are excluded in high coverage cases than in low-coverage cases. The distribution of benefits from these universal connection subsidies is nearly always progressive (Komives et al 2005). The only exceptions are Azerbaijan and Belarus, where non-poor households are as likely as poor households to be unconnected.

This assessment of connection subsidies presumes that all potential or targeted beneficiaries could and would eventually be connected (or at least that households in all income deciles could and would connect at the same rate). There are two scenarios under which this would not be the case: (a) when not all eligible households choose to connect (even when offered the subsidy) and (b) when the utility does not offer a connection to all (i.e. network or grid is not present in proximity to the eligible households **).** In either scenario, the future uptake rate among the poor would probably be lower than for the population as a whole and network or grid extension would likely favor areas where the non poor reside. In practice, errors of exclusion would most likely increase and the progressivity in the distribution of benefits would diminish, relative to the result of the first simulations. A simulation of the targeting performance of a 18
# **References**

Blanchard, O. and P. Diamond (1991);."The Cyclical Behavior of the Gross Flows of U.S. Workers," NBER Reprints 1582 Blau, D. M. (1987); “A time series analysis of self-employment in the United States,” _Journal of Political Economy_ 95:445-67.

Bosch, M. and W. Maloney (2005); “Labor Market Dynamics in Developing Countries. Comparative Analysis using Continuous Time Markov Processes,” Policy Research Working Paper 3583. The World Bank.

Bosch, M. and W. Maloney (2006); “Gross worker flows in the presence of informal labor markets. The Mexican experience 1987 – 2002,” The World Bank, mimeo.

Carneiro, F. and A. Henley (2003); “Social Security Reforms and the Structure of the Labor Market: The case of Brazil,” Report to the Inter-American Conference on Social Security.

Chenani, N., V. Mehta and R. Patel (2003); “Review and Evaluation of Health Care Reforms in Brazil,” mimeo.

Curi, A. and N. Menezes-Filho (2006); “O mercado de trabalho brasileiro e segmentado? alteracoes no perfil da informalidade e nos diferenciais de salarios nas decadas de 80 e 90.” Estudos Economicos 36(4) São Paulo.

Davis, S. and J. Haltiwanger. (1990); "Gross Job Creation and Destruction: Microeconomic Evidence and Macroeconomic Implications," NBER Macroeconomics Annual , 123-168 Davis, S. and J. Haltiwanger (1992); "Gross Job Creation, Gross Job Destruction, and Employment Reallocation," _Quarterly Journal of Economics_ , 107:819-863.

De Carvalho, I. (2002); “Old-age benefits and retirement decision of rural elderly in Brazil,” mimeo MIT.

De Souza, R. (2002); “O sistema Público de Saúde Brasileiro,” Ministério da Saúde, Brasil.

Evans, D. and L. Leighton (1989); “Some empirical aspects of entrepreneurship,” _American Economic Review_ , 79(3).

Fiess, N., M. Fugazza and W. Maloney (2006); “Informal Labor Markets and Macroeconomic Fluctuations,” mimeo. The World Bank.

27
- 75-

4.a <!-- Start of picture text --> 4.b<br><!-- End of picture text --> <!-- Start of picture text --> 01M mm mmmm<br>7 . ,3125 6 7<br>6~~~~~~~~~~~~~~~~~~6<br>a~~~~ ~ ~~ ~~ ~~~~~~~ J I- i/o'; 4.wnX<br>2 "K9< X -" , 23o"<br>2 ____ I 2 Du? liii<br>1953 195 15U ,11 1512 1%3 154 151 1 1917 116 119 1m 1971 1m<br>IN -U<br>I Iw _ __ _ _ _ _ U _ _ _ _ .- I SS- Se<br>1913 1959 153 1%1 151 1913 1914 1515 1515 ? 1516 1%5 1M 1971 172<br>U0 is 3"Is<br>In i uc s A a<br>-RU - -M. S<br>/ 1 4~~~~~~~~~~~~A<br>ii. WREI cli Exh3 1R t n<br>-In -us -"DEOFF. Ooauto acL ~~~~~~~~~~~~~~~-s Ofeu xhgRt 251<br>195W 1959 151 1 11 1513 I514 155 1 M 1 1n 1 197 1971, 192<br>-4. ~ CA .a /ura Scon $ 3oac,llo<br><!-- End of picture text --> Anumal Data DEVOFF Devaluation Rate in of ficdel £xchaoge Itatc REX : Real Exchange Race tndex CAB : O.crent AccOunlc Balances i,liow _UJS$_ WRA Real Wage Index (SowuCC: fundaCioti 141diterrafteg) YGI : AnNmacl Ggwch Rate of Real GDO P2) Other variable definitions, ibide, Figure 3. Sources World Bank Date cc _oJ_
much faster than available jobs, thereby reducing employment opportunities for them in these sectors.

# **Figure 4: Distribution of urban female** **<u>workforce across industries, by education</u>**

<!-- Start of picture text --> 50<br>Low education 1987 Low education 2011<br>High education 1987 High education 2011<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Note: Distribution of female workers across industries, including employees, self-employed and unpaid family workers. Shares are in percentage of all female workers in the respective education group. Low education is below secondary schooling; high education is secondary or higher. _Source_ : NSS Employment and Unemployment Survey.

In 1987 the vast majority of highly educated women worked in public administration, education, and health. This share declined to 50 percent in 2011, mainly driven by a declining share of public administration. Although financial and business services have become more important, they still account for only a small fraction of female employment. Consequently, highly educated women are increasingly working in industries such as manufacturing (mostly in textiles), wholesale and retail trade, and domestic services; within these sectors, many highly educated women work in 14
ity (Agrawal, 2008; Giuliano and Nunn, 2021). Ethnic groups with differing historical livelihood strategies might demonstrate different adaptive capacity in the face of changing climate conditions. Societies that have historically relied on subsistence agriculture might have developed resilience capabilities that allow them to better address climatic challenges and reap the benefits of positive shocks. To test this hypothesis, the analysis combines data from the Ethnographic Atlas database developed by Murdock (Murdock, 1967) with our primary dataset. The paper analyzes how employment responses to water shocks vary for individuals living in ethnic homelands that have historically relied on (i) agriculture; (ii) hunting, fishing and gathering; or (iii) pastoralism (herding) for their livelihoods.

This paper finds that relative to non-agricultural societies, regions where agriculture-focused ethnic groups historically resided experience a significant increase in employment in response to wet shocks, and experience worse effects from dry shocks. Homelands of ethnic groups that historically relied on hunting, fishing and gathering are also found to benefit from wet shocks but are less impacted by dry shocks (relative to other groups). Pastoral ethnic groups experience the largest negative effect from dry shocks while reaping no benefits from wet shocks. These findings suggest that local norms and institutions in historically agricultural regions have adapted well to allow workers to benefit from surplus agricultural productivity during periods of excess water but are also prone to the adverse impacts of dry periods on agricultural productivity. Pastoral ethnic groups have not developed a similar adaptation ability. These findings are consistent with studies showing that societies that experienced the Neolithic Revolution earlier have developed civilization and statehood earlier, and are wealthier and more educated today than pastoral societies (see, e.g., Diamond (1997); Hibbs Jr and Olsson (2004); Putterman (2008); Michalopoulos et al. (2019); Galor and Özak (2016)).<sup>1</sup> Interestingly, hunter-fishergather regions have better adapted to avoid being worse off during dry periods than regions that historically relied on agriculture for livelihood. This finding is possibly driven by fishingfocused ethnic groups. Our results are consistent with Dalgaard et al. (2020), who find that societies that historically relied on fishing or enjoyed an abundance of marine resources are more developed today, and that these societies differ from purely agrarian societies in terms of culture, personality traits, and institutions. The paper also finds that jobs that are less vulnerable to water variability are more frequently found in historically hunter-fisher-gather ethnic homelands today, and that these regions have more diversified economies than historically agrarian regions.

> 1The paper also finds that individuals living in historically agricultural ethnic homelands and homelands that derived most of their subsistence from hunting, fishing, and gathering are wealthier and more educated today.

4
|**Does not know whether paid tax**|4972|0.0324|0.1770|0|1<br>1896|0.0849|0.2788|0|1|<br>3076|0|0|0<br>0|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Moonlighting**|4972|0.0177|0.1319|0|1<br>1896|0.0464|0.2104|0|1|<br>3076|0|0|0<br>0|
|**Dissatisfied with her / his job**|4972|0.0815|0.2736|0|1<br>1896|0.2136|0.4100|0|1|<br>3076|0|0|0<br>0|
|**Training of which apprenticeship**|4972|0.1748|0.3798|0|1<br>1896|0.4583|0.4984|0|1|<br>3076|0|0|0<br>0|
|**Mentoring**|4972|0.0050|0.0707|0|1<br>1896|0.0132|0.1141|0|1|<br>3076|0|0|0<br>0|
|**Training in new technologies**|4972|0.0099|0.0988|0|1<br>1896|0.0258|0.1587|0|1|<br>3076|0|0|0<br>0|
|**Other type of training**|4972|0.8103|0.3921|0|1<br>1896|0.5026|0.5001|0|1|<br>3076|1|0|1<br>1|
|**Union membership**|4972|0.0720|0.2585|0|1<br>1896|0.1888|0.3915|0|1|<br>3076|0|0|0<br>0|
|**Firm’s size: 5 workers or less**|4972|0.1076|0.3099|0|1<br>1896|0.2822|0.4502|0|1|<br>3076|0|0|0<br>0|
|**From 5 to 9**|4972|0.0346|0.1828|0|1<br>1896|0.0907|0.2873|0|1|<br>3076|0|0|0<br>0|
|**From 10 to 19 size_10t19**|4972|0.0579|0.2336|0|1<br>1896|0.1519|0.3590|0|1|<br>3076|0|0|0<br>0|
|**More than 20**|4972|0.0432|0.2034|0|1<br>1896|0.1134|0.3172|0|1|<br>3076|0|0|0<br>0|
|**Does not know the size**|4972|0.1380|0.3449|0|1<br>1896|0.3618|0.4807|0|1|<br>3076|0|0|0<br>0|
|**Goals in life: Success at work**|4972|0.1138|0.3176|0|1<br>1896|0.1176|0.3222|0|1|<br>3076|0.1115|0.3148|0<br>1|
|**Giving a contribution to society**|4972|0.1273|0.3334|0|1<br>1896|0.1245|0.3302|0|1|<br>3076|0.1291|0.3353|0<br>1|
|**Earning a lot of money**|4972|0.1852|0.3885|0|1<br>1896|0.2210|0.4150|0|1|<br>3076|0.1632|0.3696|0<br>1|
|**Having a happy family life**|4972|0.3508|0.4773|0|1<br>1896|0.3370|0.4728|0|1|<br>3076|0.3592|0.4799|0<br>1|
|**Gaining all type of work experiences**|4972|0.0440|0.2052|0|1<br>1896|0.0438|0.2047|0|1|<br>3076|0.0442|0.2056|0<br>1|
|**Understanding the meaning of life**|4972|0.0603|0.2381|0|1<br>1896|0.0533|0.2246|0|1|<br>3076|0.0647|0.2460|0<br>1|
|**Gain self-esteem**|4972|0.1185|0.3232|0|1<br>1896|0.1028|0.3038|0|1|<br>3076|0.1281|0.3342|0<br>1|
|**Living in rural areas**|4972|0.4055|0.4910|0|1<br>1896|0.5527|0.4973|0|1|<br>3076|0.3147|0.4645|0<br>1|
|**Living under the poverty line**|4972|0.1961|0.3971|0|1<br>1896|0.1065|0.3086|0|1|<br>3076|0.2513|0.4338|0<br>1|

_Note:_<sup>_1_</sup> _For the definition of actual work experience adopted see the data section. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ Table A2. Descriptive statistics by <u>gender</u>

||||**All**<br>|||||**Women**<br>|||||**Men**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|Obs|Mean|Std.<br>Dev.|Min|Max|Obs|Mean|Std.<br>Dev.|Min|Max|Obs|Mean|Std.<br>Dev.|Min|Max|
|**Log of total monthly labor income (in th.**<br>**of old Manats)**|1896|5.9355|0.5171|2.3026|8.8537|658|5.8693|0.5647|2.3026|7.8240|1238|5.9706|0.4865|3.9120|8.8537|
|**Women**|4972|0.4670|0.4990|0|1|2322|1|0|1|1|2650|0|0|0|0|
|**Log of weekly hours of work**|1896|3.6412|0.3126|0.6931|4.4308|658|3.6070|0.3325|0.6931|4.4308|1238|3.6594|0.3001|0.6931|4.4308|

44
The starting point is to take a pair of subsequent port _d_ (departure) and _a_ (arrival) in a shipping sequence. The time span between departure at _d_ and the next departure T( _da_ ) (or d ) captures most possible disruptions affecting port _a_ (Figure 2), whether at _a_ or before. These include disruptions _en route_ from _a_ to _d_ , or disruptions at the _a_ (arrival) port. Therefore, looking at the lead time statistics of possible inbound TT connections reveals potential disruptions affecting _a_ .

Figure 2. Lead Time Between a Pair of Subsequent Ports Source: Authors.

For each pair of ports ( _d,a_ ), a dataframe with the following variables was constructed from vessel tracking data:

- Period _t_ (in practice monthly data from February 2016 until most recent).

- Port arrival _a_ .

- • Previous port _d_ . • Median shipping time for period _t:_ d _(t)._ Using median as opposed to mean avoids problems with outliers (vessels staying in port for noncommercial reasons, such as repairs or crew changes).

- • A reference transit time for the edge TT _(d,a):_ d is representative of normal shipping and port conditions on lane _d_ to _a_ above which vessels are considered delayed (see below).

- • Sum of vessel capacity moving from _a_ to _b_ for the period TT d _(t)._ • The same concept with vessel count d _(t)._ • Delay for period or transit time above the reference. CC NN

<!-- Start of picture text --> i d d<br>d 0 i d d<br>(tt) −TTd<br> TT (tt) ≥TT<br>ΔΔ (tt) = � TTd<br> TT (tt) < TT<br><!-- End of picture text -->

# **Estimating Reference Transit Time**

The stress index aims to capture large systemic disruptions. A too small value of the reference transit time ( d )) will capture too much noise in the form of small but frequent deviations, such as in a bell curve TT 5 5