rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Full population<br>es)<br>a ~<br>© _ = _ —~ -_— -_— ~<br>@ ee ee = -" =<br>o eee ross 7<br>£ =-<--7 -—<br>= oer<br>ct _—<—0777<br>oS —----7-77<br>= ———<br>ay<br>2 nn<br>a Totes ee ee<br>an Re eeeEe—=EE_<br>Pa a<br>ivtl ee AL Ab NO<br>a oe1 oe” opvi —p*;<br>—§ Upward mobile -====-+ Downward mobile =—-=--=-—=- Secure in both<br>Lines give the upper and lower bound shares per transition category. These are derived<br>fromNote aethat the lower panel bound estimatesof theofsecure boundsgroup on incomeis estimatedmobilityunder (Dang theandsame Lanjouw,mobility2017).scenario<br>as the upper bounds of the two mobile groups, and vice versa.<br><!-- End of picture text -->
**_Energy demand models for policy_** **_<u>formulation</u>_**

two simultaneous utility-maximization decisions – how much to allocate for different fuels and what fraction of the budget to be spent on energy as opposed to other needs. He used indirect translog utility function for the two-stage model and estimated the model using pooled time-series cross-section data for nine OECD countries.

A new trend in energy modeling and for residential energy demand studies started with the pioneering works of McFadden (1973).<sup>33</sup> Prior to his works and even now most models assume a continuum of decision options. However, in reality, many a times we are confronted with discrete choices: unless an appliance (say electric oven) is purchased, the demand for that end-use energy cannot exist for that consumer. Moreover, consumers do not buy more than one such item at a time, thereby putting a limit on the growth as well. In energy studies, logit models became quite popular – often as an alternative to the translog models. The other feature of these studies is their use of micro-data from survey or similar sources – which initiated a further new trend in the demand modeling.

More recent academic studies have relied on cointegration approach (and other advanced econometric methods) for demand analysis of the residential sector. Most of these studies tend to focus on the aggregate demand in the sector and are preoccupied with identifying the cointegrating relationships. Studies of this variety include, among others, the following: Clements and Madlener (1999), Beenstock et al (1999), Ziramba (2008), Holtedahl and Joutz (2004), Halicoglou (2007), Narayan and Smyth (2005) and Hondroyiannis (2004). In addition, studies using household survey data have also appeared (see for example Hirst et al (1982), Tuan and Lefevre (1996), and Pachauri (2004)).

A common problem with the studies on the residential energy demand using the econometric approach is the assumption of a representative consumer. Although in disaggregated models one representative consumer per group is used, still the process remains arbitrary and judgmental. Assimakopoulos (1992) suggested an approach of endogenously obtaining homogeneous groups of consumers using a two stage process: a

> 33 See Dubin and McFadden (1984), Baker and Blundell (1991) and Baker et al (1989) as well.

71
# **Annex 4: Ranking of Dirtiest Manufacturing Industries**

A conventional approach to defining dirty industries has been to identify pollution-intensive sectors as those that incur high levels of abatement expenditure per unit of output in the United States and other developed economies (Mani 1996;Tobey 1990). A more direct approach is to select sectors that rank high on actual emissions intensity. Mani and Wheeler (1998) determine the high-ranking sectors by this criterion using emissions intensities of medium U.S. manufacturing firms at the three-digit SIC level. They then compute average sectoral rankings for conventional air pollutants, water pollutants, and toxic substances as shown in the Table A4. Similar to the conventional approach, five of the six sectors with the highest overall ranks are iron and steel, nonferrous metals, industrial chemicals, pulp and paper, and nonmetallic mineral products. The strength of their approach lies in the fact that the set of dirtiest manufacturing industries appears to be fairly stable across countries and pollutants.

**Table A4: Dirty Industries** **<u>according to different Pollutants</u>**

|**Rank**|**Air**|**Water**|**Toxic/Metal**|**Overall**|
|---|---|---|---|---|
|1|Iron and Steel|Iron and Steel|Non-Fer Metals|Iron and Steel|
|2|Non-Fer Metals|Non-Fer Metals|Iron and Steel|Non-Fer Metals|
|3|Non-Fer minerals|Pulpandpaper|Industrial Chemicals|Industrial Chemicals|
|4|Petro Coal Prod|Mis Minerals|Leather Products|Petro Refineries|
|5|Pulpandpaper|Industrial Chemicals|Pottery|Non-Fer minerals|
|6|Petro Refineries|Other Chemicals|Metal Products|Pulpandpaper|
|7|Industrial Chemicals|Beverages|Rubber Products|Other Chemicals|
|8|Other Chemicals|Food Products|Electrical Products|Rubber Products|
|9|Wood Products|Rubber Products|Machinery|Leather Products|
|10|Glass Products|Petro Products|Non- Metal Minerals|Metalproducts|

_Source: Mani and Wheeler (1998)_ 28
leaders’ sample as our preferred prediction for the social costs of the program. In order to estimate WTA, we run a series of probit regressions to estimate equation 5 with different configurations of the matrix X j. Without any covariates, these estimations yield a γ that gives us an estimate of the median WTA across all of the communities. With additional conditioning, we can refine these estimates for subsamples of the population. For example, including state fixed effects as covariates gives separate median WTA estimates for each state. Further covariates permit the prediction of median WTA for communities with varying configurations of the covariates.

Table A9 contains the leader sample probit estimations used to predict willingness to accept. These estimates show specifications with no controls (column 1), with state fixed effects (columns 2-5), and with a full set of covariates intended to reflect the opportunity cost of participation, including forest type, ejidal wealth indices, primary economic activities, use of PSA land, and geographic characteristics. Beneficiaries seem more responsive to changes in the offer price than non-beneficiaries. We observe this also in a simple graph showing the proportion of yes responses against the difference between the offer and the actual payment (Figure A4), which illustrates a steeper drop for beneficiaries than non-beneficiaries when the net gains (the difference between the hypothetical and the asking price) increase. This helps justify our use of the coefficient estimates from the beneficiary sub-sample to predict non-beneficiary WTA in our simulations. The state fixed effects explain a significant amount of the variation in responses (compare columns (1) and (2) in Table A9). We use the estimated coefficients from this table (A9) to calculate γ and θ from equation 5 above, and then the median value of WTA as written in equation 6.

# 5 WTA estimates

Figure 2 and Table A10 show the averages of the estimated median values of community willingness to accept, calculated using both parsimonious specifications (the first three columns of Table A9), and a specification using a large set of relevant covariates to refine the prediction (the last column of Table A9). We also divide the calculations by sample, showing the full sample (Figure 2, panel a.) and the subsamples of beneficiaries (panels b. and d.) and non-beneficiaries (panel c.).

The sample average of the median WTA estimates ranges from 151 to 199 pesos per hectare. The lowest estimate comes from the non-beneficiary population. This could be explained either 26
The weights for the GLS specification are the predicted variances of the error terms from the OLS regression. We estimate the variance of the error term parametrically as specified in Elbers, Lanjouw, and Lanjouw (2003) and Nguyen, Corral, Azevedo, and Zhao (2017). The variance model has a logistic form with ( _ε_<sup>2</sup> _ch_<sup>)asthedepen-</sup> dent variable (equation 4). In equation 5, the dependent variable is the estimated variance, derived from the residuals of the first OLS regression. The explanatory variables of the variance model are chosen using the Least Absolute Shrinkage and Selection Operator (LASSO) method (Tibshirani (1996)).<sup>31</sup> The estimates from the LASSO regression and the residuals are then used to predict the variance of ( _εch_ ) (equation 6).

The predicted variances are used to re-estimate equation (2) using GLS (equation (7)).

Next, we estimate predicted welfare using estimates from the analysis in the first step. Since we would like to generate a joint distribution of welfare, and not solely the expected value, we use Monte-Carlo simulations to generate 100 simulated dis ̃ ̃ tributions of welfare. In equation (8), _β_<sup> ̃</sup> , _ηc_ and _εch_ are vectors from the simulations.

Following the literature on small area estimation, we make the following assumptions for the simulations. The vector _β_<sup> ̃</sup> is drawn from a normal distribution with mean ˆ _βGLS_ and variance Var(ˆ _βGLS_ ).

> 31In this method all district and household characteristics used in step 1 are included on the right- hand side. The resulting estimates include zero coefficients for several variables, thereby selecting the remaining variables with non-zero coefficients for the variance model.

12
Policy Research Working Paper 7508

# **Abstract**

Recent surveys show considerable progress in maternal and child health in Ethiopia. The improvement has been in health outcomes and health services coverage. The study examines how different groups have fared in this progress. It tracked 11 health outcome indicators and health interventions related to Millennium Development Goals 1, 4, and 5. These are stunting, underweight, wasting, neonatal mortality, infant mortality, under-five mortality, measles vaccination, full immunization, modern contraceptive use by currently married women, antenatal care visits, and skilled birth attendance. The study explores trends in inequalities by household wealth status, mothers’ education, and place of residence. It is based on four Demographic and Health Surveys implemented in 2000, 2005, 2011, and 2014. Trends in rate differences and rate ratios are analyzed. The study also investigates the dynamics of inequalities, using concentration curves for different years. In addition, a decomposition analysis is conducted to identify the role of proximate determinants. The study finds substantial improvements in health outcomes and health services. Although there still exists a considerable gap between the rich and the poor, the study finds some reductions in inequalities of health services. However, some of the improvements in selected health outcomes appear to be pro-rich.

This paper is a product of the Social Protection and Labor 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 aambel@worldbank.org.

_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
2

# **Active Labour Market Policies in the OECD and in Selected Transition Economies**

# Hartmut Lehmann

# I Introduction

Since the beginning of their transition to a market economy the economies of Central and Eastern Europe (CEE) experienced a sharp rise in open unemployment, reaching comparable levels to the worse performing economies of the OECD.<sup>2</sup> Having had continuous difficulties with the fight against inflation, governments in CEE had thus far few possibilities to reflate their economies and have tried from early on to combat the incidence of unemployment with active labour market policies (ALMPs). The rise of unemployment has, however, increasingly involved the "crowding out " of resources for ALMPs in favour of unemployment benefit payments.

Nevertheless, it seems worthwhile to discuss ALMPs in transition economies, as they can play a role at the margin in the fight against unemployment. Such policies are here understood in the narrow sense of policies targeted at the unemployed and those threatened by redundancies. Clearly, sound macroeconomic policies, policies inducing large foreign direct investment and the existence of a political and social consensus about how to distribute the costs of transition are crucial for rapid growth and thus more employment. The issue of modernisation of state-owned enterprises (SOE's) via foreign direct investment and its effect on employment will be briefly touched upon, citing the example of eastern Germany. The analysis is restricted also in so far as partial equilibrium effects of ALMPs will be the main interest when evaluating such policies. Given the very limited size of active policies in virtually all transition countries general equilibrium effects should for the most part be negligible.

There have been exhaustive surveys of ALMPs in OECD countries in the literature (most recently, OECD (1993), ch.2 and Bjorklund et al. (1991)). Section II contains a brief discussion of the merits of transplanting ALMPs developed to fight unernployment in mature market economies to CEE.

> 2 The main exception to this statement is the Czech Republic where open unemployment has stayed very low throughout the transition.
**Table 5: Reduced form probit model of the use of destructive fishing gear**

|_Independent_|_U_|_se Destructive Fishing _|_Gear _<br>_Marginal_|
|---|---|---|---|
|<br>_Variables_|_Coefficients_|_Standard Errors_|<br>_Probabilities_|
|Own destructive gear (IV)|4.463***|0.561|0.89***|
|Rent fishing gear|1.675***|0.244|0.54***|
|Fish reef species|0.932**|0.395|0.20***|
|Fish pelagic species|0.274|0.267|0.05|
|Large boat/dhow|0.130|0.344|0.03|
|Outrigger or dugout canoe|0.058**|0.028|0.02**|
|Fishing primary activity|0.004|0.267|-0.01|
|Food secure|-0.424**|0.223|-0.10**|
|Household size|0.080**|0.042|0.02**|
|Female head of household|0.900***|0.255|0.26***|
|Mean male education|-0.077**|0.038|-0.02**|
|Mean female education|0.041|0.027|0.01|
|Own land|-0.246|0.275|-0.05|
|Land area cultivated|-0.005|0.023|-0.00|
|Value of physical assets|-3.93e<sup>-07</sup>**|1.62e<sup>-07</sup>|-0.00**|
|Access to credit|0.433**|0.199|0.10**|
|Participates in meetings|0.116*|0.068|0.02*|
|Participates in AIGA|-1.032***|0.311|-0.13***|
|Aware of MPA|0.025|0.179|-0.01|
|MPA village|-0.320|0.198|-0.06|
|Observations|655||655|
|RSquared/Loglikelihood|0.5931|-155.17||

Notes:

*** , **, * indicate significance at the 99, 95, and 90 percent confidence interval, respectively Robust standard errors reported, adjusted for clustering effect Coefficient for dummy variables for each of the MPAs and constant not reported For continuous variables, marginal probabilities measure the marginal change in probability at the variable’s mean. For discrete variables, the marginal probabilities should be interpreted as the change in probability for a change of the dummy variable from 0 to 1

32
||PPR|Working Paper Series|||
|---|---|---|---|---|
||Title|Author|Date|Contact|
|WfS74|Fiscal Stabilization<br>and Exchange Rate<br>Instability: A Theoretical<br>Approach<br>and Some Policy Conclusions<br>Using<br>Mexican Data|Andrew Feltenstein<br>Stephen Morris|August 1988|A. Bhalla<br>60359|
|WPS75|Welfare Dominance and the Design of<br>Excise Taxation In the Cote d'lvoire|Shlomo Yitzhaki<br>Wayne Thirsk|August 1988|A. Bhalla<br>60359|
|WPS76|On the Shadow Price of a Tax<br>Inspector|Shlomo Yitzhakl<br>Yitzhak Vakneen|||
|WPS77|<br>Incentive<br>Policies and Agricultural<br>Performance in Sub-Saharan<br>Africa|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS78|Economists, Institutions<br>and Trade<br>Restrictions: A Review Article|J. Michael Finger|||
|WPS79|Quantitative<br>Appraisal of<br>Adjustment<br>Lending|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS80|Emerging Issues of Privatization<br>and the Public Sector|Samuel Paul|September 1988|<br>E. Madrona<br>61711|
|WPS81|Reaching People at the Periphery:<br>Can the World Bank's Population,<br>Health, and Nutrition Operations<br>Do Better?|Richard Heaver|||
|WPS82|Microeconomic<br>Theory of the House-<br>hold and Nutrition Programs|Dov Chernichovsky<br>Linda Zangwill|September 1988|<br>S. Ainsworth<br>31091|
|WPS83|Welfare Costs of U.S. Quotas in<br>Textiles, Steel, and Autos|Jaime de Melo<br>David Tarr|September 1988|<br>C. Cabana<br>61539|
|WPS84|Black Markets for Foreign Exchange,<br>Real Exchange Rates and Inflation:<br>Overnight vs. Gradual Reform in<br>Sub-Saharan<br>Africa|Brian Pinto|||
Table A12: The Complementary Effect of Internet and Roads on Firms Operations and Productivity

||(1)|(2)|(3)<br>**WBES**|(4)|
|---|---|---|---|---|
|Variables|**Telecommu-**<br>**nication is an**<br>**obstacle to**<br>**operation**|**Losses as**<br>**percentage**<br>**of annual**<br>**sales**|**percentage**<br>**owned by the**<br>**governemnt**|**Secured a**<br>**contract with**<br>**governemnt**<br>**in the last fscal year**|
|Submarine internet|-0.0678***<br>(0.00976)|-2.293***<br>(0.267)|1.268***<br>(0.0477)|0.172***<br>(0.0152)|
|Proximity to road (KM)|0.0362***|-0.712***|-0.295***|-0.00146|
||(0.00115)|(0.0248)|(0.00792)|(0.00421)|
|Submarine internet * road|-0.00361***<br>(0.00129)|-0.146***<br>(0.0451)|0.0847***<br>(0.00254)|0.0111***<br>(0.00365)|
|N|13473|11742|14525|6768|
|ymean|0.270|9.322|0.757|0.183|
|r2|0.139|0.0949|0.0301|0.167|
|r~~2~~<br>~~a~~|0.135|0.0892|0.0252|0.160|
|Kleibergen-Paap F-statistic|1.096|1.059|1.094|1.362|
|_P > F_|0|0|0|0|
|Cragg-Donald Wald F-statistic|1181.8|1051.7|1282.4|394.2|
|Windmeijer F-statistic|25.59|18.95|25.39|12.39|
|Kleibergen-Paap Wald F-statistic|25.99|18.95|25.39|183.9|
|Age of the frm|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|
|Grid (20*20) connectivity FE|✓|✓|✓|✓|

_Robust standard errors in parentheses clustered at the grid level. ∗p < ._ 10 _∗∗p < ._ 05 _∗∗∗ p <_ 0 _._ 01 _. We use a strategy that combines difference-in-differences and instrumental variables techniques. The causal effect of Internet is estimated using difference-in-differences, while the effects of the road and its interaction with the Internet are estimated using an instrumental variables approach. Our instruments are: the distance to the hypothetical road, the interaction between the distance to the hypothetical road and an indicator for whether an individual (or a firm) is connected to submarine Internet, lightning, and terrain elevation._ 62
|P084315|2004|2008|11/13/2007|9 Closed|Comoros|
|---|---|---|---|---|---|
|P084315|2004|2008|6/27/2008|10 Closed|Comoros|
|P084315|2004|2009|8/31/2008|11 Closed|Comoros|
|P084315|2004|2009|3/16/2009|12 Closed|Comoros|
|P084315|2004|2009|6/18/2009|13 Closed|Comoros|
|P084315|2004|2010|9/24/2009|14 Closed|Comoros|
|P084315|2004|2010|12/29/2009|15 Closed|Comoros|
|P084315|2004|2010|6/25/2010|16 Closed|Comoros|
|P084315|2004|2011|11/21/2010|17 Closed|Comoros|
|P084315|2004|2012|9/14/2011|18 Closed|Comoros|
|P084317|2005|2005|2/25/2005|2 Closed|Congo,Republic of|
|P084317|2005|2005|5/5/2005|3 Closed|Congo,Republic of|
|P084317|2005|2006|10/5/2005|4 Closed|Congo,Republic of|
|P084317|2005|2006|5/10/2006|5 Closed|Congo,Republic of|
|P084317|2005|2007|12/11/2006|6 Closed|Congo,Republic of|
|P084317|2005|2007|3/28/2007|7 Closed|Congo,Republic of|
|P084317|2005|2008|12/21/2007|8 Closed|Congo,Republic of|
|P084317|2005|2008|6/27/2008|9 Closed|Congo,Republic of|
|P084317|2005|2009|8/31/2008|10 Closed|Congo,Republic of|
|P084317|2005|2009|2/28/2009|11 Closed|Congo,Republic of|
|P084317|2005|2009|6/18/2009|12 Closed|Congo,Republic of|
|P084317|2005|2010|1/15/2010|13 Closed|Congo,Republic of|
|P084317|2005|2010|6/29/2010|14 Closed|Congo,Republic of|
|P084317|2005|2011|3/14/2011|15 Closed|Congo,Republic of|
|P084317|2005|2012|10/17/2011|16 Closed|Congo,Republic of|
|P084317|2005|2012|5/15/2012|17 Closed|Congo,Republic of|
|P084317|2005|2013|12/30/2012|18 Closed|Congo,Republic of|
|P084317|2005|2013|6/24/2013|19 Closed|Congo,Republic of|
|P084317|2005|2020|8/12/2019|20 Closed|Congo,Republic of|
|P084329|2004|2004|6/22/2004|1 Closed|Afghanistan|
|P084329|2004|2005|5/7/2005|3 Closed|Afghanistan|
|P084329|2004|2006|12/19/2005|4 Closed|Afghanistan|
|P084329|2004|2006|6/5/2006|5 Closed|Afghanistan|
|P084329|2004|2007|12/18/2006|6 Closed|Afghanistan|
|P084329|2004|2020|4/22/2020|7 Closed|Afghanistan|
|P084353|2003|2008|8/31/2007|1 Closed|Philippines|
|P084404|2008|2008|12/20/2007|1 Closed|Mozambique|
|P084404|2008|2008|6/2/2008|2 Closed|Mozambique|
|P084404|2008|2009|12/24/2008|3 Closed|Mozambique|
|P084404|2008|2009|6/23/2009|4 Closed|Mozambique|
|P084404|2008|2010|12/22/2009|5 Closed|Mozambique|
|P084404|2008|2010|6/18/2010|6 Closed|Mozambique|
|P084404|2008|2011|3/28/2011|7 Closed|Mozambique|
|P084404|2008|2012|1/8/2012|8 Closed|Mozambique|
|P084404|2008|2013|7/22/2012|9 Closed|Mozambique|
|P084404|2008|2013|4/12/2013|10 Closed|Mozambique|
|P084404|2008|2014|11/30/2013|11 Closed|Mozambique|
markups can explain. The results show that markup dispersion can account for between 4% 2 (China) and 18% (Colombia) of the observed _σ_ ( _arpk_ ) . Although the shares are generally significant (except in China), these findings seem to suggest that markup heterogeneity is unlikely to be the primary force behind the unexplained _arpk_ dispersion.

Table 4: Heterogeneous Markups and Technologies

|||M|oments||Effe<br>(% of|ct on_σ_<br>2 (_arpk_)<br>total_σ_<br>2 (_arpk_))|
|---|---|---|---|---|---|---|
||_σ_<br>2 (_arpm_)|_σ_<br>2 (_arpk_)|_σ_<br>2 (_arpn_)|cov(_arpk, arpn_)|Markups|Capital Elasticities|
|ARG|-|0.55|0.31|-0.01|-|0.32<br>(58%)|
|BRA|-|0.74|0.52|0.09|-|0.40<br>(54%)|
|CHN*|0.05|1.37|0.76|0.41|0.05<br>(4%)|0.48<br>(37%)|
|COL*|0.22|1.49|0.87|0.40|0.22<br>(18%)|0.59<br>(48%)|
|MEX*|0.13|1.18|0.50|0.31|0.13<br>(12%)|0.35<br>(33%)|
|MYS|-|0.93|0.49|0.35|-|0.25<br>(27%)|
|TWN|-|0.62|0.30|0.18|-|0.20<br>(32%)|
|THA|-|0.95|0.76|0.22|-|0.48<br>(51%)|
|TUR|-|0.84|0.47|0.09|-|0.41<br>(49%)|
|JPN|-|0.56|0.29|0.08|-|0.24<br>(44%)|
|USA*|0.06|0.52|0.35|0.23|0.06<br>(14%)|0.16<br>(38%)|

_Notes_ : This table reports estimates of (within-industry) dispersion in firm-level markups and production technologies. The first panel displays the data moments used for these calculations – dispersion in the average revenue products of materials ( _arpm_ ), capital ( _arpk_ ) and labor ( _arpn_ ), and the covariance of the _arpk_ and _arpn_ . For the countries denoted with an asterisk, the _arpk_ and _arpn_ moments pertain to markup-adjusted average revenue products, calculated by subtracting the firm-specific markup (measured by _arpmit_ ) from _arpkit_ and _arpnit_ , as detailed in equations (4) and (5). The second panel of the table displays the contributions of the two forms of heterogeneity to the dispersion in _arpk_ . Values in parentheses express these contributions as percentages of total observed dispersion.

**Technology heterogeneity.** Next, we analyze the contributions of dispersion in production technologies. Specifically, we generalize the production function to allow for firm-specific output elasticities of capital and labor. The key insight that informs our analysis is the observation

14