rafmacalaba/datause-encoder-data huggingface dataset:
also be sitting on huge mining resources yet to be discovered. The scenery of these mountains also could make tourism a viable export expansion sector. But developing any of these sectors poses the challenge of using the limited resource of the country in a constrained business environment.

# **Tajikistan, an economy with many major binding constraints to growth**

Haussmann, Rodrik and Velasco (2005) provide a useful framework to assess the binding constraints a country is facing. Is it the cost of financing, the low return on private investment or the poor appropriability of investment returns? A similar exercise on Tajikistan suggests the following major constraints to growth: government regulations and interventions that weaken the business climate, weak control of corruption resulting in informal taxation that adds to the cost of doing business, and costly and unreliable connectivity service raising the cost of trading and limiting access to markets and technology. We can summarize these constraints into two: a constrained production capacity and constrained accessibility to market.

# _Constraints to Tajikistan’s Production Capacity_

In countries with disadvantageous geography like Tajikistan, rent-seeking behavior and elite capture have limited the potential of positive spatial externalities, reducing access to quality intermediate goods and services and contributing to low returns on investment. This increases perceptions of risk among potential investors, both domestic and foreign, who tend to prefer to engage in activities with safe returns, such as real estate, or to operate in the shadow economy. A successful diversification strategy would support the development of backbone services and infrastructure, and business-friendly business environments.

With regards to backbone service and infrastructure, ICT plays an important role in reducing the economic distance to markets and integrating urban centers. Such technology helps with negotiations, reduces the need for travel and transportation, and reduces information distortions by allowing easy cross-checking of available information. Despite significant recent progress Tajikistan lags behind on many accounts its neighbors (Table 2).

**Table 2. ICT Performance in Kazakhstan, the Kyrgyz Republic, and Tajikistan, 2007**

|**Item**|**Kazakhstan**|**Kyrgyz Republic**|**Tajikistan**|
|---|---|---|---|
|Mobile cellular subscriptions (percent)|80|41|35|
|Telephone lines (percent)|21|9|5|
|Internet users (percent)|12|14|7|
|Digital subscriber lines (monthly payment, USD)<sup>a</sup>|31|265|104|
|Internet traffic (incoming + outgoing, terabytes)<sup>a</sup>|67,539|n.a.|56,000|

_Source:_ World Development Indicators database; countries’ statistical agencies. a Data are for 2008.

9
in sub-districts in the years in which they had KDP; the ‘non-KDP’ bar shows the mean number of violent incidents in years in which the sub-districts did not have KDP.<sup>43</sup> The mean is of the average number of violent conflicts _in one year_ in KDP and non-KDP areas. In East Java, marginally more violent conflict took place in non-KDP areas than in areas with the program (with a mean of 1 in the former and 0.92 violent conflicts in the latter). In NTT, areas with KDP reported higher levels of violent conflict (1.04 versus 0.74).<sup>44</sup> If we take all conflict, violent and non-violent, similar trends are observable.

**_Figure 3: Mean # Violent Conflict Incidents per Year per Sub-District in East Java and NTT (2001-2003): KDP and non-KDP Areas_**

<!-- Start of picture text -->
1.2<br>1.0388<br>1<br>1<br>0.9241<br>0.8 0.735<br>East Java<br>0.6<br>NTT<br>0.4<br>0.2<br>0<br>KDP (n=287) Non-KDP (n=337)<br>Source: KDP & CCN dataset<br>Mean Cases Per Kecamatan Per Year<br><!-- End of picture text -->

While KDP forums, facilitators and complaints mechanisms are used frequently and effectively to deal with conflict related to the program, we find little evidence that KDP per se 43 We matched the conflict data by year to whether a sub-district had had KDP in the previous year. We ‘lagged’ that data in order to ensure that the full KDP cycle had finished, a necessary step if we assume that KDP only has a significant impact over a full program cycle. To illustrate, take, for example, a sub-district that received KDP in cycle 2 (2000/2001) and cycle 3 (2003/2004), but not in cycle 1 (1999/2000). The conflicts recorded in Years 2 and 3 of our conflict data (2002 and 2003) would be included in the ‘KDP’ total. However, the conflicts in the first year (2001) were recorded in the ‘non-KDP’ total because the program had not yet operated in that sub-district at that time. Thus one particular sub-district will contribute towards both the KDP and non-KDP totals unless they received the program for at least three years. If a sub-district had KDP for three years, they would contribute three years of data to the KDP sum; if they had the program for two years, they would contribute two years of data to the KDP total, and one to the non-KDP total; and so on. However, once a sub-district had had KDP, all subsequent years were counted in the KDP total, as we assume that KDP programmatic effects will hold. If, for example, a subdistrict had KDP in Year 1 and 2, but not in Year 3, we would still count all three years of conflict data in the KDP total. Doing the analysis this way allows us to factor in for conflicts that took place before the program arrived in an area. If we simply compared KDP and non-KDP areas, these conflicts would bias the comparison.

44 The difference in East Java between KDP and non-KDP areas is not significant; in NTT it is significant at the 10% level.

23
1II) R \io7 ;ilfi P 'i 1 98(

# Summary findings

tI **sitig** hooseholdo( sur\e e data from 44 Demzograpliic and I lCalth Sur\veys in 3,i c ounltrics, Filmer and Pritchett dociiilcit differenit paitternls in the enrollmenlt and attainment ot children fro(m riclh and poor housellolds. t lhev find(i that: Fninrollment profiles of the poor differ across couintries but fall into distinictive regional patterns. In soiime areas (including muich of South America) the poor reach nearlv univcrsal enrollnment in first grade but then drop oUt in droves. In others (including much of South Asia and West Africa), the poor never enroll. Both patterns lead to low attainment.

*'I'here are enormious differences across countries in the "wealth gap" - the difference in enrollment and

educational artain merit bet _s%_ **_r_** ic ri . i: * soile countries tile differeiic . rw\r en tilc :'a' p in the mnedilan im hiber o, vear ( ,ni.ete( oo i n on]x a ear or t\\o; in itlier> _t:te_ **giip** i a'i great .l - f.tenl year . **_*_** ihc attaililinellt pr tilCs ctll h( :Ied as JIagnlklit tools to CXamnIeIII iCSueS **iII** tIIe Cdiicati It I " >t(cI, including the cxtent to which enrollime-it ;s low b6eca.iseof the physical uLinavailability ot schools. Filmer and Pritchctt ovecrcomile the Lack of data on inconme and consumptioni expenditures in the sur\ eys >vh constructing a proxy for longn-run houisehold \xealth. using survey informationi on asSCt" an11d Using the statistical techniqUe of principal comnponents.

-T his paper -a product of Poverty and Human Resources, Development Research G.roup - is part of a larger effort in the groUp to inforimi education policy. The study was funded by the Bank's Research Support Budget under<sup>the research project</sup> "Iducational Enrollment and Dropout" (RPO 682-1 1). Copies of this paper are available free from the World<sup>Bank, 1818</sup> Hi Street NW, Washingtoni, DC 20433. Please contact Sheila Fallon, roomi MC3-638,<sup>telephionie '02-47/3-8009, fax 202-</sup> 822-1 153, Internet address sfallonriaworldbank.org. The authors man. he contacted at dfilmer **ou** orldbank.org or lpritchett(f worldbank.org. September 1998. (38 pages)

_I11e Polioc Rese.rc \Y orking IPaper Series disseminates the findings ot_ **_u_** _owk in progrs tS r_ **_enicowaiige_** _the exchange ot ideas a.sout dcle lopnment issues. Ani objectit e of the series is to get the findigs oiut quicklv. ci 'n i th pr 7tesen'ta oins 1 less tkan jUo/K polisoai. [1⁄2e papers_ **_.arr_** _the_ **_iaoU_** _Sf the authors and should be cited according/v. Tbe fIndings. iwiterpretations_ **_ii_** _il .iJ_ **_cwlitlsionl_** _s expressedl1_ **_in_** _this paper_ **_are_** _e ntire1% trh(Sc of the authors. Thev do not necessarily represent thec 1ie it ot \t 1,1 Ia_ **_UikI_** _!ts Exricutit i Directors, or the coulntlles tiex rcpreseet._ Produced by the Policy Research D[isseminitiom (Cllntcl
**Table 3.**

|Country|Eviction<br>Formalism|Check<br>Collection<br>Formalism|Judicial<br>Efficiency<br>Index|Citizens’<br>Access to<br>Justice|Enforcement<br>Index|Control of<br>Corruption<br>Index|
|---|---|---|---|---|---|---|
|Austria|**6.30**|**6.07**|**9.50**|**7.50**|**8.25**|**8.57**|
|**Pakistan**|**4.21**|**4.21**|**5.00**|**0.00**|**3.85**|**2.98**|

_Sources: Formalism Index: Djankov et al. (2003); Judicial Efficiency Index: ICRG; Citizens’ Access to Justice: World Business Environment Survey; Enforcement Index: Business Environmental Risk Intelligence; Control of Corruption: ICRG._ There are several possible explanations for this, all of which are probably true.

First, there are other factors besides procedural complexity which affect perceptions of efficiency or access and these may be more important in some countries. Second, in rich countries administrative capacity, and other institutional designs (see Table 2) including ADR systems may be more developed; therefore citizens may be better able to deal with procedural complexity, or to counteract some of the negative effects associated with procedural complexity. Third, while comparative values for indices tell us how countries fare relative to each other, they do not tell us what is the appropriate level of the index for a particular country. For example, while a score of 2.42 may be “small” enough for Denmark, it may not be so for Malawi.<sup>31</sup> In fact, no score will be “small enough” for Malawi if Malawi does not have court structures in suitably accessible places: something which is not picked up by the data.<sup>32</sup> While the data discussed above and some of which is shown in Table 3 deals only with relatively simple and small transactions, the general principles, stemming from common sense and supported by some empirical evidence, should hold for all types of commercial transaction which are disputed in the courts.

> 31 The theoretical benefits of formalism and the practical experience is discussed later in the book. 32 For example if parties must travel long distances to courts, the likelihood of using courts falls even further and the relevance of courts declines.

29
**Figure 2.1 Gender Balance of RPL Applicants across Occupations**

<!-- Start of picture text --> Sewing machine operation 20% 80%<br>Tailor and dress making 15% 85%<br>Graphic design 68% 32%<br>IT support 84% 16%<br>Electrical installation 96% 4%<br>Plumbing 99%<br>Welding 100%<br>Male Female<br><!-- End of picture text --> _Source:_ Original figure for this publication. _Note:_ IT = information technology.

# **Sources of Skills Acquisition**

The baseline survey asked the applicants how they had gained their skills for the occupations for which they have applied for RPL certification. The responses are shown in figure 2.2. Both formal and informal ways of skilling seem to have played vital roles. Many respondents cited that they acquired the skills through short-course training programs (42 percent), which constitutes a more formal off-the-job training opportunity. Simultaneously, many applicants also indicated they have learned their skills informally from family members and friends engaged in the same work (47 percent) or learned it on the job through their work experience in Bangladesh (32 percent). Those constitute a more informal training opportunity that is widely available in Bangladesh. This finding is generally consistent with the assumption of the RPL program of the Skills and Training Enhancement Project (STEP) that many workers who want to have their skills assessed acquired their skills through these informal modes of skilling opportunities (figure 2.2).

**Figure 2.2 How the RPL Applicants Acquired their Skills**

<!-- Start of picture text --> Learned from family or friend engaged in this work 47%<br>Short course training 42%<br>Learned through work experience in Bangladesh 32%<br>Apprenticeship program 5%<br><!-- End of picture text --> _Source:_ Original figure for this publication. _Note:_ The sum of responses exceeds 100 percent as the question was multiple choice.

9
16 The still under-studied household enterprise sector generates the majority of new nonfarm jobs in most African countries, even during times of high economic growth (Fox and Gaal, 2008). Household survey data show that, for the past decades, the informal sector (non-farm) has been a growing source of employment for a large fraction of the African youth, but also for older workers trying to seize entrepreneurial opportunities. Its contribution to GDP and poverty reduction has been substantial, and it has become a major point of entry into the labor market for many. For youth in large cities such as Addis-Ababa, Lagos, Kinshasa, Abidjan, Douala, Nairobi, or Dar-es-Salaam, the informal sector is indeed the only viable option for making a modest living, even for those with secondary, vocational, and tertiary education, as the number of employers in the formal sector is limited and there is evidence of skills mismatch in the labor market. Figure 2 reveals the fundamental difference in structure between high-income and lowincome labor markets, and why the basic concepts of unemployment/employment and traditional theories have proved ineffective in helping policy makers in Sub-Saharan Africa.

Summing up, it can therefore be said that despite their increasing levels of sophistication, economic theories of unemployment and employment have so far yielded little useful policy guidance for developing countries. While they have provided important insights on specific and often limited micro issues, they have generally fallen short of proposing a framework that governments in poor countries could follow to create employment for the large, often underutilized segments of their labor force. From a policy perspective, it is important to shift the focus to concrete opportunities for creation of formal sector employment in African and lowincome countries where production costs remain potentially competitive and can provide opportunities for labor arbitrage.

# **3. Labor arbitrage: New Ways of Creating Employment**

Because the search for solutions to unemployment and underemployment has too long focused on the wrong questions, traditional, mainstream remedies have failed to provide useful answers to developing country policy makers. Learning from ignorance would require drawing lessons from economic history and the experience of other countries where structural change has involved a variety of industrial processes. The transformation of the world economy and the
|TABLEII-2 TOTALEXPENDITURES OF THECOLOMBIANGO|VERNMENT20<br>Fiscal acc|17 <br>ounts|Portion of t<br>accounts a|he fiscal<br>nalyzed|ENPH<br>Totals|Ratio<br>between<br>total in the|
|---|---|---|---|---|---|---|
||||||In|survey and|
||In billions<br>of Pesos|% of<br>GDP|In billions<br>of Pesos|% of<br>GDP|billions<br>of Pesos|<br>external<br>statistics,%|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Total expenditures**|**305,241**|**33.3%**|**122,464**|**13.3%**|**90,922**|**29.8%**|
|**Social protection**|**82,439**|**9.0%**|**50,617**|**5.5%**|**35,386**|**42.9%**|
|Pensions|33,360|3.6%|33,360|3.6%|31,921|95.7%|
|Colombia Mayor|1,054|0.1%|1,054|0.1%|961|91.1%|
|Unemployment subsidy|198|0.0%|198|0.0%|15|7.7%|
|Más Familias en Acción|2,001|0.2%|1,911|0.2%|1,936|96.7%|
|Jóvenes en Acción|191|0.0%|189|0.0%|169|88.4%|
|Victims' Policy|13,800|1.5%|13,800|1.5%|232|1.7%|
|<br>Familias en su Tierra|105|0.0%|105|0.0%|152|144.7%|
|Other social protection expenditures|<br>31,730|3.4%|||||
|**Education**|**44,505**|**4.8%**|**31,558**|**3.4%**|**28,458**|**63.9%**|
|Early childhood|3,967|0.4%|3,967|0.4%|3,432|86.5%|
|School Meals Program|1,849|0.2%|1,849|0.2%|1,500|81.1%|
|Primary and secondary education|22,713|2.5%|22,713|2.5%|22,753|100.2%|
|Higher education|3,029|0.3%|3,029|0.3%|773|25.52%|
|Other education expenditures|12,947|1.4%|||||
|**Health**|**44,902**|**4.9%**|**35,946**|**3.9%**|**22,928**|**51.1%**|
|Subsidized healthcare system|17,336|1.9%|17,109|1.9%|16,334|94.2%|
|Contributory health system*|23,595|2.6%|18,837|2.0%|6,594|79.1%|
|Other health expenditures|3,972|0.4%|||||
|**Subsidies minus contributions**|**11,633**|**1.4%**|**4,344**|**0.5%**|**4,150**|**35.7%**|
|_Utilities_|_2,833_|_0.4%_|_4,344_|_0.5%_|_4,150_|_146.5%_|
|Electricity|1,869|0.2%|2,921|0.3%|2,735|146.3%|
|Subsidy|3,058|0.3%|3,058|0.3%|2,808|91.8%|
|Residential contributions**|137|0.0%|137|0.0%|73|53.2%|
|Non-Residential contributions|1,052|0.1%|||||
|Natural gas|478|0.1%|537|0.1%|513|107.2%|
|Subsidy|571|0.1%|571|0.1%|536|93.8%|
|Residential contributions|34|0.0%|34|0.0%|23|67.2%|
|Non-Residential contributions|59|0.0%|||||
|Water, sewage and waste collection|486|0.1%|885|0.1%|902|185.7%|
|Subsidy|1,150|0.1%|1,150|0.1%|1,116|97.0%|
|Residential contributions|265|0.0%|265|0.0%|214|80.7%|
|Non-Residential contributions|399|0.0%|||||
|_Fuels_|_8,800_|_1.0%_|||||
|**Other expenses**|**121,762**|**13.2%**|||||

**Source:** World Bank Group staff estimates using information from the Ministry of Finance and Public Credit, National Statistical Office, and the ENPH, 2017. **Notes:** * For the contributory health system, the sources in 2017 corresponded to $15,105 billion correspond to RC and REE contributions, $ 7,886 billion to income tax and $ 908 billion to other sources ** Residential contributions refer to contributions made by households or buildings for residential use, non-residential contributions refer to contributions made by buildings for commercial, industrial use, among others.

7
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<!-- Start of picture text --> 5,000 Es,co}oO<br>Fa<br>— 2,500 g<br>5€<br>aC)<br>ana 10,000 zB<br>-_>. >3<br>3 7,500 a<br>==<br>%S=<br>3 5,000 5=<br>28<br>S2,500 a<br>& Ss<br>= oO<br>5 10,000<br>3 33)<br>< 8<br>7,500 gea<br>5,000 =aryoO=a<br>S|<br>2,500 Se=><br>Oo<br>2015 2031 2051 2015 2031 2051 2015 2031 2051 2015 2031 2051 2015 2031 2051<br><!-- End of picture text -->

<!-- Start of picture text --> 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100<br>Year<br>M§East Asia & Pacific Ml Europe & Central Asia BB High Income<br>M@\Latin America & Caribbean [J Middle East & North Africa!) South Asia<br>M9) Sub-Saharan Africa<br><!-- End of picture text -->
# **Table 4 Data means: male and female time allocations** (std. dev. in parenthesis)

## **<u>Panel A  SSA TUS2000 TUS</u>**

|Minutes per day|Female|Std.dev|Male<br>Std.dev|
|---|---|---|---|
|SNA work|170|(241)|373 (293)|
|Domestic work|227|(161)|77 (137)|
|Care|112|(123)|12 (30)|
|Total work|549|(211)|462(277)|
|No of records|667|||

|**Panel B EMNV98**<br>||||
|---|---|---|---|
|Minutes per day|Female|Std.dev|Male<br>Std.dev|
|SNA work|170|(254)|465 (236)|
|Domestic work|329|(194)|72 (108)|
|Care|99|(135)|19 (55)|
|Totalwork|599|(215)|556 (214)|
|No of records|544|||

The data means for both samples again indicate that women work longer than men do and that there is a high degree of specialization in domestic work and care activities by females and in SNA work by males. The female and male means for total hours of work are higher than in Tables 1 and 2 because the samples comprise families in the most intensive child rearing phases of the life cycle. In this respect the results are consistent with those of time use studies for developed countries.<sup>53</sup> However the means for care activities for both samples are much lower than those obtained for developed countries. Time use data for developed countries typically indicate the allocation of far more time to child care, even when there is a substantial input of market or bought-in child care.

Because the data means are for parents from the same household, the gender differences in leisures in Table 4 can be seen as an outcome of the intra-household allocation of resources. They therefore illustrate the extent to which a model that assumes time outside SNA activity is leisure can give entirely misleading results on the intra-family sharing rule. Instead of working longer than men, women would be seen to working less

> 53 Parents are found to work much longer in the child rearing phases in developed countries, and particularly mothers (see Apps and Rees 2001).  We attribute this, together with the strong gender division of labor over these phases, to high borrowing rates in an imperfect capital market and to government policies that preclude the development of an efficient, high quality child care sector.

52
- [32] Stock, James H and Motohiro Yogo (2005). “Testing for Weak Instruments in Linear IV Regression,”In: Andrews DWK Identi...cation and Inference for Econometric Models. New York: Cambridge University Press, 80-108.

- [33] Timmer, Marcel P., Abdul Azeez Erumban, Bart Los, Robert Stehrer, and Gaaitzen J. de Vries. (2014) “Slicing up global value chains.” Journal of Economic Perspectives, 28(2), 99-118.

- [34] United Nations (1999). Handbook of Input-Output Table Compilation and Analysis.

- [35] Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu (2014). “Quantifying International Production Sharing at the Bilateral and Sector Level,”NBER Working Paper, 19677.

43