rafmacalaba/datause-encoder-data huggingface dataset:
information if they are concerned about their apprentice eventually competing against them (Frazer, 2006). The lack of training materials and syllabi as well as reliance on older technology could also hinder apprentices’ learning and earning outcomes (Darvas & Palmer, 2014). Further, since apprentices copy their trainer’s business, their outcomes could be limited by their trainer’s knowledge and practices (Darvas & Palmer, 2014; Frazer, 2006). Our results using the randomized match between apprentices and trainers show that whom you train with matters. Training with the most profitable or the most experienced firms increased apprentices’ total monthly earnings. Because training opportunities with these trainers were limited, apprentices assigned to other trainers were relegated to less qualified trainers who delivered worse outcomes. Thus the overall results suggest that trainer quality is a constraint to the effectiveness of apprenticeships.

# **7 Conclusion**

In this paper we present findings from our experimental evaluation of the NAP in Ghana, a program that because of its implementation process is equivalent to a traditional apthe to train under the NAP were prenticeship. Among applicant pool, opportunities randomized. This allows us to compare the outcomes of participants offered training to those who were not offered the opportunity to train. Close to 75 percent of participants who were offered the opportunity to train under the NAP commenced training. However, since 62 percent of the control started an apprenticeship outside of the program, we find a relatively modest first stage effect on starting an apprenticeship. This could suggest that the program recruitment was not particularly well targeted. The implementation delays caused by political transition were likely an important contributing factor to the modest first stage effect on starting an apprenticeship. We find a larger difference in the apprenticeship completion rates between individuals who received a NAP training offer and those who did not. This suggests that apprenticeship fees present a barrier for many youth, especially when it comes to completion of training. Because many youth in this context are likely to be credit constrained, this provides some justification for government subsidies.

With respect to labor market outcomes, apprenticeships move participants out of wage but there is a slower transition into This reduces earn- employment, self-employment. ings in wage employment, with limited effects on self-employment earnings, leading to lower overall earnings. But we find that these reductions can be more than offset by the higher earnings generated if an apprentice trains with a high-quality trainer. Because the number of high-quality trainers is limited, this finding suggests that policy measures 22
results from running the main regression with district and year fixed effects in equation (1), substituting other observables as the the dependent variable. For robustness, in addition to the measure of TSC intensity, we separately regress the observed properties of children on a binary indicator that the TSC had started, meaning that any positive number of latrines had been built in that district by that year.

Table 2 demonstrates that none of the characteristics of children or households that we observe are predicted by the variation in TSC program intensity that we use to identify an effect on cognitive achievement. In particular, children exposed to more TSC latrines in their first year of life are not more likely to attend extra “tuition” classes outside of school, nor do they have better educated fathers; if anything, they have slightly less well educated mothers. Note specifically that we find no association of early-life exposure to the TSC with school enrollment among these six-year-olds. Village-level variables are also uncorrelated with TSC implementation, indicating that our results do not reflect overall trends in local government service delivery. Of course, this exercise cannot fully eliminate the possibility of omitted variable bias from another factor, but it may be unlikely that such an important factor would be uncorrelated with the variables observed here.

# **4.4 Evidence for parallel trends**

Equation 1 will only estimate the effect of the program if we can make the parallel trends assumption that test scores would have evolved along similar trends, on average, in districts where the TSC was and was not implemented, without the program. Although we cannot observe this counterfactual case directly, we can assess this assumption by looking for differences in trends established before the program, in additional datasets. In section 6 we do this in three different ways. First, we show that the distributions of district level improvements in literacy between the 1991 and 2001 censuses are quite similar among districts that did and did not receive any reported TSC latrines by 2003. Second, we study within-district trends in literacy in the India Human Development Survey, and find no difference in trends 15
and the National Bureau of Economic Research, pp. 289–328.

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53
column in Table 4 reports the odds ratio for downward nominal wage rigidity vs flexible wages and the second column the corresponding odds ratio for downward real wage rigidity vs flexible wages. Heteroscedasticity-robust p-values are given in the parentheses.

**Table 4: Downward nominal and real wage rigidity: Multinomial logit regression**

||Downward nominal<br>rigidity/<br>Flexible wage<br>|wage|Downward real wage<br>rigidity/<br>Flexible wage|
|---|---|---|---|
|Low-skilled blue-collar (%)|0.553***<br>(0.000)||1.038<br>(0.809)|
|High-skilled blue-collar (%)|0.739*<br>(0.063)||0.682**<br>(0.026)|
|Low-skilled white-collar (%)|0.730<br>(0.150)||0.684*<br>(0.066)|
|Labour cost (%)|1.479**||1.351*|
||(0.033)||(0.063)|
|Permanent workers (%)|1.487*<br>(0.073)||1.187<br>(0.301)|
|Size = 20–49|1.149||1.102|
||(0.222)||(0.278)|
|Size = 50–199|1.225*<br>(0.065)||0.995<br>(0.949)|
|Size = 200+|1.051<br>(0.695)||1.060<br>(0.505)|
|Sector = Energy|0.676<br>(0.418)||1.816***<br>(0.001)|
|Sector = Construction|0.765*||1.067|
||(0.076)||(0.649)|
|Sector = Trade|0.826*<br>(0.087)||0.960<br>(0.624)|
|Sector = Market services|0.884<br>(0.209)||0.963<br>(0.619)|
|Sector = Financial interm.|0.805<br>(0.470)||1.395<br>(0.158)|
|Sector = Non-market serv.|1.004||0.792|
||(0.987)||(0.521)|
|Observations||11981||
|Pseudo R2||0.302|0|

Notes: The table presents the estimated odds ratios for nominal wage rigidity vs flexible wage and real wage rigidity vs flexible wage. The regression includes country fixed effects (not shown). Robust P- values in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

The regression results indicate that workforce composition is related to wage rigidity in a manner that is predicted by the theoretical models discussed in Section 3. We find that firms employing a larger proportion of high-skilled white-collar workers (the reference category) 19
# **Migrants, Towns, Poverty and Jobs: Insights from Tanzania**<sup>**1**</sup>

Luc Christiaensen, Joachim De Weerdt, Bert Ingelaere, Ravi Kanbur

## **JEL classification** : J46, J61, O15, O18, R1

**Key words** : migration, urbanization, secondary town, jobs, poverty, life history,

1 Luc Christiaensen (lchristiaensen@worldbank.org, corresponding author), World Bank, United States, Joachim De Weerdt (joachim.deweerdt@uantwerpen.be), University of Antwerp and Catholic University of Leuven, Belgium, Bert Ingelaere (bert.ingelaere@uantwerpen.be), University of Antwerp, Belgium and Ravi Kanbur (sk145@cornell.edu), Cornell University, United States. This paper was prepared for the Initiative for Policy Dialogue/Japan International Cooperation Agency Africa Task Force Meeting on The Quality of Growth in Sub‐ Saharan Africa, held at Columbia University on 11‐12 April, 2017. The Task Force is chaired by Joseph Stiglitz, Akbar Noman and Ravi Kanbur. An early version of the paper has also been presented at a World Bank Jobs Group Brown Bag Lunch Seminar in Washington D.C. in January 2017. The authors thank the participants in these meetings for their feedback and the migrant survey participants for sharing their life histories. Financial support from the Strategic Research Program of the World Bank Group and the International Growth Center of the Department of International Development, UK is further gratefully acknowledged. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
**Decoupling and recoupling**

(2000-Q3 2010) period, and assuming for simplicity that trend growth remained stable within each sub-period, the specification is simple enough:

- !!" !"!!" !

<!-- Start of picture text --> ! ! !!!!" !"!!!!! ! !!!!" !"!!!!! ! !!"#$ ! !!!!" !"!!!!"#!! ! !!!!" !"!!!!"#!! ! !!"#$<br>(1)<br><!-- End of picture text --> a regression of the annual q/q growth rate of country _i_ ’s cyclical output (relative to a log linear GDP trend) on the G7 and Chinese cycles, based on quarterly data, identifying the late period (2001-Q3 2010) with an interacting dummy. In passing, such a specification also allows us to estimate the long-term growth convergence (the EM “alphas”) as the constant of the regression.

Note that we deliberately exclude China from the EM sample. For sheer size and growth dynamics, China represents a class in itself that, if grouped with other EM, could bias the conclusions of this exercise (as it does, most notably, in the typical analysis of EM aggregate output). Indeed, it remains a global driver that should be treated as an additional exogenous force behind EM growth and, thus, be included as an additional control, as we do here.

**Table 2** summarizes the results. The first thing to note is the fact that emerging economies display mostly “positive alphas” relative to the G7 group: a median 2.5% growth differential (Column 4) per year over the whole period, roughly in line with the back-of-the-envelope illustration in **Figure 5** . Is the elusive growth convergence finally materializing in the emerging world? While it is still far too early to judge, at first glance their relative growth performance is not inconsistent with the convergence view, not only vis a vis developed economies but, most strikingly, within the emerging group as well ( **Figure 4** ).

Regarding decoupling per se, in the traditional specification in which global growth is represented by the G7, the EM betas appear stable and high. In other words, there seems to have been no decoupling in the past decade, a finding mostly in line with the results in the recent economic literature (Rose, 2009; Wälti, 2009).

However, the data paint a different picture once we include China as a separate control: the explanatory power of the G7 clearly diminishes in the latest period, at the expense of the Chinese <u>6</u>
**Figure 7. Distributional Effects of Agricultural Subsidies in Mali and Niger, 2014**

<!-- Start of picture text --> 7.a Agricultural subsidies as a share (%) of market income per decile<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Mali Niger<br>7.b Share (%) of agricultural subsidies received by market income deciles<br>20<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Mali Niger<br>Source: Authors<br><!-- End of picture text --> The richest benefit the most from energy subsidies in Mali. Gas and electricity subsidies are globally regressive (figure 8), which the fact that domestic energy services are currently not affordable for the poorest partly explains. As a result, only the few better-off individuals with access to these services benefit from the subsidies. In the case of electricity, for example, only 27 percent of the population, who are overall nonpoor individuals, had access in 2014. This evidences bad targeting (at the poor) of energy subsidies. To really help the poorest individuals benefit from subsidies, the first step should be to provide access to gas and electricity services.

13
# **Table A.6.  Income Inequality: Ordered Logit**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Coefficients <br>All s|Odds ratio<br>ample|Coefficients<br>Top-pe|Odds ratio<br>rformers|Coefficients<br>Constraine|Odds ratio<br>d gazelles|Coefficients <br>Survi|Odds ratio<br>valists|
|Gross operating profit|0.00108*** <br>(0.000188)|1.001|0.000489<br>(0.000374)|1.000|0.000980*** <br>(0.000338)|1.001|0.00204***<br>(0.000450)|1.002|
|Age owner|-0.0276***|0.973|0.0298|1.030|-0.0260|0.974|-0.0252*|0.975|
||(0.0104)||(0.0656)||(0.0353)||(0.0151)||
|Age squared owner|0.000344**<br>(0.000121)|* 1.000|-0.000445<br>(0.000788)|1.000|0.000215<br>(0.000438)|1.000|0.000338*<br>(0.000172)|1.000|
|Male(=1)|-0.149**<br>(0.0688)|0.862|-0.677**<br>(0.326)|0.508|-0.0722<br>(0.151)|0.930|-0.355**<br>(0.159)|0.701|
|No diploma|Ref.||Ref.||Ref.||Ref.||
|Primary completed|0.0351|1.036|1.123|3.075|-0.0648|0.937|-0.0165|0.984|
||(0.125)||(0.687)||(0.356)||(0.167)||
|Some secondary|0.532***|1.702|1.142*|3.133|0.427|1.532|0.429***|1.536|
||(0.115)||(0.591)||(0.325)||(0.159)||
|Post secondary|1.135***|3.111|2.098***|8.150|1.134***|3.110|0.902***|2.464|
||(0.141)||(0.667)||(0.358)||(0.219)||
|Other post primary|0.619***<br>(0.219)|1.856|1.313<br>(0.871)|3.718|0.687*<br>(0.418)|1.988|0.876<br>(0.582)|2.400|
|Can read|0.447***<br>(0.0974)|1.563|0.0436<br>(0.665)|1.045|0.909***<br>(0.276)|2.482|0.404***<br>(0.129)|1.497|
|No opportunity in small firms|-0.000629<br>(0.0866)|0.999|0.795*<br>(0.444)|2.215|-0.0311<br>(0.156)|0.969|-0.0724<br>(0.136)|0.930|
|Firm size|0.00340|1.003|-0.0217|0.979|-0.205**|0.814|0.0680|1.070|
||(0.0561)||(0.174)||(0.0871)||(0.105)||
|Trade register|0.830***<br>(0.258)|2.294|0.397<br>(0.959)|1.487|1.469***<br>(0.374)|4.346|0.604<br>(0.600)|1.829|
|Financial knowledge|0.421***|1.523|-0.326|0.722|0.404***|1.498|0.396***|1.486|
||(0.0822)||(0.347)||(0.141)||(0.141)||
|Business association|-0.0507|0.951|0.359|1.431|-0.341*|0.711|0.167|1.182|
||(0.113)||(0.367)||(0.182)||(0.208)||
|Lack of customers|0.0401|1.041|0.174|1.191|0.160|1.174|-0.0109|0.989|
||(0.0602)||(0.275)||(0.111)||(0.0942)||
|Lack of credit access|-0.193***<br>(0.0645)|0.825|-0.479*<br>(0.270)|0.619|-0.237**<br>(0.119)|0.789|-0.246**<br>(0.101)|0.782|
|Management problems|0.0895|1.094|-0.208|0.812|0.269*|1.309|0.0827|1.086|
||(0.0781)||(0.363)||(0.145)||(0.122)||
|Sector fixed effects|yes||yes||yes||yes||
|Region fixed effects|yes||yes||yes||yes||
|<br>Pseudo-R2|0.0999||0.1789||0.1176||0.0902||
|Observations|4,012|4,012|265|265|1,194|1,194|1,681|1,681|
|Standard errors in parentheses **|* p<0.01, ** p<|0.05, * p<0.1|||||||

Note: Discipline problems, no opportunity in large firms, firms age and lack of qualified workforce are non significant additional control variables which are included in the regressions but not presented in this table 34
# **I. Introduction and literature review:**

# **The benefits or disadvantages of vocational education: An inconclusive debate**

Despite an extensive volume of research into the subject, the debate regarding the benefits of vocational education as opposed to general academic education is not conclusive. The arguments against vocational education usually fall into two categories: either that vocational education has a lower benefit-cost ratio (the economists’ argument); or that it inhibits the future socioeconomic attainment of students by reducing students’ access to tertiary education and subsequently gaining entry to the professions and other high-prestige occupations (the educators’ and sociologists’ argument).

Summarizing research findings from Colombia, Tanzania, Kenya, Mexico, Jordan, Sri Lanka, India, Barbados, Swaziland, El Salvador, Brazil, Nigeria, and Somalia, Psacharopoulos (1987) argues that “the costs of vocational programs are considerably higher than general education, while their benefits are comparable”. He questions the value of providing vocational instruction through the mainstream educational system and explores more efficient alternatives, such as employment-based training. In the context of the New Economy, an article titled “Training for Jobs” in a1994 issue of the Economist magazine stated that: “ _Economists have long argued that the returns on general education are higher than those on specific training, because education is transferable whereas many skills tend to be job-specific. Today this case is becoming more compelling still as jobs become less secure, the service sector expands and the lift cycle of vocation skill diminishes and the market puts an even greater premium on the ability of dealing with people and process information”._ The educators’ and sociologists’ arguments are well summarized by Arum and Shavit (1995). They argue that vocational education “inhibits the future socioeconomic attainment of students and reduces students’ chances of attending college and of subsequently gaining entry to the professions and other high-prestige occupations”. One reason for this is that vocational programs are usually less selective and represent the least prestigious of high school tracks. This can bring a certain degree of stigma that may lead to discrimination when 2
- Sylvain Leduc and Daniel Wilson. Roads to prosperity or bridges to nowhere? theory and evidence on the impact of public infrastructure investment. _NBER Macroeconomics Annual_ , 27(1):89–142, 2013.

- Lant Pritchett. The tyranny of concepts: Cudie (cumulated depreciated investment effort) is not capital. _Journal of Economic Growth_ , 5(4):361– 384, 2000.

- Luis Serven. Does public capital crowd out private capital? evidence from india. World Bank Policy Research Working Paper, 1613, 1996.

# **A Proofs**

## **A.1 Proof of Lemma 1**

The first derivative of _Y_ with respect to _kp_ is given by: In the ratio of the second derivatives, the constants _αA_ (1 _− γ_ ) will cancel out. We thus ignore them and take the derivatives: 29
apart from the rest of the workforce. These privileges were sustained by strictly limiting migration to the cities by instituting a household registration system ( _hukou_ ) in the late 1950s (see, for instance, Fan 2004).

In the latter half of the 1960s, when the Cultural Revolution was in full swing, the Third Front program dispersed industries from the urban centers in the East and Northeast to the interior provinces, especially the southwestern region, to increase their survival chances in the event of war and to plant the seeds of industrial capability throughout the country (Naughton 1988). This was followed by efforts to build small scale rural industries, and some decentralization of economic decision making to provincial or county level agencies (Riskin 1987). However, on the eve of the reform era towards the end of the 1970s, the organization of production and the working of enterprises still conformed closely to the model instituted two decades earlier. In particular, the large sized enterprises responsible for the lion’s share of manufactured products remained under the tight control of central and provincial bureaus.

The backwardness of China’s industries relative to some of its East Asian neighbors, the inefficiencies of the centrally planned economy, agricultural stagnation, and the urgent need to accelerate growth and provide jobs that would reverse the spread of poverty empowered the reformers among the senior members of the Communist Party. Their initial proposals announced at the 3<sup>rd</sup> Plenum of the 11<sup>th</sup> Central Committee in 1978 have bit by bit transformed China’s command economy (Riskin 1987) into a decentralized, socialist market economy with institutions and ownership rights that are coming to resemble those of the capitalist economies in East Asia and elsewhere. This process of institutional change has been a slow one. It has been interspersed with much experimentation through which reform ideas were subjected to trials on a limited scale before being adopted more widely. And throughout the past 25 years, each 4
**Figure 11**

# **Financial globalization through global equity funds**

This figure presents the percentage of assets invested in Advanced and Emerging markets by global equity funds. Source: EPFR <!-- Start of picture text --> 88% 16%<br>86% 14%<br>12%<br>84%<br>10%<br>82%<br>8%<br>80%<br>6%<br>78%<br>4%<br>76% 2%<br>74% 0%<br>Advanced EM<br><!-- End of picture text -->
- No Education

- Primary Education

- Secondary Education

- Tertiary Education

_Business type._ We have created a series of binary variables to indicate whether the participants who already owned a business had registered this business with the Corporate Affair Commission and if yes, which kind of status they had chosen among:

- Limited liability company

- Business name

- Other

_Tax paid_ records the amount paid in taxed in the year previous to the initial application by the individuals who already owned a business at that time.

_Loans_ are a series of binary variables indicating - for the participants who already owned a business at the time of the initial application – whether this business had a loan at that time from any of the following:

- Bank

- Microfinance Organization

- Cooperative society

_Business Challenges_ are a series of binary variables indicating - for the participants who already owned a business at the time of the initial application – whether they identified the following as major challenges for their business:

- Lack of funds to start a business

- Lack of the required skills

- Lack of business experience

- Lack of interest in business

_Projected outcomes_ record the expectations of participants who already had a business at the time of the initial application. Information was recorded regarding:

- Money needed to expand business

- Number of new people the business will employ