rafmacalaba/datause-encoder-data huggingface dataset:
specific to these countries. Specifically, we will include commodity prices as a control to understand whether the inclusion of these prices explains some of the volatility of fiscal policy. The idea is to test whether the fiscal volatility in commodity-exporting countries comes solely from commodity price volatility or not.

# **3.2 Data**

We make use of annual data over the 1990-2021 period. The choice of our sample period is dictated by data availability. The data comprises 184 countries, with 148 EMDEs and 36 advanced economies. We classify countries into ‘commodity exporters’ and ‘non-commodity exporters’ by applying the classification criteria used in World Bank (2022).<sup>9</sup> Based on this classification, our sample comprises a diverse set of 90 commodity-exporting EMDEs. ‘Non-commodity exporting’ EMDEs are simply the ones not classified as commodity exporters.

We analyze three fiscal policy variables from the government budget: primary expenditure, government revenue, and primary balance (Source: IMF World Economic Outlook). In addition, to understand whether different components of expenditures matter more than others, we also analyze government consumption as a measure of fiscal policy (Source: World Bank World Development Indicators).

Our data comes from multiple sources. For commodity exporters, we obtain data on natural resource rents (as percent of GDP) from the World Bank’s World Development Indicators. Fiscal rules are based on the IMF’s Fiscal Rules Dataset (Davoodi et al. 2022). Country-specific commodity terms of trade indices are obtained from the IMF. We use institutional and political variables from the International Country Risk Group (ICRG) and the Polity IV Database. We use the Chinn-Ito index as our measure of capital account openness. We provide details of the country coverage, variables included in the analysis, and data sources in Appendix 1.

# **4. Measuring fiscal policy volatility**

# **4.1 Characterizing fiscal policy volatility**

We start by checking the cyclicality of each of the four measures of fiscal policy, although this is not the focus of our analysis. As described above, we measure cyclicality by the estimate of the parameter β in equation (1). This parameter includes both the working of automatic stabilizers as well as any discretionary changes in fiscal policy implemented in response to fluctuations in GDP. Figure 3 summarizes our results for two groups of countries: advanced economies versus EMDEs;

> 9 An economy is defined as a commodity exporter when, on average in 2017-19, either (1) total commodity exports accounted for 30 percent or more of total exports or (2) exports of any single commodity accounted for 20 percent or more of total exports. Economies for which these thresholds were met as a result of re-exports were excluded. When data were not available, judgment was used.

7
Policy ReseaRch WoRking PaPeR 4677

# **Abstract**

This paper exploits a unique longitudinal data set from Tanzania to examine the consequences of child labor on education, employment choices, and marital status over a 10-year horizon. Shocks to crop production and rainfall are used as instrumental variables for child labor. For boys, the findings show that a one-standard-deviation (5.7 hour) increase in child labor leads 10 years later to a loss of approximately one year of schooling and to a substantial increase in the likelihood of farming and of marrying at a younger age. Strikingly, there are no significant effects on education for girls, but there is a significant increase in the likelihood of marrying young. The findings also show that crop shocks lead to an increase in agricultural work for boys and instead lead to an increase in chore hours for girls. The results are consistent with education being a lower priority for girls and/or with chores causing less disruption for education than agricultural work. The increased chore hours could also account for the results on marriage for girls.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department to study the consequences of child labor. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kbeegle@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 do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
previous decade. Although the government responded to deterioration in the education pro fi le with an investment program at the primary and “basic” level in 2000, a continuing problem has been the inability of the government to hire and retain teachers in schools.

An exacerbating factor is the HIV/AIDS epidemic. A recent report (Grassly and others 2003) calculates that the number of teachers lost to HIV/AIDS has increased from 2 per day in 1996 to 4 or more a day in 1998, representing two-thirds of each year’s output of newly trained teachers. Not surprisingly, teacher attrition has received a lot of attention, both in the popular press and in institutional reports (our data and that from the census of schools in 2002 corroborates the high rates of attrition (Das and others 2004b)). Further, absenteeism rates are high, primarily due to illness and funerals. Grassly and others (2003) for instance, fi nd that absenteeism arising from illness-related reasons will lead to the loss of 12,450 teacher-years over the next decade. The resulting teacher-shortage has led to class sizes above the 40 children per teacher norm (particularly in rural areas), teachers teaching double shifts, and limited possibilities for substitutions when teachers are absent.

In 2002 we surveyed 182 schools in four provinces of the country.<sup>9</sup> The choice of schools was based on a probability-proportional-to-size sampling scheme, where each of 35 districts in the four provinces was surveyed and schools were randomly chosen within districts with probability weights determined by grade 5 enrollment in the school year 2001. Thus, every enrolled student in grade 5 in the district had an equal probability of being in a school that participated in the survey. As part of the survey, questionnaires were administered to teachers and head-teachers with information on a host of topics including their demographics, personal characteristics, absenteeism, outside options and classroom conditions. In addition, we also collected information at the level of the school including fi nancing and the receipts of educational inputs during the academic year. Of these 182 schools, we use 177 for our analysis–for two schools we do not have the relevant school information, and examiners regarded the test scores for three schools in the fi rst year as suspect.

An extensive module linking teacher characteristics to student performance formed an integral part of the survey. As part of this student-teacher matching we collected information on the identity of each student’s teacher in the current (2002) and the previous year (2001). Based on this information, we can identify the non-movers in 2001 and 2002; this sub-sample represents 26 percent of the students tested both in 2001 and 2002. We administered a questionnaire to all matched teachers present on the day of the survey, resulting in information on 541 teachers in 182 schools. Every teacher interviewed is hence matched to a student, either by virtue of currently

> 9 Lusaka, Northern, Copperbelt and Eastern provinces were surveyed. These four provinces account for 58 percent of the total population in Zambia.

12
the unemployed and discouraged), while informal self-employed are at least as well off as formal employees.

Finally, we find a negative and significant effect of the individual-level satisfaction with the national government on the propensity to work without contract Eastern Europe, as well as in Western Europe.

# **References**

Ambrosini, M. (2001). The role of immigrants in the Italian labour market. _International Migration_ , 39 (3): 61–83.

Basu, A.K., N H. Chau and R Kahbur (2011). Contractual dualism, market power and informality. Cornell University, mimeo, http://www.arts.cornell.edu/econ/seminars/basu_chau_kanbur.pdf Bernabè, S. (2008). _Measuring Informal Employment in Transition Countries._ University of Leuven, mimeo Boeri, T. and P. Garibaldi (2005). Shadow Sorting, in C. Pissarides and J. Frenkel (eds), _NBER_

- _Macroeconomics Annual_ , MIT Press, Cambridge, MA, 2005.

- Boeri, T., P. Garibaldi and M. Ribeiro (2011). The lighthouse effect and beyond. _Review of_

- _Income and Wealth,_ 57: S54–S78.

Bosch, M., E. Goni and W. Maloney (2007). The Determinants of Rising Informality in Brazil: Evidence from Gross Worker Flows. _World Bank Policy Research Working Paper_ No. 4375.

- Bosch, M., and W. Maloney (2010). Comparative Analysis of Labor Market Dynamics Using Markov Processes: An Application to Informality. _Labour Economics_ 17(4): 621-631.

De Paula, Á. and J. A. Scheinkman (2011). The informal sector: An equilibrium model and some

- empirical evidence from Brazil. _Review of Income and Wealth_ , 57: S8–S26.

- Djankov S., I. Lieberman, J. Mukherjee and T. Nenova (2003). Going Informal: Benefits and Costs. In B. Belev (ed.) ―The Informal Economy in the EU Accession Countries: Size, Scope, Trends and Challenges to the Process of EU Enlargement‖. Sofia: CSD: 63-80.

Ekelund, J., E. Johansson, M.-R. Jarvelin and D. Lichtermann (2005). Self-employment and risk

- aversion - evidence from psychological test data, _Labour Economics_ 12(5): 649-659.

_EURES - the European Job Mobility Portal_ (2011). Living & Working.

- http://ec.europa.eu/eures/main.jsp?acro=lw&lang=en&catId=490&parentId=0

- European Commission (2008). _Employment in Europe 2008_ . Luxembourg: Office for Official Publications of the European Communities.

- European Commission (2009). _New Skills for New Jobs. Anticipating and matching labour market and_

- _skills needs._ Luxembourg: Office for Official Publications of the European Communities.

- European Foundation for the Improvement of Living and Working Conditions (2007). _European_

- _Working Conditions Survey, 2005_ [computer file]. Colchester, Essex: UK Data Archive [distributor], May 2007. SN: 5639

46
with their effects on employment. The estimates presented in Column 3 of Table 4 in the Appendix indicate that in areas close enough to an electric grid, bringing a main road 10 km closer marginally increases the number of hours worked by nearly 1.2 hours a week, but this effect is not statistically significant. Unlike proximity to roads, proximity to the electric grid has a much larger effect. In areas with high access to a main road, bringing the electric grid 10 km closer increases the number of hours worked by 12.5 hours a week. Importantly, it is more beneficial to provide both types of infrastructure simultaneously, as indicated by the positive coefficient on their interaction term. Indeed, bringing a main road 10 km closer increases the positive effect of bringing the electric grid 10 km closer, and by more than five hours of work every week. Using the DHS sample, Figure 7 shows that the impact of increasing proximity to the electric grid on the number of hours worked becomes higher for households that are closer to the road, and vice versa (the impact of increasing proximity to the roads also boosts the number of hours work for households that have greater proximity to the electric grid). Bringing an electric grid 10 km closer increases the number of hours worked by 12.5 hours for households located next to a main road, but only by just 7 hours when households are located 10 km away from the main road network. Similarly, bringing the main road network 10 km closer increases the number of hours worked by 1.2 hours when households are located next to the road, but such proximity actually reduces the number of hours when the household is located 10 km away from the electric grid. That is, increasing the proximity to the road only has a positive impact for workers located next to the electric grid.

Figure 7: Marginal impact of proximity to roads (left) and electricity (right) on employment <!-- Start of picture text --> Marginal impact of bringing Marginal impact of bringing<br>grid closer by 10 km road closer by 10 km<br>15<br>12.5<br>10<br>7.15<br>5<br>0 1.16<br>−4.19<br>−5<br>Distance to the other infrastructure<br>0 km 10 km<br>Increase in hours worked (in percentage point)<br><!-- End of picture text --> We note some consistency between the IV estimates and the OLS results reported in Table 10. In all cases but one, the IV coefficients have the same sign as the corresponding OLS coefficients; the only reversal of sign is the negative effect of electricity on the number of hours worked. Unsurprisingly, the OLS coefficients are smaller, indicating a downward bias.

Summarizing the findings, we note that supplying roads and electricity leads to significant employment 22
- Akber, N., Paltasingh, K. R., & Mishra, A. K. (2022). How can public policy encourage private investments in Indian agriculture? Input subsidies vs. public investment. _Food Policy_ , _107_ (November 2021), 102210. https://doi.org/10.1016/j.foodpol.2021.102210

- Baird, S., McIntosh, C., & Özler, B. (2019). When the money runs out: Do cash transfers have sustained effects on human capital accumulation? _Journal of Development Economics_ , _140_ (March), 169–185. https://doi.org/10.1016/j.jdeveco.2019.04.004

- Barrett, C. B., Bachke, M. E., Bellemare, M. F., Michelson, H. C., Narayanan, S., Walker, T. F., & Bank, T. W. (2012). Smallholder Participation in Contract Farming : Comparative Evidence from Five Countries. _World Development_ , _40_ (4), 715–730. https://doi.org/10.1016/j.worlddev.2011.09.006

- Bastagli, F., Hagen-Zanker, J., Harman, L., Barca, V., Sturge, G., & Schmidt, T. (2019). The Impact of Cash Transfers: A review of the evidence from low- and middle-income countries. _Journal of Social Policy_ , _48_ (3), 569–594. https://doi.org/10.1017/S0047279418000715

- Bizikova, L., de Brauw, A., Rose, M., Laborde, D., Motsumi, K., Murphy, M., ... Smaller, C. (2022). _Achieving sustainable food systems in a global crisis: Malawi. Ceres 2030 deep dives into the nexus of food systems, climate change and diets_ .

- Brugh, K., Angeles, G., Mvula, P., Tsoka, M., & Handa, S. (2017). Impacts of the Malawi social cash transfer program on household food and nutrition security. _Food Policy_ , (November 2016). https://doi.org/10.1016/j.foodpol.2017.11.002

- Carletto, C., Corral, P., & Guelfi, A. (2017). Agricultural commercialization and nutrition revisited: Empirical evidence from three African countries. _Food Policy_ , _67_ , 106–118. https://doi.org/10.1016/j.foodpol.2016.09.020

- Centre for Development Management (CDM). (2017). _Evaluation of the 2015/16 Farm Input Subsidy Programme_ . Lilongwe, Malawi.

- Chakrabarti, A., Harou, A. P., Fanzo, J., Palm, C. A., & Palm, C. A. (2024). Exploring agriculture ‐ child nutrition pathways: Evidence from Malawi’s Farm Input Subsidy Program. _Food Security_ , 201–221. https://doi.org/10.1007/s12571-023-01416-x

- Chibwana, C., Fisher, M., & Shively, G. (2012). Cropland allocation effects of agricultural input

23
We took total value added by sector from the National Accounts. We then set up a non-linear programming problem to obtain "new" factors shares for firms and households, where the new factor shares satisfy the conditions of a Social Accounting Matrix. The new factors shares for firms and households are the values that minimize the sum of the squares of the differences between original and new factor shares from the national accounts plus sum of the squares of the differences between the original and new factor shares from the household data, subject to the constraint that the new data satisfy all the constraints of a Social Accounting Matrix. In our objective function, we apply equal weights to a departure from the original national accounts data and to a departure from the household income data, although we have done sensitivity analysis where we weight either the household data or the national accounts data more heavily. .This reconciliation of the two sets of data significantly decreased the share of capital reportedly paid by firms, especially in some of the more capital intensive sectors like ferrous and non-ferrous metals.

# **III. Policy Results**

For each of our ten federal okrugs, we first discuss our central scenario (results are in table 13a). In our central scenario, we assume that tariffs on goods are cut by 50 percent, that barriers to foreign direct investment are eliminated or reduced (depending on the sector) and that seven industrial sectors receive an improvement in their market access between 0.5% and 1.5%. See table 12a for details by sector. We present the overall welfare effects for each region, the impact on wages and returns to capital, the changes in exports and the real exchange rate and factor adjustment costs.

The gains come from a combination of effects, so we also estimate the comparative static impacts of the various components to WTO accession in order to assess their relative importance. In order to obtain an assessment of the adjustment costs, we estimate the percentage of mobile labor and capital that must change industries. Next we discuss the estimates of the impact at the level of productive sectors of the economy. In table 19, we conduct sensitivity analysis with respect to some key parameters and present these results. A key aspect of the sensitivity analysis is how the results differ across regions when we assess the ability of different regions to attract FDI based on the ranking of their investment potential. Finally, we present results for all seventy representative households in table 20.

## **Welfare Effects of WTO Accession—Household and Overall Results**

We have 70 representative households in our model: ten representative households in each of the seven regions. We shall present welfare results for each of the 70 households, but in order to present 20
<mark>Soto, C. J., John, O. P., Gosling, S. D., & Potter, J., 2008. The developmental psychometrics of Big Five self-reports: Acquiescence, factor structure, coherence, and differentiation from ages 10 to 20.</mark> _<mark>Journal of Personality and Social Psychology, 94,</mark>_ <mark>718-737.</mark>

- World Bank, 2008. _Agriculture for Development. World Development Report_ . World Bank. Washington DC.

Young, A., 2013. “Inequality, the Urban-Rural Gap and Migration,” _Quarterly Journal of Economics,_ 128(4), 1727–1785.

38
**Table 3: Panel regressions**

|VARIABLES|(1)<br>TFP<br>growth<br>Long<br>Memory<br>DEA|(2)<br>TFP<br>growth<br>hicks<br>neutral|(3)<br>TFP<br>growth<br>Long<br>Memory<br>DEA|(4)<br>TFP<br>growth<br>hicks<br>neutral|(5)<br>TFP<br>growth<br>Long<br>Memory<br>DEA|(6)<br>TFP<br>growth<br>hicks<br>neutral|(7)<br>TFP<br>growth<br>Long<br>Memory<br>DEA|(8)<br>TFP<br>growth<br>hicks<br>neutral|
|---|---|---|---|---|---|---|---|---|
|WDI: Growth<br>rate of Mobile<br>and fixed-line tel.<br>subscr.|0.00115<br>(0.0292)|0.899<br>(3.046)|-0.00158<br>(0.0324)|0.590<br>(3.427)|||||
|WDI: Growth<br>rate of electricity<br>|0.171**|18.67**|||0.262***|30.75***|||
|prod|||||||||
|WDI: Growth<br>|(0.0848)<br>-0.0999*|(8.168)<br>-9.278|-0.0860|-7.787|(0.0713)|(7.108)|||
|rate of kms rail|(0.0546)|(5.856)|(0.0555)|(6.007)|||||
|WDI: Growth<br>rate of electricity<br>prod, net of<br>losses|||0.0743|8.727|||0.243***|28.15***|
||||(0.0663)|(6.455)|||(0.0693)|(7.125)|
|Constant|0.993***|-1.059|1.001***|-0.229|0.983***|-2.074***|0.985***|-1.776**|
||(0.00862)|(0.857)|(0.00798)|(0.801)|(0.00673)|(0.677)|(0.00686)|(0.704)|
|Observations|137|137|137|137|232|232|232|232|
|Number of<br>id_country<sup>7</sup>|8|8|8|8|8|8|8|8|
|r2_o|0.0496|0.0580|0.0200|0.0225|0.0677|0.0929|0.0658|0.0874|
|Rho|0|0|0|0|0|0|0|0|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Next, we turn to the results from cross-country regressions. Not surprisingly given the above results, only on country (Tunisia) has its TFP growth explained by the electricity variable – as we now lost the cross-country dimension of the panel estimations. Being a small oil producing countries, Tunisia has successfully implemented measures to roll back subsidies on electricity consumption by households and industries. The implementation of cost reflective electricity, combined with policies promoting the adoption of energy efficient technology by industries and large electricity consumers can therefore explain this finding.

> 7 Algeria, Egypt, Iran, Israel, Jordan, Morocco, Syria, Tunisia.

11
_Table 6: Economic and employment impacts by measure according to simulations with the Cyprus input-output model relative to a reference scenario without measures._

|_Effect of measures on growth and jobs (per million euros invested) compared to a business-as-usual_<br>_evolution_<br>_Measure_|_Short-term im_<br>_Economic_<br>_multiplier_|_pact (2022/23)_<br>_Net_<br>_employment_<br>_per Μ€_<br>_invested_|_Long-term imp_<br>_Economic_<br>_multiplier_|_act (up to 2030)_<br>_Net_<br>_employment_<br>_per Μ€_<br>_invested_|
|---|---|---|---|---|
|1. Immediate launch of grants for energy renovations of buildings from unused budget of 2020-21|0.28|1.40|0.01|0.66|
|2. New grant scheme for energy renovations of existing buildings, 2021-27|1.30|12.57|-0.05|-0.07|
|3. Grants for energy renovations of buildings under construction for upgrade to Near-Zero Energy Buildings|0.87|7.67|0.01|0.11|
|4. Installation of smart electricity meters|1.45|14.16|0.01|0.23|
|5. Virtual net billing for encouragement of photovoltaic installations by enterprises|1.44|14.19|0.20|1.85|
|6. Subsidy to loans of businesses certified with an environmental management system|||||
|7. Business4Climate scheme - grants to enterprises with a verified low-carbon action plan up to 2030<br>8. Implementation of existing Sustainable Urban Mobility Plans (SUMP)|1.38|5.33|-0.01|-13.66|
|9. Construction of tram in the capital city of Nicosia|||-0.07|-3.28|
|10. Scrappage scheme for old cars to be replaced with battery electric vehicles|1.22|12.51|-0.01|-1.27|
|11. Replacement of streetlights in municipalities and villages with energy efficient lighting|0.74|5.34|0.01|0.32|
|12. Tree planting along urban and intercity roads<br>13. Fiscallyneutral carbon taxation for economic sectors out of the EU Emissions TradingSystem|||||
|_Business as Usual case(economy-wide demand stimulus)_|_0.83_|_6.27_|_0.01_|_0.04_|

<u>Note: Some cells are blank because some measures were not simulated with the IO model. M€: million euros at constant prices of year 2016.</u> 17