rafmacalaba/data-use-mentions-extended huggingface dataset:
1 replicates our baseline scenario from Table 1, column 1. Under that no-tax scenario, 33 million permit sellers receive an annual rent of US$19,700. By setting the tax to 13% (column 2) and increasing it to 141% (column 5) we can see how tax policy can improve targeting by reducing the number of “beneficiaries” and henceforth increasing the basic guaranteed income of the targeted group.

For instance, with a 13% tax, the market shrinks to 31 million permit sellers and the price of the work permit drops to US$18,000. Yet, they receive the revenues from the tax amounting to US$2,300 per person and per year. The basic income guarantee, therefore, goes up to US$21,000 and poverty decreases by an extra 1.2 percentage point to 8.1%. Increasing the tax up to 141% further targets the 15 million poorest by reducing the permit price down to US$ 10,000. However, with the tax revenues hence generated, such a scheme allows a basic income guarantee as high as US$24,000 per year. Such a policy would bring the poverty rate down to 2.8% from a baseline of 12.3%.

# **6 Informal Labor Markets and the Limits of a Market for Work Permits**

We have proposed a market for work permits as a mechanism to redistribute part of the benefits of migration from migrant to native workers to compensate them for the risk of being displaced and for the potential loss in earnings. The illustration using data from the US-Mexico migration corridor gives estimates that suggest the market price for working permits would constitute a sizeable basic income guarantee for native workers who decide to sell their right to work.

The plausibility of the quantitative results hinges on the model’s assumptions. In particular, we assume the perfect enforcement of immigration and labor laws. Imperfect enforcement would result in informal markets that alter the agents’ incentives.
Policy Research Working Paper 8840

# **Abstract**

Rural economies are in transition around the world; in many countries, improved technology and linkages across sectors have expanded access to markets and accelerated production for some farmers. At the same time, rural areas globally are facing a growing base of landless and smallholder farmers, out-migration to urban areas, and persistence of low-skilled, informal, and seasonal jobs where women are often heavily concentrated. Recent global initiatives are examining programs that can effectively raise rural incomes, including how addressing shortfalls in women’s hours worked and earnings can raise rural productivity and growth. But well-designed policies to address these issues require improved counting of individuals’ employment, accounting for the complexity of measuring rural women’s labor force participation, as well as data on social, economic, and institutional constraints that women face in seeking better economic opportunities. Using recent rounds of the Ethiopia, Malawi, Nigeria, and Uganda Living Standards and Measurement Study-Integrated Surveys on Agriculture, as well as findings from recent country pilots conducted by the International Labour Organization, this paper discusses best practices and issues to consider when examining rural women’s employment in socioeconomic surveys, as well as a survey research agenda to improve measurement.

This paper is a product of the Development Data 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 author may be contacted at koolwalg@gmail.com.

_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
of scale to the deployment of solar power, and so little economic penalties to small-scale use, and in general it is deployed at the demand site *“off grid”+, obviating the need for power grids.

Solar power is therefore ideally suited to southern developing countries with high levels of solar radiation and no power infrastructure in place in rural areas: the total cost of deploying solar power in rural areas is less than that of conventional power stations plus a grid *which in the US costs about $3 million per mile+. Solar’s drawback is that is ceases when the sun sets, and so needs to be accompanied by batteries that can store power into the night.

Solar power is already available in some rural developing countries. In the Iquitos region of Peru, along the upper reaches of the Amazon, the provincial government and NGOs have combined to make solar panels, batteries and compact fluorescent bulbs available to households at less than the cost of fuel for the generators they were previously using for power. Electricity is now available at zero marginal cost, encouraging use, and with no environmental impact. In Bangladesh ex employee of the Grameen Bank runs Bright Green Energy, which sells solar panels and batteries, provides financing in conjunction with the IFC and local banks, and in conjunction with Grameen Technology Center trains women to install and service solar power sources.

The labor force link ( can work through the effect of environment on health. Better environmental policies can reduce atmospheric pollution in cities, reduce respiratory diseases, and increase labor effectiveness and reduce days lost due to illness. Graff Zivin and Neidell (2011) provide an example of this link between air pollution (in that case, ozone) and labor productivity in farms in California. Using a database of daily worker output, they find a surprisingly strong impact, with a 10 ppb decrease in ozone concentrations increasing worker productivity by 4.2 percent.

As an illustration of how environmental policies may increase capital and its productivity , one only has to think
Second, we denote the probability of undocumented migration by _Ii_ . This is only observed for the subsample of migrants. Therefore, the sample used to estimate equation (3) only includes return and current migrants. A migrant decides to undertake an illegal migration if the value of the unobservable latent variable _I_<sup>_∗_</sup> is positive and it captures the perceived gains from undocumented migration. By contrast, a migrant decides to undertake a legal migration if the value of the latent variable _I_<sup>_∗_</sup> is negative. In equation (3), we use the diplomatic exchange ( _Dipc,t_ ) between Egypt and the countries of destination of Egyptian migrants at the time of migration for identification. The diplomatic exchange variable is both country and year specific, and is derived from the Correlates of War Diplomatic exchange data set.<sup>17</sup> Our diplomatic exchange variable captures diplomatic representation at the level of _Charg ́e d’Affaires_ , minister, and ambassador between Egypt and all members of the Correlates of War interstate system, every five years for our period of analysis.<sup>18</sup> The identifying assumption is that exogenous shocks to the bilateral diplomatic relations between Egypt and the countries of destination of Egyptian migrants constitute a strong predictor of undocumented migration.

Third, we also account for return migration; i.e. not every migrant returns to Egypt within our window of observation. The probability of return migration is denoted by _Ri_ . This is only observed for the subsample of migrants (current and return migrants). A migrant decides to return to Egypt if the value of the unobservable latent variable _R_<sup>_∗_</sup> is positive and it captures the gains from return migration. By contrast, a migrant decides to stay abroad if the value of the latent variable _R_<sup>_∗_</sup> is negative. For identification of equation (4)
price effects we construct a real commodity export price index as in Keyfitz (2004) that is weighted by trade shares using the World Bank WITS data set and deflating it by the GDP deflator for each country. We take a simple HP-filter on this series to get the commodity price cycle (CMD� � �CMD� �CMD���) (see Alberola et al. (2016) for an alternative measure).

� where the weights w� are calculated using the share of each commodity (Pcomm�) in total country exports in the base period (the average observed between 2009-2011) deflated using GDP deflator (P��).

The output gap minus the fitted line on the real commodity price gap yields an estimate of the noncommodity output gap :

# **_Cyclicality of Fiscal Policy: The Role of Cyclical Lengths_**

To investigate the possibility that policy makers incorrectly identified long-term growth, and adjusted their fiscal policy accordingly, we follow Strawczynski and Zeira (2013). They show that the extent to which expenditures respond to output depends on the length of the cycle. This is related to the simple illustration in the theory section above. Fiscal policy is prone to become procyclical in cases where shocks lengthen the duration of the cycle. We follow Strawczynski and Zeira (2013) and Aguiar and Gopinath (2007) to create a variable that measures how the cycle becomes the trend: Where y� is the log of per-capita real GDP and K is the number of lagged differences. We normalize � � the above to one using σ∆��� �σ∆� and express the time sequence as: This ratio provides a measure of long-run variability to short-run variability, or the degree to which the cycle is the trend. If the standard deviation of per capita real GDP for any period lagged is the same as the difference of one period lagged, then there is no cyclical trend. However, we interpret a persistent cycle as one where the lagged variance to that of one period
it expects that otherwise it would certainly be subject to an NTB. This effect would be magni fi ed if the country already exports this product, hence we interact this variable with the export indicator as well. Moreover, the decision to export a good prior to the UR may be independent of the subsequent MFN tariff changes so that the instrument is uncorrelated with the error term. Nonetheless, we will be able to test and verify the exogeneity of the export indicator and NTBs as instruments because we employ a GMM approach. The set of overidentifying restrictions that allow us to perform these tests arise from including other instruments such as world price changes between 1992 and 1994 which can help to predict if a good was exported in 1994 but are unlikely to depend on the changes in MFN tariffs that take place in subsequent years.

The variable that captures the political economy effect, ∆x, is likely to depend on the MFN tariffs since it involves the production/import ratio weighted by the import demand elasticity, all of which are functions of the EU’s domestic prices and hence its MFN tariffs. Therefore, we employ the levels of these variables before the MFN tariff is implemented, e.g. 1978 for xt−1 and 1992 for xt. We fi nd some evidence of endogeneity and therefore instrument ∆x with a measure of scale economies (Value added/number of fi rms) and its interaction with the average world price change in the industry between 1992 and 1994. We also test this set of instruments to ensure that they are orthogonal to the error.

Finally, the reciprocity term also poses a potential endogeneity problem in the form of reverse causation since the total tariff reduction by other WTO members in the UR will partially depend on the EU’s reductions. We instrument reciprocity by using the unilateral portion of the total tariff reductions that are eventually offered at the UR. More speci fi cally, the reductions between 1986 and 1992 were unilateral because they were undertaken by various countries, outside of GATT negotiations
Policy Research Working Paper 9427

# **Abstract**

This paper develops and estimates a model of supply and demand for child care. On the demand side, one- and two-parent households make consumption, labor supply, and childcare decisions. On the supply side, non-parental care providers include child care centers, paid caregivers who operate at the child’s home, and unpaid, informal care-givers. Centers make entry, price, and quality decisions under monopolistic competition. Child development is a function of the time spent with each parent and non-parental care providers; these inputs vary in impact. The model’s structural parameters are estimated using the 2003 Early Childhood Longitudinal Study, which contains information on parental employment and wages, child care choices, child development, and center quality. The model features locations that differ in size and median family income. Parameter estimates are used to evaluate multiple policies including vouchers, cash transfers, and public provision. Vouchers that can only be used in high-quality centers or that require mothers to work are particularly effective, as - they deliver child development gains while increasing moth ers’ labor supply, thereby limiting policies’ fiscal cost.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 mferreyra@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
# **2 Green Goods Trade Landscape and Labor Markets**

## **2.1 Data**

We rely on data on NTMs, tariffs, and trade values from UNCTAD and WITS from 2014 to 2020 to calculate the AVEs. The data is disaggregated at the 6-digit level of the HS product classification. The NTM data is from the UNCTAD TRAINS (United Nations Conference on Trade and Development - Trade Analysis Information System) dataset, while the tariff and trade data are from the World Integrated Trade Solution (WITS) dataset. The NTM data uses a cut-off query every December 31st to get the annual period from 2008 to 2020, although the data was collected in 2015 and then again in 2018 in collaboration with the Economic Research Institute for ASEAN and East Asia (ERIA). The limit to this data is that this is not a true panel but a “manufactured” panel.

The second database used is the Philippines Labor Force Survey (LFS), which provides insights into the employment status of individuals in the labor force. It includes data on annual earnings, workers’ industrial affiliation, occupation, education level, and other related labor market aspects. The LFS data is collected quarterly, and for this analysis, we focused on the surveys conducted between 2010 and 2020 at the month of October to ensure consistency. It is important to note that the LFS invites different individuals in each round, making it a repeated cross-sectional dataset rather than a proper panel dataset. Although – the data is based on samples it includes weights that allow to make generalizations about the total labor force.

These two databases were merged with additional datasets that identify products classified as green goods. The green goods referred to in this note are based on the list of green products defined by the Green Transition Navigator (GTN)–a compilation of the APEC, OECD, and WTO green goods classifications. To arrive at our final database, we followed the following steps: first, we cleaned the trade data and ensured consistent
well as improved representation of hazards. Investment in research and technology in the field has been driven by the inadequacies of the historical record, mainly insurance claims data, for extreme events, and specifically by the inability of these data to robustly capture the statistics of catastrophic loss on insured portfolios. Unlike automotive or health claims, which are numerous and lend themselves to more reliable curve fitting for frequency and severity distributions, the small number of records of low-frequency and high-severity loss-making events do not provide sufficient evidence for such actuarial analysis. . While EP curves can be constructed from 20 years of observed historical loss data, they are limited to low-return-period estimation, are highly erratic and sensitive to data addition, and almost certainly underestimate loss levels, especially when considering average annual loss (Verisk 2013). Such records are rarely representative, especially in the extreme tail of the EP curve, which has few if any points to define it and is of primary importance to insurers and reinsurers. That is, history is not a good predictor of all possible future events.

Moreover, in low-income regions of the world, there is often acute data scarcity around historical evidence of natural hazards. The resulting short time series of hazards hampers the estimation of the EP curve. The increasing availability of remotely sensed weather information from satellites has helped provide global coverage for hazardrelated data sets that now range from 10 years to 40 years in length. The range of variables inferred from satellite data has also expanded to include new ones such as soil moisture, as new sensors come online and provide reliable training and validation data.

A key component of catastrophe modeling is developing statistical and physics-based methodologies, which vary by hazard type, to extend limited historical catalogs of events to many thousands of years of probabilistic events in order to adequately capture frequency, extreme severities, and full geographical location. This process is computationally intensive, and great care must be taken to ensure that resultant event sets are constrained by observed physical processes; for example, definition of hazard parameter distributions of climatological
Data from the 2015/16 HCES show that household expenditures on mobile services in 2016 corresponded to 1.4 percent of total consumption (CSA 2018). The share was lower among households at the bottom of the income distribution. Thus, the average expenditure of the lowest income decile on mobile phone services was about Br 83 a year (US$10 a year at purchasing power parity) (table 1). This represented 0.43 percent of the decile’s total consumption. Meanwhile, the top income decile spent Br 1,729 (about 2.31 percent of the corresponding decile consumption). In demand across the consumption distribution, only 14 percent of households in the poorest decile reported positive spending on telecommunication services, while the corresponding share among households in the top consumption decile was 76 percent. The low spending on mobile services may be partially explained by the lack of adequate network infrastructure. An estimated 15 million Ethiopians are out of reach of the cellular network, and 60 million are without access to the internet. This barrier is coupled with the high costs of handsets that are driven by import duties and a concentrated retail market.

**Table 1. Household Expenditures on ICT Services, by Consumption Decile, 2015/16, %**

|_Household expenditure_|_All_|_Poorest_<br>_1_|_2_|_Con_<br>_3_|_sumption_<br>_4_|_decile_<br>_5_|_6_|_7_|_8_|_9_|_Richest_<br>_10_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|ICT|1.456|0.427|0.663|0.882|1.020|0.947|0.928|1.185|1.331|1.628|2.314|
1 percent level) and showed more improvement in business practices than women who were assigned just to the basic training. This additional attention cost twice as much as the basic training alone.

# _Individual Consulting_

A related literature looks at the impact of providing consulting services on a one-on-one basis to firms in order to improve business and management practices. The closest to the business training experiments is the work of Karlan et al. (2012), who study a mix of 160 male and female tailors in Ghana with 5 or fewer workers. They used local consultants from Ernst and Young in Ghana, who met with the tailors in visits of 30 minutes to 1 hour several times a month over one year, with the average firm receiving 10 hours of consultant time over a year, at zero cost to the firm. In addition some firms receive grants of around US$133. They find some of the consultants recommendations were adapted for some months, but had been abandoned one year after training stopped. There is no significant impact of either treatment on profits or revenues, with some specifications showing negative effects in the short-run, although power is very low and confidence intervals wide.

Bruhn et al. (2012) evaluate a state government program in Puebla, Mexico, which paired small businesses with a consultant from one of several local consulting firms. Consultants spent approximately four hours per week over a year assisting the firm in overcoming constraints to growth. 432 firms applied to the program, and 150 were chosen to receive heavily subsidized consulting services (the cost was about 10% of the commercial rate). The mean number of employees was 14, and 72 percent were male-owned. The training impact was assessed with a single follow-up survey one to three months after the consulting. The authors find large point estimates for the impacts on sales and profits, which are sometimes significant depending on the measure used and the extent of trimming. However, the study faces many of the same challenges as the business training studies reviewed above. First,
Due to seasonal variations in returns to labor in farm activities, individuals might work temporarily in the non-farm sector. Some household members might work off-farm if there are diminishing returns to labor in agricultural production. Simultaneous income generation from both farm and non-farm sectors can be a strategy to cope with income risk if appropriate insurance markets are not available. Engaging in activities independent from agriculture such as manufactures can counter farm-related risk. Households in Burkina Faso that diversified their income into non-farm activities were more able to cope with droughts during the 1980s (Webb and Reardon 1992). Being able to hedge against risk in this way may enable farmers to increase the adoption of more risky high-return crops (Lanjouw and Lanjouw 1995, 2001). Other market failures can also explain income diversification. If capital markets do not exist, households investing in farm equipment will engage in non-farm activities to raise the required capital. Nonfarm income in Burkina Faso increased farm productivity by stimulating the adoption of animal traction (Savadogo, Reardon and Pietola 1998).<sup>11</sup> Missing land markets might prevent individuals whose comparative advantage is in non-farm employment from fully specializing in these activities.

Rural non-farm employment is not an option for all rural households. Zezza et al. (2008), using survey data from 15 developing countries, identify patterns along demographic characteristics as well as asset possession of households by income source. They conclude that households are more likely to engage in on-farm work if they own land, have lower levels of education, have little access to infrastructure and a greater share of their labor force members is female. Households deriving income from non-agricultural wages are usually more educated and have better access to infrastructure. Households receiving income from remittances are more likely to have a female head. Reardon (1997), focusing on African countries, draws similar conclusions but stresses the fact that households with higher income from non-farm activities also have higher total income and larger landholdings. This raises the question of causality. Either income diversification into non-farm activities leads to