rafmacalaba/datause-encoder-data huggingface dataset:
10 Information campaigns for programs that attempt to improve primary school quality or to enhance child nutrition through primary school feeding programs in a context in which virtually all primary-school-age children are already enrolled would seem less relevant than such campaigns as part of a new program to improve preschool child development where there had previously been no preschool programs.

11 The authors estimate the impact of atypically low rainfall levels by including a year’s delay because the food shortages would be apparent only one year after the drought, but before the next harvest was ready.

12 To estimate these longer-run impacts, Alderman, Hoddinott and Kinsey (2006) combine data on children’s ages with information on the duration of the civil war and the episodes of drought used in their analysis. They undertook a new household survey to trace children measured in earlier surveys.

32
# **2. Data and construction of variables**

The data on per capita GDP used for this paper are taken from World Bank (2007). The values correspond to real per capita GDP in 2000 US dollars, adjusted for differences across countries in purchasing power parity (PPP). The data on births and deaths are based on 123 Demographic and Health Surveys (DHS) covering 59 countries. The surveys include countries in Africa (33 countries, 68 surveys), Latin America (12 countries, 31 surveys), and Asia (14 countries, 27 surveys). The earliest surveys in our sample were carried out in 1986, the latest ones in 2004. Taken together, the surveys we use collect information on approximately 760,000 women and 1.7 million births. However, the sample sizes vary considerably—for example, the 1999 India DHS covers approximately 90,000 women, while the sample size for the 1987 DHS for Trinidad and Tobago is just over 3,800 women. The list of specific surveys and their sample sizes are given in the Data Appendix.

The DHS ask women a set of questions about the date of birth, current vital statistics, and date of death (if deceased) of all children ever born. We use the responses to these questions to construct retrospective birth and death histories, closely following Paxson and Schady (2005). Our measure of infant mortality is an indicator that takes on the value of one if a child died at a reported age of 12 months or younger. We use this measure of infant mortality, rather than the standard definition of mortality for children _younger than_ 12 months, because of age heaping in reports of mortality. Further, we discard information for children born within 12 months of the survey when calculating mortality rates to avoid complications with censored data.

Although the DHS are a rich source of data, they also have some limitations for our analysis. We briefly discuss two of these limitations, both of which are related to the use of _retrospective_ information in the DHS to construct birth and death histories. First, recall bias may 5
protections, vesting provisions and redemption rights are more typical in Common Law countries while milestones are less common. However, while contracts seem to differ across legal regimes, the most experienced VCs implement U.S. style contracts. Cumming and Johan (2011) also find that the legal experience of the VCs is more important than the legal regime of the country of the VC fund in structuring the contracts.

# **_5.5 Small Firm Financing_**

The effect of financial development is not uniform across all firms. In this section, we review the literature that has found a differential impact of financial development and access to finance on small versus large firms. First, studies have shown that small firms are more likely to report access to finance as being a major obstacle than large firms. Ayyagari, Demirguc-Kunt, and Maksimovic (2008) use firm level survey data from the World Business Environment Survey (WBES) and find that small firms report higher financing obstacles than large firms. Not only do small firms report higher obstacles, they are also more severely affected. Beck, Demirguc-Kunt, and Maksimovic (2005) find that the growth of smaller firms is hindered most by financing constraints, especially collateral requirements, bureaucracy, the need for special connections, and interest rate payments. They also find the lack of specific forms of financing such as leasing and long-term finance to be particularly constraining for small firms.

Just as small firms face greater information barriers to raising funds than large firms in economies with underdeveloped financial systems, they also benefit disproportionately as the financial systems develop. Beck, Demirguc-Kunt, and Maksimovic (2005) find that previously constrained small firms benefit the most from financial development. Love (2003) also finds that small firms are disproportionately more disadvantaged in less financially developed countries than are large firms. Laeven (2003) finds that small firms‟ financing constraints decrease following financial liberalization episodes such as interest rate liberalization, elimination of credit controls, privatization and bank entry where as those of large firms actually increases, reflecting the loss of political patronage and erosion of entrenched interests.

To illustrate differences in access to finance across countries, we use latest set of Enterprise Surveys, led by the World Bank, consisting of 47,745 firms in 99 developing 49
<!-- Start of picture text --> Pollcy Research<br>Agricultural Pollcles<br>WPS 865<br><!-- End of picture text --> This paper - a product **i** f the Agricultural Policies Division, Agriculture and Rural Development - Department is part of a arger effort in the department to study the impacts of food security policies. Copies of the paper are ava lable free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Cicely Spoc ner, room N8-039, extension 30464. (February 1992, 48 pages.)

# Most influential studies of malnutrition and

public policy have focused on energy availability and consumption, tending o equate hunger with malnutrition. But recent sludies have explored how other factors - notat ly infection and levels of maternal education - affect nutrition.

Alderman and Garcia'; study of nutrition levels in Pakistan shows ti at raising household food consumption, for exanple, has less impact on nutritional levels than raising a mother's education does. They found that educating motheis to at least the pririary level tends to reduce the level of child stunting (a long-term indicator of child nutrition) 16.5 percent, or roughly 10 times the impact achieved by increasing per capita income 10 perccnt. (The impact of education is not immediately realized; the diffusion of knowledge about good hygiene and child care associated with learning has a cumulative effect.) Alderman and Garcia found that in Pakistan, 1<sup>ood security alone is not enough to improve</sup> children's nutritional status. There may be welfare justifications for various food policies, but in rural Pakistan, especially, it is equally important to improve health and reduce infection.

The Policy Research Working Paper Series disseminates the findings of work under way in the Bank. An objective of theseries is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Resea;ch Dissemination Center
Average farm size increased from 738 hectares per farm in 1953, to 867 hectares in 1960, to 988 hectares in 1971, and to 1 339 hectares in 1981, but declined to 1 280 hectares per farm in 1988. From 1955 to 1988, average farm size by province increased from 1 284 to 2 663 hectares per farm in the Cape Province; 471 to 998 hectare per farm in the Orange Free State; 403 to 629 hectare per farm in the Transvaal, and 390 to 609 hectare per farm in Natal. These data show that the national average hides significant regional variations. In 1988, the median farm size was about 500 hectares, with farms in the high-potential areas significantly smaller. Such qualifications should not distract, however, from the fact that large-scale farms dominate South African agriculture, and that the average size of these farms is extraordinary by international standards.

# _Evidence of Economies of Scale_

At present, there is mixed evidence for the existence of scale efficiencies in South Africa's commercial farm sector:

- The distribution of gross farm income in commercial agriculture is highly unequal: In 1988, 3% of the farmers earned 41% of the total gross farm income; 26% earned 81%/0, while the remaining 74% of farmers earned a mere 19% of total gross farm income (calculated from the 1988 agricultural census, CSS, 1993).

- Hattingh (1986) reports evidence of a direct relationship between farm size and efficiency in sheep farming in the Karoo and in cattle ranching in north-western Transvaal. Hattingh also reports that efficiency increased between small and medium-sized irrigated farms at Vaalharts and dryland grain farms in the Orange free State, before decreasing again on the larger farms (size ranges are not specific);

- Analyzing the Department of Agriculture's Production Cost Surveys, Moll (1988) finds no significant economies of sizes both in maize-cattle regions (Western Transvaal, North-West Orange Free State and the Transvaal Highveld) and in wheat-sheep regions (Swartland). Using re-tabulated 1983 census data, however, Moll (1988) finds economies of size, but only in the maize areas and for 50-300 hectare range.

Conversely, there exists empirical evidence from South Africa to suggest an inverse relation between farm size and efficiency. Statistics from the 1988 census of agriculture (CSS, 1993) show that

> 5 Moll (1988) measures economies of size (all factors but operator labour changing) as opposed to economies of scale (all factors changing).

9
Table 2: Baseline Results.

||ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|ln(NO2)|0.113***|||||0.116***|0.123***|0.110***|0.071***|
||(0.025)|||||(0.025)|(0.025)|(0.025)|(0.019)|
|ln(Lights)||0.030*||||0.031**|0.020|0.022|0.020|
|||(0.016)||||(0.016)|(0.015)|(0.015)|(0.013)|
|ln(CO2)|||0.079***||||0.077***|0.065***|0.066**|
||||(0.028)||||(0.026)|(0.024)|(0.031)|
|ln(Electricity)||||0.103***||||0.081***|0.060***|
|||||(0.025)||||(0.022)|(0.016)|
|ln(Survey)|||||0.328***<br>(0.069)||||0.349***<br>(0.051)|
|Observations|2,752|2,752|2,472|2,548|2,303|2,752|2,472|2,371|2,071|
|Countries|178|178|174|170|154|178|174|167|146|
|Years|05-20|05-20|05-19|05-19|05-19|05-20|05-20|05-19|05-19|
|R-Squared|0.805|0.803|0.816|0.811|0.866|0.806|0.821|0.824|0.882|

Robust standard errors in parentheses

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

_Notes._ All specifications include country and year fixed effects, and a country-specific time trend. When nights lights are used, fixed effects are included for either DMSP-OLS (pre-2014) or VIIRS (2014-2020) sensor. Both NO2 and lights are per area. Robust standard errors, clustered by country are reported in parentheses. R-Squared is adjusted and within all fixed effects.

consumption distributional data from more than 1900 household surveys spanning 1967-2019 and 168 economies.<sup>13</sup> As a caveat for this data, it is worth noting that not all these surveys are comparable in design and sampling methods. There are also sizable discrepancies between national accounts and household surveys.<sup>14</sup> In Columns 6-9, we investigate the inclusion of multiple signals at once. Column 6 shows that both NO2 and lights have explanatory power when included jointly. However, when any of the additional signals are included in Columns 7-9, the statistical significance of lights disappears. Thus, NO2 is always a useful predictor of cyclical output, even when additional widely-used signals such as electricity consumption are available.The same cannot be said about lights.

**Differences by-Grade.** Table 3 Column 1 replicates Table 2 Column 4, in which both NO2 and lights were used as signals. Then, in Columns 2-4, we break down the results by data quality grade.<sup>15</sup> Lights only have statistically significant explanatory power for cyclical fluctuations in the 37 out of 178 countries with the lowest data quality, consistent with the findings in Chen

> 13Chen et al.; see Chen and Ravallion (2010) and Pinkovskiy and Sala-i Martin (2016) for representative applications.

> 14See Deaton (2005) for a discussion of the sources of these discrepancies.

> 15We consolidate A and B, and D and E, into joint groups to be more comparable with the larger sample in grade C.

17
I must avoid introducing endogenous variation into the instruments. This might occur if municipalities are on different time trends according to their values of the pre-period variables used to compute the instruments. Municipalities might also be on different time trends according to initial public pre-primary enrollment levels. I address these concerns by including the interaction of a linear time trend, _t_ with a vector of the 1997 values of per capita revenue, per capita transfer revenue, the fraction of public primary school students in state or federal schools, the fraction of 0-6 year-olds enrolled in public pre-primary, and public pre-primary enrollment squared. This allows for heterogeneous secular trends in municipalities with different pre-reform characteristics.

In the results section, I demonstrate that these instruments satisfy the inclusion restriction: they are correlated with observed per capita revenue and with its interactions with per capita income and inequality. The exclusion restriction should hold since the instruments depend only on pre-reform municipality characteristics (captured by fixed effects) and federal government rules, which are exogenous to any particular municipality’s investment during the sample period.

# **5.3 Measuring income, inequality, and pre-primary investment**

I measure income two ways, to ensure robustness: median and average per capita municipal income. I compute median per capita household income using 2000 Census data. Average income data are available annually. I measure inequality with the municipal Gini coefficient, computed from 2000 Census data. Results instead using mean over median income, and the level of income polarization,<sup>32</sup> are similar and available upon request. Table 4 presents a correlation matrix of these variables. The correlation between the Gini coefficient and per capita income (whether median or average), is always below 0.04 in absolute value. Median and average income have a correlation of 0.72.

I have panel data on several aspects of public pre-primary education quality:<sup>33</sup> pre-primary students per teacher, the fraction of pre-primary teachers with at least some post-secondary education, and the fraction of pre-primary schools with a designated school building, electricity, an

> 32I measure income polarization using individual-level data on all voting-age (16+) persons in the 2000 Brazilian Census, and following the method described in Duclos, Esteban, and Ray (2004). I set the parameter _α_ , capturing the importance of local clustering of income, to an intermediate value of 0.5.

> 33All measures of pre-primary education quality are not available for 1995, but are available for 1996-2008.

20
each month of extra experience<sup>31</sup> in using the system is associated with an average of 4% decrease in the TCC of VAT per cycle. However, the square of experience is also significant and positive; this suggests that the decrease due to experience occurs at a decreasing rate and diminishes over time, as expected.

Other coefficient estimates are generally as expected and in line with the estimates of Coolidge, Ilic and Kisunko (2009). It is important to note that PAYE is an important driver of TCC in South Africa. One would expect to see a similar result for VAT, yet our estimate of this coefficient is not significantly different than zero. These results are robust to controlling for all type of industry, province and legal form. It is important to note that experience with tax compliance (not necessarily e- filing) is never statistically significant and thus dropped from the main regression equation. This may suggest that it is the experience with e-filing, not with tax compliance relevant type of experience associated with a reduction in TCC.

# **3.2.2 Nepal**

# **3.2.2.1 Probability Estimates for E-filing**

Coefficient estimates from Equation 1 with Nepali data are presented in Table R.N2. Besides the variables present in the main model, we also control for several other perception variables: difficulties in access to finance, road infrastructure, skilled worker and electricity, computer usage in tax accounting, internet availability, corruption, political instability and whether a firm has bank account or not. This is to see if these variables are indeed important in e-filing decisions.

> 31 This is computed from responses to the questions: “How long ago (in months) did you started e-filing” for each four major taxes (income tax, VAT, PAYE and UIF/SDF). We simply took the average of e-filing experience in months with all these four major taxes

40
A third way in which labor market could be impacted is through the impact of COVID-19 related measures on commuters traveling to work. Not only could labor mobility be limited during the outbreak, but there is also a higher risk to contract the infection while using public transport due to contact with fellow commuters. About 43 percent of those employed in Tunisia rely on public transport to get to work (Figure 5). The more well-off individuals are more likely they are to use public transport. This is because a high share of the poor walk to work. Looking at regions, as expected, about two-thirds of those employed in Grand Tunis use public transport.

**Figure 5: Use of public transport to travel to work**

<!-- Start of picture text --> a. By consumption decile b. By region<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Grand Nord Nord CentreCentre Sud Sud<br>Tunis Est Ouest Est Ouest Est Ouest<br><!-- End of picture text --> Source: Author’s calculations based on EBCNV 2015 Note: Public transport is defined as the use of any of the following to go to work: metro, train, bus, taxi, driver car or passenger car A fourth way through which labor market could be impacted is by the viability of certain jobs to be performed from home. Dingel and Neiman (2020) compute a score measure of teleworking or homebased work (HBW) which illustrates the possibility of working from home. Hence, workers with the lowest HBW scores may be most likely to immediately lose their job due to COVID-19. Analysis using this approach shows that most jobs in sectors such as education, finance and communication could plausibly be performed at home, but very few jobs in agriculture and industries such as textiles and mechanical and electrical can be performed at home, putting workers engaged in these sectors at a greater risk of loss of employment. We also observe that the ability to work from home tends to be higher for higher consumption deciles in contrast to lower consumption deciles. In addition to the four ways mentioned in this note, there are other ways in which the labor market could be impacted by the outbreak and government’s measures.

Finally, households spending high share of expenditures on food are more vulnerable to a decline in real disposable income as a result of price increases. Food expenditures constitute a major part of the household budget for Tunisians, more so for the poor. Not surprisingly then, households that spend a lot of money on food items that are likely to observe increase in prices are going to be impacted more due to the crisis. Figure 6 shows the share of food expenditure in total expenditure of Tunisian households by consumption deciles and region. Food expenditure tends to be higher for households in the poorest 9
- 43 -

(19)

**rf=**

-0l-O+4,/q. 1-u

Recall the taxes generally influence r as in (7). In general taxes will have an ambiguous effect on rg. In the absence of inflation and the investment tax credit (Or, = 0), rg would be higher than in the absence of taxation because only part of the cost of holding the land would be tax-deductible (i.e., the interest cost). However, the ability to deduct nominal interests costs when inflation is present and the ability to claim an investment tax credit would both reduce the effective tax rate.

## _b. Inventory capital_

A completely general theory of the holding of inventories can be very complicated indeed owing to the dynamic nature of the problem. We make some reasonable simplifications to make the problem both manageable and intuitive. In particular, we model the firm in the steady state.<sup>2 7</sup> The firm produces an output X using as an some raw material. An amount R of the raw material Is held as inventory (or work in progress). The average holding period of a unit of inventory is T = R/X, chosen by the firm. The firm produces a unit of output using a unit of raw material drawn from inventory and incurs cost of C (X, R) where C1 > 0, C2 < 0. The price of output is Q and the purchase price of raw materials P.

The corporate tax base includes total revenues (QX) less current costs (C) less interest costs less the First-In-First-Out (FIFO) value of raw materials taken out of inventory.<sup>2 8</sup> We denote P-T as the FIFO value of goods taken out of inventory after being held there a length of time T. The problem of the firm at time zero is:

# Mx, _flo_ e -((1-u)(Qx-C(X,R))-P(X+A) +uP.X)t

## (20)

> 27 The following analysis is adopted from Boadway, Bmee and Mintz (1982). It might be noted that from an analytical point of view, the tratment of renewable resources would be similar to that of inventories.

2SWe nalyzethe caseof FIFO tax accounting for iDlustrative purposes. Some countries (e.g. the United States) allow firma to use LIFO accounting for taxes.