rafmacalaba/datause-encoder-data huggingface dataset:
# **MANY PATHS TO SKILLED EMPLOYMENT: A REVERSE TRACER STUDY OF EIGHT OCCUPATIONS IN COLOMBIA**

# **I. INTRODUCTION**

Throughout much of the developing world, publicly subsidized, preemploymen **t** raining institutions play a dominant role in govermment training policy. These institutions encompass both vocational secondary schools and government training centers. Yet evidence from industrialized countries, particularly the United States,<sup>1</sup> suggests that these public vocational and non-formal training systems constitute but two among many alternative training options. Alone or in combination they account for a small proportion of the job-skills training received by workers across many skilled occupations. Indeed, even in a given occupation, this research demonstrates that workers pursue a large variety of training paths to acquire the skills they employ on their jobs.

There are two additional findings of this U.S. research that are surprising. Informal t.aining, or "picking up the trade on the job," is found to be among the most important sources of job-related skill development. Moreover, combining different types of training programs is found to be a common practice among workers in many of the occupations analyzed in this research.

In the present study we attempt to verify whether these important findings from the U.S. hold in Colombia's less-developed economy. To explore this question, we employ the reverse tracer study technique to analyze data from surveys of workers in 1 See Foster (1970), Hills (1982), Horowitz and Herrnstadt (1968), Marshall & Glover (1975), and U.S. Department of Labor (1964 and 1970), for an analysis of trairing sources associated with traditional occupations in the United States.
# **Policy Research Worxing Paper Series**

||**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|---|
|WPS 1599|Economic Implications forEurkey<br>of a Customs Union with the<br>European Union|Glenn W. Harrison<br>Thomas F Rutherford<br>David G. Tarr|May 1996|M. Patena<br>39515|
|WPS 1600|Is Commodity-Dependence<br>Pessimism Justified? Critical<br>Factors and Government Policies<br>that Characterize Dynamic Cormmodit<br>Sectors|Nanae Yabuki<br>Takamasa Akiyama<br>y|Mav 1996|G. liogon<br>33732|
|WPS1601|The Comestic Benefits of Tropical<br>Forests:ACritical Review<br>Emphasizing Hydrological Functions|Kenneth M Chornitz<br>Kanta Kumari|May 1996|PRDEI<br>80513|
|WPS1632|Program-Based Pollution Control<br>Management: The Indones;an<br>PROKAS!H Program|Shakeb Afsah<br>Benoit Laplante<br>Nabiel Makarim|May 1996|A.Williams<br>37176|
|VW'PS<br>1603|Infrastructure Bottleniecks, F-rivate<br>Provision, and lndustriai Productivity.<br>A Study of Indonesiar and Thai<br>(,ities|Alex Anas<br>Kyu Sik Lee<br>Michael Muriay|May 1996|S. Ward<br>31707|
|WPS1604|Costs of Infrastructure Deficiencies<br>in Manufacturing in Indonesoa,<br>Nigeria. and Thailand|Kyu Sik Lee<br>Alex Anas<br>Gi-Taik Oh|May 1996|S. Ward<br>31707|
|WPS1605|VVhyManufacturinig Firms Produce<br>Some Electricity Internallv|Kyu Sik Lee<br>Alex Anas<br>Satyendra Verma<br>Michael Murray|May 1996|S. Ward<br>31707|
|'NPS1606|The Benefits of Alternative Powei|Alex Anas|May 1996|S. Ward|
||Tariffs for Nigeria and Indonesia|Kyu Sik Lee||31707|
For exportables, the post-UR border protection will be determined by the export subsidy conumitments. As described in section 2, the commitments on export subsidies are in terms of maximum volume and budget outlays defined for major group of commodities. A 36 percent reduction in the value of export subsidies by a particular country would typically require a less than 36 percent reduction in the _ad-valorem_ equivalent of the export subsidy since the decline in value would result from changes in both the rate of assistance and in the quantity exported. In the context of multilateral reforms under the Round, reductions in export subsidies are likely to raise world prices, offsetting the reduction in export volumes implied by the reductions in export subsidies. Thus, treating the export subsidy reductions in terms of reductions in export subsidy rates could be used as an approximation to the effects of the commitments. The per unit export subsidy rates at the base period is determined as follows: _,s_ = 4 where s is the subsidy rate, B is the total value of export refunds and X is total volume of exports and P is the share of exports that is subsidized. The subscript s denotes the goods which are net exports and use export subsidies. The per unit subsidy rates during the base period are translated to _ad-valorem_ rates using border prices in 1986-88. There are goods which are treated as net exportables in estimating historical protection based on the OECD and USDA databases but do not have export subsidy commitments in the Round. In these commodities, some imports occur and the post-UR border protection are estimated based on the tariffication commitments.<sup>4</sup> In most of these commodities, import restrictions are usually an essential backcstop for export subsidy programs. The tarifi' equivalent based on the tariffication commitments defines the maximum post-UR border protection.

> **14The** estimates will he slightly biased hy the amount of the margin between f.o.b. and c.i.f. prices which in most primary agricultural products are usually not more than 20 percent.

21
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Barro, R.J., 1998, Notes on Growth Accounting, NBER Working Paper 6654, July.

Bhalla, S, and H. Kharas, 1991, Growth and Equity in Malaysia: Policies and Consequences, World Bank Bosworth, Barry (1998), Productivity Growth in Mexico, Draft Working Paper Caprio, G. Jr, and D. Klingebiel, 1997, Bank Insolvency: Bad Luck, Bad Policy, or Bad Banking?, World Bank Collin, S. and Bosworth, B, 1996, Economic Grwoth in East Asia: Accumulation versus Assimilation, Brookings Paper on Economic Activity, 2 Edwards, S., Openness, 1997, Productivity and Growth: What do we really know?, NBER Working Paper 5987 Fraumeni. B., "The Measurement of Depreciation in the U.S. National Income and Wealth Accounts", mimeo, 1996.

Gavin M., and R. Hausmann, 1996, The Roots of Banking Crisis: The Macroecnomic Context, in R. Hausmann and L. Rojas-Suarez, eds, Banking Crisis in Latin America, John Hopkins Press Ghani, E., 1992, How Financial Markets affect Long-run Growth-A Cross-country Study, Working Paper 843, World Bank, Washington D.C.

Hulten, C. and F. Wycoff, "Issues in the Measurement of Economic Depreciation", _Economic Inquiry,_ vol. 24, January 1996.

Kim, J-I, and Lau,, L.J., 1994, The Sources of Economic Growth of the East Asian Newly Industrialized Countries, _Journal of the Japanese and International Economies_ Klenow, P.J. and A. Rodriguez-Clare. _The Neoclassical Revival in Growth Economics: Has it Gone too Far?_ Graduate School of Business, University of Chicago. Mimeo. March 1997.

Krugman, P. 1994. "The Myth of Asia's Miracle". Foreign Affairs 73 (6), 62-78.

Nehru, V, E. Swanson, and A. Dubey, 1995. "A New Database on Human Capital Stock in Developing and Industrial Countries: Sources Methodology and Results," _Journal of Development Economics, Vol._ 46, 379-401.

Nelson, R. and Pack, H., 1995, The Asian Growth Miracle and Modem Growth Theory, Unpublished, Pack, H, 1998, Technological Change and Growth in East Asia: Macro and Micro Perspectives, paper presented at the World Bank Conference on Rethinking the Asian Miracle.

Page, John, 1994, The East Asian Miracle: Four Lessons for Development Policy, in S. Fischer and J. Rotemberg, eds, NBER Macroeconomic Annual Radelet, S. and J. Sachs, 1998, The East Asian Financial Crisis: Diagnosis, Remedies, Prospects, HIID Rajan, R. and L. Zingales, 1999, Financial Systems, Industrial Structure, and Growth, Mimeo 12
Table 5: Rank Correlations between Relative Price Dispersion and Comnion Measures of Trade Regime

|||Income|Categor|v|
|---|---|---|---|---|
||Measure|Middle|High|Low|
|Openness|Trade Share|18|-7|-20|
|Measures'|Adjusted Trade Share|37|-28|-9|
||Leamer Openness|-24|-14|_|
|Intervention|Average Tariff|43|-|-3|
|Measures|NTB Coverage|13||4|
||Dollar (1990) Measure|-17|56|39|
||Price Level|-24|-10|13|
||Leamer (GDP)|-1|6|-|
||Leamer (Trade Share)|-60|-33||
||Leamer (R-squared)|.40|12||
|*A negative r<br>disper|elationship indicates high<br>sion is associated with lo|price<br>w trade s|hare||

price dispersion with these measures would suggest that in general outward oriented economies are more liberal.

The rank correlations listed in Table 5 reveal the relationship to be more complex. While the three measures of openness correspond to low levels of price dispersion for high and low income countries, the relationship is rather weak. With the exception of the Leamer measure, the relationship is reversed in the case of middle income countries; economies with more relative price dispersion show higher trade shares than liberal economies. The difference between the Leamer index and the other trade shares is to be expected, given that the Leamer index is more highly correlated with the raw trade share than with the "adjusted" trade share.

# 5.2 distortions **in** the price **level** and relative price dispersion

Comparisons with other measures of outward orientation confirm these results. As mentioned above, increases in import restrictions can raise the price of import substituting goods and of nontraded goods, biasing production against exports (see appendix). These price increases will be reflected in 23
the labor force required for reconstruction tends to be considerable, and because Fonden uses reconstruction schemes that are specifically designed to employ the local community.<sup>11</sup>

## **5.4 Exercises**

The identifying assumption that underpins our regression discontinuity design is that potential outcomes are smooth functions of the forcing variable as the threshold is crossed. One potential threat to this assumption is that individuals might be able to game the Fonden assignment rule. It is unlikely that third parties could have tampered with weather stations run by CONAGUA not only because these weather stations serve a variety of purposes both civilian and military, but also because few individuals outside of CONAGUA could have known the thresholds or be able to identify the subset of weather stations used to determine Fonden eligibility. Nonetheless, in order to investigate whether manipulation is possible, we examine the density of the forcing variable. If Fonden is being manipulated then we would expect to observe bunching of observations to the right of the threshold. Formally, we use the sorting test proposed by McCrary (2008). In figure 10a we plot the density of the forcing variable as it crosses the threshold. The solid line is the density of the forcing variable as estimated by local linear regression, the dashed lines represent confidence intervals. The overlapping confidence intervals suggest that there is no discontinuous change in the density across the threshold. The p-value of the McCrary sorting test associated with this graph is 0.42. We thus fail to reject the null that the forcing variable is smooth across the threshold.

We can provide further support for the validity of the identifying assumption by establishing that log night lights do not change discontinuously at the threshold in the years preceding a natural disaster. Figure 10b plots the change in log night lights between two years before a disaster has taken place and the following year. The figure reveals no apparent discontinuity at the thresholds. Moreover, when we estimate the impact of Fonden it yields a small coefficient that is statistically indistinguishable from zero, 0.016 (t=0.45).

# **6 Night lights as proxies of subnational economic activity**

While our primary interest lies in determining whether log night lights can predict changes in municipal level GDP, in the absence of this type of data we begin our analysis by investigating the relationship between log night lights and proxies of economic activity at the municipal level. Specifically, we calculate by municipality, from the 2005 population conteo and the 2010 population census, the number of dwellings with the following characteristics: dwelling has non

> 11In particular, Fonden housing reconstruction efforts occur through the temporary work program. This program is designed to hire homeowners to rebuild their own houses.

15
<u>Table 5: Simultaneous Estimates; “Multi Agriculture”</u> _<u>versus</u>_ “Single Agriculture”

|<br>**Dependant variable: Welfare Ratio**||<br>**Dependant variable: "Agriculture Multi"**|**HH-head**|
|---|---|---|---|
|Size|-0.061|Size|0.010|
||(0.004)**||(0.004)*|
|Number above age 60|-0.011|Number above age 60|0.047|
||(0.033)||(0.036)|
|HH-head Couple without children (D)|0.257|HH-head Couple without children (D)|0.023|
||(0.036)**||(0.038)|
|HH-head single parent with children (D)|-0.111<br>(0.040)**|HH-head single parent with children (D)<br>|-0.041<br>(0.045)|
|HH-head single people (D)|0.103|HH-head single people (D)|-0.091|
||(0.054)||(0.060)|
|Average level of education|0.085|Time to the nearest tarred road|0.107|
|in the household|(0.016)**|in hours|(0.039)**|
|"Agriculture Multi" HH-head (D)|0.030<br>(0.206)|||
|Time to the nearest tarred road|-0.031|Male-headed household (D)|0.014|
|in hours|(0.024)||(0.047)|
|Time to the nearest primary school|0.031|Age|0.003|
|in hours|(0.022)||(0.002)|
|Time to the nearest health center|0.012|Primary instruction (D)|-0.125|
|in hours|(0.023)||(0.039)**|
|Time to the nearest food market|-0.003|Secondary (1st cycle) instruction (D)|-0.248|
|in hours|(0.018)||(0.081)**|
|Part of people affected by malaria|-0.339|Secondary (2nd cycle) instruction (D)|-0.645|
|in the cluster|(0.178)||(0.180)**|
|Average level of education|0.107|Tertiary education (D)|-1.092|
|in the cluster|(0.043)*||(0.273)**|
|Rural Forest|0.308|Rural Forest|1.544|
||(0.204)||(0.426)**|
|Rural Savannah|-0.064|Rural Savannah|1.207|
||(0.080)||(0.322)**|
|Rural High Plateaux|-0.159|Rural High Plateaux|0.819|
||(0.082)||(0.198)**|
|Constant|-0.031|Constant|-2.470|
||(0.242)||(0.834)**|
|||Mills|Sign.**|
|District Fixed Effects|yes|District Fixed Effects|yes|
|Instrumental Variables|yes|Instrumental Variables|yes|
|Observations|1855|Observations|1855|
|R Squared|0.3644|R Squared|0.1506|
|RMSE|0.428|RMSE|0.445|
|(robust se);*:p<5%;**:p<1%||||

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The effects of camps on host communities on different outcomes – which may in turn affect attitudes – have been evaluated in a limited number of studies. Maystadt et al. (2020) investigate the refugee-driven landscape changes in Africa. The authors find that refugees cause a small increase in vegetation condition, while contributing to increased deforestation. In a related study, using satellite data on forest cover and loss, Salemi (2021) shows that refugee camp openings are associated with a small reduction in the extensive margin of forest loss (i.e., land clearing) and a small increase in intensive margin forest loss (i.e., gradual reductions in canopy cover). Using global data from 1990 to 2018 on locations of refugee communities and civil conflict (including but not limited to camps), Zhou and Shaver (2021) find no evidence that hosting refugees increases the likelihood of new violent conflict, prolongs existing conflict, or raises the number of violent events or casualties in the area. Ginn (2021) examines camps in Jordan and Iraqi Kurdistan and argues they could play an important role for both refugee and host communities in expanding the stock of housing, which could be a key driver of social cohesion in displacement settings.

Another important dimension is _de jure_ policies. Blair et al. (2021a) significantly advance the study of policies toward refugees in low- and middle-income countries by coding _de jure_ policies in more than 90 countries. They show that policies towards refugees are liberalizing over time in LMICS, unlike in high-income countries. They furthermore argue that policy changes have occurred when neighboring states are in civil war, and hence the country is likely to receive refugees. These policy changes are more likely to be liberal (reductions in restrictions) when the political elites are ethnic kin with a victimized group. In related work, the authors argue that more liberal hosting policies attract additional refugees (Blair et al. 2021b).

These _de jure_ policies relate to attitudes. Figure 1 plots an index of these data on policies against the Gallup World Poll measure of attitudes towards immigrants. The positive relationship indicates that countries with more positive attitudes also allow refugees more legal access to the labor market. Causal relationships could run 4
**Manual v. Automation: Methods Used by Organizations in GDPR Compliance**<sup>**115**</sup>

|Tools used for data inventory and mapping|60% email, spreadsheets, in-person<br>communication(manual)|
|---|---|
||31% commercial software tool designed for<br>data inventory/mapping<br> <br> <br> <br> <br>|
||21%<br>Data<br>Loss<br>Prevention<br>(DLP)<br>technology|
||20% GRC software customized in-house<br>for inventory/mapping<br>|
||8% Outsource data inventory/mapping to<br>external consultants/law firms|
||4% Don’t know|
|Method for handling Data Subject Requests|64%Entirelymanual|
||25%Partiallyautomated|
||7%Still beingdesigned|
||2% Haven’tyet addressed|
||1%Automated|

Of the 1,263 organizations surveyed by MW&E, 31% of respondents purchased insurance covering cyber risks. Of those insured, 43% had insurance coverage for GDPR fines and penalties.<sup>116</sup> Expenditures on cybersecurity insurance vary by region with 19% of Chinese respondents, 35% of U.S. respondents, 29% of European respondents, and 31% of Japanese respondents reporting an insurance purchase.<sup>117</sup> Data breach disclosure requirements continue to be a challenge for many organizations with only 18% of MW&E’s respondents confident in their ability to notify a data protection authority within 72 hours of becoming aware of the incident, as required by the GDPR.<sup>118</sup> The study suggests that many organizations need further expenditures on external cybersecurity services that would enable organizations to identify cyberattacks early on and provide data protection authorities the necessary forensic evidence within the mandated window of time.<sup>119</sup> The GDPR permits regulators to fine organizations up to €20 million or 4% of an organization’s global annual turnover, whichever is higher, in cases of noncompliance with the GDPR. For the largest companies, this could result in fines in the millions or even billions of dollars. When a personal data breach occurs, an organization must provide notification describing, at minimum, (1) the nature of the breach, (2) its potential consequences, and (3) the measures the organization proposes to mitigate any harm.<sup>120</sup> As of November 30, 2020, there have been approximately 460 instances where fines have been imposed on organizations under the GDPR.<sup>121</sup>

> 115 Hughes & Saverice-Rohan, _supra_ note 74.

> 116 McDermott Will & Emery, _supra_ note 68.

> 117 _Id._

> 118 _Id._

> 119 _Id._

> 120Council Directive 2016/679, art. 33, 2016 O.J. (L 119) 1, 2. No such notification is required if the data breach is unlikely to present a risk to the rights and liberties of data subjects or seasonable notification is rendered unfeasible by circumstance.

> 121 CMS Law, _GDPR Enforcement Tracker_ (2020), https://www.enforcementtracker.com.

20
effects by firm, year, and controls for differential time pre-trends.<sup>17</sup> They suggest that when IDP inflows in a municipality increase by 1 percent, gross production of the firms located in that municipality decreases by approximately 0.3 percent. Table I also presents estimates of the effects of IDP inflows on oil and energy consumption pointing to similar results although slightly larger in size.

We also test for dynamic effects of IDP inflows on the intensive margin of production and find no evidence of a long-term impact of IDP inflows one or two years after the IDP shock takes place. In fact, the coefficients become very small and close to zero as we lag the IDP shocks one or two years. When we estimate a model combining the contemporaneous effect and the effects one and two years after the IDP shock took place, we only observe significant effects for the contemporaneous impacts of IDP inflows on the intensive margin of production (see Appendix III).

Table II presents the estimates of the effects of IDP inflows on the extensive margin of production estimated as the probability of entry and exit for each firm. The results confirm that firms are adjusting their external margin of production in response to larger IDP inflows. In fact, we observe that larger IDP inflows are associated with a larger exit and lower entry probability for all firms. Particularly, the estimates in columns (3) and (6) suggest that when the share of IDP as a percentage of the working age population increases in 1% the probability of firm entry falls by 0.01 percentage points and the probability of firm exit increases by 0.02 percentage points.

The estimated coefficients represent a small effect taking into account the size of the migration shock (which corresponds to an average increment of 4,000 individuals in each municipality and year) and previous results from other studies that use the same data in Colombia.<sup>18</sup> The literature that studies the impacts of immigration on firm performance generally finds

> 17The number of observations drops for column (3) because all the additional controls are not available for all the municipalities in the manufacturing survey. However, these controls are available for the largest municipalities that host the bulk of the economic activity in Colombia. The same reduction in our sample occurs for the estimates presented in Tables II and III.

> 18For instance, Camacho and Rodriguez (2013) uses the same sample to study the effect of conflict on firm exit. The authors find that a one-standard deviation increase in the number of guerrilla and paramilitary attacks in a municipality increases the probability of plant exit in 5.5 percentage points or .28 SD.

17
|**Table 3: Correlates of Take‐up**|Wage S|ubsidyOnly|Sample|AnyWage|Subsidy|Treatment|
|---|---|---|---|---|---|---|
|Retail|‐0.084||‐0.102*|0.001||‐0.016|
||(0.055)||(0.055)|(0.030)||(0.030)|
|Colombo|‐0.196**||‐0.134|‐0.210***||‐0.155***|
||(0.092)||(0.094)|(0.054)||(0.055)|
|Kandy|‐0.067||‐0.037|‐0.145***||‐0.117**|
||(0.091)||(0.092)|(0.054)||(0.054)|
|Formally registered|‐0.006||‐0.014|0.030||0.016|
||(0.060)||(0.057)|(0.031)||(0.031)|
|Any paid worker at baseline|0.089||0.042|0.034||0.004|
||(0.086)||(0.083)|(0.046)||(0.046)|
|Above median assets|‐0.018||‐0.038|0.004||‐0.019|
||(0.056)||(0.053)|(0.030)||(0.030)|
|Firm five years or younger|0.067||0.066|0.026||0.025|
||(0.054)||(0.053)|(0.029)||(0.030)|
|Owner's education (years)||0.020*|0.016||0.014**|0.012*|
|||(0.011)|(0.011)||(0.006)|(0.006)|
|Baseline business practice index||0.011***|0.011***||0.011***|0.010***|
|||(0.004)|(0.004)||(0.002)|(0.003)|
|Owner's age||‐0.002|0.001||0.001|0.002|
|||(0.004)|(0.004)||(0.002)|(0.002)|
|Sample Size|250|250|250|843|843|842|
|Pseudo‐R2<br>Notes: coefficients are marginal eff<br>parentheses. *, **, and *** denote|0.044<br>ects from pr<br>significance|0.050<br>obit estima<br>at the 10, 5|0.083<br>tion. Robu<br>, and 1 pe|0.020<br>st standard<br>rcent levels|0.036<br>errors in<br>respectiv|0.047<br> <br>ely.|

39