rafmacalaba/datause-encoder-data huggingface dataset:
34 bending supply of labor, on average, in respdnse to further increases in thir wag. rate (Schultz, 1980b).

There are good reasons, therefore, to analyze separately the decision to enter the market labor force and the choice of how many hours to work when participating. The analysis of both decisLons together **as** though they were both generated by a single truncated process (iLe. Tobit moedl) **may** supprxss distinctive features of the two decisLomuaking processes 13 Svidence of this pattern can be seen for Thailand in Tables A-3 and A-4.

To this individual market nonmarket time-allocation model of participation and hours, Mincer (1963) was the flrst to note that the **earnings** of a husband should increase the reservation wage of a married woman and thereby reduce her time allocated to the labor market. Symsetric treatment of both husband and wife implies that the wage rates of both **are** appropriate detersinants of the labor supply function of husband and wif (Heckuan, .1971). 14 Empirical studies of the labor supply of married couples have found that the labor supply of men is relatively inelastic with respect to their own-wage rate and relatively unresponsive to variation in their wivesl wage rate (actual or predicted). In the case of married women, market labor supply is elastic with respect to their own-wage and inversely related to **the** wage of their husbands. Because the own-wage elasticity is generally larger than that associated with her husband's cross-wage eiasticity (in absolute terms), the net effect of a proportionate increase in the level of wages paid to women and men is to increase women's market particLpation. **These** response patterns **are** replicated in cross sectional **data** from many industrially advanced countries and from a substantial number of low income countries. 5 A general increase in the l-vel of both men's and women's **wages is** sufficien **t** o induce an
Finally, column (3) reports summary statistics for the mobility cost incurred by workers when the change sectors and move to another location. On average, this cost is 1.8 times the average annual real wage, which is larger than the sector-only and region-only mobility costs. Note that the number of estimates which are statistically significant is much larger than for the other mobility costs. Using only the significant estimates produces a larger average sector-region mobility cost.

Yet, the mobility costs across countries are heterogeneous. Sector mobility cost range from one time the average annual wage in countries such as Honduras or Argentina to more than 10 times the average annual wage in Cameroon, Ethiopia, or South Africa. Regional mobility costs range from around 30% of the average annual wage in places like Uguanda and Malawi to more than 5 times in Timor-Leste or Ethiopia. The costs incurred by individuals moving across sectors and regions ranges from at least one time the average annual wage (Argentina, the Republic of Yemen, Chile) to more than 10 times in Ethiopia or South Africa. In the next subsection we investigate some of the determinants of mobility costs.

# **4.2 Explaining the mobility costs**

The historical patterns of structural transformation in high income countries suggest that as economies develop workers move from agriculture activities in rural areas towards urban jobs in industry and services. This has been well documented in the literature (Clark, 1940; Rostow, 1959; Lewis, 1954). Yet, depending on the pattern of technological progress and frictions in labor market, workers may be misallocated across sectors and regions, slowing down structural transformation. To provide some description of the mobility costs and its association with development, we estimate the following equation: where C _c,t_ is the estimated mobility costs of moving between industry/region _k_ and _k_<sup>_′_</sup> and _εc,t_ is the error term. _γc_ and _γt_ are country and year fixed effects respectively. In the empirical analysis we use them alternatively. Because the left-hand side variable of equation (10) is estimated with error, we weight the observations by the inverse of the standard error of each mobility cost Lewis and Linzer (2005). We estimate equation (10) for each mobility cost and using a single country characteristics at a time. The country characteristics we use are the log of GDP per capita, The Gini coefficient, and the share of agriculture, manufacturing and services in employment. We expect GDP per capita to be negatively correlated with mobility costs, both across countries, and within countries over time as financial constraints which restrict mobility are likely to be lower as countries get richer. We also expect the Gini index to be positively correlated with mobility costs.<sup>7</sup>

> 7We computed the GINI index based on the information available in the surveys. It is therefore a measure of wage inequalities rather than total income inequalities.

10
Tasmania in Australia, the Great Lakes region, and the northeastern and northwestern coasts of North America have the strongest wind power potentials. If turbines were set up in all the regions with wind speeds greater then 6.9 m/s, they would generate 72 TW of electricity, which is almost five times the world's current energy use. However, it is not possible to set up turbines in every region identified due to existing buildings, land rights and other obstacles. Nevertheless, even 20% of those sites could satisfy current world energy consumption. A study initiated by the United Nations’ Environment Program (UNEP) to evaluate wind power potential in 19 African countries estimates that the wind power potential could reach 53 TW in those countries alone (InWEnt Consulting, 2004).

As a result of concerns about climate change and higher prices for fossil fuels, wind power has excellent potential for continued rapid deployment. A 2006 joint study by the Global Wind Energy Council (GWEC) and Greenpeace International (GI) estimates that wind energy can make a major contribution to global electricity supply within the next 30 years (see Table 2). The study shows that wind energy could supply 5% of the world’s electricity by 2030 and 6.6% by 2050 under the reference wind power scenario; 15.6% in 2030 to 17.7% by 2050 under the moderate scenario; and 29.1% in 2030 up to 34.2% by 2050 under the advanced scenario. GWEC and GI (2006) projections of installed capacity, electricity output and the contribution of wind power to global electricity supply by 2030 and 2050 are provided in Table 2.

**Table 2** . Projection of Wind Power Development

|Scenario|Installed ca<br>(GW)|pacity<br>|Electrici<br>(T|ty output<br>Wh)|Contribution of<br>to total electricit<br>(%)|wind power<br>y generation|
|---|---|---|---|---|---|---|
||2030|2050|2030|2050|2030|2050|
|Reference|364|577|892|1,517|5|6.6|
|Moderate|1,129|1,557|2,769|4,092|15.6|17.7|
|Advanced|2,107|3,010|5,176|7,911|29.1|34.3|

_Note_ : The reference scenario assumes 15% annual growth rate of wind power until 2010, 10% for 2011-2014 and falling to 3% per annum by 2031. The growth rates under the moderate scenario are: 19% through 2010, 16% for 2011-2014, 15% for 2015-2020 and declines to 10% through 2025 before falling to 5%. Under the advanced wind energy scenario, growth rates are up to 20% to 2015; falling to 17% to 2020; then reduces to 10% for the next five years to 2025, before falling below 5%.

_Source_ : GWEC & GI (2006) 6
the national level. The paper identifies the main pieces of information one needs to have in order to undertake such analysis and builds on a wealth of information from the government of Tamil Nadu. One missing piece of publicly available information is the maturity structure of government debt in Tamil Nadu. We did not have this information at the time of analysis but in the case of Tamil Nadu this is not a problem because of the relatively long maturity of and fixed interest rates on state public debt. We chose not to estimate the elasticities that determine the evolution of revenue receipts. These elasticities are difficult to estimate because the estimation methodology assumes constant revenue and tax policies, whereas tax and revenue policies in India, or developing countries in general, are not constant.

Finally, while we wanted to compare Tamil Nadu with subnational governments in other countries, we decided not to embark on this task as comprehensive subnational finance data are not available yet and key fiscal data are not defined in a consistent way across countries. For example, national governments monitor carefully the debt service ratio of subnational entities, but they define it differently. Brazil defines the debt service as a share of current revenue net of transfers (Brazil 2001). Colombia records it as a share of operational savings (Colombia 1997). Peru defines it as a share of current income including transfers (Peru 2003), while Russia denotes it as a share of total budgetary expenditures (Russia 1998). We however think that such comparisons based on comprehensive data and a consistent framework are important and merit further work.

31
Ansón, J., Arvis, J., Bo¤a, M., Helble, M., and Shepherd, B. 2017. “Time, Uncertainty, and Trade Flows,”Working Paper 673, Asian Development Bank, Manila, Philippines.

Antràs, P. 2016. Global Production: Firms, Contracts and Trade Structure, Princeton University Press.

Antràs, P., and De Gortari, A. 2020. “On the Geography of Global Value Chains,”Econometrica. Antràs, P., and Helpman, E. 2004. “Global Sourcing,”Journal of Political Economy 112(3): 552580.

Antràs, P., and Helpman, E. 2008. “Contractual Frictions and Global Sourcing,”in Helpman, E., D. Marin, and T. Verdier (eds.) The Organization of Firms in a Global Economy. Cambridge, MA: Harvard University Press.

Arkolaskis, C., Costinot, A., and Rodriguez-Clare, A. 2012. “New Trade Models, Same Old Gains?,” American Economic Review 102(1): 94-130.

Aslam, A.,Novta, N., and Rodrigues-Bastos, F. 2017. “Calculating Trade in Value Added,”IMF Working Paper WP/17/178.

Baldwin, R. 2012. “Trade and Industrialization after Globalization’s Second Unbundling: How Building and Joining a Supply Chain are Di¤erent and Why it Matters,” in Globalization in an Age of Crisis: Multilateral Economic Cooperation in the Twenty-First Century. University of Chicago Press.

Baldwin, R. 2016. Globalization’s Three Unbundlings. Harvard University Press.

Baldwin, R., and Taglioni, D. 2012. “Gravity Chains: Estimating Bilateral Trade Flows when Parts and Components Trade is Important,”NBER Working Paper No. 16672.

Bagwell, K. and Staiger, R. 2011. “What Do Trade Negotiators Negotiate About? Empirical Evidence from the World Trade Organization,” American Economic Review 101(4): 1238-1273.

Balié, J., Del Prete, D., Magrini, E., Montalbano, P., Nenci, S. 2019. “Does Trade Policy Impact Food and Agriculture Global Value Chain Participation of Sub-Saharan African Countries?,” American Journal of Agricultural Economics 101(3): 773-789.

Bas, M., and Strauss-Kahn, V. 2015. “Input-Trade Liberalization, Export Prices, and Quality Upgrading,” Journal of International Economics 95(2): 250-262.

Beck, T. 2003. “Financial Dependence and International Trade,” Review of International Economics 11: 296-316.

Bickerdike, C. 1907. “Review of A.C. Pigou’s Protective and Preferential Import Duties,” Economic Journal 17 (65): 98-108.

Blyde, J. (Ed.) 2014. Synchronized Factories: Latin America and the Caribbean in the Era of Global Value Chains. Inter-American Development Bank, Washington DC.

27
<!-- Start of picture text --> AFR EAP ECA<br>1500<br>4000<br>500<br>. meme<br>LCR MNA SAR<br>1500<br>1000 Type<br>% [ irigation<br>& Be incustran<br>500 By domestic<br>; a —<br>APD EC NAM<br>4500<br>4000<br>5000a a<br>2010 2020 2030 2040 2052010 2020 2030 2040 205@010 2020 2030 2040 2050<br><!-- End of picture text -->
# **Congo**

||**Prevalence amon**<br>**National Antenatal**|**g females 15-24(%)**|**Spectrum**|**estimates**||
|---|---|---|---|---|---|
||**Sentinel**|**National Demorahic**|||**Indicator 16 - HIV**|
||<br>**Surveillance**<br>**Surveys **|**gp**<br>**and Health or AIDS**<br>**Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|**.**<br>**prevalence in young**<br>**people 15-24(%)**|
|**1990**|-|-|0.81|-|-|
|**1991**|-|-|0.84|-|-|
|**1992**|-|-|0.81|-|-|
|**1993**|-|-|0.82|-|-|
|**1994**|-|-|0.76|-|-|
|**1995**|-|-|0.71|-|-|
|**1996**|-|-|0.66|-|-|
|**1997**|-|-|0.62|-|-|
|**1998**|-|-|0.54|-|-|
|**1999**|-|-|0.53|-|-|
|**2000**|-|-|0.49|-|-|
|**2001**|-|-|0.44|-|-|
|**2002**|-|-|0.39|-|-|
|**2003**|-|-|0.36|-|-|
|**2004**|-|-|0.34|2|-|
|**2005**|2.6|-|0.31|11|-|
|**2006**|-|-|0.29|12|-|
|**2007**|-|-|0.27|17|2.6|
|**2008**|-|-|0.26|-|-|
|**2009**|-|2.4|0.24|24|1.9|
|**2010**|-|-|0.21|41|-|
|**2011**|-|-|0.18|44|1.79|
|**2012**|-|-|0.16|39|-|

<u>ANC data source: Congo UNGASS Report 2008</u> <u>National DHS and AIS data source: Congo AIS 2009</u>

# **Ethiopia**

||**Prevalence amon**<br>|**g females 15-24(%)**<br>|**Spectr**<br>|**um estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral coverage**<br>**(%)**|
|**1990**|-|-|0.36|-|
|**1991**|-|-|0.44|-|
|**1992**|-|-|0.52|-|
|**1993**|-|-|0.55|-|
|**1994**|-|-|0.56|-|
|**1995**|-|-|0.56|-|
|**1996**|-|-|0.54|-|
|**1997**|-|-|0.51|-|
|**1998**|-|-|0.46|-|
|**1999**|-|-|0.41|-|
|**2000**|-|-|0.35|-|
|**2001**|12.4|-|0.29|-|
|**2002**|11.0|-|0.22|-|
|**2003**|8.6|-|0.15|-|
|**2004**|-|-|0.11|4|
|**2005**|5.6|1.1|0.08|7|
|**2006**|-|-|0.06|18|
|**2007**|3.5|-|0.04|29|
|**2008**|-|-|0.04|-|
|**2009**|2.6|-|0.03|36|
|**2010**|-|-|0.03|47|
|**2011**|-|0.5|0.02|56|
|**2012**|-|-|0.03|60|

<u>ANC data source: Report on the 2009 Round Antenatal Care Sentinel HIV Surveillance in Ethiopia</u> <u>National DHS and AIS data sources: Ethiopia DHS 2005 and 2011</u> 54
urgent policy decisions. While research must be sufficiently rigorous that, if acted upon, there is a reasonable expectation it will improve the policy decision, it must also be timely, in the sense that it has to reach the policy maker before the decision is made. This article argues that timeliness of results has received less attention than research quality—and less than it merits.

Meeting accepted professional standards has been the watchword for the research community. In consequence, efforts to feed advice and guidance into the policy-making process are usually undertaken only at the end of the research cycle, after the research has been completed and vetted. While there is an obvious rationale for this—no one wants results based on badly flawed analysis or grossly inaccurate data reaching the policy-maker’s desk—the approach runs the risk that results arrive too late to be of value. Indeed, decision makers worldwide constantly voice frustration with the slowness of the research production process. The ODI/SciDev.Net International Survey on the Science-Development Policy Interface, for example, reports that “A general consensus from the expert interviews was that a major challenge is the narrow focus and long time-scales of scientific research compared with political priorities” (Jones et al. 2008, 20). Similarly, an ODI Briefing Paper notes that “Policymakers bemoan the inability of many researchers to make their findings accessible and digestible in time for policy decisions. Practitioners often just get on with things” (Overseas Development Institute 2004, 1).

A possible remedy is to encourage the submission of preliminary results early in the research cycle in order to increase the likelihood of their reaching the policy maker before the decision has been taken. It is this thought that leads to the main proposal of this article—the introduction of what we term provisional assessments. As explained more fully in section II, provisional assessments are short papers focused on a current policy issue and delivered to the policy maker
# **5.2 Pre-trial delays**

Did the reform affect the speed of pre-trials? We start by estimating our event study specification (1). Panel A, Figure 3, plots the pre- and post-decree hearing period coefficients. The results reveal a sudden drop in pre-trial duration for cases that entered a chamber close to the application cutoff in that chamber. The drop in pre-trial duration levels off three hearing periods after the cutoff. To provide an estimate of the average decree impact net of this adjustment period, we estimate (3) removing these six hearing periods, _τ ∈_ [ _−_ 3; 2], from our sample. The results indicate an average 46.1 days reduction in pre-trial duration (p-value _<_ 0.01; col 1, Table 3) off of a 157 day baseline duration.<sup>18</sup> This is a large effect, on the order of 0.32 pre-reform standard deviations. Specification (3) allows for chamberspecific linear trends on either side of the cutoff. We obtain a remarkably similar point estimate (42.9 days reduction, p-value _<_ 0.01) when we assume a common linear trend across chambers on either side of the cutoff (col 1, Table A2), further ruling out selection on trends as a potential source of bias.<sup>19</sup> Next, we reproduce the event study result, accounting for censoring in pre-trial duration.<sup>20</sup> We estimate the Cox proportional hazard model expressed in (2). Again, the event study specification exposes a clear jump in the hazard ratio of exiting pre-trial at the decree introduction cutoffs (Panel B, Figure 3). Estimating the average effect (3) indicates that the introduction of the decree significantly increased the hazard ratio of a case finishing

> 18Leaving the adjustment period in the sample does not affect our conclusions (Table A1).

> 19We present results fitting a common linear trend across chambers while allowing for a structural break at the cutoff, for all our main results (Tables A2, A3, A6, and A7). In addition to verifying the robustness of our results to various trend specifications, these models allow us to parsimoniously report a coefficient on these pre- and post-reform trends.

> 20This censoring is visible in Panel A, Figure 3, which displays a downward trend in the effect of the entry-period dummies on pre-trial duration as we move away from the cutoff in the post period. This is because for any late entry cohort, the longest-lasting cases are still ongoing and, thus, omitted from this sample. While censoring is present, the event study results in Figure 3 indicate there is a significant break from this pre-trend at the cutoffs. Similarly, the average effects show a large and significant treatment effect despite controlling for chamber-specific linear trends (and allowing these trends to be affected by the reform; cols 1 and 2, Table 3). Hence, we can credibly rule out that censoring explains the observed drop in pre-trial duration.

14
Experimental evidence for the effectiveness of this strategy in low-income countries, however, is limited, as noted by Betcherman et al. (2004). More recently, Card et al. (2015) aggregated over 200 econometric evaluations of active labor market programs, finding no average effects on employment in the short run but positive effects over longer time horizons, while McKenzie (2017) finds modest impacts of vocational job training across low- and middle-income countries. Taking a broader look at labor market and entrepreneurship programs in low-income countries, Blattman and Ralston (2015) argue that “it is hard to find a skills training program that passes a simple costbenefit test.” In the case of our experiment, we find modest effects of the program on ICT employment (1.7 percentage points from a baseline of 6.4 percent) and no effects on employment or earnings more generally, suggesting that the job training served to shift workers to the sector targeted by the program. Heterogenous treatment impacts suggest that to some extent the program helped overcome skills mismatch present in the nascent industry, as individuals with higher scores on a baseline assessment that overlapped with the certification exam that firms began to use were much more likely to benefit from the program. Impacts also varied across cities, which suggests the presence of spatial mismatches in labor supply and labor demand, consistent with the literature that finds high returns to programs that induce internal migration for work (Jensen, 2012 and Bryan et al., 2014). After the program, the share of the treatment group meeting international benchmark scores for entry-level positions grew substantially in all cities, but the training generated varying amounts of sector switching across cities.

At baseline, we also measured applicants’ attitudes and biases related to gender norms. At the time of the experiment, the ICT sector was male dominated, with government figures indicating that 67 percent of workers in the information services sector in 2010 were men (National Bureau of Statistics 2010).<sup>1</sup> Additional heterogenous treatment impacts suggest that training can play a role in changing these norms and aspirations. While structural features of the economy and outright discrimination are likely dominant reasons for women’s lower labor market earnings, additional disadvantages may stem from beliefs that women themselves hold about their own potential. Women who hold gender-based stereotypes with respect to their mathematical abilities, for

> 1 There is no direct mapping between the goals of the training program and occupational categories in government statistics.   All other possible categories (office support, telecommunications) are also male dominated.

3
in the design (called ‘design effect’ or simply _deft_ ) can result in more precise survey estimates (smaller standard errors and confidence intervals) or in the reduction of the necessary sample size, therefore, a reduction in the budget needed for the surveys.

This paper aims to propose a new method for increasing the efficiency of two-stage household sample surveys by incorporating information from previous small area estimates of poverty in the selection process of Primary Selection Units (PSUs). Poverty estimates in small areas are spatial descriptions of the distribution of poverty that is finely disaggregated in small geographic units, such as cities, municipalities, districts, or other administrative partitions within a country.

In the context of improving the sample design in a multi-stage scheme, these poverty maps should satisfy at least two key properties: i) the small area should be directly linked to the PSUs of the design; ii) standard errors (precision) of such small area estimates should be relatively small and they should be easily calculated by the National Statistical Office conducting the household survey.

For the above reasons, the methodological originality of this contribution is twofold. First, the new method takes into account the theory of small area estimation (SAE, also referred as poverty mapping) methods developed in the context of the World Bank by Elbers, Lanjouw and Lanjouw (ELL, 2002 & 2003), later refined by Tarozzi and Deaton (2009) and Van der Weide (2014); this for a notable improvement in the design effect of household surveys and specifically for Household Budget Surveys (HBSs). The proposed method, however, is very versatile and can be implemented also using alternative approaches to poverty estimation at the ‘small area level’ (as defined in Rao, 2003): in particular, the Empirical Best (EB) of Molina and Rao (2010), and the more recent proposal H3CensusEB of Molina (2019). The present paper is based on Molina’s H3-CensusEB estimator found to be superior in terms of bias and mean square error (MSE) to ELL and Molina and Rao’s Empirical Bayes prediction estimates (Corral, Molina and Nguyen, 2020; Corral et al., 2021).

Second, the new method described in this paper implements, for the first time in a developing country, the recent JRR method, from Verma and Betti (2011), for 3