rafmacalaba/datause-encoder-data huggingface dataset:
**Table 13 Distribution of employment a) by job tenure: Croatia against selected countries;** percentages

||Under1<br>year|1and<br>under2<br>years|2and<br>under5 <br>years|5and<br>under10<br>years|10and<br>under20<br>years|20<br>years|Average<br>tenure<br>(years)|Median<br>tenure<br>(years)|
|---|---|---|---|---|---|---|---|---|
|Croatia(2001)|9.7|5.1|17.2|21.3|20.7|26.0|12.2|8.0|
|Privatesector|13.0|7.1|21.2|21.7|17.0|20.0|10.8|6.0|
|Publicsector|4.9|2.2|11.8|21.3|26.0|33.8|14.1|12.0|
|Underprivatization|2.1|0.5|6.6|12.4|30.9|47.5|17.3|19.0|
|**1-49**|15.3|7.8|23.7|21.9|14.5|16.7|9.2|5.4|
|**50-199**|6.7|3.1|13.8|19.9|24.5|31.9|14.3|13.3|
|200+|4.4|3.0|11.3|18.9|26.2|36.2|15.6|15.7|
|_Othertransitioneconom_|_ies_||||||||
|Bulgaria(2001)|14.0|9.5|25.2|20.8|19.8|10.8|8.1|5.5|
|Privatesector|20.6|13.0|32.0|18.5|10.6|5.9|5.6|3.5|
|Publicsector|7.3|5.7|17.6|23.2|30.0|16.2|10.8|8.5|
|CzechR.|19.2||36.6|12.0|14.8|17.4|9.0|2.0|
|Lithuania(2001)|15.4|8.9|21.6|25.4|16.8|11.9|8.3|5.0|
|Poland(1999)|14.5|11.7|19.0|17.7|20.3|16.7|9.6|6.2|
|_Marketeconomies_|||||||||
|Denmark|25.1|11.4|16.2|18.2|17.7|11.4,|7.9|4.4|
|France|15.0|8.0|17.7|17.4|23.3|18.7|10.7|7.7|
|Germany|16.1|9.4|22.0|17.2|18.4|17.0|9.7|10.7|
|Spain|35.5|4.9|11.1|14.4|17.7|16.5|8.9|4.6|
|UnitedKingdom<br>|19.6|10.7|19.5|23.5|17.3|9.4|7.8|5.0|
|UnitedStates|26.0|8.5|20.0|19.8|16.8|9.0|7.4|4.2|

> Note: data for the OECD countries refer to 1995.

> a) Wage and salary workers Sources:

> Croatia: Central Bureau of Statistics, Author's calculations. Bulgaria: LFS June 2001, Bank staff calculations.

> Lithuania and Poland: Rutkowski (2002)

> OECD countries - OECD Employment Outlook 1997

> The proportion of workers with job tenure less than one year (which is a

> common indicator of labor market dynamics) is much lower in Croatia than in both transition and market economies. In Croatia new hires account for less than 10

> percent of all employees. In the restructuring transition economies this proportion varies from 14 to 19 percent. But the most striking is the comparison with dynamic market economies characterized by low hiring and firing costs. In Denmark and the U.S. new hires account for some one-fourth of employment. In Spain, thanks to the

> liberalization of rules governing the use of fixed-term contract, new hires account for

> over one-third of total employment. This comparison clearly shows the extremely

_-40-_
# Using Poverty Maps to Improve the Design of Household Surveys: The Evidence from Tunisia

_Gianni Betti Vasco Molini_<sup>_1_</sup> _Dan Pavelesku_ University of Siena The World Bank The World Bank Siena, Italy Rabat, Morocco Algiers, Algeria <u>gianni.betti@unisi.it vmolini@worldbank.org dpavelesku@worldbank.org</u> JEL: C83, D63, C81. Key <mark>word</mark> s: Survey design, implicit stratification, poverty map

> 1 Corresponding author
**Figure 2:** Electoral and Bureaucratic Turnover

<!-- Start of picture text --> (a) New village head in survey data (b) Tenure of village head (years)<br><!-- End of picture text --> <!-- Start of picture text -->
-.2 -.1 0 .1 .2 -.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger Margin of victory of best-ranked challenger<br>(c)  % New appointments (d)  Bureaucrat parent served in village gov.<br>-.2 -.1 0 .1 .2 -.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger Margin of victory of best-ranked challenger<br>6<br>0.5<br>4<br>0.2<br>-0.1 1<br>New village head in survey data Tenure of village head (years) -2<br>-0.3<br>-0.6 -4<br>0.7 0.3<br>0.4 0.1<br>0.1 -0.1<br>Bureaucratic turnover<br>-0.2 -0.3<br>Parent served in village government<br>-0.5 -0.5<br><!-- End of picture text -->

_Notes_ : Panel (a) looks at the probability that the village head in our survey sample is a different individual from the incumbent candidate competing in the most recent village election. Panel (b) looks at the number of years in office of the village head in our sample. Panel (c) looks at the village-level fraction of bureaucrats (excluding the village head) who began in their current position since the last election. Panel (d) looks at the probability that bureaucrats have a parent who served in the village government. The dots are conditional means of each outcome across binned intervals of the margin of victory of the best-ranked challenger on each side of the RD threshold, with 95% confidence intervals in solid gray lines.

28
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**Figure 16. Components of government revenue, upper‐middle income countries (% of GDP)**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Indirect Taxes Social security contributions Direct taxes Other taxes Non tax revenue<br><!-- End of picture text --> Source: Authors based on CEQ Data Center. Notes: Data for Brazil 2019 covers only Central Government.

Brazil's comparative results regarding middle-income countries show that it is a country that reduces inequality the most when including all taxes and transfers and the education and health benefits (Figure 17). Brazil in 2009 was less successful in reducing inequality, excluding education and health, even when the country's results were above the average. In contrast, the drop in the Gini coefficient in 2019 is lower when including only direct taxes, indirect taxes, and direct transfers; still, it is among the three highest.

**Figure 17. Inequality reduction, upper‐middle income countries (Gini coefficient points) From Market income plus pensions to Consumable From Market Income plus pensions to Final**

<!-- Start of picture text --> 12.0<br>10.0<br>Brazil (2019),<br>6.4<br>8.0<br>6.0 Brazil (2009),<br>3.1<br>4.0 3.3<br>2.0<br>0.0<br>consumble income (Gini points)<br>Gini coefficient reduction from Market Income plus pensions to Fiji (2019)<br>Turkey (2016) Paraguay (2014) Guatemala (2014) Peru (2011) Armenia (2011) Russia (2014) Moldova (2017) Belarus (2015) Albania (2015) Jordan (2017) Dominican Republic (2018) Ecuador (2011) Brazil (2009) Botswana (2010) Mexico (2014) Colombia (2017) Namibia (2016) Costa Rica (2018) Mauritius (2017) Brazil (2019) Argentina (2017) South Africa (2015)<br><!-- End of picture text --> <!-- Start of picture text --> 25<br>Brazil (2019),<br>20 15.6<br>Brazil (2009),<br>14.3<br>15<br>9.4<br>10<br>5<br>0<br>final income (Gini points)<br>Fiji (2019)<br>Gini coefficient reduction from Market Income plus pensions to Armenia (2011) Moldova (2017) Paraguay (2014) Guatemala (2014) Albania (2015) Russia (2014) Jordan (2017) Belarus (2015) Turkey (2016) Peru (2011) Ecuador (2011) Dominican Republic (2018) Mauritius (2017) Colombia (2017) Botswana (2010) Namibia (2016) Costa Rica (2018) Mexico (2014) Brazil (2009) Brazil (2019) Argentina (2017) South Africa (2015)<br><!-- End of picture text --> Source: CEQ Data center.

37
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33
_EL_ is the ratio of water levy actually paid to water levy that should be paid; S is the matrix of time-varying variables which consist _of: Profem_ (profit per number of employees); _Lref_ (lag refund / lag levy paid); _Ratwtop_ (pollution control operation cost / total operating cost); _Cmpaccon_ (complaints or accidents or conflicts with local communities); _bi, di,_ **_w'_** are firm specific effects in the first, second, and third equation; **_Vit,_** _mit,_ **_Uit_** are the usual error terms.

The following assumptions are made in estimation:

- the firm specific effects are random;

- the three error terms are uncorrelated and well behaved;

- the lagged pollution variable is uncorrelated with the error term in the first equation and correlated with the corresponding error term in the second equation;

- all the right-hand side variables in the first equation are doubly exogenous; that is, uncorrelated with the firm specific effects as well as with the error term;

- inspection is an endogenous variable in the second equation;

- inspection and pollution are endogenous variables in the third equation.

This system of equations is recursive and dynamic. Since the model is recursive, we can estimate it equation by equation.8 Here, however, we are interested in the third equation. To eliminate the individual effects we transform equation (4) into the following:

- _f"-1/_<sup>_2_</sup> _EL =fT1 Ql 2c + Q- 11 2X ,Q, + 0- 112_ **_Zi, 2_** _+_ **_Q- 2 Si,03 +_**<sup>_1_</sup> _- /_<sup>_2_</sup> _PO,0 4_<sup>**_+_**</sup> _(5) 112insp,o_ + Q-1/2e,

where variables are defined as above, and _eit_ is the new error term, defined as the sum of the firm random specific effects (ai ) and the regular error term (ui, ). The matrix omega is the appropriate matrix to eliminate the individual effects. It is constructed as follows:<sup>9</sup>

> _8_ See Lahiri and Schmidt (1978) for further details.

> 9 See, for example, Ahn and Schmidt (1995) for details.

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55
increased class sizes, valued at $4.99. But these changes were likely more than compensated by an average government grant of $11.90, yielding a net gain of $6.51 per pupil. Given the inequality in initial fees and pupil-teacher ratios however, wealthier households stood to lose much more, and our calculations suggest all other socio-economic groups in our data experience net losses in per pupil funding under FPE.

However, the EMIS data on changes in pupil-teacher ratios underlying Scenario 1 are potentially problematic during this period. The system of per pupil grants introduced under FPE may have provided an incentive to local school officials to exaggerate enrollment figures. The total enrollment recorded in the EMIS data rose 12% per annum from 5.70 million pupils in 2002 to 7.11 million pupils in 2005. In contrast, the gross enrollment rates from the household survey data analyzed in the previous section imply an annual increase of only 2.2% annual increase, yielding a smaller absolute change in total pupils over a longer time span (from 6.63 million in 1997 to 7.85 million in 2006). If we apply the growth rate of enrollment from the household surveys to the initial enrollment level in the administrative data, this implies a downward revision of pupil-teacher ratios in 2005 by 14.4%.

Scenario 2 is identical to Scenario 1, but using the enrollment growth rates reported in the household survey data. Once again, poorer households experience funding increases under FPE while richer households experience losses, but the crossing point has shifted upward, with only households whose head has some secondary or tertiary education experiencing losses. Scenario 3 explores an alternative assumption: no socio-economic sorting before FPE, such that households from all groups enrolled in schools with equal pupil-teacher ratios on average. Under this scenario, FPE implies a reduction in per pupil funding only for the wealthiest households, whose head had some tertiary education.

Figure 2c summarizes the net changes in funding per pupil under these three scenarios. A few general conclusions appear quite robust. The impact of FPE on funding levels in public schools was likely heterogeneous across socio-economic groups. These heterogeneous changes in funding were pro-poor. Furthermore, the net change for the wealthiest households was likely negative, as capitation grants failed to fully compensate the large reduction in fees paid by wealthier house27
mainly due to the availability of new technologies in soybean production (GMO seeds plus the diffusion of zero tillage techniques<sup>2</sup> ) that were so important as to increase the profitability of the agricultural sector on average. Biofuels played a minor role in this development, though gaining some importance in recent years.

Environmentalists and agricultural experts have raised concerns about the deforestation that accompanied the expansion of soybean area in the Northern provinces of Argentina. In their opinion, the expansion of soy production over the past several years has fuelled deforestation, poor water resource management and increased land degradation (World Bank, 2009). In response, producers ́ organizations have pointed out that rotation practices have not been abandoned and that the spread of “zero tillage” practices compensates for the damages when combined with adequate fertilizer and agrochemicals adoption. However, the growth of soybean area in comparison to cereals or livestock created concerns on the possibility of persistent mono-cropping. These facts prompted interventions in the market through subsequent increases of export taxes on soybean grain, thus reducing price incentives to production of the crop. At the same time, due to the rally in international food prices, wheat and corn exports were banned transitorily. As a result, the effects on wheat and corn outweighed the diminished soybean profitability and soybean crop share kept its increasing trend in production.

Argentina started biodiesel production on a large scale in 2006. Bioethanol from sugarcane or corn did not start to develop until 2010. Previously, only anecdotal cases of biofuel production could be found. They consisted of a few producers that used own grains and oilseeds as fuels for selfconsumption through simple transformation methods. In the case of ethanol from sugar cane, a previous failed experience of mandatory blending took place between 1984 and 1988.

The rapid development of biodiesel in comparison to ethanol shows a clear response of economic agents involved in the agro-industrial activity to market incentives. These incentives became apparent to investors in the early 2000s and were the following: (i) increasing international prices of biofuels attracting new investments to the value chain of an already highly competitive domestic industry of soybean oil, (ii) attractive (but not fully secure) demand from markets such as the EU, with

> 2 Zero tillage is a planting system to improve soil conservation where the new crop is planted stubble of the previous crop with even less soil disturbance than with minimum tillage. The importance of this agronomic practice in Argentina is reflected by the existence of an influential NGO of producers promoting its adoption: www.aapresid.org.ar

4
Within the GTAP framework we endogenously determine the duty rate consistent with the actual change in imports resulting from the changes in domestic productivity and in the SSM duties. Q-SSM duty changes are modelled with the three-tiered structure in the Q-SSM proposal. Using GEMPACK’s complementary slackness feature, we allow duties to be increased to the allowed limit for each level of import surge (110, 115 or 135 per cent of base imports). No duty is imposed unless imports from a particular supplier reach 110 percent of the previous 3 years’ average; if these imports are between 110 and 115 percent of the 3-year average, a duty which is the greater of 25 percent or 25 of the existing bound rate is imposed on top of existing tariffs. If imports from a particular supplier exceed 115 percent of the 3-year average, duties are raised to 40 percent or 40 percent of the existing bound rate. Finally, we allow for a duty of 50 percent, or 50 percent of the bound rate, when imports reach 135 percent of the historical average.

# **Micro model**

For each run of our Monte Carlo simulation, we collect all of the relevant outputs from the macro model and use them as inputs in our household-level model. Specifically, we apply the productivity change as a shock to the output of each crop produced by the household; and changes in domestic producer and consumer prices and wages to the households in our survey sample. Using the resulting impacts on the distribution of income, we calculate changes in national poverty rates following the approach of Ivanic, Martin and Zaman (2011), including second-order impacts for household consumption. This method can be simply described as follows: in the first step, we calculate a compensating variation measure of the implications of price and wage changes for household welfare. In the next step, we evaluate the number of households whose new welfare level places them below the poverty line. Finally, we use household weights to calculate an updated poverty rate.

# **Global extrapolation**

We extrapolate the results from our sample of 31 countries into global estimates of poverty, change expressed in millions of people. We do so by using our sample of 31 countries with their respective population weights following the methodology in Anderson et al (2013). Because our sample is fairly representative of Sub-Saharan Africa and includes almost all the South Asian countries, we are able to extrapolate global poverty estimates with relatively small standard errors.

10