rafmacalaba/datause-encoder-data huggingface dataset:
percent increase in employment and 20 percent increase in earnings, although men saw no change in these outcomes.<sup>2</sup> This paper builds on and extends this literature by providing the first randomized experiment of a large-scale vocational training program for the general unemployed population (not just for disadvantaged youth) in a developing country. This is also the first paper on a developing country that is able to trace longer-term impacts up to three years post-training, by complementing a follow-up survey with administrative data from the social security agency. We employ an over-subscription design to evaluate the impact of the Turkish National Employment Agency’s vocational training programs.<sup>3</sup> These programs average 336 hours over three months, are available for a wide range of subjects, and are offered by both private and public providers. These training services were provided to over 250,000 registered unemployed in 2011, hence we are evaluating a program operating at scale and not just a pilot. A large sample of 5,902 applicants randomly allocated to treatment and control within 130 separate courses, coupled with a detailed follow-up survey with only 6 percent attrition one year after training allows us to measure both the overall impact of training, and heterogeneity in training impacts along dimensions pre-specified in a pre-analysis plan. These features contrast with the small existing literature in developing countries, which has focused on pilot programs for youth, evaluated over relatively short time horizons, with higher rates of attrition, and no sources of administrative data on employment outcomes.

We estimate that being assigned to training had an overall effect on employment and earnings that was small in magnitude and not statistically significant: individuals assigned to treatment had a 2 percentage point higher likelihood of working at all, a 1.2 percentage point higher likelihood of working 20 hours or more per week, and earned 5.6 percent higher income. We find similar magnitude, but statistically significant, impacts on measures of the quality of employment: a 2.0 percentage point increase in formal employment, 8.6 percentage point increase in formal income, and an increase in occupational status. Our point estimates suggest the training impacts were largest for males aged above 25, even though this group was least likely to take a course conditional on being assigned to treatment. We cannot, however, reject

> 2 In addition a small pilot study of 658 women offered tailoring and stitching training in India found a 5 percent increase in employment (Maitra and Mani, 2012), while two studies of youth in Uganda which offer vocational training in combination with a grant (Blattman et al, 2013) or in combination with life skills training (Bandiera et al, 2012) also found positive impacts on employment and/or earnings.

> 3 This study is also to our knowledge the first randomized experiment of any social policy in Turkey.

3
control variables. As a second robustness check, I use a panel dataset constructed from the original dataset. Evidence from the panel data is in accordance with the main regression results.

The lack of a long panel dataset makes it difficult to interpret the relations as causal. However, the strong correlation between direct and indirect measures of growth and trade under a rich set of control variables shows the importance of the relation between importing, exporting, and firm evolution. The evidence from the panel data analysis reinforces this conclusion.

# **10 References**

Aghion, Philippe and Peter Howitt (1992), “A Model of Growth through Creative Destruction,” Econometrica 60: 323 - 351.

Almeida, Rita and Ana M. Fernandes (2008), “Openness and Technological Innovation in Developing Countries: Evidence from Firm-Level Surveys,” Journal of Developing Studies, 44:5, pp 701-727 Alvarez, Roberto and Raymond Robertson (2004), “Exposure to Foreign Markets and Plant-Level Innovation: Evidence from Chile and Mexico,” The Journal of International Trade and Economic Development, 13:1 pp 57-87 Amiti, Mary and Jozef Konings (2007), "Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia," American Economic Review, vol. 97(5), pg 1611-1638.

Aw, Bee Y., S. Chung and M.J. Roberts (1998), “Productivity and the Decision to Export: micro evidence from Taiwan and South Korea”, NBER WP Series 6558 Aw, Bee Y., Mark J. Roberts and Tor Winston (2007), “Export Market Participation, Investments in R&D and Worker Training, and the Evolution of Firm Productivity,” The World Economy, 14:1, pp. 83-104.

Aw, Bee Y., Mark J. Roberts and Daniel Y. Xu (2008), “R&D Investments, Exporting and the Evolution of Firm Productivity,” American Economic Review, Papers and Proceedings, 98:2, pp. 451-456.

Bartelsman, Eric J. and Mark Doms (2000), “Understanding Productivity: Lessons from Longitudinal Microdata,” Journal of Economic Literature, 38(3), pp. 569-594.

Becheikh, Nizar, Rejean Landry and Nabil Amara (2006) “Lessons from innovation empirical studies in the manufacturing sector: A systematic review of the literature from 1993-2003,” Technovation, 26: 644-664.

Bernard, Andrew B. and J Bradford Jensen (1995), “Exporters, Jobs, and Wages in U.S Manufacturing: 1976-1987,” Brookings Papers on Economic Activity, 67-119 _____ (1997), “Exporters, Skill Upgrading, and the Wage Gap,” Journal of International Economics, 42, pp. 3-31.

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organization cannot be overruled by the central government. The relationship between central and district government agencies therefore accords closely to the theoretical literature’s notion of de facto ‘agent authority’. In contrast, within an organization a manager has the ability to overrule the agents she employs on business decisions. By contrasting the differences in information between principals (central tiers of government or managers) and agents (district governments or employees) under these two regimes allows us to assess the impact of the nature of authority on information acquisition.

We can also use the fact that different organizations in the Ethiopian context are managed differently (as will be shown below) to investigate the impact of environments that differ in terms of how they value the acquisition of information. As the culture of an organization becomes more empirical, we can interpret this as an increase in the individual rewards for information use. We can thus test the impact of mediating incentives on the likelihood of information acquisition. **2.2 Survey of Public Officials** The implementation of any of the three core tasks of public administration outlined above requires information on the characteristics and thus needs of the citizens they serve. To elicit an understanding of the information public officials have about their environment and to understand the incentive environments in which they operate, between June and September 2016 we held face-to-face interviews with 1,831 public administrators in 382 organizations across the three tiers of Ethiopia’s government. Table A1 lists the organizations included in our study, and Figure A1 provides a map of the district governments we sampled. Our analysis focuses on the agriculture, education, health, revenue, and trade sectors only. For each of these five sectors, we sample the main ministry, all the corresponding regional offices, and a geographically representative sample of corresponding district offices from across the country.<sup>7</sup> Within each organization, we interviewed senior managers and a representative sample of their staff. We limit our scope to the professional grades of technical and administrative officers, excluding grades that cover cleaners, drivers, secretaries, etc.

Table 1 provides descriptives for the 382 organizations we study. All officials work within a relatively standard structure, with a manager overseeing levels of hierarchy below him within a clearly defined organizational structure. As is common, the more centralized an authority is, the larger it is in terms of both the number of managers and the number of employees. Federal organizations also have a higher number of employees per director than either regional or district government offices.

In terms of bureaucrat characteristics, around 20% are women (at managerial grades, 8% are women, at non-managerial grades 24% are women); 82% of Ethiopian bureaucrats have some form of university

> 7Our survey, and therefore analysis, necessarily excluded the majority of the Somali region for security reasons.

8
- Deng, X., Huang, J., Rozelle, S., and Uchida, E. (2008). Growth, population and industrialization, and urban land expansion of China. _Journal of Urban Economics_ , 63(1):96–115.

- Dercon, S., Gilligan, D. O., Hoddinott, J., and Woldehanna, T. (2009). The impact of agricultural extension and roads on poverty and consumption growth in fifteen Ethiopian villages. _American Journal of Agricultural Economics_ , 91(4):1007.

- Doll, C. N., Muller, J.-P., and Morley, J. G. (2006). Mapping regional economic activity from night-time light satellite imagery. _Ecological Economics_ , 57(1):75–92.

- Donaldson, D. (2015). The gains from market integration. _Annual Review of Economics_ , 7.

- Donaldson, D. and Hornbeck, R. (2016). Railroads and american economic growth: A “market access” approach”. _The Quarterly Journal of Economics_ , 131(2):799.

- Donaldson, D. and Storeygard, A. (2016). The view from above: Applications of satellite data in economics. _Journal of Economic Perspectives_ , 30(4):171–98.

- Duranton, G. and Puga, D. (2015). Urban land use. In _Handbook of regional and urban economics_ , volume 5, pages 467–560. Elsevier.

- Elvidge, C., Hsu, F.-C., Baugh, K., and Ghosh, T. (2014). National trends in satellite-observed lighting. _Global Urban Monit. Assess. Earth Obs._ , page 91–118.

- Elvidge, C. D., Baugh, K., Zhizhin, M., and Chi Hsu, F. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights. In _Proceedings of the Asia-Pacific Advanced Network_ .

- Ethiopian Roads Authority (2015a). Road sector development program 19 years performance assessment.

- Ethiopian Roads Authority (2015b). The road sector development program phase V.

- European Space Agency (2017). 300 m annual global land cover time series from 1992 to 2015.

- European Space Agency (2019). New Release of the C3S Global Land Cover products for 2016, 2017 and 2018 consistent with the CCI 1992 – 2015 map series.

- Faber, B. (2014). Trade Integration, Market Size, and Industrialization: Evidence from China’s National Trunk Highway System. _The Review of Economic Studies_ , 81(3):1046–1070.

- Fiorini, M. and Sanfilippo, M. (2019). Roads and jobs in Ethiopia. WIDER Working Paper 2019/116.

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|**Ta**|**ble 3: Hum**<br>% of<br>population<br>with no<br>primary<br>education|**an Capital**<br>Average<br>years of<br>schooling<br>per<br>capita|**in 2000: S**<br>Average<br>years of<br>primary<br>schooling<br>per<br>capita|**ADC count**<br>Average<br>years of<br>secondary<br>schooling<br>percapita|**ries**<br>Average<br>years of<br>tertiary<br>schooling<br>percapita|
|---|---|---|---|---|---|
|Botswana|24.0|6.279|4.762|1.416|0.100|
|Congo,DR|<br>47.7|3.03|2.374|0.619|0.038|
|Lesotho|28.8|4.232|3.628|0.568|0.036|
|Malawi|40.7|3.204|3.017|0.170|0.017|
|Mozambique|63.8|1.105|0.983|0.117|0.005|
|South Africa|22.1|6.138|4.590|1.353|0.195|
|Swaziland|21.7|6.010|5.016|0.880|0.114|
|Tanzania|42.8|2.705|2.515|0.161|0.029|
|**Zambia**|**17.3**|**5.457**|**4.296**|**1.113**|**0.047**|
|Zimbabwe<br>Source: Barro and L|<br>12.7<br>ee.|5.354|3.456|1.775|0.123|

Zambia’s performance in generating human capital is mixed. In 1980 Zambia ranked 2<sup>nd</sup> in the set of 28 African countries for which data is available and first in the set of countries included in Table 3 in terms of the average years of schooling per capita with the absolute value being 3.9. However, in terms of tertiary schooling per capita, in 1980 it ranked second from bottom within the SADC countries in the table and 4<sup>th</sup> from bottom in the set of 28 African countries with the absolute value being .006. From Table 3 we can see that both these absolute values have increased significantly in 2000 but Zambia’s performance relative to other countries is mixed. It’s rank in the set of all African countries for overall schooling has slipped to 6<sup>th</sup> in 2000 and 4<sup>th</sup> within SADC countries. For tertiary education, the country’s rank has improved to 17<sup>th</sup> within Africa (29 countries) and to 5<sup>th</sup> within SADC. The improvement in tertiary education notwithstanding, Zambia continues to be an average performer in the continent and within SADC.

A similar picture emerges when we look at the number of physicians in the country (Table 4), another indicator of skill availability. Currently there are .116 physicians per 1000 population in Zambia which is 6<sup>th</sup> lowest among the SADC countries in the table. Comparable figures stood at 1.6 (World in 1998), and 0.179 (least developed countries as per UN classification in 2004).

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SEPLAN natural vegetation cover was used. These tabulations implicitly assume that there is no compliance with the RL requirement.

Table 7 contains the mean and total land value per road distance categories and deforested areas in 1996-2002 in Mato Grosso. This tabulation finds:

- (i) Most remaining forest and most deforestation is located in “high cover” areas (and would presumably be legal if properly permitted). However, deforestation rates are higher in “low-cover” areas (presumed illegal) areas although low cover deforestation rates decrease, especially in 2000-01. Contrary to expectation, the value of deforested land is not much higher in low cover areas (perhaps because remaining forest in these heavily exploited areas is on poorer soils), although the difference increases after 2000.

- (ii) In absolute terms, most deforestation occurs within 2 kilometers of the existing road network. Deforestation rates are far higher here than in more remote locations.  And land values are much higher.  The causal connections here could be debated – does road access confer value, or does high-quality land attract roads?  However, there is a clear association between high-value land, road access, and deforestation.

- (iii) There is a strong association between land quality, land value, and deforestation rates. Deforestation rates and land values are highest in the land rated as being best for annuals and perennials.  Deforestation rates and land values are the lowest in land rated as unsuitable or suitable only for natural pasture.  Land rated as ‘restricted’ for crops has low imputed value but rather high deforestation rates.  In 2002, only about 8.6% of deforestation took place on these three classes of low-suitability soils, but this represented a total of 608 km<sup>2</sup> .

- (iv) In 2002, Amazonian forest had lower land values and a higher deforestation than the

Cerrado. (However, effective land values would be higher in the Amazon if RL requirements were enforced.) 38
# **6.5 Processing the Simulated Vectors**

A set of commands that facilitate the processing of the outputs from the simulation are provided. This subroutine is useful for post-estimation calculations based on the output of Mata data created in the simulation stage (6.3). It provides the user the ability to further calculate poverty and inequality indicators. Those indicators may be based on new aggregated levels, new poverty lines, or different weights that were not implemented in the simulation stage. In the case of new weights, it is necessary for the user to have included these new weights in the `addvars` option.

## **6.5.1 Process Indicators**

This sub-routine allows for processing the simulated vectors to obtain poverty and inequality indicators.

The command’s structure is: ``` saeprocindicator,matasource(string)aggids(numlistsort)[INDicators(string)plinevar(string) plines(numlistsort)area(string)weight(string)] ```

- `matasource():` The `matasource` option allows users to specify the source ydump file created by the `sae simulate` routine. Because the size of the file can be quite large, it is advisable to use this with the `numfiles` option.

- `aggids():` The `aggids` option indicates the different aggregation levels for which the indicators are to be obtained, values placed here tell the command how many digits to the left to move to get the indicators at that level. Using the same hierarchical id specified in the `area` option, AAMMEEE, if the user specifies 0, 3, 5, and 7, it would lead to aggregates at each of the levels E, M, A and the national level.

- <u>`indicators():`</u> The `indicators` option is used to request the indicators to be estimated from the simulated vectors of welfare. The list of possible indicators is:

- The set of Foster Greer Thorbeck indexes (Foster, Greer, and Thorbeck 1984) _FGT_ 0, _FGT_ 1, and _FGT_ 2 ; also known as poverty headcount, poverty gap, and poverty severity respectively.

- The set of inequality indexes: Gini, and Generalized Entropy Index with _α_ = 0 _,_ 1 _,_ 2

- Set of Atkinson indexes

- `plinevar():` The `plinevar` option allows users to indicate a variable in the target data set which is to be used as the threshold for the Foster Greer Thorbeck indexes (Foster, Greer, and Thorbeck 1984) to be predicted from the second stage simulations. The user must have added the variable in the `sae data import` command when preparing the target dataset. Only one variable may be specified.

- `plines():` The `plines` option allows users to explicitly indicate the threshold to be used, this option is preferred when the threshold is constant across all observations. Additionally, it is possible to specify multiple lines, separated by a space.

- `area():` The `area` option is necessary and specifies at which level the clustering is done, it indicates at which level the _ηc_ is obtained. The only constraint is that the variable must be numeric and should

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Recent literature has focused on the “discovery process” of exporting (Hausmann-Rodrik, 2003). It contends that firms in developing economies tend under-invest in discovery because would-be first movers into export markets fear their initially high returns would be eroded by subsequent new entry, resulting in an under-investment in searching for new export activities. This hypothesis of “super-easy entry” is seen as the key market failure impeding diversification. If this could be overcome, then presumably diversification would increase and exports would grow more rapidly.

A policy corollary is that governments can usefully deploy industrial policies to stimulate discovery and hence diversification. Klinger and Lederman (2004) find that overall export diversification increases at low levels of development. They create a model to test the hypothesis that the threat of imitation inhibits the rate of “discovery”. They proxy barriers to entry by using the average time it takes to register a formal firm (from the _Doing Business_ surveys, World Bank), and find that indeed barriers to entry are associated with increased discovery and diversification. From this they deduce that some type of subsidy to the diversification process is warranted.

This view of discovery may eventually be sustained by subsequent research. However, there are intuitive reasons to continue investigation of this hypothesis. First, it is not clear that aggregate data affecting all markets apply equally across industries and hence to the individual industry failing to export. Second, anecdotal evidence often points to the opposite conclusion: that imitating entry broadens the market through agglomeration and industrial level economies of scale affecting key inputs and lowers transportation costs for all firms in the industry. Finally, even if market failures constrain discovery, it is not clear that policy makers should devote scarce resources to this problem – if discovery turns out to be a small part of the export growth problem.

This last concern is the point of departure for this paper. The discovery channel is only one way of looking at the export diversification cycle. This paper attempts to look at the 3
population remaining on January 1<sup>st</sup> , 1975, in columns 7 and 8, assuming migration away. The same exercise can then be repeated using the 1975-79 death probabilities reported in columns 9 and 8. The number of casualties during the 1975-79 period is then computed in columns 11 and 12 and added to the 1970-74 death tolls to give the 1970-79 figures in columns 13 and 14. These figures can then be compared with the excess deaths figures reported by Heuveline (2001 (b)) for the period from January 1<sup>st</sup> 1970 until January 1<sup>st</sup> 1980, reported in columns 15 and 16. The ratio of both figures is computed in columns 17 and 18.

Table A1 indicates that when the Heuveline’s reconstruction is used as a benchmark, the sample based method very substantially underestimates the extent of mortality. The ratios in columns 17 and 18 indicate that the sample based method would only account, depending on gender and birth cohorts, for between 28% and 65% of the mortality figures obtained using the reconstruction method. If one considers that the denominator in these ratios is only “excess deaths” while the numerator is supposed to represent all deaths, the underestimation is actually even worse. It also seems that the mortality of younger cohorts is more underestimated than for older cohorts, and also that female mortality estimates suffer from a somewhat larger bias than estimates for males.

Several factors can account for such large differences in the estimates of the extent of mortality. First, it should be emphasized that in computing the ratios in columns 17 and 18, both the numerator and the denominator were point estimates within substantial confidence intervals. The death probabilities reported in columns 3, 4, 9 and 10, are all estimated with standard errors reported in table 1. Heuveline (1998(a), 2001(b)) also report central estimates within a substantial range. However, the general pattern of underestimation by the sample based approach seems to reveal a more substantial bias not explained by the regular variance of estimates.

Heuveline (2001(b)) indicates that reconstruction approaches using “two population estimates are sensitive to the underestimation or overestimation of population size at the beginning of the period _relative_ to population size at the end of the period, and to migration, the other cause of changes in cohort size”. Since in the current exercise the same baseline population for 1970 was used, discrepancies could come from an underestimation of the 1980 population by Heuveline (2001(b)). After examining in detail his analysis, I have no elements that point in this direction. Estimates of net migration are always difficult to get, especially during chaotic times like the Khmer rouge period in Cambodia. Heuveline (1998(a)) gathers ancillary data and uses a midrange estimate. Some of the variation between our estimates might plausibly come from an underestimation by Heuveline (2001(b)) of migration out of Cambodia between 1970 and 1979. On the other hand, my own estimates, based on the sample approach do not 15