rafmacalaba/datause-encoder-data huggingface dataset:
inputs on children in Sub-Sharan Africa. Or during their school years: For example, Brown and Hunter (2004), Ansell (2008), Huber and Stephens (2012), and Harding and Stasavage (2014), show that more democratic regimes are more likely to spend more on school education. On the other hand, democratic institutions might also benefit people during their work years: Workers might enjoy more bargaining power as a result of the political power of unions (Rodrik, 1999), or they might have access to job opportunities that were previously available only to the elite.

Institutional effects unfold over time, sometimes a great deal of time, and are cumulative. Think for example of health and education. If our parent’s generation benefited from high public spending on health during their early childhood, they are more likely to have grown healthier and have acquired competitive cognitive and social skills (Heckman, 2011). Add onto that high public spending on education, which may had provided them also with good educational resources to develop their cognitive skills. Now, our generation would have benefited as well from the benefits our parents enjoyed, as it is well known (for example) that more educated parents are more likely to invest more time and money in the education of their children. Furthermore, we may also benefit from high public spending as our parent’s did, or from the capacity that was built previously. It is precise to explore the long-term effects of these legacies; democracy is likely to have intergenerational impacts on social outcomes.

# **3 Data**

The data come from several harmonized household surveys of nine Latin American countries: Argentina, Brazil, Bolivia, Colombia, Honduras, Peru, Panama, Paraguay and Uruguay. To maximize the number of countries and periods I use surveys between 1995 and 2009, biannual, and restrict myself to urban areas in Bolivia due to the lack of rural data for the 1995 and 2009 surveys. Peru and Honduras surveys for 1995 are not harmonizable, but the countries represent sources of variation too important to exclude them; therefore, my pseudo-panel is unbalanced. Table A1, in the appendix, reports the data sources.

I restrict myself to individuals born between 1936 and 1977. However, I drop individuals between 18 and 24 years of age in 1995 and between 21 and 24 years of age in 1997 to consider that by 2009 life expectancy in the region was about 74 years –to keep a representative sample of “old” individuals- and that a person’s educational attainment is likely to remain unchanged after age 25 –to avoid truncation in the distribution of educational attainment.

The source of income for every observation is monthly monetary labor income (constant prices of 2005, US$, adjusted by purchasing power parity), which is the sum of earnings from wages, 6
# **Excise duties**

Specific excise duties are levied on tobacco products, alcohol products (malt and traditional beer, wine and other fermented beverages, spirits) and petroleum products (petrol, distillate fuel, residual fuel and base oil).

# **Fuel levies**

Fuel levies include general levies and specific excise duties on petrol, diesel and illuminating paraffin. Petrol and diesel fuel are also levied with a contribution to the Road Accident Fund which pays compensation to victims of traffic accidents.

Total general government spending in South Africa is also somewhat higher than the average for middle-income countries.<sup>14</sup> Total general government spending, excluding interest payments amounted to 32.2 percent of GDP in 2010/11 (table 2). As we can see in figure 2, South Africa’s social government spending (as a share of GDP) is among the highest in our (comparable) sample. Compared with other "big" social spenders in this graph, Bolivia and Brazil, South Africa spends somewhat more on education and less on health and direct cash transfers than Brazil does, but more on direct cash transfers than Bolivia does.

Table 2: South Africa: General government expenditure, 2010/11

||2010/11<br>(% of<br>GDP)|Incidence<br>analysis<br>(% of<br>GDP)|
|---|---|---|
|**Total General Government Expenditure**|**34.8**|**14.9**|
|**Primary government spending**|**32.2**|**14.9**|
|_Social spending_|_17.6_|_14.9_|
|Total Cash Transfers|3.8|3.8|
|Child Support Grant|1.1|1.1|
|Old age pension (non-contributory)|1.3|1.3|
|Disability grant|0.6|0.6|
|Foster care grant|0.2|0.2|
|Other grants|0.6|0.6|
|Free Basic Services|0.5|0.5|
|In-kind transfers|12.6|11.1|
|Education|7.0|7.0|
|Health|4.1|4.1|
|Housing and urban|1.5|...|
|Other social spending|1.1|...|
|_Nonsocial spending (incl.public sectorpensions)_|14.6|...|

Source: : Statistics South Africa, 2012b , “Financial Statistics of Consolidated General Government,” November 22, 2012, for totals. Line items under direct and indirect taxes from 2013 Budget Review, National Treasury. For Free Basic Service, data represent the amount transferred under the equitable share formula for 2010/11 to municipalities to compensate them for providing basic services to poor households, and was provided by the Financial and Fiscal Commission of South Africa.

Just over half of South Africa’s total general government expenditure was devoted to social spending. Some 3.3 percent of GDP was dedicated to direct cash transfers to individuals and includes items such as non-contributory pensions and child grants (table 2) but excluding free basic services. Over the past decade, the number of beneficiaries receiving social grants doubled from almost 8 million in 2003/04 to 15.8 million in 2013/14 mainly reflecting the expansion of direct cash transfers to children and the elderly. The Child Support Grant (CSG) was introduced in 1998 and was initially targeted at children aged 0-7 years, with the age limit progressively raised to its current level of 18 years. The age limit for the old-age grant for men was also lowered from 65 to 60 years to

> 14 General government excludes State-Owned Enterprises.

6
18 for men and between 51 and 55 years for women) have only approximately one tenth of the re-employment probability of the base group. Not only for insured unemployed but also for exhaustees are age and educational attainment powerful predictors of flowing into employment (cf. Lazar and Szekely, op. cit., p. 8). Boeri's (1994) contention that individual characteristics are not significant determinants of outflows from unemployment is, therefore, not supported by the evidence of the discussed Hungarian micro data sets.

The estimated hazards to go to training for men and women seem to indicate a wrong targeting of this ALMP measure. Those people who have a relative higher probability of flowing into regular employment, i.e. the young and the well educated, also have a relative higher probability of being taken onto a training scheme. In other words, those unemployed who seemingly have the least difficulty finding reemployment on their own are above all targeted for training measures. That Hungarian employment offices engage consciously in "creaming off' when targeting training measures will be discussed below. The estimated hazards to go to public works show for men an inverse relationship between education and the probability of entering the scheme. This might be a reflection of the type of work performed by men in this scheme or a reflection of targeting the underprivileged among the unemployed. Age is not a significant determinant in this instance. For women who are much less likely to exit into public works education is not a significant predictor of this hazard rate, while non-manual occupation is. That this latter covariate has predictive power points to the often heard fact that municipal administrations use unemployed women in clerical and health care related jobs and that fiscal substitution effects are present in the application of public works.

# 3.1.2. Evaluation of active labour market policies

Hungary is the only transition economy where "controlled experiments" have been undertaken to evaluate ALMPs. In O'Leary (1994) we find such an experiment to evaluate the Hungarian retraining and public works measures. There, the sample to be evaluated consists of three sub-samples: registered unemployed who did not participate in ALMPs, those who participated in retraining in the second half of 1991 and those who participated in public service employment (PSE) in September 1991. This sample was interviewed in November 1993.19 The impact analysis of ALMPs looks above all at differences in the re-employment probabilities and earnings of the three sub-samples. O'Leary uses various methods to control for differences in 19Strictly speaking, O'Leary's analysis is not a controlled experiment as he does not select persons with the same observable characteristics and then assigns them to the three groups. Instead he tries to control for observable characteristics _ex post._
|**Table**|**2a. Housing Prices and Current Accou**<br><br>|**nt Deficits**<br>||||
|---|---|---|---|---|---|
||**Country**<br>**Real Housing Prices**|**CA Deficit2008**||**Country**<br>**Real Housing Prices**|**CA Deficit2008**|
||_(as % change 2002‐06)_|_(as % GDP)_||_(as % change 2002‐06)_|_(as % GDP)_|
|1|Ukraine<br>230|‐7.19|21|China<br>31|9.46|
|2|Estonia<br>150|‐10.78|22|Thailand<br>29|3.06|
|3|Lithuania<br>130|‐14.91|23|Australia<br>25|‐4.92|
|4|Latvia<br>120|‐15.12|24|Finland<br>25|3.39|
|5|South Africa<br>96|‐7.96|25|Norway<br>25|19.13|
|6|Poland<br>66|‐4.72|26|Italy<br>22|‐2.78|
|7|New Zealand<br>65|‐9.26|27|Serbia<br>20|‐18.57|
|8|Iceland<br>55|‐18.2|28|Argentina<br>15|0.82|
|9|Denmark<br>50|1.32|29|Czech Republic<br>13|‐2.23|
|10|Spain<br>50|‐10.05|30|Colombia<br>12|‐2.19|
|11|France<br>48|‐2.8|31|Netherlands<br>12|5.62|
|12|United States<br>45|‐4.63|32|Switzerland<br>9|9.29|
|13|Belgium<br>42|0|33|Malaysia<br>5|14.8|
|14|Ireland<br>42|‐4.98|34|Singapore<br>4|19.25|
|15|Canada<br>40|0.95|35|Austria<br>‐3|2.81|
|16|HongKong<br>40|11.71|36|Philippines<br>‐5|2.44|
|17|Hungary<br>40|‐5.45|37|Indonesia<br>‐7|0.1|
|18|Slovenia<br>40|‐4.69|38|Germany<br>‐15|7.31|
|19|Sweden<br>40|6.44|39|Japan<br>‐25|4.01|
||United<br>38|‐3.64||||
|20|Kingdom|||||
|Source|: Authors;The Economist Magazine,April 200|9;and April 2009 W|EO,I|MF||

46
where _θs_ is the value added share of sector _s_ , and _S_ is the number of sectors in each country.

Each sector’s output _Ys_ is obtained by aggregating the output of individual establishments using a CES technology: where _Ysi_ is a differentiated product by establishment _i_ in sector _s_ , and _σ_ is the elasticity of substitution across producers within industry.

Each establishment produces a differentiated product according to the standard Cobb-Douglas production function: where _Asi_ stands for establishment-specific productivity, _Ksi_ is establishment’s capital stock, _Lsi_ is labor input, and _αs_ is industry-specific capital share.

Each establishment maximizes current profits: where _Psi_ is establishment-specific output price and _PsiYsi_ is value added of firm _i_ , _w_ and _R_ are the common wage rate and the rental cost of capital, respectively. _τKsi_ denotes establishment-specific “capital” distortion (which increases the cost of capital _relative_ to labor). A large (small) value of _τKsi_ increases the cost of capital (labor) relative to labor(capital). A wide range of factors could potentially cause such distortion, e.g. credit market imperfection and labor market regulations that differ across firms. “output” distortion is denoted by _τY si_ . Such distortions could arise because of government policies such as tax regulation that favor particular firms or corruption. These distortions could also reflect monopoly power or adjustment costs.

The first order conditions imply that _MRPKsi_ = _R_ <u>(11+</u> _−ττYsiKsi_ <u>)</u> and _MRPLsi_ = 1 _−wτYsi_<sup>.Fromthe</sup> first-order conditions, the optimal capital-labor ratio is given by: Building on the work of Foster et al. (2008), Hsieh and Klenow (2009) distinguish between two productivity measures: one expressed in physical units (TFPQ) and the other in monetary values (TFPR) Their analysis shows how measures of TFPR relate to the wedges. More specifically, HK show that 10
Table A.3: Qayarrah field

|**Dates Removed**<br>**Temperature(K)**|
|---|
|June 9,2016<br>1040|
|June 12,2016<br>1051|
|June 13,2016<br>1055|
|June 14,2016<br>1049|
|June 15,2016<br>1163|
|June 16,2016<br>1156|
|July3,2016 to August 24,2016<br>Average = 1160 Max = 1488|

**Qayarrah Area:** Prolonged confrontation between Daesh fighters and Iraqi forces occurred at the site near Qayarrah city, southeast of Mosul. While Al Jazeera reported the presence of Iraqi forces in the area as early as June 2016 [8], the earliest reports of fires at the field date to early July [21]. These fires continued throughout the second half of 2016: Daesh lost control of the site 24 August 2016 and fires continued until at least December 2016 [15].

Daytime images are consistent with the news reports that large fires began in July 2016. Images were available at four dates around the drop in temperature at this site: 1 May 2016, 28 June 2016, 16 July, and 19 July. Analysis of these images shows large smoke plumes on 16 and 19 July but no apparent smoke 1 May and 28 June.

While several low temperature nights were observed in June 2016 (June 9 – June 16), the last date with temperature observed above 1500 K was 2 July 2016. A structural break test of temperature indicated the greatest likelihood of a change in the temperature pattern on 9 June 2016. To remain conservative in our estimates and consistent with the qualitative evidence, we drop observations from this site June 9 to June 16, 2016 included and all dates after 3 July 2016 included.

**Ajil and Hamrin Area:** As Daesh rapidly lost territory in Iraq in March 2015, three adjacent sites were all lit on fire as Daesh ceded control. Landsat images of the sites were available

34
ers: 18 _UNEMPs = S-SG- SdP_ (29) Note that here we assume that skilled workers who are unable to find a job in the formal economy do not enter the informal economy, in contrast to unskilled workers. In principle, any worker who is not hired in the formal economy could get a job in the informal economy at the going wage. However, there are several reasons why they may choose not to do so. An important consideration may be that skilled workers are concerned about possible adverse signaling effects associated with employment in the informal economy and may prefer instead to remain openly unemployed (Gottfries and McCormick (1995)). Indeed, the existence of "luxury" unemployment is a well-documented feature of the labor market in developing countries. The skilled labor force evolves over time according to: Because we assumed that workers are born unskilled, the skilled workforce does not grow at the natural growth rate of the urban population, but rather at the rate at which unskilled workers choose to acquire skills. Note also that, for simplicity, we assume no "de-skilling" (or obsolescence) effect, although this could be easily captured.

It is important, to conclude this discussion of wage formation mechanisms, to return to our assumption that skilled workers' wage is determined on the basis of an efficiency effect of compensation on productivity. In practice, it is notoriously difficult to discriminate among various forms of efficiency wage theories; these theories tend to be "observationally equivalent." At the same time, however, this makes our choice here of an explicit formulation based on the wage-productivity link less restrictive than it appears at first glance; in fact, a wage-setting equation fundamentally similar to (28) can also be derived by considering, say, turnover costs. A bargaining framework between firms and a centralized trade union could also lead to a similar wage-setting specification. The important point is to assume in each case that whereas firms are concerned with the _product_ wage, workers (or the union that represents them) are concerned with the _consumption_ wage. This

> 18As noted earlier, skilled employment in the public sector is set exogenously by the government.

26
**Figure 5. Skill content of jobs (O*NET-based) by level of GDP per capita, all countries, latest year.**

<!-- Start of picture text --> Non-Routine Analytical, US Non-Routine Interpersonal, US Routine Cognitive, US<br>LB<br>SB LB<br>ETKMZWGMBFUGSLNPTZKHBDCMCIZMGHMMNIMDVNPKGYINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECMVPEDOCNZACORSTHCRMEIQBGBRPAVEMXMUUYARLVTRCLRULTPLHUSCEESKGRPTSICZESCY PR ITGBFRCAISFIBEDEIESEDKATNLUSNOLU ETKMZWGMBFUGSBSLNPTZKHBDCMCIZMGHMMNIMDVNPKGYINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECMVPEDOCNZACORSTHCRMEIQBGBRPAVEMXMUUYARLVTRCLRULTPLHUSCEESKGRPTSICZESCYPRITGBFRCAISFIBE DE IESEDKATNLNOUS LU ETKMZWGMBFUGSLNPTZKHBDCMCIGHZMMMNIMDYEVNGYPKINCVSZAOALBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNBATNLKECPEMVDOCNZACORSCRMEIQBGBRPAVE MX MUUYARLVTRCLRUHUPLLTSCEESKGRPTCZSIESCYPRITGBFRCAISFIBEDEIESEDKATNLUSNOLU<br>LB<br>SB<br>YE<br>YE<br>700 800 900 1000 1100 700 800 900 1000 1100 700 800 900 1000 1100<br>GDP level, log points GDP level, log points GDP level, log points<br>Routine Manual, US Non-Routine Manual, US<br>YE<br>PA<br>ETKMZWGMBFUGSLNPTZKHBDCMCIZMGHMMNIMDVNGYPKINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECMVPEDOZACNCORSTHCRIQBGBRLBVEMXMUUYARLVTRCLRUHUPLLTSCEESKGRPTCZSIESCYPRITGBFRCAFIBEDEIESEDKATUSNOLU ETKMZWGMBFUGSLNPTZKHBDCMCIZMGHMMNIMDYEVNPKGYINCVSZAOALBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNBATNLKECMVPEDOZACNCORSTHCRIQBGBRPALBVEMXMUUYALVTRCL R HUPLLTSCEESKGRPTUCZSIESCYPRITGBFRCAISFIBEDEIESEDKATUSNOLU<br>ME ISNL SB ME NL<br>SB<br>700 800 900 1000 1100 700 800 900 1000 1100<br>GDP level, log points GDP level, log points<br>.5 .4<br>.4<br>.2 .2<br>0<br>0<br>0<br>Skill index Skill index -.2 Skill index<br>-.5<br>-.2<br>-.4<br>-1 -.6 -.4<br>1<br>.6<br>.4<br>.5<br>.2<br>0<br>Skill index Skill index 0<br>-.2<br>-.4 -.5<br><!-- End of picture text --> Note: Each point shows the skill content of jobs using O*NET indexes per country and year. The horizontal axis measures GDP per capita in PPP from WDI.

When looking at changes over time, both measures show the same patterns with regards to the non-routine cognitive, non-routine interpersonal and routine manual task content of jobs (Figure 6). The correlation is also positive, but considerably smaller, for the routine cognitive task content of jobs. However, O*NET and STEP measures do not necessarily lead to the same conclusions regarding the evolution of the non-routine manual content of jobs over time, since the correlation coefficient is relatively low.

16
# **7 Conclusion**

This paper estimates the price elasticity of African elephant poaching using gold prices as instruments of ivory prices. A low poaching elasticity with respect to price may partly explain why the African elephant did not experience the fate of the 19th century American bison, despite sharp increases in ivory prices. On the other hand, the low elasticity also means that the challenge of medium- to long-term conservation of African elephants remains largely unresolved.

# **References**

- **Adda, J ́erˆome, Brendon McConnell, and Imran Rasul** , “Crime and the Depenalization of Cannabis Possession: Evidence from a Policing Experiment,” _Journal of Political Economy_ , 2014, _122_ (5), 1130–1202.

- **Angrist, Joshua and Adriana Kugler** , “Rural windfall or a new resource curse? Coca, income and civil conflict in Colombia,” _Review of Economics and Statistics_ , May 2008, _90_ (2), 191–215.

- **Barbier, Edward B, Joanne C Burgess, Timothy M Swanson, and David W Pearce** , _Elephants, economics and ivory_ , London: Earthscan Publications, 1990.

- **Becker, Gary S., Kevin M. Murphy, and Michael Grossman** , “The Market for Illegal Goods: The Case of Drugs,” _Journal of Political Economy_ , February 2006, _114_ (1), 38–60.

- **Berman, Nicolas, Mathieu Couttenier, Dominic Rohner, and Mathias Thoenig** , “This Mine Is Mine! How Minerals Fuel Conflicts in Africa,” _American Economic Review_ , June 2017, _107_ (6), 1564–1610.

- **Burbidge, John B., Lonnie Magee, and A. Leslie Robb** , “Alternative Transformations to Handle Extreme Values of the Dependent Variable,” _Journal of the American Statistical Association_ , 1988, _83_ (401), 123–127.

- **Burn, Robert W., Fiona M. Underwood, and Julian Blanc** , “Global Trends and Factors Associated with the Illegal Killing of Elephants: A Hierarchical Bayesian Analysis of Carcass Encounter Data,” _PLoS ONE_ , 09 2011, _6_ (9), e24165.

24
output expands in labor-intensive sectors, particularly other agriculture, to meet the new demand from countries involved in the dispute.

Although the United States plays a major role in the world economy, accounting for close to a quarter of global GDP, the expansion of China and other developing countries has reduced its relative importance in the global trading system. In 2016, the United States accounted for 8.7 percent of all goods exports and 13.3 percent of goods imports (World Development Indicators, World Bank). With the exception of Canada, Mexico and several Central American economies, the United States is not a major trading partner for many economies. The majority of EU trade is intra-EU. Europe is Africa’s main export destination. Globally, the United States is less important than high-income Asia or Europe in terms of total trade. Faced with an increasingly inward-looking United States, the global trading system will likely rebalance and adjust.

There are a few important findings that emerge from this work:

- Retaliatory action by developing countries is the least desirable strategy in the face of new protectionist measures. No action is preferred to retaliation, as aggregate losses are found to be nearly twice as big when developing countries impose retaliatory measures.

- No action could translate into benefits for developing countries if they can respond to trade diversion opportunities in regions involved in the trade dispute.

- Trade liberalization and improved intra-regional integration could not only help offset the negative terms of trade effects of increased protectionism on developing countries, but also represent significant benefits that are, so far, untapped.

- “Turning the other cheek” could benefit countries in Latin America and the Caribbean that are deeply interconnected with the United States, the source of most of their imports.

- Faced with an increasingly inward-looking United States, the world trading system will likely rebalance and adjust, reinforcing existing trade ties and creating new ones.

# **5 Conclusion**

Faced with escalating protectionist measures and a potential trade war between major economies, policymakers in developing countries are grappling with the impact of these measures on their domestic markets and the choice between different policy responses. In this paper, 11