rafmacalaba/datause-encoder-data huggingface dataset:
# **3 Summary Statistics**

## **3.1 Gateways and Geographical Distribution of Migrants**

According to our survey, around 340,000 Venezuelans migrated to Ecuador between 2016 and the summer of 2019.<sup>4</sup> As a result, the share of Venezuelan migrants in Ecuador’s population grew from close to zero to 2% in less than 3 years. The majority of Venezuelans entered Ecuador by land from Colombia, while the rest mostly arrived by plane.<sup>5</sup> Perhaps not surprisingly, about 10% of the college-educated Venezuelans arrived by air, compared to fewer than 3% of the less educated migrants. As shown in Olivieri et al. (2020), most migrants spread across Ecuador’s geography in search of economic opportunity and the particular gateway of entry has little explanatory power regarding their ultimate choice of location.

Table 1 summarizes the geographical distribution of Venezuelan migrants across provinces in Ecuador. Columns 1 and 2 report the raw counts of respondents born in Ecuador (4,406) and in Venezuela (1,780), respectively. Columns 3 and 4 elevate the raw counts to population estimates, amounting to 16.8 million Ecuador-born individuals and 340,633 Venezuelan migrants, or about 2 Venezuelans for each 100 Ecuadorans.

The top two provinces by the estimated number of Venezuelan migrants are Pichincha (with capital Quito) and Guayas (with capital Guayaquil). We estimate that around 145,000 Venezuelan migrants were living in Pichincha at the time of the survey, followed by 61,000 in Guayas and over 38,000 in Los Rios. Thus, about 72% of all Venezuelan migrants were living in these three provinces. These figures also show that, relative to the population in the province, Venezuelan migrants made up around 2% to 3% of the population in Pichincha and Guayas, and over 4.5% in the province of Los Rios.<sup>6</sup>

## **3.2 Sample Descriptive Statistics**

Table 2 presents summary statistics for the main variables that will be used in our analysis for the whole sample, including both natives and foreign-born individuals. The

> 4By the end of 2019, this figure had risen to approximately 390,000 individuals.

> 5As shown in Appendix Table .1, the majority of Venezuelan migrants (92%) entered by land through the Rumichaca bridge that connects Ecuador and Colombia (82%), and other land entry points were San Miguel (7%) and Huaquillas (3%). Most of the remaining arrivals (6%) were by air, with an even split between the airports of Quito and Guayaquil.

> 6The table also indicates that the population density of Venezuelans may have been over 10% in some provinces. However, the small sample size in those provinces implies large confidence intervals around these values.

5
What would we expect the data to show from a theoretical perspective? If the nascent rate represents early stage activity, we expect this to be higher than the young entrepreneurship rate. This is because many people that take “some steps” towards starting a business do not actually succeed. We also expect the young entrepreneurship rate to be larger than the formal rate, since many firms first are initially established under sole proprietorship but incorporated at a later stage. In fact, for the United States, these rates are 8.12%, 4.98% and 2.55% respectively. This does not, however, hold across developed and developing countries.

In fact, it appears that in many countries - developed and developing - the young entrepreneurship rate and the nascent entrepreneurship rate are _less_ than the formal entrepreneurship rate. This is case not only in Hong Kong, but also in Latvia, the Netherlands, Norway, Israel, Iceland, New Zealand, Denmark, Sweden, Belgium, etc. In many developed countries, therefore, the formal entrepreneurship rate actually exceeds the young entrepreneurship rate and even the nascent entrepreneurship rate.

There are several possible explanations. In developing countries, a lower corporate rate might actually represent a shift towards increased formalization of the economy. Newly registered companies may represent some aspect of formalization, where businesses that were not previously LLCs have newly converted their legal status. It is also important to note the unit of analysis is different in the datasets: GEM measures the number of individual entrepreneurs, possibly overlooking individuals that are involved in multiple new businesses. The WBGES dataset instead measures number of businesses, and can capture this dynamic. However, a possible complication also results from the WBGES measure: Formal entrepreneurship includes both actual businesses _and_ LLC’s that are a legal vehicle for purposes other than starting a new business. For instance, entrepreneurs might use registrations to achieve other business ends such as reducing taxes (e.g. shell companies) and avoiding regulatory burdens (e.g. labor laws).<sup>9</sup> For example, in the United States, firms may register several LLC’s as a way to limit liability for different lines of businesses. In Hong Kong, where the formal rate far surpasses the young business formation rate, all real estate sales are first converted to an

> 9 For instance, laws on hiring and firing employees in Italy applies only to firms with more than 15 employees, which might encourage business owners to register multiple smaller firms (Klapper, et al 2006).

7
Figure 20: Part-time employment by age groups. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s<br>Russia Georgia Kyrgyzstan Turkey Armenia Albania Moldova<br>>64 years old 50‐64 40‐49 25‐39 15‐24<br><!-- End of picture text --> Source: Own calculations based on Household surveys Figure 21: Temporary employment by age groups. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s<br>Georgia Kyrgyzstan Turkey Armenia<br>>64 years old 50‐64 40‐49 25‐39 15‐24<br><!-- End of picture text --> Source: Own calculations based on Household surveys A less clear picture is observed in the case of temporary employment. Indeed, in the four countries where temporary employees are identified, we observe different dynamics in the age profile. On the one hand, Kyrgyzstan and Turkey do not evidence significant changes in the age profile of temporary workers in the last 10/15 years. On the other hand, Georgia and Armenia present changes in the age profile but in opposite directions. Armenia shows today a higher share of the older groups within temporary employees while Georgia evidences a younger profile of temporary workers.

Finally, analyzing the composition of non-standard employment by gender, we have a heterogeneous picture by types of non-standard employment in the levels but with a similar trend (Figure 22 and 23). In the case of part-time employment, there is observed a larger female participation while Page 28 of 51
# **1. Introduction**

It takes little analysis to see that education levels differ dramatically between developing and developed countries. Building upon several decades of thought about human capital – and centuries of general attention to education in the more advanced countries – it is natural to believe that a productive development strategy would be to raise the schooling levels of the population. And, indeed, this is exactly the approach of the Education for All initiative and a central element of the Millennium Development Goals.

But there are also some nagging uncertainties that exist with this strategy. First, developed and developing countries differ in a myriad of ways other than schooling levels. Second, a number of countries – both on their own and with the assistance of others – have expanded schooling opportunities without seeing any dramatic catch-up with developed countries in terms of economic well-being. Third, countries that do not function well in general might not be more able to mount effective education programs than they are to pursue other societal goals. Fourth, even when schooling policy is made a focal point, many of the approaches undertaken do not seem very effective and do not lead to the anticipated student outcomes. In sum, is it obvious that education is the driving force, or merely one of several factors that are correlated with more fundamental development forces?

The objective of this study is to review what is known about the role of education in promoting economic well-being. We are interested in assessing what research says about these issues. More than that, we pay particular attention to the credibility of this research in establishing a causal relationship between education and economic outcomes and between policy initiatives and educational outcomes.

The discussion also has one distinctive element. We have come to conclude that educational quality – particularly in assessing policies related to developing countries – is THE key issue. It is both conventional and convenient in policy discussions to concentrate on such things as years of school attainment or enrollment rates in schools. These things are readily observed and measured. They appear in administrative data, and they are published on a consistent basis in virtually all countries of the world. And, they are very misleading in the policy debates.

We will show in graphic terms the differences in educational quality that exist. Most people would, in casual conversation, acknowledge that a year of schooling in a school in a Brazilian Amazon village was not the same as a year of schooling in a school in Belgium. They would also agree that families, peers, and others contribute to education. Yet, research on the economic impact of schools – largely due to expedience – almost uniformly ignores these. The data suggest that the casual conversation may actually tend to understate the magnitude of differences.

1
and their employees, and show that larger firms are more compliant since collusion costs increase with firm size. Hsieh and Olken (2014) similarly argue that the effective burden of taxation in developing countries falls more heavily on larger firms. Empirically, Kumler et al. (2015) find that compliance indeed increases with firm size in Mexico. Naritomi (2019) provides evidence suggesting that larger retail stores have more customers and are therefore less able to sustain evasion. Both Bronnenberg and Ellickson (2015) and Lagakos (2016) categorize retailers as either ‘traditional’ (small, labor intensive retailers - our categories 1 to 3) or modern (large, capital intensive retailers - our categories 4 and 5), and argue that traditional retailers are mostly informal.<sup>10</sup> We provide direct evidence that formality and firm size are positively correlated. We use data on formality status and firm size available in the retail firm censuses of four countries in our core sample (Cameroon, Mexico, Peru and Rwanda). Panel A in Figure 1 shows the share of formal firms as a function of log employment in each country.<sup>11</sup> In all countries, retailers with a few employees are overwhelmingly informal, but more than 80% of retailers with 20 or more employees are formal. In Mexico, the census classifies retailers in categories which are similar to our broad place of purchase categories. This enables us to go one step further and directly observe retailer size and formality status. Figure 1 shows for our categories (1) to (5) the log median number of employees (Panel A) and the share of firms paying value-added taxes on their sales (Panel C). We observe that non-brick-and-mortar stores and convenience stores are small and rarely formal, whereas nearly all large stores are formal.

Given this evidence, our baseline formality assignment assigns categories (1) to (3) to the informal sector, and categories (4) and (5) to the formal sector. Goods in category (1) - non-market consumption - are by definition not purchased in markets and are therefore untaxed. Categories (2) and (3) (non brick and mortar stores and corner stores) are likely very small and mostly informal, whereas category (5) (large stores) consists mostly of supermarkets which are unlikely to be non-compliant. Our approach assigns retailer-types to informality which are all classified as ’traditional’ tax-evading retailers in Lagakos (2016).

For services, we assume that institutions pay taxes while individual providers do not. This leads us to assign category (6) to the formal sector and category (7) to the informal sector. We follow the same logic in assigning expenditures in the unspecified category to

> 10Relatedly, Gordon and Li (2009) explain the high shares of taxes on capital (such as corporate income taxes) in developing countries relative to rich countries by the fact that capital is more observable than labor in these countries. This also implies higher compliance rates among larger retailers.

> 11Formality is defined as ’being registered with tax authority’ in Cameroon and Rwanda, and ’paying value-added-taxes on sales’ in Mexico and Peru.

8
significant subjective judgments (Deutsch Karlekar 2011; Schneider 2014). The comprehensive raw data compiled for its computation nevertheless offer systematic and objective information on punitive actions against individual media outlets. The detailed reports collected for each country and harmonized by the coordinating staff contain factual details on the most salient censorship events taking place during the year of analysis. _Beacon for Freedom of Expression,_ a project of the National Library of Norway, also maintains a global database of country–year individual censorship events.

To test the implications of our model––namely that various types of media are unequally exposed to coercion because of the size of their audience and the transaction costs entailed by punitive actions––we generated the first original data set of individual censorship events between 2001 and 2015, covering 196 countries and territories. The data set contains 8,979 observations and uses factual information from several sources to code a series of variables of interest on both targets and censors. These include the name of the individual journalist or outlet facing censorship, the type of ownership, the type of censored news, the severity of censorship, the level of censorship, accusations, the frequency of censorship, and many other event-specific characteristics. The Online Appendix includes more information about the construction of our data set.

First, by developing our own data as opposed to relying on existing data sets, we aimed to work with objective rather than subjective variables capturing censorship, and at doing so consistently across countries and years. Second, the data set goes beyond government-led punitive actions to record censorship committed by editors, non-government entities such as organized crime networks, extremist religious groups and others. Figure 3 illustrates the extent of censorship by types of censors across 9,000 events.

26
- 10 -

## **<u>Table 2. Summary Statistics</u>**

|Variable|Abbr.|Obs|Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Rural Access Index (0 to 1)|_RAI_|691|0.372|0.280|0.009|1|
|Road density in rural areas (km per km<sup>2</sup>of|_RdDen_|691|0.509|0.706|0.004|4.163|
|land)|||||||
|Share of roads in good condition to the total<br>road length in rural areas (0 to 1)|_Quality_|691|0.330|0.261|0.0001|1|
|Rural population density (per km<sup>2</sup>of land)|_PopDen_|691|236.7|393.9|0.9|4007.6|
|Share of population living within 2 km of a<br>road to the total rural population (0 to 1)|_PopConc_|691|0.618|0.322|0.019|1|
|Urbanization rate (share of urban population)<br>(0 to 1)|_Urban_|691|0.114|0.213|0|0.997|
|Country dummy for Bangladesh|_v_(_Bangladesh_)|691|0.093|0.290|0|1|
|Ethiopia|_v_(_Ethiopia_)|691|0.094|0.292|0|1|
|Kenya|_v_(_Kenya_)|691|0.064|0.244|0|1|
|Mozambique|_v_(_Mozambique_)|691|0.182|0.386|0|1|
|Nepal|_v_(_Nepal_)|691|0.074|0.262|0|1|
|Tanzania|_v_(_Tanzania_)|691|0.230|0.421|0|1|
|Uganda|_v_(_Uganda_)|691|0.161|0.367|0|1|
|Zambia|_v_(_Zambia_)|691|0.103|0.304|0|1|
|Povertyrate(0 to 1)|_POV_|281|0.499|0.210|0.00003|0.922|

# **III. MAIN RESULTS**

The ordinary least squares (OLS) method is applied to estimate equation 2. The findings show that road density and quality are both important determinants of the RAI (table 3). The coefficient of _RdDen_ is significantly positive at 0.324, and the coefficient of _Quality_ is also significant. The latter has a greater coefficient of 0.543, indicating its relative importance for improving the RAI. Even if the country-specific fixed effects are taken into account, the result remains unchanged. Both are significant, and the effect of road quality is greater than the road density effect.

The findings also show that the RAI is affected by population distribution patterns. It tends to be higher where more people live along the road network: the coefficient of _PopConc_ is positive and significant. Contrary to our prior expectation, however, population density, which is often used as a proxy to measure the efficiency of rural road investment, turned out
_Brazilian spatial labor dynamics Page 13_

# **References**

ANSELIN, L. _Spatial econometrics_ : _methods and models_ . Dordrecht: Kluwer Academic, 1988.

ARAUJO, C., A. de JANVRY, and E. SADOULET. 2004. Spatial patterns of nonagricultural rural employment growth in Mexico during the 90s. Department of Agricultural and Resource Economics, University of California, Berkeley. Processed.

AZZONI, C. Economic Growth and regional income inequality in Brazil. _The Annals of Regional Science_ , v. 35, p.133-152, 2001.

AZZONI, C., MENEZES, T., MENEZES, N., NETO, R. Geografia e convergência da renda entre os estados brasileiros. In: HENRIQUES, R. (Org.). _Desigualdade e pobreza no Brasil_ . Rio de Janeiro: IPEA, 2000, p.299-343.

AZZONI, C., SANTOS, L. (2002). Education, cost of living and regional wage inequality in Brazil in the 90 ́s. _Papers in Regional Science_ , v. 81, p. 157-175.

BAICKER, C. (2005). The spillover effect of state spending. _Journal of Public Economics, 89_ (2-3), 529-544.

- BAR-EL, Raphael et al.  2002. _Reduzindo a Pobreza através do Desenvolvimento Econômico do Interior do Ceará._ Fortaleza: Edições IPLANCE.

BARRO, R. J. (1991) Economic growth in a cross section of countries. _Quarterly Journal of Economics_ , v.106, n.2, p.407-43, 1991.

BARRO, R.J., SALA-I-MARTIN, X. (1991) Convergence across states and regions, _Brookings Papers on Economic Activity_ (1991) (1), pp. 107–182.

CARVALHO, A., da MATA, D., CHOMITZ, K. _Estimation of Multiequation Cross-Section Models in the Presence of Spatial Autocorrelation_ . 2005, in prep.

CASTRO (2003). Logistic cost and Brazilian regional development. processed.

FAN, S., CHAN-KANG, S. (2004). Returns to investment in less-favored areas in developing countries: a synthesis of evidence and implications for Africa. _Food Policy 29,_ 431-444.

FAN, S., HANZELL P., HAQUE, T. (2000). Targeting public investments by agroecological zone to achieve growth and poverty alleviation goals in rural India. _Food Policy 25,_ 411-428.

FAN S., L. ZHANG, X. ZHANG (2002) Growth, inequality and poverty in rural China : the role of public investments. International Food Policy Research Institute, report 125.

FERREIRA, A. H. B. _A distribuição interestadual e inter-regional da renda no Brasil_ : tendências recentes. 1995. 101f. Tese (Professor titular) - Departamento de Ciências Econômicas, Universidade Federal de Minas Gerais, Belo Horizonte, 1995.
Table 5A: Effects of embodied _foreign_ services on manufacturing export RCA, for MORE services intensive manufacturing sectors

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Df|2.445<br>(3.766)||||||
|forshf|0.246<br>|||0.989<br>|||
||(0.499)|||(1.295)|||
|**Df*forshf**|**-8.962****<br>|||**-32.749**<br>|||
||**(4.376)**|||**(22.928)**|||
|Db||16.921***<br>(5.080)|||||
|forshb||<br>1.070|||2.801**||
|||(0.660)|||(1.113)||
|**Db*forshb**||<br>**-20.873*****|||<br>**-45.683*****||
|||**(8.025)**|||**(15.308)**||
|Dfb|||6.959**||||
||||(3.374)||||
|forshfb|||<br>0.991|||3.265**|
||||(0.820)|||(1.548)|
|**Dfb*forshfb**|||<br>**-12.261*****|||<br>**-35.116*****|
||||**(4.499)**|||**(12.198)**|
|log(GDP/capita)|-0.183|-0.182|<br>-0.168||||
||(0.204)|(0.220)|(0.217)||||
|TFP|<br>0.071**|<br>0.078**|<br>0.073**|0.051|0.057|0.050|
||(0.034)|(0.032)|(0.034)|(0.050)|(0.050)|(0.051)|
|log(emp)|<br>0.880***|<br>0.888***|<br>0.889***|<br>0.894***|<br>0.907***|<br>0.918***|
||(0.080)|(0.081)|(0.080)|(0.089)|(0.089)|(0.090)|
|SKratio|0.320|0.179|0.256|0.317|0.283|0.315|
||(0.713)|(0.689)|(0.716)|(0.935)|(0.910)|(0.924)|
|K/L|<br>0.145**|<br>0.150**|<br>0.151**|<br>0.178**|<br>0.189**|<br>0.182**|
||(0.072)|(0.072)|(0.072)|(0.090)|(0.088)|(0.088)|
|rwage|-0.018*|-0.016*|-0.017*|-0.021|-0.017|-0.018|
||(0.010)|(0.010)|(0.010)|(0.014)|(0.013)|(0.013)|
|GVC Participation|0.913***|0.940***|0.928***|1.033***|1.004***|1.024***|
||(0.289)|(0.293)|(0.290)|(0.335)|(0.338)|(0.337)|
|Country FEs|Yes|Yes|Yes||||
|<br>Sector FEs|Yes|Yes|Yes||||
|Year FEs|Yes|Yes|Yes||||
|Country*Year FEs||||Yes|Yes|Yes|
|<br>Sector*Year FEs||||Yes|Yes|Yes|
|Observations|2,901|2,901|2,901|2,901|2,901|2,901|
|R-squared|0.596|0.598|0.598|0.622|0.626|0.626|

Notes: Dependent variable is manufacturing export RCA. We keep only the last seven sectors with high financial and business service input intensity, as listed in Appendix 4. Robust standard errors in parentheses, clustered by country*sector. *** p<0.01, ** p<0.05, * p<0.1.

39
forecasts based on yearly data. The largest part of these studies employs univariate time series methods with exogenous regressors. Few other studies use multivariate time series methods or state space econometric models. With the exception of Baltagi et al. (2002), none of these studies attempt to compare the forecast accuracy of different forecasting models.<sup>3</sup> Given significant variation in country coverage, time frame, forecast horizons, and econometric methods, the results of these studies are difficult, if not impossible, to reconcile.

The purpose of this study is to assess the accuracy of different econometric methods in forecasting electricity demand in developing countries. Based on the time series econometrics literature we first develop an econometric framework for forecasting electricity demand. We then obtain a number of electricity demand forecasts based on historical time series of 106 developing countries over the period 1960-2012. Finally, we evaluate the accuracy of the electricity demand forecasts resulting from different econometric methods and model specifications.

Our results demonstrate that time-series econometric forecasts yield highly accurate predictions for the evolution of electricity demand in the majority of developing countries. The forecasts based on the best performing method do significantly improve over the predictions of two heuristical models, commonly used by development practitioners, which assume that electricity demand grows at an exogenous rate or is proportional to real GDP growth. The quality of the forecasts, however, diminishes for the countries and regions, where rapid economic and structural transformation or exposure to conflicts and environmental disasters makes it to establish stable historical demand trends.

# **2 Forecasting Methods and Accuracy Tests**

This section briefly documents the econometric framework for forecasting electricity demand and evaluating its forecast accuracy. It first discusses implications of the stationarity property on forecastability of electricity demand time series. It then summarizes econometric methods employed for forecasting electricity demand. Finally, it describes measures of forecast errors for assessing forecast accuracy and comparing the quality of different forecasting methods.

> 3Baltagi et al. (2002) only focus on a small set of estimators within Autoregressive distributed lag (ARDL) model.

4