rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4: Expenditure Shares of Subsidized Food Products across Countries and Quintiles <!-- Start of picture text --> Flour Bread<br>7 6<br>6<br>5<br>5<br>4<br>4<br>3<br>3<br>2<br>2<br>1 1<br>0 0<br>Djibouti Morocco Libya Iran, Islamic Rep. Iran, Islamic Rep. Libya<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Sugar Vegetable Oil<br>9 4<br>8 3.5<br>7 3<br>6<br>2.5<br>5<br>2<br>4<br>1.5<br>3<br>2 1<br>1 0.5<br>0 0<br>Djibouti Libya Morocco Djibouti Libya<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Expenditure share (in %) Expenditure share (in %)<br>Expenditure share (in %) Expenditure share (in %)<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

# A Policy Dilemma

It should be clear by now that there is a certain trade-off between the share of expenditure on subsidized products in total household expenditure and the dollar amounts of subsidies received. To illustrate this trade-off, figure 5 plots these two dimensions across population percentiles for LPG in different countries. For most countries, the curves are negatively sloped for the expenditure shares, meaning that poorer households spend a larger share of total expenditure on subsidized products than richer households (figure 5, panel a). Also in most countries, richer households receive larger amounts of subsidies in per-capita terms (figure 5, panel b). This rule is not, however, always true. For example, the data for LPG in the Islamic Republic of Iran show a negative slope in both graphs, demonstrating not only that this product is more important for poorer households but also that these households receive a larger amount per capita in subsidies than richer households. This is less evident for food products, such as flour (figure 6). We can 14
early pregnancy, low education attainment, and drug use. The key findings based on our survey data collected in Fortaleza, the Northeast of Brazil are the following.

- Poor youth are at considerable risk of growing up without their father; only 7 percent grow up with their father present in the household.

- The intergenerational transmission of low education attainment is at play, but it is diminishing; two percent of the youth never attended school, compared to 19 and 23 percent of their mothers and fathers, respectively.

- Poor people are at considerable risk of hunger and malnourishment; 27 percent of the youth and 55 percent of the adults experienced hunger at least once in their life and 43 percent of the youth did so in the last 12 months.

- Risk of early pregnancy and fatherhood is large; 31 percent of the youth had their first child before age 16. This is triple that of the adult population, where only 9 percent entered into parenthood before age 16.

- Risk of sexual abuse and violence within the household exists; six percent of the youth answered that they had their first sexual relationship with a family member and 13 percent grow up in household with violence.

- The youth are at risk of falling into drug use; 34 percent of the youth population are taking or did take drugs and 14 percent use drugs on a daily basis

- Social capital levels are low; only 5 percent of the youth and 9 percent of the adults have measurable social capital. Moreover, 35 percent have no confidence in the other dwellers of the neighborhood.

- Risk of growing up in a violent neighborhood is large; 59 percent of the youth claim that they live in a violent neighborhood, 80 percent feel unsafe in their neighborhood and 50 percent feel unsafe at home.

- The risk of engaging into violence is considerable and 47 percent of the youth claim that family problems are the main reason for youth engaging in violent activities, while 23 percent suggest that lacking opportunities in general is the main reason.

- Risk of unemployment and is huge; 39 percent of the youth declared themselves unemployed compared to 28 percent of the adult population.

22
before rebounding between 2016 and 2020. Even in the first period of overall employment decline, “new” exporters increased employment, but not enough to offset employment declines in the “never” exporting group. During the employment increase between 2016 and 2020, overall employment increased in the “always”, “new”, and “never” groups, while it decreased in those that stopped exporting. New exporters appear to be the main driver of job creation.

# **5. Empirical Analysis**

## **_5.1 Empirical Approach_**

We are interested in estimating the relationship between exports and employment. All our models are estimated using the 2013-2016-2020 firm panel data.

As a first step, we consider the characteristics which make a firm more likely to export. We begin by estimating the extensive margin to identify what the main drivers of a firm being an exporter are.

exporter are. ′ ′ e e e e i i i i (5.1) ii ii where, e e e e i is a dummy indicating a firm = αα+ XX γγ+ ZZ δδ+ DD + μμ + εε _i_ ’s export status in year _t_ . We alternatively consider the determinants of being a “new” exporter (one that did not export in 2013). We control for a selection of firm characteristics ( i i ), and year dummies ( ). Firm characteristics ii included in i are those that we expect would impact the firm’s export sales and employment. XX , ZZ DD Exclusion restrictions in XX i are characteristics that we would expect to impact a firm’s decision to export but not have any direct impact on its employment levels. Our choices of control variables ZZ are guided by the empirical literature (Dixit and Pal 2010, Abbey et al. 2017, Marques 2015).

To control for firm productivity, we use as proxies the value of machinery and labor costs. The value of machinery is taken as the book value of all machinery the firm currently uses if it were to hypothetically purchase all of it. Together, the value of machinery and labor costs help 18
strument using teacher and parent reports (Brinkman et al., 2017). Data collected on a cohort of 4 year-olds also shows that as children get older their performance on each of the domains goes up and their vulnerability declines (Hasan et al., 2013).

Figure 5 provides a visual depiction of the association between maternal education and child development as reported by teachers and caregivers. Across the board we see a positive relationship between the two variables. Language and cognitive development, communication skills and general knowledge and social competence are dimensions with strongest relationship. Literate mothers have kids with raw social development scores 0.30 s.d. above those of kids whose moms do not read. Language and cognitive development scores are 0.43 s.d. higher, and communication skills and general knowledge scores are 0.26 s.d. higher, respectively.<sup>9</sup> Once again, Appendix Table A5 reports the descriptive statistics regarding all dimensions (Panel C).

# **2.3 Parenting, child activities and human capital investments**

## **2.3.1 Parenting**

Given the association between parenting styles and child outcomes posed by the broader developmental literature, we make full use of our survey instruments to take a unique approach and investigate elements of parenting that may influence child development. In particular, we presume some of these can be influenced by the amount of formal education caregivers acquire.<sup>10</sup> The focus is on how families influence their children’s development through their relationships, such as the level of warmth or anger that mothers feel for their children and the kind of discipline that mothers tend to use when children misbehave. These practices are important because children living in an environment with higher-quality parenting are believed to be more likely to have higher pre-academic skills, better language skills, more social skills, and fewer behavior problems than children who received lower quality parenting (NICHD, 2002).

In our survey, mothers were asked to answer a series of questions about their parenting prac-

> 9The corresponding numbers for physical health and well-being and emotional maturity are 0.09 and 0.12. 10Psychology and sociology have a long tradition of associating parenting with socio-economic status (Baumrind, 1991; Kohn, 1969).

12
**_5_**

Net debt g Net debt + market value of equity _where:_ Net debt =Interest bearing debt -cash and short term securities The net debt figure should be estimated from the book value information from the accounts.7 Ideally, the net gearing figure should be calculated for every year that is included in the equity beta calculation. This allows both an average over the period and an end point gearing figure to be used in the calculations explained below. This is important if companies are undergoing rapid changes in their financial structures, as has been seen in the UK over the last two years. If possible, it would also be useful to know the national gearing level, or at least the average gearing of all quoted companies, since this may have an impact on changes in relative financial structure, which is a practical area that has not been explored closely by academia.

# 2.2.3 Estimating **the** asset beta

Once we have the equity beta and the net gearing it is possible to establish an estimate of the asset beta. This is done by using the following formula: _13a = ex (1-g)_ Where _fia_ is the asset beta and _.1e_ the equity beta.

So, if the net gearing was 50% and the equity beta was 1, the asset beta would be 0.5. This is a simplification of the overall relationship. Some doubts have been raised with respect to the assumption of a zero debt beta and it may be worth investigating the impact of non-zero debt betas in examples where the companies are highly geared, or the financial markets are likely to place a high premium on the company owing to the perceived general riskiness of the company. This could be a very important issue in the less developed countries or where high levels of debt have been employed in project financing deals.

# **2.2.4 Does this approach over or underestimate risks?**

Recent work has raised some questions relating to the simplified model adopted in the majority of countries regarding the relationship between the equity and asset betas. Traditionally, the approach set out earlier in this section is used. This is based on: _,Ba_ =:fie X _(1-g)_ The alternative approach can be defined as:

> 7 A net rather than gross value of gearing is used because a regulator should be interested in the financing of productive assets. A company can easily change its gross level of gearing by borrowing and then investing the proceeds in short-term financial instruments. If a management chooses to do this, customers should not face higher prices because of it.

> 8 The recent gas arbitration in Victoria provides a good example of the discussions relating to this alternative approach. See, for example, _The Weighted Average Cost of Capitalfor the Gas Industry_ by Professor Kevin Davis, March 1998, available on the Office of the Regulator General web site.
should become more immediately apparent to government staff that we have relatively little idea as to what identifies these firms, and so there is a role for expanding the program to serve a broad range of firms and then scientifically measuring impact to see who the program works best for.

3. _Focus more on eligibility criteria and on making it easier for firms to apply:_ the most innovative companies may not be those which are already formal, large, and long-established. Market failures may apply more to younger and smaller companies, so ensuring that eligibility criteria do not rule these firms out seems a good idea. But just as important as the initial criteria is making it easy for firms that meet these criteria to find out about the funds, apply without undue burden, and receive the funding promptly. Incentives for both government and World Bank operational staff are often focused more on getting projects launched and ensuring money is spent, rather than on the efficient delivery of services once projects have been launched.

4. _Use evaluation techniques that can obtain more power out of relatively small samples:_ despite all these efforts, in many African countries there are just not that many firms with more than one or two workers, and the projects will likely end up funding fewer than 100 firms a year. McKenzie (2011b, 2012b) discusses how one can obtain more power out of such sample sizes by restricting attention to a more homogeneous set of firms, and taking multiple measures on them. An extreme case is illustrated in Bloom et al. (2012), which conducted an experiment with only 20 textile firms in India, but as these firms were all in the same sector and weekly production data were collected, impacts of management consulting were still possible to measure. In practice, matching grant programs are unlikely to have the same level of homogeneity of firm production technologies and the same richness of data, but steps in this direction can still be made.

5. _Practice “little IE” as well as “big IE”:_ our goal in these failed experiments was impact evaluation (IE) around the overall impact of these grants. We refer to this as “big IE”. While important, we have seen the difficulties in doing this. Given the large number of design issues that matching grant programs grapple with that do not have a strong evidence basis for decision-making, it seems possible to also conduct more “little IE”. By this we mean experiments embedded in the overall broader project that can help learn not whether or not matching grants have an impact overall, but instead about what the impacts of changing different design features in these grants could be. This shifts the question from “what is the impact of the matching grant scheme?” to “what is the most effective way to maximize the impact of the matching grant scheme?”. For 27
23 " "Services: An International Comparison, 1960-1983" (paper written for the Fifth Annual Seminar on the Service Economy, Geneva: 1989b, monograph).

- Fuchs, V.R., ed., Production and Productivity in the Service Industrieq, Studies in Income and Wealth, vol. 34, NBER (New York: Columbia University Press, 1969).

- Gershuny, Jonathan, "The Future of Service Employment," in The EmerQina Service Economy, ed. by Orio Giarini (Oxford: Pergamon Press, 1987) 105-126.

- Giarini, Orio, ed., The EmerQing Service Eccnomv (OxZord: Pergamon Press, 1987)

- Gregory, P. R. and R. C. Stuart, Soviet Economic Structure and Performance.

- (Harper and Row, 1990).

- Griliches, Zvi, ed. The Measurement of Services in the U.S. Economy, NBER Studies in Income and Wealth (Chicago: the University of Chicago Press, 1?92).

- Grossman, Gene and Helpman, Elhanan, "Endogenous Product Cycles," Working Paper No. 2913 (Cambridge, Mass: National Bureau of Economic Research, March 1989a).

- , "Growth and Welfare in a Small Open Economy," Working Paper No. 2970

- (Cambridge, Mass: National Bureau of Economic Research, July 1989b).

- Giarini, Orio and Walter R. Stahel, The Limits to Certaintv (Kluwer Academic Publishers, 1989).

- Heston, Alan and Robert Summers, "Measuring Final Product Services for International Comparisons" (mimeo, 1992).

- IMF/IBRD/OECD/EBRD, A Study of the Soviet Economy, 3 volumes (Paris: OECD, 1991).

- Johnson, Simon, "Private Business in Eastern Europe" (mimeo, April 14, 1992).
As in Case II however, the tax-to-GDP ratios do not reflect the underlying and severe changes in the economy. Real revenues in fact are now all lower than in Case 11. Morever, import taxes still do better. Consumption levels are also down but less with imports. Investment, driven by savings, have the revcrse story like in Case II. In all variables, the risks or standard deviations are higher.

Another source of possible error is the government's forecasts about the external shocks. For example, if die collapse of the CMEA exports and the rise in import prices turn out to be higher, e.g. near 80% and 60%, respectivcly (instead of the projected 50% and 30%, respectively) and everything else the same as above, real revenue will fa ll to much lower levels but with less decline in the import-tariff case: 59.1 in the domestic indirect tax case; 61.4, import tariffs; 59.2, capital income tax.

# 4.3. Downsizing the State Sector

The simulation **s** o far sidestrack some fundamental questions - Whether public expenditures in transitional economies are already 'optimal'? Whether they should be at all Financed by high taxes? And whether other measures, such as privatization, will help? These issues are examined next. The extremely high tax-to-GDP ratios resulting from keeping the current revenue structure instandy confirm that tax policy should never be divorced from the expenditure fimction of government. As the former socialist countries move towards market-oriented economies, public expenditures also need to be examined, cut back, and restructured. Any attempt to finance current levels is not sustainable and will only lead to reasonably high taxes, as demonstated above. One single item like government transfers and subsidies, for example, can take up 45 % of gross value added at factor prices (see Table 1.) Just eliminating a third of the revemne demanded for that item alone, even while giving up non-tax revenue, will reduce the required tarff rate in Case I from 31.1 % to a much lower 85%.

_24_

**_Go: Ramnm Unrainia_**
consideration applies to changes in other sources of non-labor incomes. In Table 2, we show that, in Brazil, the observed changes in incomes from sources different from the labor market were substantial and varied across the income distribution. For instance, incomes from public transfers grew for all income quintiles and they increased by more for the richest ones who were receiving very little public assistance before the crisis. However, the importance of incomes from public assistance was small for the richest households both before and during the pandemic period. On the contrary, for the poorest quintile this source of income increased its participation in the initial household income from 16% to 39%.

Incomes from other transfers, such as pensions and unemployment insurance, declined for all and the reduction was especially large for the second and fifth quintiles. This source of income represented between 16% and 29% of the initial household income both before and during the pandemic period, indicating that not accounting for this source of income in the analysis could be biasing the microsimulation projections.

**Table 2. Observed changes in incomes from sources different from labor between 2019 and 2020**

||Other<br>transfers|Public<br>social<br>assistance|Other<br>sources of<br>income|
|---|---|---|---|
|Observed change between 2019 and 2020||||
|Q1|-8.0%|149.1%|-32.5%|
|Q2|-23.6%|723.8%|-35.2%|
|Q3|-2.1%|1422.9%|-34.4%|
|Q4|-3.8%|2193.4%|-30.7%|
|Q5|-14.8%|1420.7%|-40.3%|
|Share of the initial housold income in 2019||||
|Q1|17.2%|15.6%|4.1%|
|Q2|24.5%|2.6%|1.9%|
|Q3|28.9%|0.6%|1.4%|
|Q4|19.7%|0.2%|1.1%|
|Q5|18.5%|0.0%|1.0%|
|Share of the initial housold income in 2020||||
|Q1|15.8%|38.8%|2.8%|
|Q2|18.7%|21.0%|1.2%|
|Q3|28.3%|8.7%|0.9%|
|Q4|19.0%|4.0%|0.8%|
|Q5|15.7%|0.5%|0.6%|

Source: PNAD-C 2019 and 2020.

Notes: Other transfers include poverty pension, regular pension, unemployment insurance and fishers’ insurance. Other sources of income include alimony, donations, allowance, rents, scholarships, and financial gains.

When comparing the observed changes in poverty rates between 2019 and 2020 with our projections, we find that our model correctly predicts an increase in poverty, although the size of the change is smaller than the observed values (Table 3). The poverty rate according to the 2.25 USD-a-day poverty lines increased by 4.9 percentage points and our prediction was 2.7 percentage points. For the 6.85 USD-a-day poverty line, the observed increase was 7.7 while our projection 26
through these large multipliers will, in most circumstances, also directly contribute to reducing poverty (Dollar and Kraay, 2002; World Bank, 2004a).

A related literature on the role of non-farm enterprises as engines of rural development, income growth, and poverty reduction has grown rapidly in the past decade (Mellor, 2000; Foster and Rosenzweig, 2004; Lanjouw and Lanjouw, 2001; Datt and Ravallion 1998, Ravallion and Datt, 1996, 1998, 2002; Haggblade, Hazell and Reardon, 2002). A key question addressed in this literature is whether non-farm enterprises are predominantly a low productivity supplementary activity which households undertake to diversify their income sources and insure against shocks to their agricultural income, which are important roles for the rural poor. The more promising hope, however, is that rural non-farm enterprises are potentially a source of growth and poverty alleviation in rural areas. Most of the literature suggests that non-farm activities fulfil both important functions.

These two literatures are bridged in a broader literature which attempts to understand the nature of pro-poor growth, which inevitably must encompass the rural poor and their potential pathways out of poverty (Ravallion 2004, World Bank, 2004a, 2006b). One theme of this literature argues that empirically, growth originating in the agricultural sector has tended to be more “pro-poor” than growth originating from the industrial or service sectors (Mellor, 1976; Ravallion and Datt, 1996; Ravallion and Chen, 2004; Timmer, 2002). Indeed, agricultural growth has often been an important ingredient in the formula that connects economic growth to the poor (Ravallion and Huppi, 1991; Ravallion and Datt, 1996; Ravallion and Chen, 2004; Sumarto and Suryahadi, 2003; Fan, Zhang and Zhang, 2004; Fan, Thorat and Rao, 2004; Timmer, 1997, 2004). New agricultural technologies that improve farm productivity strengthen this connection. Separate reviews by Thirtle, et al. (2003) and by Majid (2004) confirm the strong empirical link between higher agricultural productivity and poverty reduction.

One explanation for why agricultural productivity gains stimulate more rapid and pro-poor economic growth than urban-based growth is provided by Mellor’s model of agricultural growth, rural employment and poverty reduction. This model emphasizes the role of the rural non-tradable sector in pulling underemployed workers out of agriculture into the non-agricultural rural economy. The Mellor model explicitly integrates 6
10 IV Fool* - StrUctire of kEXA3ITS Model The objective of this model Is to provide a shnulatlon tool at the disaggregated lvel of Individual agricultural commodity markets that will permit experiments explorig the effect on those markets of policies at the donestic macroeconomlc or International (i.e., tradig partner) macroeconomic and sectoral levels. The effects are A. be transmitted by changes In variables that are specilled as exogenous deteminants of quantities der .anded or supplied, In turn, the values of these Ihkago variables are dtormhod In upstream models In an experental framework of rocursive causation. The structure of this framework Is given In Figure _1._ Note that MEXAGMKTS receives values of ik*ago variables from both the Mexian macroeconomic and the US (and rest of the world) agricultural markets models.

Model design specifies the Interaction of markets for several kportant food/feed crops with markets for representative livestock commodities. iputs are the prknary factors of labor and capital and the Intermedlate Inputs of fortIlizer and food crop commodities. Land Is omitted from the specification through the use of supply functions whose key arguments are price varlables. ThIs approach Is taken since the set of markets modeled does not Include sno markets for all agricultural conunodltles and knportant substItution relatlonships between factor Inputs, especlally land, exist between the markets modeled and those omltted. In additlon, the supply of agricultural labor Is linked to markets for unskillod labor nationwido (and even intortionally). Thus, the wage of labor Is a key linkage variable whose value Is determinod In the Mexican, macroeconomic model.