rafmacalaba/data-use-mentions-extended huggingface dataset:
3455 municipios reporting industrial employment and these municipios are therefore excluded from the analysis.

In the ensuing analysis, emissions for the Brazilian plants are imputed according to pollutants per employee, within a specific sector, for plants in a given size category, from the Mexican data base. To achieve this matching, both the Mexican and Brazilian data are first aggregated to the 3-digit level of International Standard Industrial Classification (ISIC).

# **4. Results: Pollution Intensity,** Scale and **Dirty** Sectors

The results from the Mexico data are presented first in this section, followed be the results of applying the Mexican emission intensity measures to the Brazilian plant data.

# **4.1 Scale and Pollution Intensity: Evidence from Mexico**

The first empirical exercise measures the relative air pollution intensity of small and large plants in the Mexican database. Intensities are computed as particulate emissions per unit of labor, because comparable output data are not available.<sup>3</sup> Using the sample of 6,000 plants, the logarithm of emissions per employee is first regressed on logarithm of actual plant level employment, with dummy variables to control for sectoral differences. The estimated elasticity of emission intensity with respect to employment is -.65, with an extremely small standard error (the t-ratio for a zero null hypothesis is 34.19). In other words this suggests that plant-level emissions per unit of labor decline by .65% for each 1% increase in employment.

Table 1 shows the average emission intensity for each sector and plant size category. While the general pattern is consistent with a negative relationship between pollution per employee and plant size, it also reveals substantial variation. Indeed in some sectors (such as basic foods, petroleum products and industrial chemicals), large plants are more pollution-intensive than small ones.

> 3 If small enterprises are more labor-intensive (per unit of output) than large facilities, then a negative relationship between plant size and the pollution/labor ratio implies
0.43|–0.43|–22.4|–27.8|–5.66|–9.85|
||(26.4)|(16.9)|(7.95)|(6.30)|(26.8)|(20.1)|(10.6)|(8.59)|
|Mean of dep. var.|0.50|0.50|0.20|0.20|0.72|0.72|0.36|0.36|
|Control variables|No|Yes|No|Yes|No|Yes|No|Yes|
|No. of municipalities|153|153|153|153|153|153|153|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ The running variable of the RD design is the incumbent margin of victory. All regressions are weighted by a triangular kernel with a bandwidth of 0.05. Reported values are marginal effects from Poisson regressions, calculated at the sample means. Standard errors are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels. Control variables are: population, area, as well as share of households with electricity, piped water, indoor plumbing, roof made of strong building material, and wall made of strong building material. Following the classification of the 2000 Census of the Philippines, “strong” building materials for walls are defined as concrete, brick, stone, wood, galvanized iron or aluminum, asbestos and glass. “Strong” building materials for roofs are defined as concrete, galvanized iron or aluminum, clay tiles and asbestos.

21
household consumption across the Jordanian population, the sharp decline in poverty observed in 2002-2010 is mostly due to the strong rise in consumption levels rather than the reduction in inequality. It is however interesting to note that the contribution of the redistribution effect becomes larger as we move away from the decomposition of the headcount index and examine poverty incidences that reflect poverty severity such as P1 and P2. Indeed the contribution of the redistribution effect more than doubles from 12.3 percent when looking at the decomposition of P0 to 29.7 percent when examining the decomposition of P2. These findings endorse the argument that the poorest in Jordan have been successful in reducing the gap with the rich in the past decade, a positive contribution brought forward by the strong economic growth of the past decade.

|**Chane in incidence of overt (% )**<br>**Table 4: Grow**|**th and R**|**edistribut**|**ion Decompositi**|**on of Pover**|**ty Changes**||
|---|---|---|---|---|---|---|
|**g py**<br>**Poverty Line= JD813.7**|**2002**|**2010**|**Actual change**|**Growth**|**Interaction**|**Redistribution**|
|**Poverty Headcount Rate (P0)**||||**_Cont_**|**_ribution to Chan_**|**_ge (%)_**|
|Kingdom|32.0|14.9|-17.1|83.3|4.4|12.3|
|Urban|29.4|14.4|-15.1|83.1|3.
transfers); a positive contribution is a reduction in Gini, negative contribution is an increase in Gini.

Budget Is the budget identified in the survey. Effectiveness is marginal contribution / budget. GST and SST include the cascading indirect effect of GST exemptions. Total electricity and water subsidies include residential subsidies and the indirect effect of commercial and industrial (cross) subsidies.

Source: 2017-18 HIES, 2018 Government Budget, administrative data and World Bank analysis.

# Results in International Context

In this section we compare Jordan’s performance to other countries in terms of fiscal space and distributional impact (all data are included in an appendix). The database we use is from the CEQ Institute and includes 42 different countries, some with multiple years of studies. We begin by looking at the overall progressivity of Jordan’s fiscal system relative to other countries. Figure 9 presents the extent to which fiscal policy in 42 countries (with five countries having two entries) reduces inequality as measured by the Gini Index. It shows both the reduction in Gini when moving from market income to consumable income— that is, taking only cash taxes and benefits into account (upper panel)—and from market income to final income—that is, including public spending on health and education (lower panel). The charts are ranked from most inequality reducing to least. Whether we consider inequality in strictly monetary terms or also
cuts. Second, in light of the time and skills required, specialized production surveys relying on crop cutting tended to neglect broader socio-economic variables, making it difficult to analyze results as an outcome of rational decision-making by an agricultural household. A second method that has been used traditionally to provide production information relied on periodic reports from agricultural extension workers. The lack of a micro orientation and the potential interest of such agents to report favorable outcomes tend to limit the credibility of such estimates. On the other hand, remotely sensed data may be useful for aggregate assessment of crop conditions and expected harvest amounts but too crude to provide the micro-evidence needed to address the multitude of issues -from determinants of technology adoption, interactions between crops, and equity impacts of improving technology across and within householdsthat are of considerable interest to policy makers. Given different costs, strengths and weaknesses by each of these methods, collecting meaningful data in a cost-effective way will invariably entail trade-offs. Failure to understand or address these trade-offs can undermine the credibility of agricultural statistics and the ability to generate meaningful research and policy prescriptions.

In light of these constraints, multi-purpose household surveys have emerged as a key tool to provide agricultural production information as well as complementary information that would allow analysis of agricultural supply, rural welfare, and links to the non-farm economy<sup>1</sup> . It is well known that a number of factors that include inadequate training of enumerators, long lags since completion of the harvest and the implied recall error, control of plots by people other than the respondent, use of non-standard units of measurement, intercropping, and limited reliability of self-reported area measurements from respondents without actual cross-checking in the field, may limit the accuracy of information provided by such surveys. A combination of these factors can result in agricultural production data of poor quality with large measurement errors. Surprisingly little attention has been devoted to measuring the extent of such errors and ways in which it could be minimized, although there are some initial attempts at taking this more seriously,
unreasonable to assume that at least part of this contribute this to the underlying IPA characteristics. In addition, to the extent that IPAs can align with the wider government’s sectoral growth strategy (or push sector-specific reforms through internal advocacy), this itself reflects an “effective” IPA.

# **5. Results**

## **5.1 Sectoral Gravity Model**

The results for the sectoral gravity model are presented in Table 5. The first equation uses traditional gravity model variables from the CEPII database (Head and Mayer, 2014) for the OLS and PPML model, respectively, and combines this with simple year fixed effects. Both models operate in line with expectations, as GDP source country and GDP host country are significant and positively correlated to FDI stock, while source-host distance is negatively correlated. Countries that are more culturally proximate (e.g. through a common language, colonizer or religion) also appear to be more likely to invest in one another in the OLS model, though the common religion variable is the only cultural approximation variable that is significant in the PPML model. In both cases, IPA sector targeting is highly significant and positive.

In the second set of regressions (equation 2 and 4), the traditional gravity variables are replaced with country-pair fixed effects, and time-varying dynamics are captured through source-year and host-year fixed effects. In both cases, the IPA sector targeting coefficient remains significant and positive, though reduces in size (thus capturing part of the unobservable country-pair FDI dynamics through fixed effects).

The third set of regressions further include sector-year and host-sector fixed effects. Again, the IPA sector targeting coefficient remains significant and positive, but further reduces in size (suggesting that IPAs are likely targeting those sectors which see larger increases in their FDI stock across the board). Our most conservative, and preferred, estimate is from the PPML model with the widest set of controls (equation 6). This suggests that an IPA’s targeting of a specific sector is associated
a measure of household permanent income in the parental generation, and we estimate returns to education equation (1) above using this measure of permanent income. For returns to education at the household level in children’s generation, we use the household expenditure data from the last two waves (2007 and 2014).

An important data issue for estimating intergenerational mobility is sample selection bias that arises from the fact that many of the existing household surveys such as LSMS and DHS include information on only those household members coresident at the time of the survey. The criteria used for household membership may vary, but when nonresident parents or children are not included in a survey, it can cause substantial biases in the estimates of intergenerational persistence in education. We utilize the household roster, nonresident parents module, and mothers’ marriage module to gather the education information on fathers and children. For details, please see the online appendix on data.

We use the Raven test scores and two memory tests to construct an index of cognitive ability. An advantage of these measures is that they do not require any knowledge of numeracy or literacy to do well in the tests. For a detailed discussion on the construction of the cognitive ability index, please see pp. 18-19 in section (5.1) below. Table 1 provides the summary statistics for our various estimation samples, separately for rural and urban areas. In our “mobility sample”(18-40 years old children in 2014 wave), the average education of rural fathers is 5.66 years, and 7.54 years for urban fathers. The rural children attain 9.05 years of schooling, while the urban children acquire more than a year more schooling at 10.18 years on average. The household income and education expenditure are expressed in the units of 10,000 rupiahs and are deflated by rural and urban average rice prices in each wave of the survey. In the education expenses sample (pooled 1993, 1997, 2000, 2007), the average educational expenditure
of potential program effectiveness<sup>16</sup> . The total effect of targeting on the enrollment of hectares at above median risk of deforestation is captured by the area under the dashed lines in Figure 5. These comparisons show that while WTA targeting enrolls the most hectares, it enrolls the least area at high risk of deforestation. The highest enrollment of hectares at high risk is under pure risk targeting, followed by targeting on risk per WTA.

Finally, the figure includes the mean simple assets index for enrolled properties under each scheme. Mean assets are slightly lower under both risk and risk per WTA targeting than under the other two schemes indicating more targeting to poorer communal properties. Appendix A.2 statistically examines shifts in the average values of deforestation risk and poverty for enrolled properties and demonstrates that the wealth levels of beneficiaries from a risk or risk/WTA targeting strategy are statistically significantly lower than those of the current program, while the average levels of deforestation risk are higher. In summary, the simulation results indicate that by combining referendum-based WTA with measures of deforestation risk, it is possible to retarget the program to achieve more avoided deforestation at less social cost and without adverse distributional consequences.

# 7 Using WTA estimates to set prices

The other lever for changing program cost-effectiveness is to adjust the payment scheme. This section presents two possible approaches to adjust the payments offered within the six zones described in Table 1. In both cases, the payment schedule offers a single price for forest in each zone, which in effect maintains the system currently used by the government but changes the amounts offered.

The first approach, which we call “adjusted payments”, sets payment rates so that the supply of forest derived using our WTA estimates is equal to the area enrolled in each zone type under the present scheme. We then exploit our community-level median WTA estimates as best guesses of the payments that would induce participation of land submitted by the communities that we inter-

> 16Since deforestation tends to occur in small increments in Mexico
BLS. (2013) Green Goods and Services survey: results and collection. U.S. Bureau of Labor Statistics. Monthly Labor Review. September 2013.

BLS. (2013) The Green Goods and Services Occupational survey: initial results. Warren, Zack. U.S. Bureau of Labor Statistics. Monthly Labor Review. January 2013.

Canada Survey of Environmental Goods and Services, 2012. Environment Accounts and Statistics Division. Statistics Canada Environmental Goods and Services Sector (EGSS): list of products and activities. Excel. Version as of 6/1/2016 Eurostat (2009). The environmental goods and services sector. doi 10.2785/31117 Federal Planning Bureau (2014). The Environmental Goods and Services Sector in Belgium 2010-2011. Report for Eurostat OECD, Eurostat (1999). The environmental goods and services industry: manual for data collection and analysis.

# **Process approach**

BLS (2012). Green Technologies and Practices Questionnaire. O.M.B. No. 1220−0184 BLS. (2011) Counting Green Jobs: Developing the Green Technologies and Practices (GTP) Survey. Conference paper: Stang, S. and Jones, C. Section on Survey Research Methods : JSM 2011 BLS. (2012) Green technologies and practices: August 2011. U.S. Bureau of Labor Statistics. News release: June 29, 2012.

Oyunbileg, D., Stoevska, V. (2017) Employment in the environmental sector and green jobs in Mongolia. Pilot Study. International Labour Organization (ILO) Stoevska, V., Elezi, P., and Muraku, E. (2014) Report on the pilot project towards developing statistical tools for measuring employment in the environmental sector and generating statistics on green jobs. Albania. Intenrational Labour Organization (ILO)

# **Task approach**

Dierdorff, E.; Norton, J.; Drewes, D. W.; Kroustalis, C.; Rivkin, D.; and
<mark>stringency of the social distancing measure, a mobility measure, and a measure of scale-up of cash transfer programs at the country level to understand what is behind the lack of precision in some</mark> of the specifications.

<mark>Second, we apply a macro-micro simulation methodology in five countries that combines preCOVID-19 household surveys with national accounts data for 2020. The methodology follows the household income generation model proposed by Bourguignon and Ferreira (2005), and instead of using a CGE model or macro projections to go from macro changes to micro impacts, we use actual 2020 data on employment level, sectoral GDP, private consumption, and remittances. The results of the simulation are used to project changes in household per capita income or consumption by quintiles between 2019 and 2020, changes in poverty rates at different lines, and</mark> changes in the shares of the vulnerable population and middle class.

<mark>Third, we compare the changes in poverty, vulnerable population, and middle class obtained using actual 2020 employment data in the macro-micro simulation methodology versus using</mark> employment projections based on the calculation of employment elasticities.

<mark>Finally, we validate our estimations for Brazil using 2019 and 2020 microdata from the PNADContínua. Using this survey, we calculate actual poverty rates and poverty rates excluding incomes from emergency assistance programs implemented since the pandemic started. This allows us to isolate the poverty changes that are explained by the job loss, labor income, and remittance</mark> channels.

<mark>Our results can be summarized as follows:</mark>

- <mark>(i) In most countries, employment estimates for 2020 based on elasticities were reasonably accurate. The projected levels of employment are within 5 percent of the actual levels in 11 of the 15 countries. However, in four countries the projections significantly overestimated employment levels and therefore underestimated job loss due to the crisis. This bias tends to be larger (i) for labor markets that were more
- 44 -

current main occupation and the time worked in the previous main occupation, if any. This is greatly preferable to the customery proxy variable (age minus six minus years of schooling/training) which only measures potential experience. Moreover, in the C8te d'Ivoire, this proxy could underestimate actual experience for people without periods of unemployment or inactivity because many peorle combine schooling and work.15/ The dependent variable for the analysis is the natural logarithm of earnings. There is a debate in the earnings function literature as to what is the most appropriate time period for this variable (see for example, Blinder (1976) and the references in there). The main argument in favor of s.hort periods (day, hour) is tnat this Deat reflects the transaction price of labor and excludes the effects of the labor-leisure choice (see for example, Griliches, 1977). on the other hand, many workers get paid ovet longer time-units, which for them establishes the relevant transaction price since neither supply nor demand takes place in smaller units. The CILSS questionnaire gave respondents the choice of time-unit to report earnings. More than 90% of modern sector employees reported earnings on a monthly basis, and even in the informal sector, 55% of employees did so. This is not surprising since the Government of

- 15/ In eight cases, people reported work experience on their current job to equal or exceed their age. In about 1.5% of the cases, the difference between age and experience was less than 6 years. We considered those cases to be reporting errors for experience and capped experience at age minus 6 years.
>of<br>rooms<br>in<br>the<br>household|2.2|2.3|2.1|2.4|UNHS (2016)|
|Household owns a cellphone|74.0|72.9|75.4|74.3|UNHS (2016)|
|Household<br>has<br>access<br>to<br>electricity|15.4|15.1|15.6|21.0|Census (2014)|
|Household has access to safe<br>water|75.2|72.2|71.7|72.0|Census (2014)|
|Household owns the dwelling|81.9|82.9|81.1|71.8|UNHS (2016)|
|House has brick walls|67.2|63.8|64.2|66.6|UNHS(2016)|

Table 6.3 breaks down the share of working population by area of residence. The calibrated GWSM base estimator (82.0%) seems closer to the official statistics than the current UNPS estimates (78.8%) in representing the official statistics in rural areas (82.6%). As indicated earlier, these differences, although small, are statistically significant. More controversial are the estimates in the urban areas, where we do not see a best estimator.

<u>Table 6.3. Employed population by area of residence</u>

||Unweighted|UNPS|Calibrated GWSM<br>base|<br>Off|icial Statis|tics|
|---|---|---|---|---|---|---|
||Mean|Mean|Mean|Mean|Year|Source|
|Rural|79.6|78.8|82.0|
# **3 Empirical Strategy**

A key advantage of using loan-level data is that we can set up our dataset at the country-sectoryear level and look at new loans in a given country-sector pair after an MDB has entered that country-sector co-financing a syndicate loan, controlling for time-varying unobservable factors both at the country and sector level. Thus, we evaluate whether the presence of an MDB attracts private capital by estimating the following equation: where _ycs_ , _t_ is the outcome variable in country-sector pair _cs_ at time _t_ . As anticipated in the previous section, in the paper we explore effects on different outcome variables defined at the countrysector-year level: 1) the number of syndicated loans; 2) the total size of syndicated loans, scaled by GDP; 3) the average number of banks involved per syndicated loan; and 4) the average loan maturity of syndicated loans, in years. When measuring the outcome variables, in the baseline analysis we focus on indirect mobilization and we always exclude the loans with MDB participation. In additional analyses, we exploit the richness of the dataset to disentangle direct and indirect mobilization effects by computing the number of banks and loan size including also the partners of the MDBs and the amount they lend in the loans with MDB participation; see Section 5.2.

We also include a lagged dependent variable, _ycs_ , _t−_ 1, as lagged syndicate lending in the countrysector _cs_ could be an omitted variable that is time-varying within sector and that represents a threat to our identification strategy.

Our key explanatory variable ( _MDBcs_ , _t_ ) is a dummy equal to one if there is at least one deal supported by MDBs in the country-sector pair _cs_ at time _t_ .<sup>12</sup> The coefficient _β_ 0 of _MDBcs_ , _t_ measures the contemporaneous MDBs’ (indirect) mobilization effect. However, to allow for the possibility that mobilization effects show up with a lag, we include up to two lags of _MDBcs_ , _t−k_ and then calculate the cumulative effect between year