rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9272

# **Abstract**

This paper reports on electronically-monitored improved use of the “Mirt” biomass stove in Ethiopia over a relatively long period of three-and-a-half years, using stove use data collected at five points in time. The results show that 62 percent of the households surveyed still retained their stoves after more than three years, which is a low level of abandonment, as the lifetime of the Mirt stove is approximately five years. Dis-adoption of the stove is not correlated with any of three monetary incentives provided at the time of distribution. With and without adjusting for dis-adoption, no longer-run differences in stove retention are found across treatments. Among those who retained their stoves, average regular stove use increased over time, but generally it is statistically the same toward the end of the first year. Thus, despite the relatively long timeframe, no decline is observed in regular usage. Comparing the persistence of the treatment effects, the paper finds that, in the longer run, subsidizing the cost most effectively promotes increased regular use over time.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at mtoman@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
3 Section III analyses the mix of passive and active labour market policies and the specific ALMPs instruments in economies of transition. The Czech Republic, Hungary and Poland will be discussed as concrete examples.

The evaluation of ALMPs in these countries will be attempted in section IV. As data availability and quality varies, this will be done using different approaches. For Hungary evaluation studies using micro data have already been undertaken and the main results of these studies will be presented and discussed. For Poland augmented matching functions will be estimated using administrative data with the aim to establish whether there is a positive correlation between the level of participants in a measure and the flow of hirings. Additionally, "problem groups" among the unemployed will be identified using stock variables and estimated transition probabilities calculated from the Polish LFS data. Furthermore, the raw data from the Supplement on Labour Market Policies attached to the August 1994 Polish LFS (Lehmann and Wadsworth, 1995) will be used for a preliminary evaluation of Polish ALMPs. The situation with Czech micro data availability looks somewhat less promising. To my knowledge there are no serious attempts thus far to evaluate the effectiveness of Czech ALMPs, using such data. Burda and Lubyova (1995) have estimated augmented matching functions for the Czech Republic using regional administrative data. They show convincingly that ALMPs could have only marginally affected the unemployment rate in that country. Especially interesting is, therefore, a general assessment of the situation in the Czech labour market. This assessment tries to identify all the determinants of the low unemployment rate. The effects of Czech ALMPs will then be seen in this broader context.

The possible lessons that the Russian Federation might learn from the experience of the three above mentioned Central European countries will be discussed in Section V while section **VI** concludes.

# **11 The Applicability of OECD Active Labour Market Policies in Transition Economies**

In OECD countries the following programme categories are considered part of ALMPs:

**a.** public employment services ("job brokerage") and administration;

**b.** labour market training;

**c.** youth measures;
**<u>Table 7.</u>** <u>Effects of gender role attitudes and personality traits on labor productivity (men sample)</u>

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Norms|Norms+<br>Controls|Norms+<br>Controls+<br>Personality|_Norms+_<br>_Controls+_<br>_Impulsive_|_Norms+_<br>_Controls+_<br>_Work_<br>_Central_|_Norms+_<br>_Controls+_<br>_Tenacity_|_Norms+_<br>_Controls+_<br>_Polychronici_<br>_ty_|_Norms+_<br>_Controls+_<br>_Locus of_<br>_Control_|_Norms+_<br>_Control+_<br>_Achieveme_<br>_nt_|_Norms+_<br>_Control+_<br>_Power_<br>_Motivation_|_Norms+_<br>_Controls+_<br>_Organized_|_Norms+_<br>_Controls+_<br>_Optimism_|
|Traditional|-0.020***|-0.013*|-0.013|0.023|-0.005|-0.063*|-0.020|-0.036|-0.010|0.052**|-0.031|-0.002|
||(-3.03)|(-1.97)|(-1.54)|(0.75)|(-0.15)|(-1.89)|(-0.59)|(-1.38)|(-0.25)|(2.21)|(-0.97)|(-0.03)|
|Impulsive|||0.091|0.716|||||||||
||||(0.53)|(1.36)|||||||||
|Work centrality|||0.044||0.188||||||||
||||(0.33)||(0.51)||||||||
|Tenacity|||-0.150|||-0.604|||||||
||||(-1.20)|||(-1.61)|||||||
|Polychronicity|||-0.033||||-0.147||||||
||||(-0.23)||||(-0.23)||||||
|Locus of control|||-0.045|||||-0.367|||||
||||(-0.34)|||||(-0.94)|||||
|Achievement|||-0.017||||||0.210||||
||||(-0.09)||||||(0.44)||||
|Power motivation|||0.283**|||||||1.098***|||
||||(2.05)|||||||(3.73)|||
|Organized|||0.052||||||||-0.107||
||||(0.39)||||||||(-0.30)||
|Optimism|||0.137|||||||||0.196|
||||(0.76)|||||||||(0.23)|
|Traditional*Impulsive||||-0.015|||||||||
|||||(-1.34)|||||||||
|Traditional*Work centrality|||||-0.002||||||||
||||||(-0.23)||||||||
|Traditional*Tenacity||||||0.013|||||||
|||||||(1.50)|||||||
|Traditional*Polychronicity|||||||0.003||||||
||||||||(0.21)||||||
|Traditional*locus control||||||||0.007|||||
|||||||||(0.80)|||||

36
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Zambia’s infrastructure situation is more hopeful than that of many other African countries. Infrastructure spending needs—though large—are not beyond the realm of possibility, and Zambia’s resource wealth and relatively well-off population provide a more solid financing basis than is available to many other countries. Zambia’s infrastructure funding gap—though substantial—can be dramatically reduced through measures to stem inefficiencies and lower costs.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in some 40 Sub-Saharan countries, including Zambia. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, and WSS—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Zambia, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that Zambia’s economy is dependent on mining, it will be benchmarked against other resource-rich economies in Africa, as well as against other African low-income countries. Detailed comparisons will also be made with immediate regional neighbors in the Southern Africa Development Community (SADC).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

During the five years 2003–07, Zambia’s economic performance was relatively strong; by 2007 it had neared the 7 percent growth rate needed to make a significant impact on poverty reduction. The overall contribution of infrastructure to improved growth in Zambia in the early 2000s was 0.6 percentage points, substantially less than for other countries in the region (figure 1). Zambia’s ICT sector was responsible for most of this growth as it added 0.47 percentage points to the per capita growth rate, while the power sector reduced per capita growth by 0.13 percentage points. If Zambia could improve its infrastructure to the level of middle-income countries in the region, performance could grow by as much as 2.6 percent per capita.

2
variables developed by Coulombe and MacKay (1995) and used by other analysts of these data. The poverty line is de-fined as 132,300 cedis which amounts to $25 per month per person in 1992 prices. The GLSS surveys, which cover about 3000-4000 households, are nationally representative mu,lti-purpose surveys conducted over a period of one year.<sup>4</sup> For Uganda, we use twvo household surveys, the Integrated Household Survey from 1992 (IHS 1992) and the fourth Monitoring Survey from 1996 (MS 1996). Like the GLSS, the IHS 1992 survey was modeled after the LSMS to measure welfare. The MS 1996, like the other annual mnonitoring surveys implemented after 1992, was designed to collect vital welfare data such as consumption and labor data. We use the regional poverty lines and mean per capita expenditure variables developed by Appleton (1999). The resulting weighted average national poverty line is 16443 Ugandan shillings per adult equivalent per month (in 1992 prices) or about $34 per person per month.

Since this paper exploires the gender dimensions of non-farm activity, we chose to make maximum use of individual level data wherever available. We used all of the information available for economic activity, main and secondary occupations (and tertiary where given), since many people-especially women-participate in more than one sector. In fact, most of the interesting information about non-farm participation comes from the analysis of the different combinations of activities. We also use imputed individual level incomes for Ghana that shed light on the different gender dimensions of the labor market. We estimate individual incomes by combining existing salary data with a proportional share of household income based on the individual's reported participation

> 4 GLSS 1 had a 14 day recall period while GLSS3 had a much smaller 3 day recall period. Since longer recall periods are associated with recall error the expenditure figures had to be corrected using a correction factor of 2.7 for each additional day of longer recall period in order to avoid unwanted bias in the analysis.

7
**Table 1:  Mean Values of key variables analyzed for children 36 to 59 months**

||**Re**<br>|**p. of Con**<br>|**go**<br>|**São T**<br>|**omé & Pr**<br>|**íncipe**<br>|
|---|---|---|---|---|---|---|
|**ECD Indicator**|**All**|**Urban**|**Rural**|**All**|**Urban**|**Rural**|
|**Child height-for-age (HAZ)**|-0.62|-0.18|-1.37|-0.87|-0.80|-1.00|
|**Child is stunted**(=1 if HAZ<-2, 0 otherwise)|19%|12%|31%|13.6%|13.2%|14.5%|
|**Child is underweight**(=1 if WAZ<-2, 0 otherwise)|10.7%|8.2%|15%%|5.7%|5.7%|5.9%|
|**Child is wasted**(=1 if WHZ<-2, 0 otherwise)|10.5%|8.0%|14.8%|2.73%|2.67%|2.83%|
|**Years of education of the mother**|7.5|8.8|5.1|6.8|7.2|6.2|
|**On track in ECD:**= 1 if child is on track in at least 3<br>of the 4 domains,= 0 otherwise|62%|69%|49%|58%|58%|59%|
|**Total score (ranging from 0 to 10) in 10 ECD**<br>**questions:**number of positive responses across the<br>literacy-numeracy, socioemotional, learning and<br>physical domain items|5.55|5.82|5.06|5.51|5.59|5.34|
|**On track in literacy/numeracy:**=1 if the child can<br>do at least two of the following: identify/name at<br>least ten letters of the alphabet; read at least four<br>simple, popular words; and/or know the name and<br>recognize the symbols of all numbers from 1 to 10;|16%|20%|8%|17%|20%|11%|
|**On track in physical development:**=1 If the child<br>can pick up a small object with two fingers, like a<br>stick or rock from the ground, and/or the<br>mother/caregiver doesnotindicate that the child is<br>sometimes too sick toplay|98%|99%|96%|97%|97%|97%|
|**On track in learning**=1 if on track if one or both of<br>the following are true: child follows simple<br>directions on how to do something correctly; when<br>given something to do, is able to do it<br>independently|86%|88%|82%|82%|83%|81%|
|**On track in social and emotional development:**=1<br>if two of the following are true: the child gets along<br>well with other children; the child doesnotkick,<br>bite or hit other children; and the child doesnot<br>get distracted easily|65%|70%|56%|65%|63%|70%|
|Source: Authors’ estimates based on the 2014-15 MIC|S from the|Rep. of Con|go and São T|omé & Prín|cipe||

# **Methods**

The main question addressed in this paper is an inference question: how does the predicted value of an indicator of early childhood development change if the incidence of stunting (a binary variable) or height-for-age-z score (a continuous variable) or a mother’s years of education are increased by one unit, holding other confounding factors fixed. This question can be answered within the context of a partially linear model, such as 1 1 2 2 , 1 2] = 0 (1) YY= ββ DD + ββ DD + ff(ZZ) + εε EE[εε|ZZ, DD , DD

8
**Table 9: The Impact of Minimum Wages on Women’s Relative Employment Prospects by Level of Education**

|Dependent Variable:||Me|n|||Wo|men||
|---|---|---|---|---|---|---|---|---|
|% of Production Workers|||||||||
||||||||||
|who are Men/Women<br>with Education Level:|o<br>Primary|Primary|Junior|High+|o<br>Primary|Primary|Junior|High+|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Panel A: Baseline Model**|_(N=87971)_||||||||
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Log Minimum Wage|0.003|-0.074***|-0.012**|0.060***|0.011***|-0.012*|0.008|0.025***|
||(0.003)|(0.007)|(0.006)|(0.008)|(0.003)|(0.006)|(0.005)|(0.005)|
|R<sup>2</sup>|0.083|0.067|0.015|0.067|0.040|0.065|0.026|0.068|
||FE|FE|FE|FE|FE|FE|FE|FE|
|Log Minimum Wage|-0.005|0.027***|-0.030***|-0.001|-0.000|0.039***|-0.023***|-0.009|
||(0.004)|(0.008)|(0.008)|(0.009)|(0.004)|(0.008)|(0.007)|(0.006)|
|R<sup>2</sup>|0.016|0.095|0.011|0.087|0.014|0.069|0.028|0.059|
|**Panel B: Model with Firm**|**Controls**_( _|_N= 87785)_|||||||
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Log Minimum Wage|0.003|-0.036***|-0.006|0.024***|0.017***|-0.004|-0.006|0.004|
||(0.003)|(0.007)|(0.006)|(0.007)|(0.003)|(0.006)|(0.005)|(0.004)|
|R<sup>2</sup>|0.083|0.195|0.101|0.401|0.156|0.246|0.152|0.263|
||FE|FE|FE|FE|FE|FE|FE|FE|
|Log Minimum Wage|-0.005|0.027***|-0.030***|-0.001|-0.000|0.039***|-0.023***|-0.009|
||(0.004)|(0.008)|(0.008)|(0.009)|(0.004)|(0.008)|(0.007)|(0.006)|
|R<sup>2</sup>|0.016|0.095|0.011|0.087|0.014|0.069|0.028|0.059|

_Note:_ *** p<0.01, ** p<0.05, * p<0.1. Standard errors, reported in parentheses, are heteroscedasticity robust and clustered by establishment. Minimum wages, wages and value-added are all measured in real terms. Controls included but not presented in Panel A: Baseline Model are year dummies (OLS and FE) and province dummies (OLS only). Controls included but not presented in Panel B: Model with Firm Controls are exporter status dummy, unskilled ratio, log firm age, foreign owned, government owned, log L, log V/L year-dummies (OLS and FE) and province- and industry- dummies (OLS only). Minimum wage is demeaned.

39
variables. Therefore, in order to assess the relative impact of each of the above on the dependent variable, we implement a simple simulation using the coefficient estimates from Table 7.

The object of the simulation is to calculate the effect that eliminating exclusions and delays in tariff reductions would be likely to have on trade flows in tariff lines above 3 percent MFN between members of the 17 PTAs for which we have MLI data. Although the number of PTAs in the simulation is small relative to the total number of PTAs considered in the regression model (163), trade flows between the simulation countries account for approximately 18.6 percent of world trade, which is more than half of all trade between PTA partners in our sample. This is due to the fact that the simulation covers most of the large PTAs in our sample (such as NAFTA, EFTA, and MERCOSUR). We estimate the effects of removing exclusions and phase-in periods by means of three simple simulations. First, we give every PTA with a positive _dexc_ value the maximum score for this variable ( _≈_ 5 _._ 44), effectively eliminating exclusions and sensitive lists for the agreements that feature them. We then construct a second simulation by reducing the value of the _dyrs_ variable to zero for any PTA that is scheduled to continue implementing tariff reductions beyond 2001, therefore eliminating phase-in periods for the agreements that have them. Finally, we implement a third simulation where we fully liberalize the exclusions and phase-in delays simultaneously to account for any potential non-linearities in the model. The results of these simulations are summarized in Table 8.

Table 8: Simulation results: removing exclusions and phase-in periods

||Trade in tarif lines above 3%|MFN (USD)<br>Percent|Change|
|---|---|---|---|
|Initial||93,716,155||
|Exclusions||156,697,583|67.2|
|Years||117,554,741|25.4|
|Both||196,556,763|109.7|

_Source:_ Author’s estimates.

The first column of Table 8 reports the average trade in tariff lines with the MFN duty above 3 percent between the members of the 17 PTAs for which we have data on exclusions and phase-in periods. The “initial” row contains the average predicted trade value (ˆ _xij_ ) for these countries, calculated using the coefficient estimates from Table 7. The next three rows show the average trade value following each of the above simulations, while the “percent change” column reports 41
_Table 1: The impact of precipitation on GDP growth_

|Spatial Aggregation|(1)<br>Cell|(2)<br>Adm 1|(3)<br>Country|
|---|---|---|---|
|Rainfall|1.261***|1.075***|1.185|
||(0.325)|(0.244)|(1.139)|
|Rainfall<sup>�</sup>|-0.211***|-0.205***|-0.310|
||(0.064)|(0.052)|(0.253)|
|Temperature|0.574**|0.424*|1.727**|
||(0.213)|(0.216)|(0.580)|
|Temperature<sup>�</sup>|-0.015*|-0.016**|-0.061**|
||(0.006)|(0.006)|(0.020)|
|Observations|869,447|60,530|4,128|
|R-squared|0.359|0.251|0.355|

Columns 1 and 2: Clustered standard errors at the administrative 1 level in parentheses. Column 3: Clustered standard errors at the country level. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1 12
Policy Research Working Paper 5864

# **Abstract**

The 2007–2008 upsurge in agricultural commodity prices gave rise to widespread concern about investors causing a “global land rush”. Large land deals can provide opportunities for better access to capital, transfer of technology, and advances in productivity and employment generation. But they carry risks of dispossession and loss of livelihoods, corruption, deterioration in local food security, environmental damage, and long-term social polarization that led some countries to recently pass legislation restricting foreign land acquisition. To stimulate evidence-based debate, this paper explores determinants of foreign land acquisition for large-scale agriculture. It quantifies demand for land deals, showing it focused on Africa where land expansion is about 20 times the level it was in the past. The analysis uses data on bilateral investment relationships, together with newly constructed indicators of agro-ecological suitability in non-protected and forested areas with low population density as well as land rights security. It estimates gravity models that can help identify determinants of foreign land acquisition dedicated to large-scale agriculture. The results confirm the central role of agro-ecological potential as a pull factor. In contrast to the literature on foreign investment in general, the quality of the business climate is insignificant, whereas weak land governance and tenure security for current users make countries more attractive for investors. Implications for policy are discussed.

This paper is a product of the Agriculture and Rural Development Team, Development Research 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 rarezki@imf.org, kdeininger@worldbank.org and hselod@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
observed or recorded migration between many country pairs, for example, between Rwanda and Mongolia, due to high geographic, cultural and economic barriers. Furthermore, censuses or alternative surveying instruments are unlikely to capture small migration corridors should any sampling strategy be followed. As a result, we observe zero values for about 48.5 percent of the 18,900 observations ((190-1)=189 destination x 100 origin countries) in the aggregate migration matrix from OPSW for 2000. The ratio of zero observations is 52.6 percent for low-skilled males, 52.9 percent for high-skilled males, 52.8 percent for low-skilled females and 54.0 percent for high-skilled females.<sup>14</sup> Two main reasons explain why a high proportion of zero observations in the dependent variable typically results in inconsistent parameter estimates. The first is selection bias. Since observations including a zero value in the dependent variable will be dropped from estimation, an inherent selection bias will be introduced since the occurrence of zero observed flows are non-random. In a double log regression model, the norm therefore is to add ‘one’ to the value of the dependent variable and then take the log. The second bias has been well documented by Santos Silva and Tenreyro (2006) who demonstrate in their influential paper, in the presence of numerous zeroes in the dependent variable, that the expected value of the error term will be correlated with some of the independent variables should the variance of the error term also be correlated with the independent variables. In other words, in the presence of numerous zero observations in the dependent variable and heteroscedasticity, that

> 14Similarly, in 1990, we have zero values for 43.2 percent of the 11,529 observations ( 190-1 destination x 61 origin countries) in the aggregate matrix. The ratio is 46.9 percent for low-skilled males, 49.0 percent for high-skilled males, 47.2 percent for low-skilled females and 50.9 percent for high-skilled females.

21