rafmacalaba/data-use-mentions-extended huggingface dataset:
it is not just the presence of an important commodity sector, but the size of the sector also matters.

The introduction of this new variable also reduces the size of the EMDE dummy as some of these countries have substantial rents coming from natural resources and it, therefore, absorbs some of the variation in fiscal policy volatility. Note that GDP per capita becomes significant once this variable is introduced, suggesting that higher levels of GDP per capita are associated with lower fiscal policy volatility. Therefore, taken together these results suggest that both the level of development and the degree of commodity dependence help explain fiscal volatility. Finally, the fact that a limited set of variables explains about 45-50 percent of the cross-country variation in fiscal policy suggests that our measure of volatility is not simply capturing noise from the initial regression in equation (1).

Next, we exploit the richness of our diverse sample of commodity exporters by separating them into three groups—agriculture, metals, and energy exporters—depending on the main type of commodity exported to examine whether there are differences in fiscal behavior across different types of commodity exporters. Table 3 presents the results. We find evidence of additional volatility for all three groups, but it is for the energy exporters where the coefficient is always

> 16 We chose to use ‘resource rents’ instead of resource revenues due to data availability issues. The ‘Natural Resource Governance Institute’ provides data on resource revenues (as percent of GDP) complied from the Extractive Industries Transparency Initiative’s (EITI), IMF, and the International Centre for Tax and Development (ICTD). The database provides data for about 90 countries from the 1980s onwards although data availability varies across countries. The correlation between our measure of total resource rents and the resource revenues from this dataset is about 69 percent.

11
**Figure A.1 Employment Rates by Age Cohort in China: Cohort Averages from the Population Census and One Percent Population Sample**

<!-- Start of picture text -->
1.0 1.0<br>Urban Male Urban Female<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>1990 2000 2005 1990 2000 2005<br>0.0 0.0<br>1.0<br>1.0<br>Rural Male Rural Female<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>1990 2000 2005<br>1990 2000 2005<br>0.2 0.2<br>0.0 0.0<br>40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70+ 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70+<br>40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70+ 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70+<br><!-- End of picture text -->

Source: National Bureau of Statistics, 2000 Population Census Statistical Materials (2002) and 2005 One-Percent Population Sample Statistical Materials (2007), China Statistics Press, Beijing.

37
Goldberg, P. and N. Pavcnik. 2007. Distributional Effects of Globalization in Developing Countries. Journal of Economic Literature, 45(1): 39-82.

Goldberg, P. and N. Pavcnik. 2005. Trade, Wages, and the Political Economy of Trade Protection: Evidence from the Colombian Trade Reforms. Journal of International Economics 66(1): 75–105.

Ghani, E., A. Goswami and W. Kerr. 2016a. Highway to Success in India: The Impact of the Golden Quadrilateral Project for the Location and Performance of Manufacturing. The Economic Journal, 126(591): 317-357.

Ghani, E., A. Goswami and W. Kerr. 2016b. Highways and Spatial Location within Cities: Evidence from India. World Bank Economic Review, forthcoming.

Ghani, E., A. Goswami, and W. Kerr. 2013. The Golden Quadrilateral Highway Project and Urban/Rural Manufacturing in India. World Bank Policy Research Working Paper # 6620.

Ghani, E., W. Kerr, and S. O'Connell. 2013. Local Industrial Structures and Female Entrepreneurship in India. Journal of Economic Geography, 13(6): 929-964.

Ghani, E., W. Kerr, and S. O'Connell. 2014. Political Reservations and Women’s Entrepreneurship in India. Journal of Development Economics, 108(C): 138-153.

Ghani, E., A. Mani, and S. O'Connell. 2013. Can Political Empowerment Help Economic Empowerment? Women Leaders and Female Labor Force Participation in India. World Bank mimeo.

Ghani, E., W. Kerr, and C. Stanton. 2014. Diasporas and Outsourcing: Evidence from oDesk and India. Management Science, 60(7): 1677-1697.

Government of India (GOI). 1997. Report of the Expert Committee on Small Enterprises. New Delhi. Available at: <u>http://www.isedonline.org/uploads
to a detailed analysis of mobility with different data cleaning methods. We will compare the results with different cleaning approaches.

# **VI. Mobility: Egypt’s panel data in perspective**

Table 5 uses the most commonly used tool to present mobility: the transition matrix. The sample is divided into _n_ equally sized income classes (e.g. deciles, 10). The matrix shows what share of each decile population in 2005 stayed in the same position in 2008 (diagonal) or moved to a different decile.

16
in India (Yadav, 2007; Solomon, 2016). Mechanizing the rice and wheat harvest is done using combines, which data from the Agricultural Input Survey shows are present throughout India prior to the implementation of MNREGA but tend to be present at higher levels in areas with higher use of fire.

The relationship between mechanization and the use of fires is driven by the fact that harvesting with a combine leaves more residue on the field than harvesting by hand (Yang et al., 2008). Specifically, combine harvesting leaves stocks that tend to be around 30-40cm as opposed to the 10-15cm that harvesting by hand leaves. The higher stalks interfere with the ability of farmers to plant the following season’s crops and must be removed to facilitate planting (Jain et al., 2014; Smil, 1999; Cassou, 2018; Bhuvaneshwari et al., 2019).<sup>35</sup> Burning is the least expensive way of dealing with this residue (Cassou, 2018), as interviews with farmers indicate: “Ankit Choyal Jat...offers an answer. ‘If I can clear my farm using a one-rupee matchbox, why will I spend thousands? (Jitendra et al., 2017)’” While combines were used in harvesting prior to the implementation of MNREGA, the shock that MNREGA provided to agricultural labor markets led to a substantial increase in their use. The only existing study of the impact of MNREGA on agricultural production processes (Bhargava, 2014) shows that the use of labor saving animal-based production processes increased after the implementation of MNREGA. Data from the Agricultural Input Survey shows a substantial increase in the average number of combines from 2006 to 2011 (Figure A3) – an increase that is much larger both in absolute and percentage terms than the increase from 2001 to 2006. Such an increase is consistent with broad state level trends from the Cost of Cultivation survey that show a decline in the amount of labor used in agriculture and an increase in the amount spent on machine
so that fiscal revenues from the scheme would equal As expected, a tax on work permits is passed on to the seller through higher equilibrium prices. A regulated market for work permits could thus create a binding floor for the earnings of domestic workers, a floor that could further be altered through taxes (and subsidies).

We can also define and solve the inverse problem. For this, we take it as given that the ̄ government wants to attain some desired basic income guarantee Π<sup> ̄</sup> and find tax rate _τ_<sup>_p_</sup> so that market-clearing condition (7) holds, i.e.

# **3 Calibration**

To calibrate our model and give some empirical estimates to the variables we derived, we look at the specific US-Mexico migration corridor. We use data from the 2018 Annual Social and Economic Supplement of the US Current Population Survey (CPS), US Census Bureau (2019), and the Mexico National Survey of Occupation and Employment (ENOE) (INEGI, 2021). The CPS is a monthly survey of approximately 60,000 US households. The survey provides information on labor force participation, employment, unemployment, hours of work, earnings, and other demographic and labor force characteristics. The March Supplement of the CPS includes detailed questions on income received in the previous calendar year. We use the official poverty thresholds for the US, which gives a poverty rate of 12.3% in 2018 (Semega et al., 2019).

For Mexico, we use the 2018 round of ENOE, a tri-monthly survey applied to a representative household sample in Mexico. The survey aims to provide statistical information on the population’s occupational and substantive socio-demographic characteristics at the national 8
/sup> The 2018 survey collects information on democracy and governance, intergroup relations, education, crime and security, poverty, the labor market, household characteristics and assets. The ENAHO likewise uses face-to-face interviews and a probabilistic three-stage sampling method to collect information on personal and household characteristics of all household members and their living conditions. While its primary aim has been to monitor the evolution of monetary poverty, ENAHO has become a key source of information for wider policy uses. The 2019 survey, which encompassed 23,347 households, included data collection modules on citizen participation, governance, democracy, transparency, discrimination, perception of insecurity, and access to justice.<sup>8</sup> As such, SASAS and ENAHO provide a unique, long-term account of the speed and direction of changes in the underlying public perceptions, values and social fabric of South Africa and Peru, making them an ideal tool to inform policy-making focused on enhancing social sustainability and reducing fragilities of exclusion.

Our empirical application seeks a common denominator consistent with all the reported dimensions across both surveys and proposes to assess social sustainability across four dimensional pillars: inclusion, resilience, social cohesion, and legitimacy. We opt for inclusion, instead of equity, to analyze equal access to economic, political, civic, and physical spaces. Inclusion thus refers to the process of creating opportunities for all people and addressing deep systemic inequalities. It involves improving the ability of all people to access basic services like running water, human capital services like schools, and markets (including the labor market) regardless of their personal or community characteristics. We select resilience and social cohesion, , to capture readiness for all kinds of shocks while accounting for the strength of inter-personal relationships and the broader sense of solidarity among members of a society. Resilience thus refers to the ability of communities and groups in both fragile and nonfragile environments to cope with shocks such as climate change, pandemics,

> 7 <mark>Small area layers (SALs) were used as primary sampling units
vs. recall|Recall|Recall|Recall|Recall|Diary|Recall|Recall|Diary/<br>Recall|
|Reference period<br>(food<br>consumption)|Daily/7<br>Days|Daily<br>/<br>weekly<br>(for<br>2<br>weeks)|7<br>days/1<br>month/1<br>year|1 month|1 month|1 week /<br>typical<br>month|<br>2 weeks /<br>one month|1 week|
|Food<br>quantities<br>available|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes<sup>(2)</sup>|
|Non-food<br>expenditures (no.<br>of items)|38|221|122|338|483|95|99|97<sup>(3)</sup>|
|Reference period<br>(non-food exp)|1/12<br>months|1/3/12<br>months|1/12<br>months|1/12<br>months|1/12<br>months|1/12<br>months|1/12<br>months|1/6/12<br>months|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). (1) They included 6 liquor and tobacco COICOP codes among food expenditures.

(2) Units of measure are specified in the questionnaire but not in the datasets. (3) Tobacco and drugs excluded.

The collection of consumption data also varies from country to country. Consumption data are generally collected using two methods, (i) diary – where the household records all the consumption data over a certain period in a notebook, or (ii) recall
2001 and 2007. Note: Tariff data as of mid 2000s In less than two decades, Mali has managed to provide access to improved water and sanitation to more than half of its population, or in the worst case (figure 10). The share of the population with access to improved water doubled from less than 30 percent in 1987 to 60 percent in 2007 (figure 9a). Over the same period, access to improved sanitation rose from a 40 percent share to over 50 percent. The migration of people from no services to improved services has been achieved by providing shared low-cost alternatives. For instance, 40 percent of the 60 percent of the population with improved water access, has 19
is not available in the underlying national account data used in GTAP. The next best thing was to use sectoral employment data that matches the GTAP data.

The United Nations Industrial Development Organization (UNIDO) provides such employment data at a relatively high level of disaggregation (4-digit ISIC sector) for a large number of countries and years but the data are only available for the industrial sector. Therefore we resort to the ILO statistics, which compile sectoral employment data for various countries and years. However the country and sectoral coverage is considerably more limited than that of GTAP, which reduces the number of countries and sectors for which the jobs content of exports is available. In particular, the prevailing aggregation of sectors in the ILO statistics consists of 11 sectors (see Table A3 in Appendix 1), which we match with the original GTAP sectors.

We replace the diagonal matrix of the compensation of employees in total output �<sup>�</sup> with the equivalent matrix of employment in output �<sup>�</sup> , which is obtained by the number of employees per total output Z. By replacing �<sup>�</sup> with �<sup>�</sup> in equation (3) we can compute the J matrix ���<sup>�</sup> � and then derive the counterparts of the _G_ and _H_ matrixes in terms of jobs contained in output and exports. Appendix 3 lists the steps needed to reconstruct the LACEX database from GTAP and Appendix 4 for the list of variables in the labor value added and jobs content of exports data sets.

One drawback of this approach is that the employment data are not always consistent with the compensation of employees data used to compute the labor content of exports. The original source of the former is typically national labor force surveys. These are often not easily reconcilable with national account data, which are the source of compensation of employees data. This discrepancy is particularly acute at the sectoral level also
the WBES is that it includes not only a module measuring management practices of firms but an additional “Green Economy” module that solicits responses on firms’ energy use, type of pollution emissions, and (a limited set of) practices relating to energy conservation (e.g., energy target-setting, monitoring, and external audits).

Our analysis advances the literature on the energy-management nexus in the following ways. _First_ , we characterize the variation in energy intensity with respect to generic management practices as well as energy-centric management practices. _Second_ , the WBES Green Economy Module provides information on the physical quantity of energy used, which allows us to measure the association of management practices with a close proxy for environmental impact. _Third_ , the presence of information on both general and energy-specific management practices within the same survey allows us to consider their separate and combined influence, since either could be an omitted variable when examining relationship with energy intensity. _Fourth_ , our data allows us to explore the relationship of generic and energy-centric management practices and firm outcomes in services, a sector that remains largely ignored in such discussions. _Finally_ , of the sample of 31 countries in WBES dataset, eight are in the “Lower Middle” or “Low” income categories. This enables us to also explore the similarities and differences in the relationship between management practices and energy intensity in advanced and developing economies.

We report four main findings. _First_ , while generic management practices negatively correlate with energy expenditure, they bear either a positive or no relationship to physical energy use, suggesting that management can help firms save energy costs without significantly reducing environmental impacts. _Second_ , among the structured management practices examined, target-setting practices are most strongly associated with reduced output-indexed energy expenditure. In both cases, these results are mainly driven by manufacturing firms, while the relationship between management practices and energy intensity is weaker in services.<sup>4</sup> _Third_ , general management practices are correlated with greater discipline around

> 4The coefficient on target-setting practices was negative but insignificant
>or with an “a<br>|ey design.<br>ror terms an<br>he estimate<br>consumpti<br>” respective|All estimates<br>d Method 2 u<br>d parameters<br>on data in TA<br>ly fall within|(1.9)<br>are obtaine<br>ses the empi<br>based on the<br>1 and the po<br>the 95% CI|d with the po<br>rical distribu<br>2019/20 dat<br>verty line in<br>or one stand|pulation wei<br>tion of the er<br>a. 1000 simu<br>NPS 2019/2<br>ard error of t|ghts. Metho<br>ror terms. B<br>lations are im<br>0 deflated to<br>he true pove|d 1 uses the<br>oth methods<br>plemented.<br>2022 prices<br>rty rate. The|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. The imputed poverty rates for TA 1 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.5.

52
0.107|0.105|
|Observations|12,342,065|12,294,413|12,341,959|12,294,343|
|_D. Switched to private school_|||||
|Warning index x 2019|0.000|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)*|
||[0.130]|[0.146]|[0.130]|[0.087]|
|Warning index x 2020|-0.003|-0.003|-0.003|-0.003|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Pr(2019=2020)|0.000|0.000|0.000|0.000|
|Mean outcome|0.032|0.032|0.032|0.032|
|Adjusted R<sup>2</sup>|0.003|0.053|0.069|0.103|
|Observations|12,342,065|12,294,413|12,341,959|12,294,343|
|Controls|No|Yes|No|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|Municipality|Municipality|School|School|

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : The table reports placebo estimates of the effects of differential exposure to COVID-19. For this procedure, we drop data from 2021. Then
variable estimates are similar in significance and magnitude to the results of the wage regressions in table 3.

We conduct a further sensitivity analysis (see table S.1 to S.4).<sup>19</sup> This analysis suggests that our conclusions are fairly robust overall. First, we include destination fixed effects rather than source fixed effects. The results with destination fixed effects are similar to those with source fixed effects.<sup>20</sup> We also examine whether results are sensitive to a specific destination country. We run “rolling” regressions, in which we omit one destination country each time, to confirm that the results are basically unchanged.

Second, we include regional dummies and a dummy variable that indicates whether the source country also belongs to the OECD<sup>21</sup> instead of fixed effects as controls to consider institutional differences, which may affect income differences, in an alternative way. Third, we employ an emigration data set by Defoort (2006) to construct an alternative proxy for the total emigration rate. The data set contains emigration to six important destination countries in the year 1975. The proxy is constructed analogously to equation (24) and is used as an instrument for the skilled migration rate. Finally, we use the stock of high-skilled and low-skilled migrants rather than migration rates as regressors. Our main conclusions remain qualitatively unchanged and overall robust.

# _III. CONCLUDING REMARKS_

In this paper, we analyzed the impact of an increase in the international bilateral migration of high-skilled and low-skilled workers on relative income and relative TFP between pairs of source and destination countries of expatriates. Our theoretical model suggested that an increase in the number of skilled migrants increases international wage 24
Policy Research Working Paper 8043

# **Abstract**

It has long been recognized that household decision-making may not result in outcomes consistent with the unitary household model. Within the collective bargaining framework, consumption decisions would be driven by the spouse with greater bargaining power, while the outcomes would still be Pareto efficient. Within the non-cooperative framework, households would not achieve Pareto efficient outcomes, and under the simplest representation, bargaining power would not affect consumption decisions. This paper develops a model that allows consumption patterns and labor supply to be affected by both bargaining power and non-cooperation. The model highlights the potential gains from improving bargaining power versus increasing cooperation between spouses, and presents conditions under which relatively large gains would be expected from moving to more equitable bargaining power versus achieving intra-household cooperation. The model’s predictions are in turn tested using a unique panel data set on married couples in rural Malawi. The analysis shows that, relative to increasing wives’ bargaining power, improving cooperation between spouses would exert larger and statistically significant positive impacts on total household income and consumption expenditures per capita, as well as the share of household consumption devoted to public goods. Supported also by cross-country qualitative research, the results suggest that household public goods are relatively important to both women and men in rural Malawi, husbands’ capacity to control wives’ incomes is relatively limited, and development programs that promote intra-household cooperation could lead to greater gains in income and household public goods provision compared with interventions focusing exclusively on women’s empowerment.

This paper is a product of the Development Data Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tkilic@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