rafmacalaba/data-use-mentions-extended huggingface dataset:
|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.39|0.54|0.89|0.89|0.67|0.67|0.54|0.43|
|N1(base survey)|1,179|1,179|1,179|1,179|1,179|1,179|1,179|1,179|
|N2(target survey)|698|698|698|698|698|695|698|698|
|**True poverty rate in TA 1**<br> <br>**Note**: The standard errors in parentheses are adjuste<br>linear regression model with the theoretical distribu|d for complex<br>tion of the err|survey design.<br>or terms and M|All estimates<br>ethod 2 uses|17<br>(1<br>are obtained<br>the empirica|.7<br>.9)<br>with the popul<br>l distribution o|ation weight<br>f the error te|s. Method 1 u<br>rms. Both me|ses the normal<br>thods employ|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Models 3.1 and 4.1 employ non-standardized variables. Models 3.2 and 4.2 employ variables in TA 3 standardized by those in NPS 2019/20. Food and non-food expenditures, household size, and age in both surveys are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA
Community Characteristics. Community characteristics at the time of schooling are meant to capture the effect of differences in communities, levels of economic development, public services, and school availability. The level of community economic development determines opportunities for work in the local labor market and, thus, returns to schooling in the area. The level of public expenditures in the area contributes to its economic health and the availability of transportation, schools, and other facilities. For our sample, we do not have measures of public community characteristics at the time of schooling. In their absence, we use two city-noncity dummy variables that take on a value of 1 if the individual lived in a city at age 8 ar<sup>4</sup> 13, i.e., ages corresponding to the official ages of entry into the primary and secondary levels.14/ The premise for this is that city dwellers generally have greater access to public facilities, such as schools, and more active labor markets than do rural residents. To the exteni that the availability of schools influences an individual's choice of residence, these variables may be endogenc . However, this is less likely to be the case for 8- and 13-year olds than for older youths.

School Characteristics. The primary school characteristics we use in equation (3) refer to the school the respondents attended, not to the primary school that is generally available to the community. Thus, it can be argued that the values of these variables are themselves a result of family decision-making which would bias the coefficients. To the extent that only 14/ These variables were constructed from migration data and there is some error in their measurement. For those individuals who migrated more than twice during their school-age years (about 5 percent of the sample), we are unable to identify residence. These individuals were assigned the location to which they had first migrated.

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# **2. India’s Labor Market**

India is predominantly rural and 71% of its more than 1 billion people live in rural areas. During the last decade Indian labor force grew at an annual rate of 2.27% in urban and 0.66% in rural areas (Chadha 2001), totaling 397 million in 1999-2000. Labor force participation (LFP) of prime-age male workers was relatively stable between 1993 and 1999 at 85%, while LFP of prime-age females declined from 34% to 28%. It seems that the increased attendance of younger women in educational institutions accounts for the majority of this decline. Unemployment rates are higher in urban (7.86%) as compared to rural areas (3.74%) and, on average, women are more likely to be unemployed than men. Unemployment rates are higher among young and more educated individuals.

In India, one of the main official sources for labor statistics – the Directorate General of Employment and Training, Ministry of Labor – makes a distinction between organized and unorganized sectors based on the size of employment. The organized sector consists of public and private components. Employment in the <u>public</u> sector consists of government civilian employees and employees in public enterprises. Public sector enterprises take three forms: the so-called _departmental undertakings, public companies_ and _public corporations_ . _Departmental undertakings_ are an integral part of government, with the same status as the government departments. _Public companies_ are incorporated under the Companies Act of 1956 and have the same status as private companies. Some of them are wholly owned by the government, while in others the government has a controlling interest. _Public corporations_ are individually created by specific legislation as separate legal entities with defined powers, management structure and duties (Roy, 1997). In the <u>private</u> sector, a distinction between organized and unorganized sectors is made on the basis of size of the enterprises. Units in the private sector that employ 10
although this will be affected by country-specific enforcement factors; and positive for the agricultural opportunity value of the land (β7>0).

# **5.2  Estimation Areas**

We estimate the model for all cells in WWF’s moist tropical forest ecoregions of each country, which are discussed more fully in Section 7. Figures 4-6 identify the relevant ecoregions, which dominate northern and central Bolivia, southern Cameroon, and most of Myanmar.

# **5.3  Variable Measures**

Our data and sources are as follows.

**Hansen pixels cleared** : per 250 m cell, the percent of 30 m Hansen pixels cleared.  This incorporates two measures:  percent cleared prior to 2001, and annual clearing from 2001 to 2014 (Hansen et al. (2013)).

**Distance from road segment** :  distance from the centroid of each cell to the nearest road segment, calculated in ArcGIS 10.

**Distance traveled to the nearest urban center via secondary and primary road segments** : Calculated in ArcGIS 10 from Delorme digital road maps.

**Elevation** :  Average elevation for a cell, calculated from the CGIAR-SRTM dataset (3 seconds resolution), aggregated to 30 seconds resolution by DIVA-GIS (http://www.diva-gis.org/gdata).

**Terrain slope:** Standard deviation of pixel-level elevation measures within a cell.

**Legal protection status** :  1 if the cell is in a protected area identified by the World Database on Protected Areas (WDPA); 0 otherwise.  The WDPA shapefile has been downloaded from http://www.protectedplanet.net/.

**Agricultural opportunity value** :  mean value for a cell, calculated from the high-resolution global grid developed by Deveny et al. (2009).  Raster resolution: .0025 decimal degrees. The Deveny data have been drawn from two sources: (1)  Foregone agricultural rents from Naidoo and Iwamura (2007), who integrate
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and its informational benefit for households’ financial decisions (Hong et al., 2004) and further back up the finding that internet access mainly has an informational role by showing that it acts as a substitute for social interaction. Deuflhard et al. (2015) show for the Netherlands that financially literate individuals, who use the internet and online accounts achieve higher returns.

In transition economies in particular, non-participation in formal savings may also be driven by lack of trust. Malmendier and Nagel (2011) highlight the importance of past experience of economic crises for households’ financial decisions. Stix (2013) and Brown and Stix (2015) show that memories of previous economic crises during transition have an impact on trust in the financial system and affect the demand for informal versus formal savings as well as the currency of savings. For advanced economies, a growing body of research shows that more trusting individuals and those who trust in the stock market are more likely to participate in the risky assets (Guiso et al., 2008; Georgarakos and Pasini, 2011).

# **3. Data and descriptive evidence**

# **3.1. OeNB Euro Survey**

The main data source for the analysis is the “OeNB Euro Survey” conducted by the Austrian central bank since 2007 on a regular basis as a repeated cross-sectional survey in 10 Central, Eastern and Southeastern European countries: 6 EU Member States which are not part of the euro area (Bulgaria, Croatia, Czech Republic, Hungary, Poland, Romania) and four (potential) candidate 4
Overall, the studies suggest an economically large impact of financial development on economic growth.<sup>14</sup> The literature on the channels through which access to finance affects firm growth is however, more limited. Clearly, access to external finance can facilitate capital accumulation. However, on a macro scale, historians have identified innovation and technological progress as the principal causes of material progress over extended periods of time (see for example, Landes, 1969; Rosenberg, 1982; and Mokyr, 1990). Solow‟s (1957) path breaking analysis of growth in labor productivity in the U.S. has established that technological advances (broadly defined) and skill, rather than capital accumulation are the prime drivers of increases in labor productivity. Solow (1957) argued that approximately 80% of the increase in labor productivity in the U.S. over the period 1909-1949 was due to more productive use of capital and increases in the skill level of the labor force. More recently, Levine, Loayza, and Beck (2000) have shown that financial sector development helps economic growth through more efficient resource allocation rather than through increases in the scale of investment or savings mobilization. Cross-country time-series studies by Bekaert, Harvey, and Lundblad (2001, 2005) also show that financial liberalization boosts economic growth by improving the allocation of resources and the investment rate.

Klapper, Laeven, and Rajan (2006) identify an entrepreneurship channel. They use data on more than 3 million firms across Europe across the Amadeus database and find that easier access to external finance is positively related to the number of start-ups. While they find that onerous entry regulations such as high registration costs impede firm entry, they find that regulations associated with improving access to external finance such as accounting standards and property rights protection have a positive effect on firm entry.

Ayyagari, Demirguc-Kunt, and Maksimovic (2011) use Enterprise Surveys (from the World Bank) from 2002 to 2005 across 47 developing economies and posit innovation as one of the
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey labor supply, while Haanwinckel (2018) includes changes in demand and minimum wage. Consequently, it comes as no surprise that almost all changes in inequality are driven by movements in the relative supply of skills. Although the push towards more structural models that account for the role of occupations is certainly a valuable contribution, future research should try to extend them in the direction of accommodating other features of labor markets and workers’ characteristics. Particularly, a dynamic model in which workers build up experience would not only help in accounting for the contribution of other worker characteristics, but also in understanding the reasons behind the observed decrease in the experience premium.

# **3 Changes in Demand: Trade Liberalization, Commodity Shocks, and Technological Change**

In the first years of the 21st century, Brazil was hit by a positive demand shock that resulted in favorable terms of trade and spurred growth in the commodity sector of the economy, together with economy-wide gains. Similarly, in the early 1990s, Brazil underwent important economic reforms that liberalized markets both through reduced tariff and non-tariff barriers on imports. Traditional neoclassical models predict that both shocks, by changing the relative price of goods that are intensive in a particular kind of factor - in our case, low or high-skilled labor - can lead to changes in income distribution. At the same time, important technological changes took place around the world, which researchers point out as potential sources of polarization and increasing wage inequality in the developed countries since these new technologies increased the demand for high-skilled workers (Autor and Dorn, 2013; Autor and Handel, 2013). In this section we investigate the possible impacts each of these factors may have had in the Brazilian wage distribution.

## **3.1 Trade liberalization and its impact on inequality**

It is widely agreed among economists that international trade can affect the distribution of earnings within countries by changing the relative prices of products and factors. For instance
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Bottazzi, Laura, and Giovanni Peri (2003)
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" Nicaragua (74 percent), and Pakistan (63 percent) were without either sewer connections or septic tanks.

The poorest households in each country had lower rates of coverage of sewer connections and septic tanks than the population as a whole. Bulgaria is the only exception. In Bulgaria nearly all households had either a sewer connection or a septic tank, but the poorest households were more likely to have septic tanks than sewers.

Rural households of all income levels had lower rates of coverage of all sanitation facilities than urban households (Figures 13 and 14). Very few urban households were without a toilet or latrine in their home. By contrast, approximately 30 percent or more of each rural decile was without any sort of sanitation facilities. Not surprisingly, the greatest sanitation deficit was among the poorest rural households. Between 80 percent and 90 percent of households in the poorest two deciles in the pooled rural sample had no latrine or toilet in their homes. Approximately one quarter of households in the poorest urban decile of the sample have no sanitation facilities.

6.4. Telecommunications For households without a private telephone in their home, having access to a public telephone in their community can be a real advantage. In the absence of a public phone, the presence of at least some private telephone connections in the community may still give households without a phone a means of communication. Phone owners may rent out their phones or allow others to use the phone for emergency communications. Information on such uses of private phones is not available in the LSMS surveys, but the community questionnaires in three countries (Ecuador, Kyrgyz Republic, and Panama) do ask about access to public phones.

In these three countries poor households were less likely than the population as a whole to have access to public telephones in their communities. In Panama and Nepal access to public telephone service increases with aggregate household income (as measured by our income proxy). In Ecuador
five presidential elections from 2006 to 2024. The data come from the Consejo Nacional Electoral (2023) and the Venezuela 360 (2023) project. Specifically, we analyze two outcomes: turnout (total votes divided by the electoral census) and opposition support (votes for candidates other than Ch ́avez or Maduro as a percentage

> 13We confirm the validity of this measure as a proxy for economic growth and income inequality in Tables B.1, B.2, and B.3, and test the sensitivity of our main results to controlling for oil production in order to account for concerns related to biases due to brightness from gas flares in the satellite data.

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# **4. Results**

The stochastic frontier model discussed in section 2 is estimated using the data presented in section 3. Equations 1 and 2 are simultaneously estimated using the maximum likelihood method discussed by Battese & Coelli (1995). The results allow an assessment of the relationship between public education expenditure and access to quality education, to gauge the efficiency in the use of public education spending and to explore the correlates of the inefficiency.

# **4.1 Public education expenditure and the learning‐adjusted years of schooling**

Model 1 in Table 3 provides an estimation of Equation 1 only, while Model 2 gives the results of the simultaneous estimation of Equations 1 and 2. After controlling for country‐level inefficiencies related to public expenditure on education, there seems to be a positive correlation between public education spending and the learning‐adjusted years of schooling. A higher expenditure per school‐age individual is associated with increased access to quality education. However, these results must be interpreted with caution as there might be some endogeneity surrounding this relationship. In fact, the decision made by the government and the international partners to invest in education may be linked to the performance of the education sector. For instance, in countries with poor access to education, there might be a rationale for increased expenditure on education with the hope that this could drive better educational outcomes. Increased expenditure could also contribute to improving the access to quality education. These results should then be understood as a correlational relationship rather than a causality relation.

Although public expenditure on education seems to be correlated with increased years of quality education overall, the relationship appears to be concave and decreases with the level of spending. One additional unit of spending is less associated with increased access to quality education, compared to the previous unit. In other terms, countries that seem to already spend too much on education may not expect to significantly increase the access to quality education by spending more. However, countries that initially spend relatively less on education could
for every year in the period. 2. _Intermediate_ : More than 25 battle-related deaths per year and a total conflict history of more than 1000 battlerelated deaths, but fewer than 1000 per year. 3. _War_ : At least 1000 battle-related deaths per year” (Strand et al. 2005, 10).

Table 1 presents, in compact form, the entire PRIO/Uppsala data (Version 3) for the 7
OLS technique with a single equation model to estimate the supply response to price. The analysis is based on data from 1961 to 1976. He found an inelastic supply response with elasticities of 0.178, 0.376 and 0.349, respectively for the linear, semi-log and log-log specifications of the supply response model. Kargbo (1979) argued that the low elasticities reflected the government’s pricing policy, which effectively held down the price paid by consumers and prevented it from rising to provide farmers with an incentive to increase production or adopt new methods.

Annual commercial imports of rice averaged over 400,000 metric tons per year, over the five-year period to 2018, at an average cost of US$140 million per year, as domestic production has failed to keep pace with Sierra Leone rapidly growing population. There is no consistent data on rice food aid that enters the market. Although the channel and structure for the marketing of domestic and imported rice are similar (see Spencer et al, 2009), consumer surveys suggest that the imported rice is more available. Overall, nearly 90 percent of households across both urban and rural districts indicated that imported rice was always available in their nearest market, compared with only 49 percent of households for domestic rice. The exception being the Western urban area, where 95 percent of households indicated that domestic rice was always available. The Western area is also the most urban and inhabited by the relatively higherincome population with the means to afford imported rice.

16