rafmacalaba/data-use-mentions-extended huggingface dataset:
forestry, and fishing Mining and quarrying Agriculture, forestry, and fishing Mining and quarrying<br>Rest of industry Non-industrial services Rest of industry Non-industrial services Rest of industry Non-industrial services<br>Source: World Bank and ZRA staff calculations based on CIT database<br><!-- End of picture text --> Across the same period, formal firms in mining and quarrying generally had the highest labor productivity, followed by formal firms in the rest of industry and services, then formal firms in agriculture. However, in more recent years, the labor productivity of formal firms in agriculture has exceeded that of formal firms in rest of industry, as will be discussed further (see Figure 10).

8 At the ISIC 1-digit level, the largest constituent sectors within rest of industry are manufacturing (12 percent of formal firms’ labor in 2021) and construction (5 percent). The largest constituent sectors within services are retail trade (20 percent), wholesale trade (7 percent), administrative and support service activities (5 percent), financial and insurance activities (4 percent), and transportation (4 percent). For additional details, refer to Table 3.

9
the four proposals (as described below) and four using decisions on each proposal at a time. “Mean giving rate”is the share of accepted proposals to give up the fuel voucher (out of possible four); “Frequent giver”is an indicator variable equal to one if individual i’s mean giving rate is greater than 0.5, i.e. if the individual gave up the voucher in the majority of the proposals. The other four outcomes are binary indicator variables equal to one if individual i indicated that he or she would give up his/her voucher for that specific proposal (unconditional cash transfer, unconditional food transfer, unconditional cash transfer and school, or cash transfer conditional on training).

> 17 Clustering at the PSU level was used to account for the first stage of the sampling strategy, which picked PSUs from the census sampling frame. Clustering thus adjusts the standard errors to take into account intra-cluster correlation, which could be relatively high for outcomes related to redistribution preferences. Given that Huber-White heteroskedastic-standard errors (commonly known as “cluster-robust”) are potentially underestimated when the number of clusters is small (as discussed in Bertrand, Duflo, and Mullainathan 2004; Cameron, Gelbach, and Miller 2008; Cameron and Miller 2015), in this paper we recomputed all standard errors with wild cluster bootstrap-t statistics following the procedure by Cameron, Gelbach, and Miller (2008), which avoids standard error underestimation in the presence of few clusters.

10
disaggregating the nonpoor group into two groups: the vulnerable (those that are nonpoor but still face a significant risk of falling into poverty) and the secure (or “middle class”). A common, but rather ad-hoc, approach is to arbitrarily scale up the poverty line by a certain factor to obtain the vulnerability line. In India, vulnerability has in the past been proposed to occur within a fixed income range between 1.25 times and twice the national poverty line in India (NCEUS 2007). Other countries similarly define the vulnerability line as twice (Pakistan; Lopex-Calix et al, 2014) or 30% above (Vietnam; World Bank, 2012) the national poverty line. This approach has the advantage of being simple and easy to understand, but it appears to be based on no underlying welfare theoretical framework.

The recent approach proposed in Dang and Lanjouw (2017) instead derives the vulnerability line from a specified vulnerability index in the spirit of vulnerability to poverty. While sharing a similar conceptual approach and motivation with existing studies of vulnerability (e.g. Pritchett, Suryahadi, and Sumarto, 2000; Chaudhuri, 2003; Christiaensen and Subbarao, 2005), this approach differs from them in several important respects. First, it explicitly provides a framework to estimate a vulnerability line which is associated with a vulnerability index that can be derived in various ways, including budgetary planning, (ideal or desirable) social welfare objectives, or relative concepts of well-being. Second, unlike in Pritchett et al. (2000) and Chaudhuri (2003), the target population consists of the currently nonpoor households rather than all households. Finally, the Dang and Lanjouw (2017) approach employs simpler nonparametric estimation methods to estimate vulnerability as a function of consumption alone. It works with either actual panel data or synthetic panel data that can be constructed from cross-sections. We employ a vulnerability index of 20% and the associated vulnerability line for our welfare analysis in the next sections. The dividing line (vulnerability line
interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned.

# **_Training personnel in the police and judiciary and other parts of the justice system_**

Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000). In other settings, NGOs such as Rozan in Pakistan (Rashid, 2001), Profamilia in the Dominican Republic (Guedes et al., 2002), and the Musasa Project in Zimbabwe have trained law enforcement personnel on issues related to gender-based violence. Elsewhere, governments have collaborated with the United Nations to provide training and support for the police and judiciary. For example, ILANUD is a joint institute of the government of Costa Rica and the United Nations that works with governmental agencies throughout Latin America to improve the work of prosecutors, judges, lawyers, police and other professionals in criminal justice generally, and gender-based violence specifically (Villanueva, 1999; ILANUD, n.d.). Most of these initiatives have been evaluated using key informant interviews and pre and post questionnaires before and after training-if they have been evaluated at all. Nonetheless, training appears to be both constructive and urgently needed (Rashid, 2001; Villanueva, 1999). Other lessons learned include the finding that changing attitudes of law enforcement is a challenging, long-term process. The quality of the trainings’ content and the skills of the trainer are essential. Training appears to be most effective when all levels of personnel (especially high-level officials) participate, and when training is backed up with changes throughout the institution, such as policies, procedures, adequate resources, and continual monitoring and evaluation.

# **_Special
21 case nonpoor households which are now receiving food subsidies will need to be excluded from this new administrative program. Excluding such nonpoor households may be politically difficult. In some cases, narrowly focused, admLinistratively targeted food subsidy programs -- like the food stamps program in Jamaica -- do not last long, because they lack the political support of the excluded middle class.

Because of the political and administrative costs associated with shifting to an administratively targeted food subsidy program, it is useful to consider several less radical methods of reforming the Egyptian food subsidy system. Each of these possible reforms seeks to reduce the fiscal costs of the present food subsidy system in Egypt.

In order to predict the distributional impact of these possible reforms, it is necessary to estima,te a complete matrix of food demand parameters for poor and nonpoor income groups in urban and rural Egypt. This can be done by using a new "food characteristic demand system" proposed by Bouis<sup>33</sup> mnd applied to the Egyptian data set by Bouis and Ahmed.<sup>34</sup> This new system uses a hedonic demand system to calculate a comrplete demand matrix of own-price, cross-price and expenditure elasticities for food and nonfood goods under specific restrictive assumptions.

Expenditure elasticities generated by the food characteristic demand system for 15 foods are presented in Table 11. In this table expenditure elasticities are presented separately for the poor (lowest 40 percent of the population in the policy simulations) and for the nonpoor (top 60 percent of the population), eLnd for urban and rural areas. In the table subsidized and non-subsidized sugar, and subsidized and non-subsidized oil, are treated as single, aggregate goods, because only a srmall percentage of households in urban and iural Egypt rely exclusively on purchases of these two subsidized goods.
Data permitting, a satisfactory approach might be to develop a fully structural model in which heterogeneous firms decide whether or not to enter the textile market, then select a destination (i.e. EU or US or both) and finally decide under which trade regime to export (i.e. under a preferential regime with fixed costs associated with proving origin, or under MFN with no fixed costs).<sup>9</sup> The alternative to a structural model is to ‘let the data speak’ in a less ambitious framework. In the Appendix, we sketch such a model in which a representative apparel producer sells all its production either to the US or the EU market or to both under preferential status (these are the two main export destinations for AGOA beneficiaries). The producer sells differentiated products (or since we do not have firm data, heterogeneous firms sell a homogenous product to both markets with fixed entry costs to each market). The firm uses textiles as an input and faces a downward sloping demand curve in each one of the two destination markets. Under the single transformation (‘third country provision’) rule introduced by the Special Rule, the firm chooses its textiles from the low-cost suppliers while under the double or triple transformation rules, it is forced to purchase textiles from the high-cost partner. The comparative statics of the model show that export sales to a market respond positively to: (i) a fall in tariffs (i.e. to an increase in preferential access under AGOA to the US), and (ii) a relaxation of the rule of origin which lowers its production costs.

# **4. Data and Econometric Strategy**

# **4.1. Specification and Data**

_Specification_ The model sketched above suggests that, after controlling for idiosyncratic factors in each market, export sales of individual apparel products towards the EU and US 9 This is the route followed by Cherkashin et al (2010). They rely on cross-section data
<!-- Start of picture text --> Japan: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Spain: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>10<br>5<br>0<br>-5<br>-10<br><!-- End of picture text --> <!-- Start of picture text --> UK: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> 43
student in grade 9 in Finland would be coded as 0, whereas in Mexico the same student would be coded as -1. Thus, this variable is sensitive to country-specific grade progression and is comparable in pooled or cross-country regressions.

Information on formal school entry date cutoffs, and on how well these cutoffs are enforced, is hard to locate and verify. Moreover, school entry dates often vary within country and over time. Given these difficulties, rather than using the legal cutoffs, as a first step to screening countries we examine discontinuities in the data in relative grade by birth month. We restrict our sample to countries with large samples on both sides of the cutoff. Four countries—Mexico, the Republic of Korea, Slovakia, and Thailand—meet this criterion. We find large and significant shocks to relative grade: being born just before the school entry cutoff (versus being born just after it) exogenously increases relative grade by 0.55 to 0.6 grades. Figure 5.4 shows graphs of how month of birth indeed affects relative grade in these four countries; and the month of the discontinuity corresponds to legal school entry month. For example, in Korea the school entry cutoff is January 1<sup>st</sup> , and we see a sharp discontinuity in relative grade between December and January. In Mexico and Slovakia, the school entry cutoff date is September 1<sup>st</sup> and we see corresponding discontinuities between August and September. It is worth noting that in Mexico the discontinuity occurs right at the start of August, whereas in Slovakia this starts to occur earlier. This suggests that while this cutoff is enforced in Slovakia, it might be less strict.

# **Figure 5.4: Relative grade by birth month**

<!-- Start of picture text --> Month Month<br>Source: Authors’ analysis of PISA 2015 data.<br>Relative grade<br>Relative grade<br><
-Tahers-Non-teachers Pay Differenocs 18

The equations also confirm that hours worked is not a significant determinant of teacher salaries. For other occupations the elasticity of earnings with respect to hours worked is positive but small (a 10% increase of monthly hours is associated with only 22 salary increase). This is not surprising if one knows that all of teacher contracts and the majority of private employment contracts in Cote d'Ivoire set salary on a monthly basis and that hours worked is an important earnings determinant only for low-level workers. (5) The splitting of the sample to estimate separate equations for teachers a ' non-teachers assumes that there **are** no significant omitted variables which also determine the selection into the teaching profession. To test for the presence of possible selection bias, we applied Heckman's (1979) two-stage technique, whereby the first stage was a probit equation which explained occupation choice as a function of personal characteristics (human capital, sex, nationality, place of origin) and parental background. No evidence of selection bias was found.

# VI. CONCLUSION.

This paper used multivariate analysis based on the monthly wage rate functions to investigate differences in earnings between teachers and other occupations in Cote d'Ivoire. The analysis controlled for differences in human capital endowments, hours worked, and various personal characteristics. It was found that the base salaries of teachers contain an economic rent component, largely due to the wage setting behavior of the Ivorian government. This salary premium is largest outside Abidjan. The premium disappears, however, when the total remuneration package is considered, i.e. including in-kind benefits, bonuses and commissions, which are more widely received by non-teachers.
of the price decline in the six months ending in April 2020 has been due to falling demand.<sup>4</sup> The demand and supply factors surrounding the recent plunge in oil prices are discussed below.

**_Demand decline resulting from lockdowns._** The single largest factor driving the collapse in oil prices has been the sharp reduction in oil demand arising from government restrictions to stem the

> 4 In contrast, other research finds that only around one-third of the fall in oil prices can be attributed to demand conditions, while supply factors explain most of the remainder of the fall (Groen and Nattinger 2020). Instead of industrial production as a proxy for oil demand, these other models use asset prices which have considerably more resilience than real activity indicators (in part reflecting monetary policy measures). If anything, other factors, in particular the widespread anticipation of a delay in OPEC+ negotiations, point to an even greater role of demand than estimated here.

6
# **G Detailed acknowledgments**

The preparation of the Firm-level Adoption of Technology survey questionnaire involved the contribution of several sector experts within and outside the World Bank.

First, we would like to thank the following World Bank Group colleagues: Erick C.M. Fernandes (Lead Agriculture Specialist), Holger A. Kray (Manager, Agriculture), Michael Morris (Lead Agriculture Economist), Madhur Gautam (Lead Agriculture Economist), Robert Townsend (Lead Agriculture Economist), Ashesh Prasann (Agriculture Economist), Aparajita Goyal (Senior Economist, specialized in Agriculture), Harish Natarajan (Lead Financial Sector Specialist), Erik Feyen (Lead Financial Sector Economist), Laurent Gonnet (Lead Financial Sector Specialist), Arturo Ardila Gomez (Lead Transport Economist), Victor A Aragones (Senior Transport Economist), Edson Correia Araujo (Senior Health Specialist), Irina A. Nikolic (Senior Health Specialist), Brendan Michael Dack (Chief Industry Specialist at IFC), Emiliano Duch (Lead Private Sector Specialist), Blair Edward Lapres (Economist), Kazimir Luka Bacic (Operations officer), Justin Hill (Senior Private Sector Specialist), Etienne Raffi Kechichian (Senior Private Sector Specialist), Justin Yap (Private Sector Specialist), Austin Kilroy (Senior Economist).

Similarly, we would like to thank several external experts. From Embrapa-Brazil, we would like to thank Alexandre Costa Varella (Head of Embrapa research unit in the South region, expert on livestock), Fl ́avio Dessaune Tardin (Researcher, Maize and Sorghum), Edison Ulisses Ramos Junior (Researcher, Soybean), Isabela Volpi Furtini (Researcher, Rice and Beans), Alberto Duarte Vilarinhos (Researcher, Cassava and Fruits), Carlos Estev ̃ao Leite Cardoso (Researcher, Cassava and Fruits, Technology transfer), and other participants of the internal seminars to validate the sector specific questionnaire for agriculture and livestock. We also would like to thank a group of senior consultants from Patina Solutions, who contributed with the preparation and validation exercise
.0524)|
|_Treat ∗Y_(_G_1)1997||-0.139**||0.014||-0.078|
|||(0.0555)||(0.0572)||(0.0644)|
|Constant|4.061***|8.173***|-16.51**|30.07***|11.61|55.85***|
||(0.705)|(0.625)|(6.932)|(5.891)|(9.296)|(7.894)|
|Observations|1,891|1,891|1,880|1,880|1,354|1,354|
|R-squared|0.089|0.391|0.054|0.357|0.069|0.423|
|_Mean C Y_(_G_1)1997|2.71|2.71|38.89|38.89|42.46|42.46|
|_Mean C Y_(_G_2)2017|7.76|7.76|40.51|40.51|42.36|42.36|

This table analyzes the inter-generational mobility relation to the parent’s (Generation 1, G1) distributional position to understand how poorer or richer households manage to change their distributional position for specific outcomes and how _Treatment_ affects this relation. Inter-generational mobility is defined as ∆ _Y_ = _Y_ ( _G_ 2)2017 _− Y_ ( _G_ 1)1997, where _Y_ ( _G_ 2)2017 and _Y_ ( _G_ 1)1997 are defined as the outcome of the offspring (G2) or parents (G1) observed in 2017 or 1997, respectively. The outcomes of interest considered are: (1) years of schooling; (2) a durable asset index formed with a principal
efciency shock|_ρe_|0.70|II|
||Correlation of energy efciency shocks across _i_=_{_1_,_2_}_|_χe_|0.11|II|
||Persistence of non-U.S. oil supply shock|_ρ_<sup>_o_</sup><br>3|0.98|BC|
||Persistence of U.S. oil supply shock|_ρ_<sup>_o_</sup><br>1|0.91|II|

_Note:_ BC: Backus & Crucini (2000), HP: Heathcote & Perri (2002), GO: Gars & Olovsson (2017), II: indirect inference.

where **V** is a diagonal matrix with diagonal elements as the variances of Ω.

Figure 8 displays the empirical impulse responses, along with the response functions estimated using model simulated data. The persistence of U.S. oil supply shock is estimated as 0.91 and innovation standard deviation is 0.012. The persistence and correlation for U.S. energy efficiency is estimated as 0.7 and 0.11, respectively. I follow the standard assumption in the literature that the shock process is symmetric across countries. Thus, the estimated energy efficiency shock process is given as following where the standard deviation of innovations is 0.02.

# **V.3 Baseline Results**

In this section, I discuss the impulse responses to U.S. energy efficiency and U.S. oil supply shocks. In particular, I focus on understanding the mechanisms by which changes in U.S. oil production and consumption can affect the GDP of oil importing and oil exporting countries. Each shock has direct implications for either U.S. oil demand or U.S. oil supply and, thus, results in changes in oil price and, consequently, U.S. and international macroeconomic aggregates. Figure 9 shows the effects of a one-standard deviation improvement
17 550 vehicles per kilometer of road. The maximum value in the sample, 425, is found in Paris. It is interesting to note that Paris is often considered to be one of the more successful world cities in effectively controlling the use of motor vehicles. In contrast, no saturation level is found from the national level data for both total road and paved road networks. The result is not surprising as the national road networks are rarely congested.

# **4. Concluding Remarks**

This paper explores alternative specifications for the relations of vehicle ownership, road length, and the vehicle-road ratio with various independent variables at the national and urban level. It examines three issues: dependent variable definition, equation specification, and urban historical spatial endowments. We first summarize the results of these efforts and then comment on the overall implications of the findings.

**Dependent variable definition.** The analysis here compared the results of normalized (e.g., vehicles per thousand persons) and non-normalized (e.g., vehicles) dependent variables in regression equations to explain vehicle ownership and road length. For vehicle equations at the national and urban level, the normalized and non-normalized results were very similar because the elasticity of population with vehicle ownership is approximately 1, and population has a constant scale effect on vehicle ownership. The road length regressions at the national level produced similar results with normalized (road length per square kilometer) and non-nornalized (road length) dependent variables. This was not true at the urban level where the nornalized dependent variable had been road length per capita. In the urban road length equation, population had an elasticity of around 0.8, and because it differed from 1, the non-normalized equation elasticities differed from the normalized equation elasticities. The general lesson from this exercise is that normalized dependent variables should not be used without testing for their validity. Nonnormalized variables are generally preferable.

**Equation specifications.** The first question addressed was whether the elasticities of the various dependent variables