rafmacalaba/datause-encoder-data huggingface dataset:
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# **8. Concluding Remarks**

Pandemics are unique in that they disrupt human capital accumulation at both school and work. We present an accounting framework to improve our understanding of their impact. Poorer nations are _more_ likely to be insulated from schooling shocks since there is little schooling initially. Despite the lower losses relative to HICs and MICs, LICs are still heavily and durably impacted by the pandemic shock given preexisting vulnerabilities. Richer nations are _less_ likely to be insulated from employment shocks, due to higher returns to experience and higher quantity of schooling.

Through the lens of the COVID-19 pandemic, we find that the schooling shock is most significant for the middle- and high-income economies, causing significant economic damage in the long run. Even after 10 years, the world economy will still continue to suffer significant losses due to lost experience in richer nations. This emphasizes the role of experience in the global learning losses due to COVID-19.

Finally, by estimating global returns to education and experience separately for men and women using household surveys for 145 countries, differential effects by gender of the COVID-19 pandemic are explored. Surprisingly, the effect on female relative income is small and short-lived. This turns out to be because differences across genders in returns to human capital are too small to make much difference in transition dynamics. That said, for low income economies, the relative lack of schooling among women does turn out to be a factor worsening the impact of the pandemic somewhat.

Our analysis has several implications for policy. First, the huge losses that we estimated highlights the urgency of reopening schools. As of November 2021, about half of the global student population live in countries where schools are still not fully open. Second, our results show the importance of bridging the digital divide between and within countries and improving online education. Third, recovery from the pandemic may require investments in remedial education for students who experienced schooling loss. Fourth, it suggests that payment protection programs are likely preferable to unemployment insurance, at least to the extent that they can protect jobs that continue to provide experience that generates human capital without exacerbating the spread of infection. Alternatively, retraining programs may be useful.

Our analysis also has limitations. We do not model the impact of pandemics on
# **4. Empirical Strategy**

This section describes the specification and estimation strategy used, and discusses identification issues. The analysis uses household-level panel data to examine land transactions, machine investment and services, and crop incomes.

In the analysis of land transactions and machine investment and services, the following firstdifferenced equation is estimated, ' ∆ _yij_ (1,0) = a + β 1 ∆ _w j_ (1,0) + β 21 ∆ _w j_ (1,0) _land ij_ 0 + β 22 ∆ _w j_ (1,0) _eduij_ 0 + _xij_ 0 d + _provij_ + ∆e _ij_ (1,0) (1)

> where ∆<sup>_y_</sup> _ij_ (1,0) is change in self-cultivated land or net rent-in land, or machine investment or change in machine services purchased, for household _i_ in village _j_ , from time 0 to 1, ∆ _w j_ (1,0) is the village-level real wage growth rate (agricultural and nonagricultural wages, treated separately) between 2007 and 2010, _landij_ 0 is the own-land or self-cultivated land size in time 0, _eduij_ 0 is the average years of

> schooling in time 0,<sup>_x_</sup> _ij_ 0 is a vector of initial household characteristics, _provij_ is a province dummy,

> and ∆e _ij_ (1,0) is the difference in shocks (assume that e _ijt_ is an ex-post shock after household decisions are

> made). Note that β 1 is the effect of change in the village-level real wage rate on the dependent variable,

> and β 2 captures how the initial household characteristics affect the impact of change in the village-level real wage rate. The estimated village-level real wage growth rate is interacted with the key household characteristics: the initial own or self-cultivated land size and the average years of schooling. In the

> estimation, we also include as ∆ _w j_ (1,0) changes in the proportion of non-agricultural income and migration rate, both calculated at the village level.

> We hypothesize that β 21<sup>></sup> 0 and β 22<sup><</sup> 0 . That is, facing rising real wages, relatively large holders tend to increase their operational size and invest in machines or increase their demand
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market in recessions (Kahn 2010; Oreopoulos, von Wachter, and Heisz 2010); and the long-term consequences of high minimum wages for low-skilled youth (Neumark and Nizalova 2007).

- Fourth, much of the research concentrates on whether specific institutions have effects on outcomes, not really considering the magnitude of the effect (i.e., paying attention to significance tests more than the size of the coefficient).  Clearly, the magnitude is important for assessing trade-offs in making policy.

- Fifth, the importance of country context can make it complicated to generalize findings. Although this point is routinely acknowledged in cross-country studies, there are few examples of studies that attempt to rigorously assess the impacts of different institutions in different country contexts (e.g., degree of openness, economic structure, level of development, demographic structure, cultural values, etc.).

These points are not meant to constitute a complete list of challenges related to modelling, but they (and others) can influence the interpretation of empirical analysis in ways that have real consequences for analyzing the effects of labor market institutions and their policy implications.

# **_2.2 Methodology for this review_**

This literature review is organized according to the conceptual framework used for the _2013 World Development Report_ . That framework puts jobs at the center of a development process that leads to improved living standards, productivity, and social cohesion. The methodology for the review involves identifying indicators in these three areas that can be affected by labor market institutions. The limiting factor is that the literature has looked at a relatively narrow set of outcomes which can inform the WDR framework. Most studies focus on the impacts of institutions on standard labor market outcomes—employment, unemployment, earnings (including distribution), and job and labor turnover. A much smaller number of studies have evaluated productivity effects. There has been very little on the effects of different labor market institutions on social cohesion, and any relevant evidence must be deduced from (mostly) indirect indicators.

The review of labor market institutions will look at the impacts listed below.<sup>10</sup> Note that the level of evidence is very uneven.

# _Living standards_

For most households, employment is the primary source of income for improving living standards, providing economic security, and avoiding poverty. This set of indicators looks at the impacts of labor market institutions on access to employment and earnings from work.

- Access to jobs/good jobs

> 10 Note that there is a potential overlap in the outcome variables for each category.  Some indicators listed in this section could arguably be put into another category.  This overlap is especially relevant for distributional and equity variables that are relevant for both living standards and social cohesion.
of spending an enormous amount of time and resources to come up with a single number as the PPP for the whole country. In the wake of recent results on sharp regional variation in prices within large heterogeneous countries, the present results on PPP variation between expenditure percentiles point to a new research agenda on subnational and expenditure group varying PPPs that the ICP could explore in its future exercises.

# **References**

Almas, I. (2012), “International Income Inequality: Measuring PPP Bias by Estimating Engel Curves for Food”, _American Economic Review,_ 102(1), 1093- 1117.

Almas, I, A. Kjelsurd and R. Somanathan (2013), “A Behaviour-based Approach to the Estimation of Poverty in India”, NHH Department of Economics, Discussion Paper Balassa, B. (1964), “The Purchasing Power Parity Doctrine: A Reappraisal”, _Journal of Political Economy,_ 72 (6), 584-596.

Banks, J., R. Blundell and A. Lewbel (1997), “Quadratic Engel Curves and Consumer Demand”, _Review of Economics and Statistics,_ 79, 527-539.

Barrett, G and M. Brzozowski (2010), “Using Engel Curves to Estimate the Bias in the Australian CPI”, _Economic Record,_ 86, 1-14.

Beatty, T. and E. Larsen (2005), “Using Engel Curves to estimate bias in the Canadian CPI as a cost of living index”, _Canadian Journal of Economics_ , 38 (2), 482- 499.

Bhagwati, J. (1984), “Why Are Services Cheaper in Poor Countries”, _Economic Journal_ , 94 (374), 279-286.

Cassel, G. (1916), “The Present Situation of the Foreign Exchanges”, _Economic Journal_ , 26 (103), 319-323.

Chakrabarty, M, A. Majumder and R. Ray (2015), “A Framework for the Simultaneous Measurement of Spatial Variation and Temporal Movement in Prices in a Heterogeneous 32
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The Thai company income tax provisions included in our calculations below are as follows: First, we examine foreign companies operating as non-SET firms (up to 100% ownership), facing a company tax rate of 35%, and SET firms (less than 50% ownership), facing a company tax rate of 30%. We do not include tax holiday firms owned by foreign companies.7 Withholding tax rates also vary since SET companies can remit dividends and capital gains abroad without paying Thai withholding taxes. Second, we assume that companies use tax depreciation allowances that conform with book depreciation. As remarked in the previous section, companies may try to use higher book depreciation figures to maximize their tax allowances even though true economic depreciation is much lower. Third, interest costs associated with either offshore or local debt are deductible from Thai corporate taxable income although interest on offshore debt is subject to Thai withholding taxes.

The capital-exporting countries selected for this study provided the bulk of direct foreign investment for Thailand, as shown in Table 4. Some of the countries, such as Hong Kong, fully exempt foreign-source income. Others exempt remitted dividends and retained profits of subsidiaries in Thailand (Netherlands, Switzerland, France and, for qualifying ownership of 10%, West Germany). Moreover, several countries tax remitted earnings of subsidiaries and provide a tax credit for the underlying corporate income and withholding taxes (Japan, Singapore, Taiwan, the United Kingdom and the United States). Almost all countries

> 2/ Tax holidays have been analyzed by Mintz (1989) for five countries including Thailand. The model used in the analysis is applicable to domestic firms or foreign affiliates where the home countries exempt foreign-source income. The regimes of deferral and accrual taxation have not been incorporated.
manufacturing or services (see B.2 in the appendix). Formal plants then not only start their life cycle with more workers relative to informal plants (three times more), but also grow at a markedly faster rate.

Figure 2: Cumulative growth rates in the life cycle of formal and informal plants in the Mexican census.

<!-- Start of picture text --> 2.50<br>2.36<br>2.27<br>2.21<br>2.01 2.00<br>2.00 1.92<br>1.88 1.86<br>1.74<br>1.71<br>1.77<br>1.62<br>1.70<br>1.48 1.49 1.64<br>1.50 1.54<br>1.48<br>1.30<br>1.36<br>1.22<br>1.00<br>1.00<br>Age<br>Informal plants Total Formal plants<br>Average cumulative growth rate<br>[0-4] [5-9] [10-14] [15-19] [20-24] [25-29] [30-34] [35-39]<br><!-- End of picture text -->

**Notes:** Formal establishments are registered with SAT, IMSS, or both, as self-reported by the respondent. Establishments that are not formal are informal. These cumulative growth rates are (rescaled) marginal effects from least squares regressions that also control for fixed effects for 2-digit sector and cohort, and a dummy for whether the panel identifier is synthetic (before or after 2008).

In Figure B.3 in the appendix, we compute growth rates for formal and informal plants according to our definition but exploiting the 2018 cross-section only, as in Hsieh and Klenow (2014). Interestingly, the cross-sectional estimates seem to bias downward the growth of informal establishments (40% in the cross-section vs. 77% exploiting the panel) but overestimate growth among formal plants (170% in the cross-section vs. 140% in our estimations). We show in Figures B.4 and B.5 the life cycles for formal and informal plants using different definitions of informality. Figure B.4 looks at a strict definition that does not sort non-responses into the informal category: plants registered with both SAT and IMSS are formal, while plants not registered with SAT nor IMSS are informal. Figure B.5 follows the formality definition on the intensive margin of 9
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _6_ by replicating observed patterns).

- 2) Next, a regression model for column _A_ is estimated, using the other columns as predictors _A_ = _f_<sup>_A_</sup> ( _B, C_ ). Typically, the rows are selected so that the dependent variable consists fully of observations, while the covariates may be partially imputed data. The predictions of the model are used to replace the initial imputations in _A_ using predictions from this model.

- 3) Next, a model _B_ = _f_<sup>_B_</sup> ( _A, C_ ) is estimated and used to update imputations in _B_ in the same manner.

- 4) The process then cycles multiple times over all columns, and keeps updating the estimates for missing prices until the process converges.

It is noted by van Buuren and Groothuis-Oudshoorn (2011); van Buuren (2012) that convergence occurs relatively fast in this type of Markov Chain Monte Carlo (MCMC). A stopping criterion is provided by Andr ́ee (2021) based on convergence in cross-validation performance of the prediction models. Specifically, since updates to imputations always enter on the covariate side of the regressions, each regression benefits from improved data produced by the previous regression. Throughout the process of estimating, predicting, and then re-estimating, the regression models improve and can be cross-validated against holdout data at each step. When the cross-validation performance does not improve any further, the process can be terminated. Typically, there may be stochastic elements involved and the whole process is repeated several times to produce multiple imputations. Andr ́ee (2021) builds an ensemble predictor by aggregating the results.

# _B. Implementation_

There are two main ingredients needed to carry out the procedure. First, the sequential imputation process needs to be initialized at some, preferably reasonable, initial imputation. Second, the regression model used to make predictions throughout the process needs to be specified.

# Initialization

In real-time settings, the imputation process can be initialized with the estimates produced in the previous period, requiring only a simple one-step ahead extrapolation that can be generated using standard time series methods, see the Appendix for notes. This means that an initialization is only needed once, when the price imputation process is deployed. When the price imputation process is deployed in real-time, it can continue to run as a continuous MCMC process that integrates new observations on the fly and use them to update imputations on an ongoing basis.

Each imputation, _m_ , that underlies the final ensemble prediction, is initialized uniquely by adding a small disturbance term around these initial imputations to
5 of the personal exemption; wealthy farmers should pay tax. Even many charitable organizations need not be exempt from income tax _ex-ante._ In an atmosphere with weak admiuistrativ.e capability, profitable business activities can easily hide behind t1r' 'charitnblel characterization. Income earned specifically frot charitable activities can be effectively exempted through the definition of taxable income<sup>6</sup> rather than the definition of taxpayer.

In most countries two separate income tax regimes coexist, one for businesses and one for individuals. Each has its own rules for determining taxable income and its owr. rate structure. Most revenue tends to come from the business income tax,<sup>7</sup> which is easier to enforce (especially for medium and large firms). Specifying the dividing line between the two regimes can bi problematic. Questions such as these must be faced: Should the business tax apply to unincorporated firms such as partnerships or only to corporations? If it applies only to the latter, why should the mere act of incorporation throw a business into a completely separate tax regime? If it applies to unincorporated firms as well, where do individual entreprene."rs fit? Does the business tax cover small family-run businesses such as restaurants, or do these fall under the individual income tak law?

Many countries draw the dividing line between the application of individual and business income tax laws at the point of<sup>incorporation,</sup> arguing that having the corporate form confers certain legal<sup>advantages</sup> that offset the %ax conssquences. Other countries try to cover<sup>all</sup> firms--in^i:o 'ioted or nDt-with their b..s't.Ifss tax law, leaving the dividing ! -Lr. etween firm3a end indiv'.duals rather ad hoc. Indonesia's 6 See pp. 17-l6 below.

7 World DeveloRwtme Report 1983, p. 84.
within a given household, a child with one standard deviation higher ability compared to the average ability of their siblings is 31 percent more likely to be enrolled. On the intensive margin, controlling for household fixed effects, we find that a child with one standard deviation higher ability receives 20 percent more discretionary school expenditures by the parents.

The remainder of the paper is organized as follows. Section 2 discusses the conceptual framework about sibling rivalry and the household schooling investment decision. Section 3 describes the survey data used in the analysis and explains the construction of the different child ability measures. Section 4 describes the empirical identification strategy and section 5 presents the main results as well as robustness tests. Section 6 concludes.

# **2. Sibling Rivalry Conceptual Framework**

A number of studies examine the interaction of siblings to understand schooling outcomes and why girls often receive less education than their brothers. Butcher and Case (1994), using United States data that contains explicit information on an individual’s completed education and the education of their brothers and sisters, find that women with only brothers receive significantly more education on average than women with any sisters.<sup>2</sup> Their finding differs from what is typically found in developing country studies. Parish and Willis (1993) examine how sibling sex composition influences girls’ education in Taiwan. They emphasize that cultural traditions favoring male descent can cause parents to manipulate daughters for the benefit of their sons. Garg and Morduch (1998) emphasize sibling rivalry using data from Ghana. Child education decisions in credit constrained households are influenced by the number of children they have, resource dilution, and the sex composition of their children, sibling rivalry. Resource dilution

> 2 They highlight three potential explanations for why sibling sex composition might influence education decisions: sibling resource competition, sex-typing of tasks, and peer effects. Resource competition occurs if boys and girls have different relative prices for educational investments or investment returns. Sex-typing stems from parents sending messages to children describing appropriate behaviors and goals, while peer effects come from children developing traits that depend on how they interact with their siblings.

4
Finally, we explored whether differences in the extent of economic liberalization affected savings rates in the cross-section of transition economies. We found this to be the case: greater liberalization is associated with lower savings with a one-year lag. To the extent that liberalization is perceived as an indicator of likely future growth, the behavior is consistent with smoothing in the face of a J-curve output evolution.

# **References**

Bayoumi, Tamim, Paul Masson and Hossein Samiei (1995); "International Evidence on the Determinants of private savings"; IMF Working Paper 95/5 1.

Bennett, John, and Maxim Boycko. "Savings and Stabilization Policy in a PrePost Socialist Economy." Journal of Money Credit and Banking, Vol.27, No. 3 (August 1995).

Calvo, Guillermo, Ratna Sahay, and Carlos Vegh. "Inflation Stabilization in Transition Economies: A Comparison with Market Economies." [MF Working Paper 95/8.

Collins, Susan (1991); "Saving Behavior in Ten Developing Countries"; in D. Bemheim and J. Shoven (eds.); National Savings and Economic Performance; Chicago, NBER and University of Chicago Press.

Conway, Patrick. "Saving in Transition Economies: The Summary Report." World Bank, Washington, D.C., 1995 Davis, Cahremza and Wojciech Charemza. Models of Disequilibrium and Shortage in Centrally Planned Economies. London and New York: Chapman Hall 1989.

Deaton, Angus. "Growth and Saving: What do we know, what do we need to know, and what might we learn?" (Draft), Research Program in Development Studies, Princeton University, 1995.

De Melo, Martha, Cevdet Denizer, and Alan Gelb, "From Plan to Market; Patterns of Transition." Working Paper No. 1564, World Bank, Washington, D.C., 1996.

Dornbusch, Rudiger and Holger Wolf (2000); "Monetary Overhangs"; in Maurice Obstfeld (ed.) Festschrift for Robert Mundell, University of Chicago Press.

Easterly, William and Stanley Fischer. "The Soviet Economic Decline: Historical and Republican Data." World Bank, Washington, D.C., 1994.

Edwards, Sebastian. " Why are Latin America's savings rates so low? An International Comparison" Journal of Development Economics, Vol. 51, pp. 5-44, (1996).

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