rafmacalaba/datause-encoder-data huggingface dataset:
WPS4177

# **ENTERPRISES, WORKERS AND SKILLS IN URBAN TIMOR-LESTE**

**Maitreyi Bordia Das and Philip O’Keefe**

## **Abstract**

Like many low-income countries, Timor-Leste faces challenges in providing employment for and increasing the skills of its labor force - challenges made more acute by high fertility rates, a very young population, and the capacity constraints of a new nation. However, there is limited information for policymakers to formulate appropriate policies. The paper presents findings of the first urban enterprises survey in independent Timor-Leste. It explores several aspects of the Timorese urban labor market, including the profile of formal and informal enterprises, their behavior in terms of employment and wage-setting practices, and constraints on firm growth. It also presents findings on the skills and training needs of urban employers, and constraints faced in overcoming skills shortages. It finds a highly informalized urban enterprise scene, where even “formal” enterprises are largely micro-enterprises. While there has been considerable action in terms of new firm creation since independence, there is already surprisingly low job creation or destruction. This is driven by a number of constraints in and outside the labor market. With respect to wages, the impacts of the informal minimum wage policy inherited from the interim international administration suggest the need for caution in future wage policy development. While employers identify many skills gaps, basic literacy, numeracy and language skill needs dominate, and employers appear to value short course and less formal modes of skills training to address their needs. The paper concludes with suggestions for addressing the key constraints identified.

The World Bank Human Development Unit East Asia and Pacific Region World Bank Policy Research Working Paper 4177, March 2007 _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._
<!-- Start of picture text --> 2000<br>f<br>o 1500<br>2<br>£<br>a<br>c<br>a<br>c 1000<br>S<br>rom<br>w<br>o<br>=<br>oO<br>2<br>= 500<br>=<br>O1nov2014 Oljan2015 Oimar2015 O1may2015 O1jul2015 Oisep2015 Oinov2015<br>Oidec2014 O1feb2015 D1apr2015 O7jun2015 D1aug2015 D1oct2015 Didec2015<br>Trivia date<br>—— Total quizzes answers<br><!-- End of picture text -->
nonstate sector, where the foreign enterprises account for an overwhelmingly large proportion, is closely associated with the upward trend of foreign investment in Shenzhen.

Also a study based on the 1993 data indicates that, in the Shenzhen SEZ, foreign firms, as well as those Hong Kong, Macao, and Taiwan, China invested firms, are generally more efficient than their domestic counterparts (Ge 1999). The data on sector output after 1993 were no longer segregated by type of enterprise ownership, so it is difficult to conduct a similar type of analysis; but a comparison of productivity growth between two sectors—the primary sector with very little FDI and the transportation, postal, and telecom sector where FDI is very heavy—shows that FDI is still very positively linked to the sectoral productivity improvement after 1993 (see figure 1.7).

**Figure 1.4    Output and Foreign Investment in Shenzhen’s Secondary Sector, 1979–2006**

<!-- Start of picture text --> 70 Output 3.5<br>60 3.0<br>50 Foreign 2.5<br>40 Investment 2.0<br>30 1.5<br>20 1.0<br>10 0.5<br>0 0.0<br>ource:ce:e:: Shenzhen Statistics Bureau,Statistics Bureau,Bureau,eau,, various years.years.ears..<br>Note: FDI = foreign direct investment.= foreign direct investment.foreign direct investment.oreign direct investment.reign direct investment.eign direct investment.n direct investment. direct investment.investment.vestment.estment.<br>Figure 1.5 Output and Foreign Investment in Shenzhen’sShenzhen’s’ss Tertiary Sector, 1979–2006 ector, 1979–2006 ctor, 1979–2006 tor, 1979–2006 or, 1979–2006 , 1979–2006 1979–2006<br>70 Output 2<br>60<br>50 Foreign Investment 1.5<br>40<br>1<br>30<br>20<br>0.5<br>10<br>0 0<br>Output Foreign Investment<br>(Billion USD)<br>(Billion RMB at 1979 price) 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br>Output<br>(Billion USD)<br>Foreign Investment<br>(Billion RMB at 1979 price)<br>1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text --> _Source:ce:e::_ Shenzhen Statistics Bureau,Statistics Bureau,Bureau,eau,, various years.years.ears.. _Note:_ FDI = foreign direct investment.= foreign direct investment.foreign direct investment.oreign direct investment.reign direct investment.eign direct investment.n direct investment. direct investment.investment.vestment.estment.

**Figure 1.5    Output and Foreign Investment in Shenzhen’sShenzhen’s’ss Tertiary Sector, 1979–2006 ector, 1979–2006 ctor, 1979–2006 tor, 1979–2006 or, 1979–2006 , 1979–2006 1979–2006**

_Source:_ Shenzhen Statistics Bureau, various years.

19
80

# **Table 39. Cost of diesel engine exhaust emissions control technology**

|Technology<br>Estimate<br>first co|d extra cost as percentage of engine<br>st (excluding development costs)|
|---|---|
|Baseline engine, no emissions control equipment.<br>Developed for performance only|Nil|
|Injection timing retard|Nil|
|Low sac volume/valve covering orifice nozzle|Minimal|
|Turbocharging|3 -_5%_|
|Charge cooling|5 - 7%|
|Improved high pressure fuel injection|13 - 15%|
|High pressure fuel injection with electronic control|14 - 16%|
|Variable geometry turbocharging (assuming it is already<br>applied to the engine)|1 - 3%|
|Particulate trap|4 - 25%|

Source: [ECMT 1990] Excess evaporative emissions can also result from disabled systems, either as a result of tampering or defective components. A recently discovered source of vehicle evaporative emissions are _running losses_ which occur when a vehicle's fuel tank heats up while the vehicle is running. Recent investigations suggest that a lot of these vapors escape through the gas cap or evaporative canister while the vehicle is running. This is especially a problem with tampered vehicles. Overall emissions will increase from these vehicles with higher volatility fuel. The U.S. EPA has estimated that running loss HC emissions could be about 2 gpm on hot summer days from vehicles using gasoline with a high volatility.

There are two sources of hydrocarbon evaporative emissions associated with vehicle refueling operations, displacement and spillage. _Displacement losses_ refer to the gasoline vapors in the fuel tank of the vehicle that are displaced by the incoming liquid fuel and directly emitted
range of past experience and consistent with seemingly reasonable expectations for the near future in the absence of major global crises.

After reviewing what we know about recent growth, distributional change and absolute poverty reduction in the developing world, the paper discusses the proposed targets for absolute poverty. The discussion then turns to a broader concept of poverty, tailored more closely to the evolving ideas about social inclusion in the developing world. The final section concludes.

# **Recent trends in growth, redistribution and poverty reduction**

Roughly speaking, the measure of poverty obtained for a given distribution of consumption (or income) depends on the mean of that distribution (relative to the poverty line) and the extent of “inequality” in the distribution.<sup>4</sup> Consider the mean first. Using nationally representative surveys, the mean will naturally depend in part on national income. As is well known, the developing world as a whole has been maintaining a growth rate for GDP of around 6% over most of the last decade, though dipping substantially (and temporarily) in 2008-09, due to the global financial crisis. This is a full 2% points higher than the average growth rate of about 4% from the 1960s through to the mid-1990s.

The World Bank’s current growth projections assume that a full-blown Euro crisis will be avoided and the annual growth rate of GDP for the developing world will still be 6% in the coming few years (having fallen slightly this year) (World Bank, 2012b). Given current population projections, a 6% growth rate in the GDP over the coming decade would represent a 4.9% rate for GDP per capita.<sup>5</sup> While the recent growth has not been even across all regions, the three regions that account for the bulk of absolute poverty—East Asia, South Asia and SubSaharan Africa (SSA)—have also seen strong growth in recent years, namely around 8% in East Asia, 7% in South Asia and 5% in SSA. Current expectations are that these rates will be maintained, though serious risks are faced that further crises emanating in the “rich world” will spill over significantly into growth and poverty reduction in the developing world, as discussed in World Bank (2012a,b).

However, the mean for the distribution of household consumption on which poverty measures are based is not (of course) the same thing as GDP or even the private consumption

> 4 Strictly, it is not “inequality” as normally defined, but rather a more complex function of relative distribution (Datt and Ravallion, 1992). However, for the present purposes it is defensible to refer to this as “inequality.” 5 The compound rate of population growth over 2012-17 and 2012-22 is 1.1% per annum.

3
- 25 -

# FIGURE 3a

# FOREIGN ADJUSTED AND CURRENT PRICES AND RERI

<!-- Start of picture text --> _ I'...................... .4<br>0 0<br>io-<br>ma'm<br>{~~~~~~ I IeCn 58 D<br>0~~~~~~~~~~~~~~.<br>*F . . . . , , , . . . IUOCC<br>is __ _ toe, . m e tees<br>l@It" * _ *t s<br><!-- End of picture text -->
**<u>Table 3: Comparison of the consumption data collection methods</u>**

||**H**|**BS**||**NPS**||
|---|---|---|---|---|---|
||**2007**|**2011/12**|**2008/09**|**2010/11**|**2012/13**|
|**Number of items**|193 food items and<br>around 250 non‐|171 food items and<br>over 300 non‐food|59 food items and<br>46 non‐food items|59 food items and<br>46 non‐food items|66 food items and<br>52 non‐food items|
||food items|items||||
|**Food products**<sup>**1**</sup>||||||
|**Capture method**|Diary: 28 days|Diary: 28 days|Recall: 7 days|Recall: 7 days|Recall: 7 days|
|**Food consumed**<br>**outside home**|‐|Diary: 28 days (an<br>additional diary<br>completed only by<br>adult household<br>members)|Recall: 7 days|Recall: 7 days|Recall: 7 days<br>(asked to all<br>household<br>members)|
|Valuation of<br>consumption from own<br>production|Using the<br>household's<br>estimate of the<br>monetary value of<br>the total quantity of<br>food consumed|Using the<br>household's<br>estimate of the<br>monetary value of<br>the total quantity of<br>food consumed|‐|‐|Using prices paid<br>by households<br>that purchased<br>similar food items<br>in the same month<br>and in the same<br>region or stratum|
|Distinguish between<br>expenditure and<br>consumption<br>**Non‐Food Products and**|No (hence cannot<br>identify if a food<br>item that was<br>purchased was<br>actually consumed)<br>**Services**<sup>**2**</sup>|Yes|Yes|Yes|Yes|
|Clothing and footwear: #<br>questions in recall|3<sup>3</sup>|70|‐|‐|6|
|Garments for men|Recall: 12 months|Recall: 12 months|‐|‐|Recall: 12 months|
|Garments for women|Recall: 12 months|Recall: 12 months|‐|‐|Recall: 12 months|
|Garments for children<br>and babies|Recall: 12 months|Recall: 12 months|‐|‐|Recall: 12 months|
|Other articles and<br>clothing accessories<br>(caps, hats, turbans etc.)|‐|Recall: 12 months|‐|‐||
|Material/Fabric for<br>clothing|‐|Recall: 12 months|‐|‐|‐|
|Footwear for men|‐|Recall: 12 months|‐|‐|Recall: 12 months|
|Footwear for women|‐|Recall: 12 months|‐|‐|Recall: 12 months|
|Footwear for children<br>and babies|‐|Recall: 12 months|‐|‐|Recall: 12 months|

12
**<u>Table 5: Estimated impacts of changes in neighboring large farm area/distance on smallholders’ fertilizer use</u>**

||**Maize**|**Wheat**|**Sorghum**|**Teff**|
|---|---|---|---|---|
|||**Panel A: Dist**|<br>**ance measures**||
|**Distance any farm**|||||
|Distance|-0.00165*|-8.75e-06|0.00111|0.00222*|
||(0.000854)|(0.00206)|(0.000677)|(0.00118)|
|Distance<sup>2</sup>|1.24e-05**|5.05e-06|-7.95e-06**|-1.66e-05**|
||(5.52e-06)|(1.53e-05)|(3.36e-06)|(7.65e-06)|
|R<sup>2</sup>|0.375|0.407|0.266|0.494|
|**Dist. same crop**|||||
|Distance|-0.00123*|-0.000877|0.000460|0.00212**|
||(0.000655)|(0.00148)|(0.000541)|(0.000805)|
|Distance<sup>2</sup>|5.18e-06*|-1.29e-06|-1.50e-06|-8.26e-06***|
||(2.67e-06)|(5.70e-06)|(2.40e-06)|(2.85e-06)|
|R<sup>2</sup>|0.375|0.410|0.265|0.494|
|||**Panel B: Ar**|**ea measures**||
|**Area, all farms**|||||
|0 - 25 km|0.000733***|0.000424|-0.000189|-0.00105***|
||(0.000215)|(0.000367)|(0.000294)|(0.000278)|
|25 - 50 km|-0.000432|8.96e-05|-1.59e-05|-0.000518*|
||(0.000339)|(0.000234)|(8.33e-05)|(0.000270)|
|50 - 100 km|0.000129|0.000182|1.09e-06|-0.000212|
||(0.000131)|(0.000159)|(3.24e-05)|(0.000147)|
|R<sup>2</sup>|0.376|0.408|0.265|0.494|
|**Area, same crop**|||||
|<br>0 - 25 km|0.00119***|0.000701|-0.000775***|-0.00408**|
||(0.000429)|(0.000855)|(0.000203)|(0.00189)|
|25 - 50 km|-0.000386|0.000221|0.000116*|-0.00284**|
||(0.000665)|(0.000669)|(5.96e-05)|(0.00140)|
|50 - 100 km|<br>0.000165|<br>0.000581|<br>-1.17e-05|<br>-0.000993|
||(0.000259)|(0.000477)|(1.86e-05)|(0.000988)|
|R<sup>2</sup>|<br>0.375|<br>0.408|<br>0.265|<br>0.494|
|No. of obs.(hhs)|170,519|52,885|89,557|92,435|

_Note:_ Woreda fixed effects and year trends included throughout. Standard errors clustered at woreda level.

21
# **1. Introduction**

The world is undergoing the most extensive school closures ever witnessed. To combat COVID19, more than 180 countries mandated temporary school closures, leaving, at its peak in early April, close to 1.6 billion children and youth out of school. By the end of May 2020, 20 school systems had opened partially, and about 1.2 billion students remained out of school.<sup>2</sup> Most countries are projecting school closures to last through the summer (or winter break). The education system is witnessing an extraordinary twin shock: school closures needed to fight the pandemic and a widespread global economic recession.<sup>3</sup> Unemployment numbers are on the rise, family incomes are falling, and government fiscal space is shrinking. Moreover, this shock is being observed simultaneously across the planet<sup>4</sup> , and most likely, international aid budgets will also be affected.

This crisis is making a dire situation worse. Before COVID-19 shut schools down, the world was already in the midst of a global learning crisis that threatened countries’ efforts to build human capital – the skills and know-how needed for the jobs of the future. Data from the World Bank and UNESCO showed that 53% of children at the end of primary in low- and middle-income countries suffer from learning poverty (World Bank, 2019). And progress in reducing it was far too slow to meet the aspirations laid out in SDG4 – to ensure inclusive and equitable quality education.

At the rate of improvement that prevailed prior to COVID-19, about 43% of children will still be learning-poor in 2030. Figure 1 shows that prior to COVID-19, if countries were to reduce learning poverty at a more ambitious yet achievable pace, the global rate of learning poverty could drop to 27%. This would have meant on average nearly tripling the then-prevalent global rate of progress.

> 2 World Bank. Education Systems’ Response to COVID-19. Brief. May 10, 2020.

> 3 World Bank, 2020b

> 4 As per the latest World Bank Global Economic Prospects (World Bank, 2020b), as many as 90% of the 183 economies it examined are expected to suffer from falling levels of gross domestic product (GDP) in 2020, even more than the 85% of nations suffering from recession during the Great Depression of the 1930s.

2
are literate(people who can read and write) in Ghana, while only 16% of the females are literate. Women on average have received less years of education than men. Among the literate over 50% of men have education beyond the primary level, only 30 percent of the women have received primary schooling (Table 10). Wage differentials between male and female could also lead to lower female participation rates, if the way individual's productive characteristics are rewarded in the labor market depends on whether it is a male or female.

<!-- Start of picture text --> Fig 1: Labor force participation rates by age-groups,<br>1987-91.<br>1 00<br>80 - -<br>-60 -<br>~. 40<br>20<br><16 17-25 26-45 46-60 >60<br>age-groups<br>1987 - -x-- 1991<br><!-- End of picture text --> Source: GLSS 1,,3.

The increased rates have been brought about by women in the 26-45 year agegroup whose participation rate has increased from 72% in 1987 to 92% in 1991. Naturally, younger and older women have lower participation rates than prime age women (Figure 1). This is a commonly observed pattern in many developing countries, and Ghana is no different in that aspect. The main argument being school enrollment for younger groups and the existence of pensions/savings for the older groups. The increased rates for females of prime age could be due to a variety of reasons ranging from increased hourly wages/earnings, increased levels of education, and lower fertility rates (issues which will be discussed in the later sections). The participation rates have increased substantially in the rural areas from 62% in 1987 to 75% in 1991.

One of the features of the this increasing labor force is that it is an educated labor force, with most of the workers having middle or secondary education. The increase in the stocks of educated labor (primary and above) and increased enrollment rates have also contributed to an increased supply of educated labor force.
carpentry, tailoring, barbering, shoe-repair, domestic work, vehicle and tool repairs, and transport. In addition, small-scale entrepreneurs manufa _re_ textiles, garments, footwear, household utensils, musical instruments, metal products, furniture and wood products, and leather goods. They process foods and beverages, and recycle junk. Some firms are also involved in such illicit ventures as smuggling and processing alcohol and cocaine.

This research analyzes the informal sector in Peru, particularly the role of women, based on a theoretical model of informal retail trade that uses data from the Peru Living Standards Survey (PLSS). Retailing is the domirnant nonfarm family enterprise. The central questions are: What factors explain differences in the performance of retail businesses? Assuming that these cc.nsiderations can be identified, what types of policy initiatives might improve the performance of firms, particularly those run by women? The analysis is confined to urban areas where most of these bus.nesses are located .<sup>4</sup> 4We attempted to provide an analysis of nonfarm enterprises in both urban and rural areas. But the limited size of the sample in rural areas precluded the estimation of a mod-l that we were confident of using. The resu- ts for rural areas are in appendix L.

3
<!-- Start of picture text --> 30<br>25<br>8<br>20<br>7<br>6<br>15<br>5<br>10 4<br>3<br>5 2<br>1<br>0<br>0<br>c. At-risk-of-poverty (AROP) rate anchored at a fixed moment in<br>time (2019) and household median income growth, Percent,<br>2007-2022<br>80 25<br>20<br>60<br>15<br>40<br>10<br>20<br>5<br>0 0<br>Percent<br>Poverty Headcount (Percent)<br>CY SI FI CZ BE DK IE MT LU FR NL EE PL AT LT PT HR IT SE ES HU SK LV EL BG RO<br>Poverty Headcount Daily Income<br><!-- End of picture text --> Poverty Rate, National anchored (adult equivalents) 2019 Daily median disposable income (2011 USD PPP per capita) _Source:_ For Poverty Headcount Rates (UMI): World Bank estimates using 2007-2022 EU-SILC UDB-C. Estimates are reported using income year, not survey year. No more recent estimates are available. For Anchored AROP: For 2006-2021, World Bank estimates using 2007-2022 EU-SILC UDB-C. This corresponds to the headcount poverty at 60 percent of the national median adult equivalent disposable income anchored to 2019 for time comparability. Estimates are reported using income year, not survey year. There is a break in the series in 2015, due to methodological changes. No more recent estimates are available.

**Income inequality rose between 2015 and 2018 but has declined since then. Despite the recent positive trends, income inequality persists at a notably high level, ranking the country as the most unequal country in the EU.** Despite solid income growth among poorer households, growth has not been inclusive enough **.** The per adult equivalent Gini Index increased from 37.7 in the income year 2015 to 40.8 in the income year 2018, in contrast to the trends in inequality in the region, which remain primarily stable (Figure 2, panel a).<sup>3</sup> More recently, income inequality has decreased, but the country continues to have the highest inequality among EU countries (Figure 2, panel b). The at-risk-of-poverty rate (AROP), a measure of income inequality rather than absolute poverty, has shown an upward trend, with 22.9 percent of the population having incomes below the national poverty line in 2021 (income year), the highest in the EU (Figure 2, panel c). Most of the observed inequality increase can be attributed to widening disparities in labor market income, with poorer individuals tending not to work or to be concentrated in lower-wage occupations. By contrast, the tax ratio, share of adults employed, pension income, and social income have only marginally helped slow the increase in the Gini coefficient (World

> 3 Similar trends are observed using the per capita Gini Index.

3