rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> ° California schools so Education - PISA<br>=o yo 5<br>LsBos aan. , Dae. on ywsoatoo. oe . . . rao 23 tusa* NK| *S swEBEl — » FIN<br>x = SUC OS eC 2 Stata SARA Vegye OND ay<br>6 o RGU TY CANT Corae ee . Fs coe sin oR<br>Bo8 eee RR ees waeegNa eee c eae bn |<br>gS A ass i Bea i Rega aa aaa 6668 “GRC Ot glen a<br>labs Risa pogencieet gs cote eae<br>_ . so : : ee ane ea . + amid<br>oO o as - rT<br>250 300 350 400 450 300 350 400 450 500 550<br>Average test score Average PISA test score<br>» Education exp per pupil ' Education exp per student<br>» SFM- efficient exp ' SFM- efficient exp<br>—— SFM- deterministic section —— SFM- deterministic section<br>— Hybrid frontier — Hybrid frontier<br>S OECD health systems Vietnam hospitals<br>o .:.o|..SS .<br>P = :<br>ss a a o aes<br>Oo afnt.. “7 7) : :<br>oe a {fee . © ar<br>z Boadty: My oat Ee ST<br>Do] Pk im 2” ree<br>S [eet preparers pete are rere ae o _ . ~ ;<br>1,000 2,000 3,000 4,000 5,000 0 10,000 20,000 30,000 40,000<br>Potential years of life lost per 100,000 Cases<br>* Health exp per capita ’ Average cost<br>» SFM- efficient exp ' SFM- efficient exp<br>—— SFM- deterministic section —— SFM- deterministic section<br>— Hybrid frontier —— Hybrid frontier<br>Notes: Stochastic frontier model is time-invariant half-normal panel-data model. California data refer to 313 unified school districts, and cover the period 2003/2004 - 2008/2009.<br>The PISA data refer to 49 countries, and cover some or all the years 2000, 2003 and 2006. The OECD health system data refer to 29 countries, and cover the period 1960-2005.<br>The Vietnam data referto 795 district hospitals with between 50 and 500 beds, and cover the period 1998-2000.<br><!-- End of picture text -->
**– Table A5 continued from** **<u>previous page</u>**

||cohort|mean years|less primary|primary|secondary|tertiary|
|---|---|---|---|---|---|---|
|Saint Lucia|1950|9.4|72.3|3.9|20.8|3.0|
|Saint Lucia|1960||52.7|8.8|35.6|2.8|
|Saint Lucia|1970||||||
|Saint Lucia|1980||||||
|Suriname|1950||11.3|69.8|16.1|2.9|
|Suriname|1960||7.1|70.9|18.5|3.5|
|Suriname|1970||6.4|66.3|22.7|4.6|
|Suriname|1980||4.9|57.9|30.8|6.4|
|Trinidad and Tobago|1950|9.0|15.8|44.6|36.4|3.1|
|Trinidad and Tobago|1960|10.1|12.1|31.6|52.9|3.4|
|Trinidad and Tobago|1970|11.5|6.7|20.6|67.8|4.9|
|Trinidad and Tobago|1980|12.1|5.4|15.9|72.0|6.8|
|Uruguay|1950|8.9|17.7|53.0|23.3|5.9|
|Uruguay|1960|9.2|12.1|57.5|22.6|7.8|
|Uruguay|1970|9.7|11.8|53.0|26.9|8.3|
|Uruguay|1980|10.2|6.6|54.0|31.9|7.4|
|Venezuela|1950|7.4|26.0|46.2|25.5|2.2|
|Venezuela|1960|8.1|18.7|46.3|34.0|1.1|
|Venezuela|1970|8.6|14.6|43.0|42.1|0.2|
|Venezuela|1980||||||

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they are risk seeking.<sup>8</sup> The final column uses business assets at baseline as a proxy for wealth, as a test of liquidity constraints. The coefficient is positive and statistically insignificant, suggesting that it is not the case that poorer firms are more likely to respond to the incentives.

Overall we view the results as indicating that most of those becoming formal are informed owners who rationally weigh the costs and benefits of formalizing, or who face legal barriers because they operate on public property.

# **5. What Are the Consequences of Formalizing?**

We carried out three follow-up surveys to measure the impact of formalizing on firms (Figure 2). The first follow-up was in August 2010, corresponding to a period of between 12 and 18 months after firms were induced to register. We were able to re-interview 465 of the original 520 firms (89%), with the most common reasons for attrition being not being able to find the owner (20 out of the 55 cases), the owner being abroad (9 out of 55 cases), and firm owners refusing to be re-interviewed (9 out of 55 cases). We cannot reject the null hypothesis that attrition is unrelated to treatment status at conventional significance levels (p=0.17). The second follow-up survey was conducted in March 2011, at period averaging 22 months after the start of the intervention We reached 445 firms in this round, with attrition again unrelated to treatment status (p=0.35). The final follow-up survey was conducted in December 2011, an average of 31 months after treatment. In this last survey 424 firms were interviewed with attrition again unrelated to treatment status (p=0.40). In addition, proxy reports were used to determine whether the business was still open for a further 59 firms. The remaining 37 firm owners were unable to be located, largely due to their having moved out of the study areas.

Table 4 examines whether any of the treatments is significantly associated with firm survival in the fourth round survey, or with reporting profits in any survey round. It does this by estimating equation (1) with survival or reporting profits as the dependent variable. In all cases, we find no significant relationship between treatment and the likelihood of survival or reporting profits in our follow-up surveys. Moreover, Appendix Table 2 shows that treatment does not appear to be associated with which firms survive. The Appendix reports the same characteristics as in Table 1, and shows balance on baseline characteristics for the firms surviving to the final survey. As a result we ignore attrition in what follows.

> 8 Risk preferences are measured on an 11-point scale taken from the German Socioeconomic Survey, which asks ―are you generally a person who is fully prepared to take risks or do you try and avoid taking risks‖. Hyperbolic discounting is measured by asking firms hypothetical questions about how much they would be prepared to take today compared to 10,000 LKR in one month, and similarly for 5 months vs 6 months. 20 percent of the firms in our sample are classified as hyperbolic.

15
regulations who are also more likely to export. Finally, I control for the number of times that a firm was inspected or required to meet with agents from labor and social security services<sup>16</sup> . Although the coefficient of this variable is negative, it is not significant.

# **Tests with Sub-Samples**

In the data, small firms, with less than 20 employees, are the ones that want to grow most. These firms have net job creation rate of 7.6% which is almost four times higher than medium size firms (firms between 20 and 100 workers). Large firms had the least net creation rate. Small firms are also the least likely group to export. More than half of the data is formed by small firms. The negative relation between net job creation in sectors and exporting can be affected by the existence of large number of small firms in the data. To control for this effect, I perform the regression analysis excluding small firms. The result is given in Table 11. The coefficient of net job creation is significantly negative<sup>17</sup> .

Firms that are constrained by labor regulations may be more inclined to hire temporary workers. Use of temporary workers can allow firms to escape the heavy burden of EPL as contracts of temporary workers are usually less restrictive for firms than the contracts of fulltime permanent workers. Pierre and Scarpetta (2004) find that firms that perceive labor regulations as constraining are more likely to use temporary workers. Hallward-Driemeier and Helppie (2007) also find that firms are more likely to hire temporary workers in countries with more restrictive labor laws. In the next two specifications in Table 11, I exclude firms that hire temporary workers assuming that these firms would be more likely to be constrained. The regression result shows that even among firms in this group, net job creation is negatively related to exporting. However the coefficient is only significant when I use the conservative definition of exporting.

# **Analysis with the Panel Data**

A group of firms in the data were surveyed twice in three years. Despite of its small size, this panel dataset has great advantage over the cross-sectional data in handling the concerns on the endogeneity in net job creation. In the regressions with the panel dataset, current export status of firms (at time _t_ ) is regressed on the past values of net job creation, employment level,

> 16 This question is only available for the 2005 survey.

> 17 In these tests with sub-samples, only those firms that are included in the sub-samples are used to calculate net job creation rate.

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# **2. Internet Use in Developing Countries: Anecdotal Evidence**

Although futurists regularly extol the Internet's potential benefits for poor countries, skepticism is common among development researchers. Duncombe (2000), for example, argues that Internet access may mean little to poor Africans because lack of education will prevent them from using the technology effectively. In Duncombe's typology,<sup>2</sup> telephone systems require no literacy for independent operation while high literacy and language skills are prerequisites for using e-mail and other Internet services. Presumably, this distinction applies to technology use by poor Asian and Latin American households as well.

Limited anecdotal evidence suggests that this typology may be too restrictive. Where Internet services are available, poor households do not seem hesitant or incompetent to use them. Anand (2000) reports active participation by poorly-educated village women in an Internet-based rural information system in Pondicherry, India. Similarly, Bayes (1999) notes the rapid adoption of e-mail as a cost-effective alternative to telephone calls by poor families in Bangladesh. Village women in Lethem, Guyana have used an Internet connection to sell hand-crafted hammocks for as much as $1,000 each (Romero, 2000). Faiola and Buckley (2000) report that Ashaninka Indian villagers in Peru have raised their incomes 10% by using the Internet to market organically grown oranges in Lima. In a recent survey of over 100 developing-country manufacturers, Rajkumar (2000) finds that using the Internet has significantly expanded their customer base, sales, and exports. The new service seems to have been particularly helpful for small enterprises with low er-wage, lower-skill employees. Similar findings have emerged from a survey of north Indian small enterprises by Lal (1996).

These cases, while promising, do not fully address Duncomb's concern because speciallytrained personnel o'ten maintain such pilot systems. For example, the weavers of Lethem, Guyana have depended on the Internet skills of a village student who was sent to the capital, Georgetown, for special training. The Peruvian Ashaninkas have depended on six tribal leaders who received eight weeks of computer training from the sponsoring agencies. In Uganda, a pilot community telecenter operates through trained personnel who access the Internet for clients (IDRC, 2000). According to Arunachalam (1999), the Pondicherry center cited by Anand (2000) also relies on the skills of specialized staff.

One counter-examrple is offered by Mitra and Rana (1999), who report the results of an experiment with a high-speed touch-screen Internet link in a slum area of New Delhi. The link was provided to the community with no instructions and very little guidance, but local children demonstrated remarkable facility in learning how to use the Internet. Mitra and Rana conclude that formal training may be unnecessary for basic Internet access, although more advanced applications will undoubtedly require some instruction.

> 2 Duncombe (2000), Fig. 5, p. 40.

2
Estimating this system (using instrumental variables) on quarterly data for (the log of) real M2<sup>5</sup> in the period before 1984 gives a reasonably good fit. Specifically, the exogenous variables x were a constant and two permanent intercept **shift** dummies, taking the value unity after the first quarter of 1979 and the second quarter of 1981 respectiveiy; the exogenous variables z were a constant term and the level and lagged value of a third dummy being the sum of the other two. The equations were estimated by instrumerLtal variables, using bank credit to government, ban'> credit to private sector, total credit and special deposits (all expressed as a share of GDP), as instruments, together with the lagged endogenous and predetermined variables in the first equation, and the first difference of these variables for the estimation of the second equation. The estimated equations are as follows.

M2 = - 0.328 P - 0.655 D79 - 0.486 D81 - 0.342 (3.7) (6.4) (4.0) (6.2) RSQ - 0.894 SEE - 0.196 DW - 0.665 72:4-84:2 dM2 - - 0.304 dP - 0.302 U + 0.144 D3 - 0.065 D3(-l) -0.051 (2.6) (2.3) (3.4) (1.6) (2.1) RSQ **-** 0.376 SEE - 0.110 DW - 1.906 74:1-84:2 The dummy variables in the first equation correspond to the dates where special levies were applied to financial assets. The implication of these demand functions are that the levies exerted a permanent impact on the demand for money. The estimated impact of the two shocks depressed the level of the demand for money by 62 percent, in addition of the inflation impact that is represented by the inflation term in the equation.

Note that this equation predicts only an average growth rate 1984-87 of 1.2 per cent compared to the actual 6.2 per cent; this difference is found to be significant. Furthermore, a dummy trend variable<sup>6</sup> included for the period after 1984 turns out to be significant when the equation is estimated over 1972-87. Indeed this dummy variable "explains" a growth of five per cent per quarter.

M2 - - 0.253 P - 0.665 D79 - 0.558 D81 + 0.019 D84 - 0.315 (3.4) (6.2) (4.7) (2.5) (6.3) RSQ - 0.904 SEE - 0.183 DW - 0.702 72:4-88:2 dM2 - - 0.273 dP - 0.31.9 U + 0.151 D3 - 0.082 D3(-l) -0.034 (3.1) (2.3) (4.5) (2.4) (1.8) RSQ - 0.477 SEE - 0.102 DW - 1.847 74:1-88:2 Analysis of the demand for money in Kenya thus suggests that inflation is an important determinant, but that confidence in the overall policy stance is also relevant in predicting money holdings.

5 We have therefore used the real level of financial assets where the deflator is the CPI index. There are somie obvious pitfalls that follow from this procedure and we will try to talke them into account. For the determination of the inflation rate, we have first computed the rate of change between the price level in a given quarter and that of the same quarter in the previovs year, and then we average these values over four consecutive quarters.

6 This variable is zero before the second quarter of 1984, and is a time trend thereafter.

_68_
6 employable in the private sector. Thus fiscal substitution effects can be more detrimental in transition economies than in OECD countries.

One strand of policies contributing to the solution of regional mismatch in OECD countries focuses on helping the unemployed move to regions with better employment opportunities. Further training and re-training schemes can be an important ingredient in such policies, although more direct interventions by government like subsidising public housing in high opportunity regions and government contributions to moving expenses are more prominent components of OECD policies intent on "taking the workers to the work" (Jackman, 1995). As there are very high barriers to mobility in transition economies<sup>4</sup> ALMPs like further training and retraining which in OECD countries also increase the mobility of workers might be rather ineffective when combating regional mismatch in CEE.

The second strand of regional policies of "bringing work to the workers", i.e. creating jobs in high unemployment regions, involves investment grants or subsidies to firms if they locate or undertake new capital investment in such regions. As nearly all governments in transition economies have severe budgetary problems and these traditional regional policies are very expensive, it is inconceivable that such policies will solve regional mismatch to any degree in transition economies. What might be relatively useful in this context, though, is the targeting of subsidised public employment in high unemployment regions.

Further training and retraining is in principal an important tool to combat skill mismatch in transition economies. However, in the light of the above stylised facts of labour markets in transition it seems questionable whether targeting these measures at identified problem groups can be efficient.

In the fight against long-term unemploymen **t** he Restart Programme in Britain seems to be an especially effective tool. The long-term unemployed are invited to a counselling interview which is supposed to help the long-term unemployed find out about available vacancies and training courses. Such an interview might also help employment offices weed out those who are not interested in taking up work. Both White and Lakey (1992) and Lehmann (1993a) find that in Britain the stock of long-

> 4 1ecause there is a severe housing constraint inherited from the old regime and to date no housing policy exists, which could help promote the movement of workers to high opportunity regions, transition economies are characterised by such high barriers to mobility (cf. Boeri and Scarpetta, 1995). The available empirical evidence from the supplement to the August 1994 wave of the Polish LFS (G6ra and Sztanderska, 1995) shows virtually no inter-voivodship mobility while, for the Czech Republic, Erbenova (1995) finds that people commute but do not n-ove to high opportunity regions.
<!-- Start of picture text --> Graph 1.6: Consumption Volatility and Country Size<br>20<br>o Cnbbco,r<br>+ Other<br>18 -<br>0<br>16<br>14 +<br>o + + ++ + S +'¢++<br>0. +<br>to - +~~~+ +.'+ At 4<br>loit 12 14 16 10 20 22<br>Logarithm of Popuhatiou, t997<br>Sorce: SIMA database ( 102 countrnesincluded).<br><!-- End of picture text -->

> The above variables are tested empirically by regressing consumption variability on a constant,

> proxies of financial and insurance risk management mechanisms, per capita GDP and (the logarithm of) population (Regression 6 in Table 1.2). With the exception of the insurance mechanism proxy, the coefficients of each explanatory variable have the expected signs.

> However, the coefficients of the financial and insurance mechanism proxies and of per capita GDP are not statistically significant due mostly to multicollineary between them. When the

> insurance consumption mechanism proxy and per capita GDP are dropped, the coefficients of the remaining explanatory variables have all the expected signs and are statistically significant (Regression 7). The remaining three explanatory variables explain half of the volatility of

> consumption. When both the insurance and financial mechanism proxies are dropped, the

> coefficients of the remaining explanatory variables also have the expected signs and are statistically significant (Regression 8). The remaining three explanatory variables explain about

> 60 percent of consumption volatility.

14
this procedure results in higher pollution levels in large countries with known pollution problems in cities (notably China, Nepal, Pakistan) or those with forests (notably Lao PDR, Indonesia, Senegal), and in lower concentrations in areas with known deserts (notably Chad, Tunisia, Morocco).

<!-- Start of picture text --> Difference between pollution level in urban areas and country averages<br>20<br>15<br>10<br>5<br>0<br>−5<br>−10<br>Chad Tunisia Morocco Cote d'Ivoire Cameroon Ghana Guatemala Thailand EthiopiaBenin Swaziland Sri Lanka Zimbabwe TogoTurkeyGabon Botswana Solomon Islands Guinea MozambiqueFijiRwanda El Salvador Zambia Bolivia MadagascarNetherlands UgandaGermanySierra Leone Malawi NicaraguaCambodia Costa Rica Denmark Australia Venezuela, RBTanzania ParaguayDominican RepublicFinland Timor−Leste Congo, Rep.BelgiumNigeriaNamibia Panama NorwayIreland Colombia KenyaEcuador PhilippinesLesotho JapanSpainHonduras Sweden Brazil BangladeshFrance MongoliaSwitzerland Vietnam Suriname Iran, Islamic Rep.Greece United KingdomGuinea−Bissau Austria UruguayAzerbaijanUnited States Central African RepublicCanada Indonesia ArgentinaItalyMexico Uzbekistan MalaysiaSenegalAngolaSouth Africa GeorgiaLao PDR Peru Chile India Armenia TajikistanKazakhstan Pakistan NepalChina<br><!-- End of picture text --> Figure 1: Difference between average pollution in urban areas and country-wide average PM2 _._ 5 data.

# **3.3 Vegetation control data**

We use the NDVI from the Moderate resolution Imaging Spectroradiometer (MODIS) from NASA’s Terra satellite to control for effects that relate to a variety of physical characteristics and natural assets of a country.<sup>11</sup> This dataset provides spatial and temporal comparisons of global vegetation conditions. The original data have a monthly frequency at a resolution of 1km. We calculated the mean NDVI value for each year in our analysis, we use 20002015 data, and summarized the data to the country-level using the mean, minimum, and maximum value to get a broad description of the vegetation in a country.

# **3.4 Other data**

For our economic variables and data on carbon dioxide emissions, we rely on the World Bank’s World Development Indicators (WDI). To ensure cross-country comparability, we

> 11Available at https://modis.gsfc.nasa.gov/data/dataprod/mod13.php.

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