rafmacalaba/datause-encoder-data huggingface dataset:
price to obtain non-energy cost of fertilizers' production. This cost does not vary much across time because fossil fuels' and nitrogen fertilizers' prices are highly correlated and follow the same trend (USGAO 2003).

# C.1.4 Crops Sector

The initial amount of food crops (measured as the global physical production of agricultural crops) and global expenditures on food crops in 2004 come from the FAOSTAT database. We set the production of cellulosic feedstocks close to zero, as the production of second generation biofuels was practically non existent in 2004. The elasticity of substitution of nitrogen fertilizers for agricultural land is based on Hertel et al. (1996) estimates for the US corn production over the 1976-1990 period. We obtain the economic rent of global cropland from GTAP v.7 database.

The crop technology index and value shares of agricultural land and fertilizers in 2004 are calibrated from known values of agricultural output, fertilizers, and the agricultural land as described in Rutherford (2002). Based on the agronomic literature (Cassman 1999, Cassman et al. 2010) we assume that crop technology index, _θ_<sup>_g,c_</sup> is a logistic function with declining growth over time: where _θ_ 0<sup>_g,c_</sup> is the initial value of crop technology index, _θT_<sup>_g,c_</sup> is crop yield potential, i.e., the yield an adapted crop cultivar can achieve when crop management alleviates all abiotic and biotic stresses through optimal crop and soil management (Evans and Fischer 1999), and _κg,c_ is the logistic growth rate. In a comprehensive study Lobell et al. (2009) report a signicant variation in the ratios of actual to potential yields for major food crops across the world, ranging from 0.16 for tropical lowland maize in Sub-Saharan Africa to 0.95 for wheat in Haryana, India. Following global estimates of Licker et al. (2010) we assume the average ratio of 0.53. We calibrate the value of the logistic growth rate _κg,c_ to match recent crop yield dynamics, and allow for yield plateau when they reach 7080% of the potential yield, _θT_<sup>_g,c_.</sup> As regards the cellulosic feedstocks, we assume that their yield grows linearly across time, adding a constant amount of technology gain per annum 16
positive effect of direct transfers. In other words, the most vulnerable households are more than compensated by direct and indirect tax payments.

|TABLEIV-1 CHANGES IN POVERTY LEVELS BY I|NCLUDING TAXES A<br>|ND TRANSFERS<br>||
|---|---|---|---|
||Market entry +<br>Pensions|Disposable<br>income|Consumable<br>income|
||(1)|(2) = (1) - direct<br>taxes -<br>contributions to<br>the S.S.S. + direct<br>transfers|(3) = (2) - direct<br>taxes +<br>subsidies|
|**_Poverty incidence_**||||
|Official extreme poverty line (US $2.5 PPP per day)|16.4<br>(0.024)|12.8<br>(0.019)|13.3<br>(0.018)|
|Official moderate poverty line (US $5.4 PPP per day)|39.1|37.6|38.8|
||(0.043)|(0.042)|(0.042)|
|US $3.2 PPP per day|21.8|18.6|19.2|
||(0.031)|(0.026)|(0.025)|
|US $5.5 PPP per day|40.3|38.8|40.0|
||(0.044)|(0.043)|(0.043)|
|**_Poverty gap_**||||
|Official extreme poverty line (US $2.5 PPP per day)|7.8|5.4|5.7|
|Official moderate poverty line (US $5.4 PPP per day)|18.5|15.9|16.4|
|US $3.2 PPP per day|10.3|7.7|8.1|
|US$5.5 PPPper day|19.1|16.5|17.1|
|Source: World Bank Group and staff estimates based on ENPH 2017||||
|Notes: The values in theparentheses correspond to the standard erro|rs associated with|the measure||

The gap and incidence from poverty tend to decrease when moving from market income to consumable income (Table IV-1), but once again, these changes are not statistically significant. In other words, the fiscal system seems to induce an improvement in the levels of well-being of the poorest by decreasing the average distance to the poverty line. However, part of the positive effect is reversed when indirect taxes and subsidies are included. Given that households do not observe the benefit received by health and education services, this analysis does not include poverty calculations for final income.

<!-- Start of picture text --> FIGURE IV- 7: INCIDENCE OF URBAN AND RURAL POVERTY<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Total Urban Rural Total Urban Rural<br>Official moderate povery line Official extreme poverty line<br>Market income + Pensions Disposable income Consumable income<br>Source: World Bank Group staff estimates based on the 2017 ENPH<br><!-- End of picture text --> 32
_Séléka occupation in Bangui (2012, excluded, up to 2014, excluded). The subsequent period translates into localized conflicts areas in Northwestern and Eastern parts of the country, outside of major cities._

**Figure 7: Manufacturing value added (percent of GDP) in CAR and its synthetic control, 2009-**

<!-- Start of picture text --> 17<br><!-- End of picture text --> <!-- Start of picture text --> 130<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>Seleka occupation CAR Synthetic CAR<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>2009 10 11 12 13 14 15 16 17<br>Seleka occupation<br>Year<br>=2012)<br>Manufacturing value added (base 100<br>Gap Manufacturing value added (index 100 =2012)<br><!-- End of picture text --> _Source: Manufacturing value-added series are from the World Development Indicators_ (35) _, from 2009-17. Note: In upper chart, the blue (lighter in greyscale) line indicates actual manufacturing value-added series, and the orange (darker in greyscale) line indicates manufacturing value-added synthetic series. The grey bar indicates the beginning of the civil war up to the departure of Séléka rebel coalition from Bangui (from December 2012 to January 2014). In_ 18
**Figure 9:** Yearly Survival rates for firms (left) and products (right) in Senegal, Tanzania, Mali, Malawi and Lao PDR

<!-- Start of picture text --> 100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>2nd 3rd 4th 5th 6th 7th 8th 2nd 3rd 4th 5th 6th 7th 8th<br>year year year year year year year year year year year year year year<br>Senegal Tanzania Mali Senegal Tanzania Mali<br>Malawi Laos Malawi Laos<br><!-- End of picture text --> _Source:_ For Lao PDR, authors‟ calculation based on Lao PDR Customs data; for the African countries, Cadot, et al (2010) For all countries, the slope in the early stages is very similar, despite their different starting points. For firms, Malawi begins with the lowest rate (~30 percent) and Laos the highest (65 percent), but by the fourth year they are all in the 70-80 percent range, even though this means that Lao experience grew at a slower pace. A different picture emerges for products, where Laos starts off with an incredibly high rate (~75 percent, compared to 10 percent for Malawi) and then seems to level off quite rapidly, meaning that here too it grows at a slower pace. A cautious conclusion, given the short time period, is that Lao firms improve their survival rates at a relatively slower pace, albeit starting from higher levels.

We have seen in this section, through various methods, that exit rates for all units (firms, products, FPDs etc.) are very high, although in a similar range to those countries for which we have comparable data. In the next section we analyze survival per se and its determinants, with a view to drawing policy conclusions that might improve the rates of survival of Lao exports.

# **4 SURVIVAL AND INITIAL VALUE – REGRESSION ANALYSIS**

We proceed to identify some of the factors that help a firm‟s export activity last longer than one year as well as those which help firms to start an export activity (a new product or a new destination) at a higher initial value. This is performed through a regression analysis which requires us to carry out some modifications to the structure of the data.

First, transactions are aggregated to annual totals for unique firm-product-destination (FPD) triplets. The starting year is recorded as that in which the FPD first made its appearance in the data. A surviving quartet (firm-product-destination-starting year) is an FPD that has no activity in the previous year but which appears in the following one. This step essentially reduces the _Exporting from a small landlocked economy_ _17_
24 Consider the following exchange-control scenario in Culbertson's study: an overvalued currency produces a supply of foreign exchange; a black market price of foreign exchange emerges as the government allocates this quantity to the market (e.g. to importers, investors, travel abroad, etc.); consider further that the government may choose to add to its reserve holdings, in which case the supply of foreign exchange is reduced; or the government may choose to draw down its reserves, thus increasing the supply of foreign exchange. Consequently, changes in the level of foreign exchange holdings represent a potential influence on the parallel market rate.

**37.** Culbertson's study for **10** countries with long-existing exchange controls and black market activity lends support for the hypothesis that the black market rate depends on the level of the official rate, the foreign-to-domestic price level (which proxies the unobservable equilibrium rate), and government reserve-level policy. The strongest coefficients are those relating the black market rate to changes in the foreign-to-domestic price level. While the R' are relatively robust for most of the countries (see the following chapter for a discussion of purchasing power parities as conversion factors), the large degree of serial correlation is disturbing. This indicates the danger of inferring that only those variables with significant coefficients are important.

**38.** The supply of foreign exchange in this market may come from various sources. Overand underinvoicing of exports and imports could potentially attract significant amounts of foreign exchange to the black markets over time. Also, if the premium on foreign currency is significantly higher than the official rate, a significant proportion of remittances would
22 of household expenditures is smaller for female‐headed households; in all other countries, the share is the same or greater than for male‐headed households. Lastly, the expenditure share is greater in every country in rural areas than in urban areas.

Table 5: Expenditure share of monthly consumption of 30 kWh by location, quintile, poverty status, and gender of household head

|||All|househo|lds||Poo|r househ|olds|Househo|ld head|
|---|---|---|---|---|---|---|---|---|---|---|
|Country|Urban|Rural|Total|Q1|Q5|Urban|Rural|Total|Female|Male|
|Angola|0.1|0.2|0.1|0.2|0.1|0.1|0.2|0.2|0.1|0.1|
|Botswana|2.4|5.5|3.7|15.6|1.0|6.7|9.0|8.0|4.3|3.2|
|Burkina Faso|6.8|9.6|8.8|15.7|5.4|11.8|12.6|12.5|13.4|8.1|
|Côte d'Ivoire|1.4|2.2|1.8|4.6|0.9|2.8|3.3|3.2|2.4|1.7|
|Ethiopia|1.1|1.3|1.3|2.7|0.6|3.3|2.2|2.3|1.8|1.1|
|Ghana|1.1|1.8|1.4|3.1|0.8|2.6|3.0|2.9|1.6|1.3|
|Madagascar|6.8|10.4|9.6|18.8|5.3|9.9|11.8|11.6|13.8|8.6|
|Malawi|1.3|2.3|2.2|3.8|1.3|2.3|3.4|3.2|2.7|2.0|
|Mali|1.0|1.5|1.4|2.5|0.7|1.8|2.2|2.2|1.4|1.4|
|Mozambique|1.5|2.6|2.3|5.5|0.8|3.1|3.5|3.5|3.0|2.0|
|Niger|2.2|3.9|3.6|5.2|2.4|4.1|4.4|4.4|5.3|3.3|
|Nigeria|0.3|0.4|0.3|0.6|0.2|0.4|0.4|0.4|0.5|0.3|
|Rwanda|5.3|9.6|8.9|16.8|3.7|11.7|13.3|13.2|11.4|8.0|
|São Tomé and Príncipe|1.7|1.8|1.7|2.7|1.2|2.1|2.2|2.1|1.9|1.6|
|Senegal|2.1|3.7|2.9|6.5|1.9|3.7|5.0|4.6|2.6|3.0|
|Sierra Leone|3.8|5.9|5.1|8.7|3.1|5.6|7.1|6.8|5.1|5.1|
|South Africa<sup>a</sup>|1.0|1.6|1.2|2.8|0.3|2.1|2.0|2.1|1.3|1.1|
|Swaziland|1.7|2.0|1.9|4.2|1.0|3.1|2.6|2.7|1.9|1.9|
|Tanzania|1.2|2.2|1.9|3.7|1.0|3.4|3.6|3.6|2.3|1.7|
|Togo|4.1|9.0|6.9|16.4|3.5|6.6|11.1|10.0|9.3|6.3|
|Uganda|3.1|4.9|4.4|8.6|2.3|8.6|8.8|8.7|5.3|4.0|
|Zambia|2.1|5.2|4.1|8.6|1.4|4.1|6.2|5.9|4.9|3.8|
|Median|1.7|2.5|2.2|4.9|1.1|3.3|3.6|3.5|2.7|2.0|

_Source_ : World Bank staff calculations based on utility information and household survey data. a These numbers are for Johannesburg. Depending on eligibility, poor households are provided with 50, 100, or 150 kWh of free electricity a month in Johannesburg.

Table 6 presents results that are more easily compared to those of Briceño‐Garmendia and Shkaratan (2011), except that, thanks to significant progress in energy efficiency improvement, power consumption at the subsistence level has declined from 50 kWh a month to 30 kWh in the intervening years. The table shows the hypothetical expenditure shares for those with and without actual expenditures on electricity as well as for those with and without access to the grid (definition 1 on page 6). It also calculates the percentage of households for which the expenditure share lies below 5 percent. The expenditure shares for those who reported non‐zero expenditures on electricity lie close to the expenditure shares of those with access to the grid due to a considerable overlap between these groups. As expected, with the exception of Angola and Nigeria, a greater percentage of those not connected to the grid found electricity unaffordable than grid‐ connected households.
Table 7: Conditional estimations for services sector

||||DV: Fem|ale-owned ent|rant share|||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|||A. E|stimations usi|ng unorganiz|ed services sa|mple||
|Log total incumbent employment|0.002|0.003|0.000|0.002|-0.003|-0.003|-0.001|
|in district-industry|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Log female-owned incumbent|||||0.014+++|0.012+++|0.012+++|
|businesses in district-industry|||||(0.002)|(0.002)|(0.002)|
|Local Industrial Conditions by Incum|bent Firms:|||||||
|Index of labor market strength||-0.006||-0.017+++|||-0.016+++|
|for district-industry||(0.005)||(0.005)|||(0.005)|
|Index of labor market strength,|||0.014+++|0.018+++||0.004|0.008+|
|female-owned businesses|||(0.004)|(0.004)||(0.004)|(0.004)|
|Observations|4,292|4,292|4,292|4,292|4,292|4,292|4,292|
|Adjusted R-squared|0.232|0.232|0.235|0.237|0.241|0.241|0.242|
|||B.|Estimations u|sing complete|services sam|ple||
|Log total incumbent employment|-0.005+|-0.005|-0.006++|-0.005+|-0.009+++|-0.009+++|-0.008+++|
|in district-industry|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Log female-owned incumbent|||||0.013+++|0.012+++|0.011+++|
|businesses in district-industry|||||(0.002)|(0.002)|(0.002)|
|Local Industrial Conditions by Incum|bent Firms:|||||||
|Index of labor market strength||0.000||-0.008|||-0.007|
|for district-industry||(0.006)||(0.007)|||(0.007)|
|Index of labor market strength,|||0.013+++|0.015+++||0.006|0.007+|
|female-owned businesses|||(0.003)|(0.004)||(0.004)|(0.004)|
|Observations|4,458|4,458|4,458|4,458|4,458|4,458|4,458|
|Adjusted R-squared|0.218|0.217|0.221|0.221|0.226|0.226|0.226|

Notes: See Table 5. Conditional estimations include district-year and industry-year fixed effects.
# **Stringent employment protection legislation**

Employment protection regulation in Croatia is among the strictest in Europe.'<sup>8</sup> Laying-off redundant workers is difficult and costly due to both high procedural and monetary costs of dismissals. This highly rigid nature of the<sup>Croatian</sup> labor market is reflected in a high value of a composite index of the strictness of employment protection legislation (EPL) developed by OECD.1<sup>9</sup> Table 14 documents that employment protection legislation is stricter in Croatia than in any transition economy accessing EU (which tend to have relatively rigid labor market), and<sup>in fact</sup> stricter than in virtually all EU and OECD economies. It is noteworthy that EPL<sup>in</sup> Croatia is much stricter than in countries characterized by low unemployment<sup>flexible</sup> labor markets, such as Hungary among the transition economies, and<sup>Denmark,</sup> Ireland, the Netherlands, and the U.K. among the mature market economies. For illustration, the summary EPL index amounts to 1.1 in Ireland or to 1.7 in Hungary, compared with 3.6 in Croatia. This indicates a truly dramatic difference in<sup>termsof</sup> constraints facing employers who need to adjust the size and composition of<sup>their</sup> workforce. Hence, strict employment protection, such as in Croatia, does not prevent high unemployment (resulting from the cumulative growth of the number of<sup>persons</sup> who cannot enter the labor market). Conversely liberal employment protection does not necessarily lead to high unemployment, to the contrary it tends to be associated with better labor market outcomes (anglo-saxon countries - U.S. and U.K. - provide the best example of relatively low level of employment protection associated with<sup>low</sup> unemployment).

**18** Labor laws and regulations in Croatia are discussed in more detail in Fuenzalida (2002).

> **'9** The index is a weighted average of 22 indicators of the strictness of employment protection legislation in three areas: (1) regular contracts (with the weight of 5/12, (2) temporary contracts (5/12), and (3) collective dismissals (2/12). Regular contract are divided into (a) procedural<sup>inconveniences</sup> (1/3), (b) notice and severance pay (1/3), and (c) difficulty of dismissal (1/3). Temporary contracts include (a) fixed term contracts (1/2) and (b) temporary work agency employment (1/2). The index for collective dismissals is directly computed by averaging four indicators relating to<sup>special requirements</sup> and costs associated with collective dismissals. The higher the value of the summary index,<sup>thestricter</sup> the employment protection legislation. The methodology of calculation of the index is described in OECD (1999).

_-42-_
Policy Research Working Paper 7166

# **Abstract**

Romania faces an acute population crisis with an aging workforce and an increased number of emigrants particularly from the young, highly educated/skilled population. This paper uses a new cross-sectional data set of Romanian emigrants to find which factors are related to plans to return home permanently. The analysis pays particular attention to differences in expected earnings and skills and training acquired as a migrant. The study finds that higher expected earnings in Romania and investment in Romanian firms are positively correlated with plans to return migrate. Policies that boost productivity and therefore wages as well as policies that improve the business climate could therefore encourage Romanian migrants to return to Romania, moderating the negative consequences of the declining and aging population, and increasing the skill stock of the Romanian labor force.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sdavies@worldbank.org. <mark>.</mark> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
categories currently used by Statistics South Africa i.e., African, Coloured, Indian/Asian and White while the location variable is a dummy with urban=1 and rural=0. The province vector is a 9-category variable of the nine provinces in South Africa namely, Western Cape, Eastern Cape, Northern Cape, Free State, KwaZulu-Natal, Northwest, Gauteng, Limpopo, Mpumalanga. Industry dummies represent each of the nine one-digit groups i.e., agriculture, mining, manufacturing, utilities, construction, trade, transport, finance, and community services or other private household enterprises.

# _2.3.2_ Factors associated with being in a greener occupation

Restricting the sample to only those in a strictly green job, we estimate an ordinary least square (OLS) model of the factors associated with being in a greener occupation, as captured by the GTI, as per equation (3). = X′ + η + δ + θ (3) l ln YY is the log of the continuous measure of strictly green task intensity (GTI-strict) in ii iiββ+αpp ll tt ss + εεii o o o o and are individual characteristics gender, age, race, and level of education. ii Location, province, location, industry of employment and time dummies are represented by YY ii ii α , η , θ and ooll δ respectively. XX pp ll ss tt

# _2.3.3_ Do green jobs pay higher wages?

To understand whether there is a wage premium as a result of being in a green job, we estimate the commonly used wage regression model (Mincer 1974). We augment this regression model with industry dummies and our variable of interest, a binary variable of whether an individual is in a green job, as specified in equation 4. = X′ ′ + η + δ + θ + ⍴ (4) _lnWi_ is the log monthly wage of individual lnWWii iiββ+ Diiγγ+αpp ll tt _i_ , ss are individual characteristics (age, race, gender, CC + εεii education level). The binary variable _Di_ takes a value of 1 if the individual _i_ is in an occupation ii XX that has a positive share of green tasks (GTI-strict), and 0 otherwise. We estimate equation (4) again using the GTI-broad to indicate green jobs. The location, province, industry and time dummies are denoted by α , η , θ and δ respectively, and is the error term. We estimate two models, where the control variables differ across models. The first model pp ll ss tt ii εε only includes the variable of interest _Di_ and controls for province and time dummies. In the second model, we add controls for gender, age, education, and race ( ), as well as α (location) and ⍴ (vector of nine occupation dummies). Our sample includes working-age ii pp XX individuals (age 15-65) who are employed and report positive earnings. CC We create a variable to proxy for how skill intensive an occupation is, which we use in the descriptives section. The three skill categories follow SASCO 2003 and ISCO-88 classification as market outcomes and people’s livelihoods even decades after the demise of apartheid. During apartheid, discrimination was institutionalized with policies such as _the Bantu education act_ and _the job reservation act_ ensuring that Black (Coloured, Indian and African) South Africans received inferior education and could not access professional occupations.

10