rafmacalaba/datause-encoder-data huggingface dataset:
(modern facilities and less crowded classrooms), but not better education, as teachers in the 75% of private centers do not attain the average standards in formation and professionalism (UNDP, 2010).

**Figure 7: Children attending private schools, by quintile**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>2007<br>0.3 2008<br>2009<br>0.2<br>2010<br>0.1<br>0<br>Q1 Q2 Q3 Q4 Q5<br>Source: Bank staff calculations, ENFT survey<br>% enrolled in private schools<br><!-- End of picture text --> Is the population opting out for private education services? It seems that with the economic slowdown in 2008-09 the share of the households sending their children to private schools noticeably declined respect to 2007 across income levels, except for quintile 1. Interestingly, nowadays the share of children in quintile 2 attending to private schools is lower than that of the lowest quintile. A possible explanation is that a portion of the poorest is settled in areas where public schools are overcrowded or access is limited. The proportion of children going to private centers in quintile 5 is significantly higher than among the rest of the population, suggesting the idea of education for elites that can afford it. However, differences in opting out behavior between quintiles 1 and 4 are not pronounced, and when conducting logit regression, contrary to the electricity case, all the quintile dummies are significant. As it will be discussed, this suggests a different pattern in opting out behavior in the case of education, where differences across socioeconomic classes are less pronounced (except for the richest), than in the cases of electricity or health services.

In sum, again, the education sector in the DR presents faulty public service delivery, which originates a private offer that is more of a reactive upshot to deficiencies in state education than a high quality alternative (at least not in every case). The result is an inefficient

[13]
probabilities. Our framework for incorporating ambiguity in the inspection decision is a form of Choquet Expected Utility (CEU) theory, as described in Schmeidler (1989). In CEU theory, inspectors’ beliefs about probabilities of non-compliance are represented as normalized and monotone set functions known as _capacities_ . Importantly, capacities allow the model to separate inspectors’ beliefs from the conditional probability that a good is non-compliant (in contrast to standard assumptions about beliefs in the expected utility framework). We adopt a particular form of capacities: the non-extreme-outcome additive (neo-additive) capacity described in Chateauneuf _et al._ (2007). In addition to a parameter that defines the ambiguity surrounding each type of shipment, the framework describes (in terms labeled _optimism_ or _pessimism_ ) how the agency’s beliefs about levels of compliance among unsearched shipments differ from the underlying probabilities. We show that a resource-constrained agency that is sufficiently pessimistic will inspect shipments with greater levels of ambiguity more frequently. While this is optimal behavior given the agency’s objective function, relative to a no-ambiguity benchmark the agency’s search activity is misallocated.

We develop this framework to illustrate the manner in which ambiguity-reducing investments in IT can contribute to successful risk management reforms. The goals of modern risk management reforms are typically to reduce the overall inspection burden while improving (or at least maintaining) compliance. We calibrate a numeric version of the model to the Serbian customs data (which represent a post-reform equilibrium) and consider the effects of substantially increased ambiguity on equilibrium rates of inspection and compliance (a reverse reform). The agency responds to higher levels of ambiguity by shifting even more inspection resources towards the shipments about which there is greater ambiguity (i.e., the infrequent shipments) and this reduces the effectiveness of search as a deterrent against non-compliance among other types of shipments. In order to maintain compliance in the face of increased ambiguity the agency must undertake increased levels of search. Modern reforms reverse this dynamic; IT investments that reduce ambiguity allow better targeting of search activity, which maintains or improves compliance, even as levels of search activity are dramatically reduced. Our calibrated model produces changes in inspection activity that 3
Gust and Marquez (2004) present empirical evidence that across industrialized countries there is a negative correlation between ICT investment and employment protection laws. They develop a dynamic model of vintage capital and skilled-biased technological change. In each period a firm decides whether to upgrade technology, which in turn requires to upskill the labor force. Firing costs delay or prevent firm decisions to adopt the latest technology.

Employment protection legislation can also be viewed as discouraging to undertaking risky activities such as investment in innovation and in ICT-intensive sectors, as in Bartelsman, Gautier and de Wind (2016), Saint Paul (2002), Koeniger (2005), Bartelsmand and Hinloopen (2005), and Samainego (2006). Bartelsman et al (2016) argue that, because of the experimentation and changes required in organizational structure, the outcome of investment in ICT is highly uncertain. This is supported by the empirical finding that productivity is more dispersed in ICT-intensive sectors (Brynjolfsson et al., 2008). If for a given firm investment in ICT is unsuccessful, the firm might be forced to exit the market because it cannot break even. Thus, incentives to invest in ICT depend on exit costs, with higher exit costs being detrimental to investment in ICT. In this scenario, firing costs are a barrier to investment in ICT.<sup>6</sup> Using a cross-industry and cross-country panel dataset of the US and the EU (EUKLEMS), Bartelsman et al (2016) show that high-risk ICT-intensive sectors are smaller in terms of employment in countries with stricter labor regulations. These empirical facts hold when comparing the EU to the US, where labor regulations are more flexible, and when comparing countries within the EU. Further, the effect of labor regulations is increasing in the risk of the investment. The paper also develops and calibrates a model and their simulations show that, due to lower investment in ICT, aggregate productivity in the US would be 10 percent lower if severance payments in the US were similar to the average cost in Europe.

# _Digital technology adoption and productivity_

Empirical studies find that ICT is important in explaining productivity differences across countries, across sectors, and across firms. Treating ICT as an input that enters into the production function, a large group of papers has focused on studying the role of ICT in the resurgence in labor productivity in the US during the 1990s, from the growth accounting and cross-industry econometric perspectives. Jorgenson, Ho and Stiroh (2008) and Oliner, Sichel and Stiroh (2007) argue that the ICT producing sectors have been responsible for the increase in productivity from 1995 to 2000, whereas after 2000 further growth in investment

> 6 Acharya, Baghai and Subramanian (2013) make the opposite argument that stricter labor laws work as a commitment device by preventing firms from dismissing workers after short‐run failures and thus encouraging employees to engage in risky innovative activities that are profit maximizing in the long‐run.

7
electricity for few months may not reap the same benefit as one that has had it for 10 years. After all, it does take time for the household to purchase appliances. The addition of a television, radio, fan or extra lights increases the benefits no doubt, but that also requires that a household have enough income for the purchase of such items. These expenses in all likelihood will be spread over a longer period of time. In this section we are going to investigate whether the length of time a household has electricity matters in terms of the level of benefits.

Household electricity consumption goes up as households acquire more and more electric appliances (Table 10). Although lighting is the most significant use of electricity, households, as indicated, gradually diversify their electricity use patterns. The applications might include entertainment (radio, television), space conditioning (fans, refrigerators) and other domestic appliances, thus increasing electricity consumption. To examine this impact, households are grouped according to the length of time their household has had electricity.<sup>14</sup> As expected, the use of electricity increases with the duration of having electricity. Interestingly, the rate of electricity use increases at a fairly high rate until about the 6th year, then levels off in the 8th year, after which consumption again begins to grow. At the second year of having electricity a household consumes about 36 kwh per month, and by the 9th year it increases to about 60 kwh/month.

Before turning to the modeling results, we first examine the cross-sectional relationship between duration of electricity use and household outcomes. It should be kept in mind that these cross-sectional results are influenced by the fact that electricity distribution companies generally extend electricity to more developed communes and then only later move on to more remote and generally poorer ones. Thus, it might be expected that these cross-sectional results may have an inherent bias against the more recently connected rural households. However, the results do not

> 14 This exercise is restricted to grid users of 2005 only.

27
America and the desire of many governments to expand social program provisions, a better understanding of the distortions generated by them can help to inform policy decisions. Our contributions to this literature are threefold. First, to our knowledge, no other studies have considered the effect of the SR expansion on informality in Colombia using individual-level data. The health reform was a major reform; health insurance coverage in the population increased from levels below 30% prior to the reform to more than 90% today. Second, our findings are derived from two different methodologies and datasets, and the results are consistent for different robustness checks. Third, the reform took place almost two decades ago, and one of our datasets covers years before and after the reform, allowing the labor market to adjust to the reform and allowing us to test for effects in the longer term.

# **III Data**

## **Household Survey Data**

We use repeated cross-sections of the Colombian Household Survey for periods before and after the implementation of the health care reform. Specifically, the informality modules included the second trimester every two years from 1990–1996 and once every year between 1997 and 2005.<sup>13</sup> These cross-sections are representative samples of the population living in Colombia’s ten largest cities.<sup>14</sup> Informality is defined as employees between 12 and 65 years who do not contribute to health insurance through employment. This definition of informality is most appropriate for our study, which evaluates the effect of a health insurance expansion.<sup>15</sup> Table 1 shows the descriptive statistics for total, informal, and formal workers in columns

> 1, 2, and 3, respectively. There is a higher proportion of men in the labor force (56%). Men

> 13In Spanish: _Encuesta Nacional de Hogares_ (ENH) 1990–2000 and _Encuesta Continua de Hogares_ (ECH) 2001–2005.

> 14Bogot ́a, Cali, Medell ́ın, Barranquilla, Bucaramanga, Pereira, Manizales, Villavicencio, Pasto, and C ́ucuta.

> 15Alternative definitions of informality are based on the firm size, occupation, or education level of workers.

9
# The Structure of Labor Markets in Developing Countries

Time Series Evidence on Competing Views _William F. Maloney*_ I thank the Mexican National Institute of Statistics, Geography, and Information (INEGI) for the use of the data. INEGI is in no way responsible for any incorrect manipulation of the data or erroneous conclusions drawn from it. I also thank Lee Alston, Hadi Esfahani, Kevin Hallock, Steve Kamin, Christopher Pissarides, and Doug Steigerwald for helpful comments, and Taimur Baig for research assistance.
Table A20: Impact of T2 on outcome indexes

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||_HIV knowledge_|_HIV attitudes_|_HIV testing_|_Attitudes_<br>_towards_<br>_risky sexual behav-_<br>_ior_|_Risky sexual behav-_<br>_ior (for sexually ac-_<br>_tive)_|
|**Panel A: Cross-section**||||||
|T2|-0.089|-0.065|0.019|-0.075|0.057|
||(0.209)|(0.137)|(0.174)|(0.122)|(0.132)|
|Observations|3,402|3,402|3,402|3,402|2,487|
|R-squared|0.091|0.057|0.093|0.029|0.084|
|**Panel B: Conditional speci**|**fcation**|||||
|T2|-0.074|0.037|0.101|-0.049|-0.053|
||(0.195)|(0.128)|(0.156)|(0.121)|(0.148)|
|T2*Y_t−_1|0.036|0.057|-0.028|-0.027|0.005|
||(0.044)|(0.044)|(0.037)|(0.040)|(0.067)|
|Y_t−_1|0.301***|0.334***|0.447***|0.304***|0.209***|
||(0.032)|(0.031)|(0.027)|(0.029)|(0.050)|
|Observations|3.402|3.402|3.402|3.402|2.117|
|R-squared|0.188|0.184|0.246|0.107|0.135|
|Controls<sup>(</sup><sup>_a_)</sup>|Yes|Yes|Yes|Yes|Yes|
|Mean Dep. Var. (Control)|1.080|0.534|0.278|0.176|0.0740|
|Std Dev Dep. Var. (Control)|6.043|3.373|4.609|3.433|3.562|

Notes: Standard errors in parentheses adjusted for clustering at the screening centre level (54 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. Increasing values of the dependent variable correspond to outcomes more consistent with the messages of Shuga. (a) Controls in each regression include: female, age, currently attending school, years of education, english spoken at home, single, does not live with the family, household size, wealth index, home owner, father obtained higher than secondary education, mother obtained higher than secondary education, muslim, speaks Yoruba as a native language.

xxvii
14 and Kravis and Lipsey<sup>(1988) discuss, there are good reasons to believe that</sup> own currency prices systematically<sup>exaggerate real income differences between</sup> rich and poor countries because<sup>non-traded goods are less expensive in poorer</sup> countries but nominal exchange<sup>rates do not reflect these differences.</sup> The purpose of the International<sup>Comparison Program is eo create</sup> internationally comparable<sup>figures for real GDP, the components of real GDP,</sup> and to produce purchasing<sup>power parity exchange rates [see: Summers and Heston</sup> (1988) and Kravis and Lipsey<sup>(1990)]. Purchasing power parity exchange rates</sup> are the rates that would<sup>have to be used such that a ziven basket of goods</sup> that cost $X in the United<sup>States (the numeraire country) could be purchased</sup> in any other country - say<sup>Chile-by changing the X dollars into Chilean</sup> pesos at the PPP exchange<sup>rate and then buying the identical basket of goods</sup> in Chile. Thus, the International<sup>Comparisons Project (ICP) collects prices</sup> for about 150 basic categories<sup>of goods for a large number of benchmark</sup> countries and then constructs<sup>purchasing power parity indexes. These PPP</sup> indexes are aggregated to<sup>form PPPs for summary groups of goods (e.g., capital</sup> goods) and further aggregated<sup>up to GDP. These PPP indexes are then used to</sup> compare national incomes.<sup>Extrapolation is used to provide estimates for</sup> countries not covered by benchmark<sup>studies, and, since prices are not</sup> collected every year, ICP<sup>extrapolates PPP indexes intertemporally.</sup> Although thes2 data are subject<sup>to data limitations, index number</sup> problems, questions associated<sup>with the extrapolation techniques, and</sup> quandaries associated with<sup>distinguishing the quality of the "same" good in</sup> different countries, Heston<sup>and Summers (1988), Kravis and Lipsey (1990), and</sup> Marris (1984) argue that<sup>international prices provide a better estimate of</sup>
**Figure 2. Mobile cellular and fixed broadband price baskets and coverage in Mexico relative to similar income regions, 2012–17**

# **a. Mobile cellular price basket b. Mobile cellular price basket vs. subscriptions**

<!-- Start of picture text --> 40 40 100<br>35 35 90<br>80<br>30 30<br>70<br>25 25 60<br>20 20 50<br>15 15 40<br>30<br>10 10<br>20<br>5 5 10<br>0 0 0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Mexico LDC Developed<br>Mobile Cellular Subscriptions Mexico<br>Developing World<br>Source: International Telecommunication Union.<br>Note: LDC = least developed countries. PPP = purchasing power parity. Mobile-cellular basket is composed of the price of standard mobile monthly<br>usage for 30 outgoing calls per month (on-net/off-net to a fixed line and for peak and off-peak times) in predetermined ratios, plus 100 SMS messages.<br>For more information, refer to https://www.itu.int/en/ITU-D/Statistics/Pages/definitions/pricemethodology.aspx.<br>In 2014, 99.9 percent of the market was divided among four companies: Telcel (66.9 percent),<br>Telefónica (21 percent), Iusacell-Unefon (9.1 percent), and Nextel (2.9 percent). The rest of the market<br>was divided between a few virtual mobile operators that had just entered the Mexican market by<br>2014. 23 The simulations in this study use data from 2014; hence, they do not consider the full effects<br>of the reforms, but they highlight the potential benefits these reforms could have in the future.<br>Figure 3. The Herfindahl-Hirschman index in mobile telecommunication, OECD, 2017<br>5000<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>US$ PPP US$ PPP<br>population)<br>Mobile Cellular Subscriptions (per 100<br>Herfindahl-Hirschman index Mexico Switzerland Australia Greece Korea, South Slovenia Hungary Japan Norway Luxembourg Ireland New Zealand Turkey Belgium Estonia Austria Czech Republic Portugal Germany Netherlands OECD Iceland Finland Slovakia Italy Chile Spain France United States of... Canada Poland Sweden Denmark<br>United Kingdom<br><!-- End of picture text --> In 2014, 99.9 percent of the market was divided among four companies: Telcel (66.9 percent), Telefónica (21 percent), Iusacell-Unefon (9.1 percent), and Nextel (2.9 percent). The rest of the market was divided between a few virtual mobile operators that had just entered the Mexican market by 2014.<sup>23</sup> The simulations in this study use data from 2014; hence, they do not consider the full effects of the reforms, but they highlight the potential benefits these reforms could have in the future.

_Source:_ GSMA Intelligence 2018.

23 ITF (2015). Informe Estadístico 4to Trimestre de 2014. Instituto Federal de Telecomunicaciones, July 15, 2015. http://www.ift.org.mx/sites/default/files/comunicacion-y-medios/informes/4ite14-vf-02.pdf 12
41

|Hurricane force wind frequency<br>(proportion)||‐<br>0.5151*<br>**|0.54<br>‐2.0504***|1.97<br>‐2.1608***|
|---|---|---|---|---|
|||‐3.42|‐4.83|‐7.45|
|Hurricane force wind<br>frequency*ln(hours) from 500k<br>city|||0.4566***|0.4584***|
||||3.68|5.79|
|**_Household characteristics_**|||||
|log of household size|‐|‐|‐0.2474***|‐0.2832***|
||0.2419*|0.2447*|||
||**|**|||
||‐36.06|‐36.83|‐37.38|‐56.634|
|dependency ratio|‐|‐|‐0.0046***|‐0.0047***|
||0.0046*<br>**|0.0046*<br>**|||
||‐28.82|‐28.96|‐28.92|‐36.03|
|1 if ethnic minority; 0 otherwise|‐|‐|‐0.2396***|‐0.2422***|
||0.2388*|0.2411*|||
||**|**|||
||‐16.32|‐16.49|‐16.66|‐29.05|
|1 if household head is female|0.0123*|0.0104*|0.0081|0.0005|
||1.93|1.65|1.3|0.10|
|log of age of household head|0.1233*<br>**|0.1236*<br>**|0.1217***|0.1317***|
||13.44|13.54|13.47|18.71|
|education household head = 1 if|||||
|primary school|0.1556*|0.1564*|0.1553***|0.14073***|
||**|**|||
||22.64|22.91|22.96|26.99|
|Lower secondary school|0.2618*|0.2659*|0.2659***|0.2524***|
||**|**|||
||33.02|33.83|34.16|44.32|
|Higher secondary school|0.4159*|0.4187*|0.4186***|0.39063***|
||**|**|||
||37.96|38.45|39|47.55|
20-a FIGURE 8

# REAL INTEREST RATES

ARGENT INA BRAZIL <!-- Start of picture text --> ao<br>6-30<br>-40<br>-so * twlt ~~OSI ~~~~~US NI ~~~U, .NI<br>mento. US * t-..)j(AWIW" -<br><!-- End of picture text --> <!-- Start of picture text --> FIGURE 9<br><!-- End of picture text --> <!-- Start of picture text --> ,<br>no Ints<br>065~~~~~~~ao me Jis? .86 .;s<br><!-- End of picture text --> CO':POSITION OF MONEY: '11, M3, AND '14 <!-- Start of picture text --> '-I/t* MV. 4S 111UA 111ws<br>045<br>:n~~ ~ ~~~~ 6:::7tS7<br>---<br>25 '.-JC:h<br>L I1 4; 0L54 . ,,, . ,,~~~~~~~~~~ 3b<br><!-- End of picture text --> <!-- Start of picture text --> .<br><!-- End of picture text --> <!-- Start of picture text --> I<br>I l FI<br>WI *A.-<br>.... . s C't.......,^..:<br>La a --<br><!-- End of picture text --> FIGURE 10 RESIDUALS DEMkND FOR '!0NEY -375 0l!F 5 **_fILMS_**

> *00 wa,_

<!-- Start of picture text --> a I S<br>015<br>-DYX . . W .<br>!l M L0115 t 0 Laos<br><!-- End of picture text --> <!-- Start of picture text --> 09I : .fjoc - :<br>___________~~~~~~~~~~~~~~~~-05 A<br>-l 0 Z 0X e e " Z " -' t7 "~~~~~~/ 0<br>o a 1<br><!-- End of picture text -->
- Heckman J., Ichimura H. and Todd P. (1997) “Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme”, _Review of Economic Studies_ 64 (4): 605-654.

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