rafmacalaba/data-use-mentions-extended huggingface dataset:
the greater complexity of an industrialised economy would require a larger role for government.

Empirical tests of this relationship have, however, proven largely inconclusive and certainly sensitive to the selection of deflators. While Easterly and Rebelo (1993) for a wide range of countries, and Oxley (1994) for Great Britain 13, find that either government revenue or expenditure to GDP rises with per capita income in both crosssection and time series estimations, Ram (1987) only very weakly replicates this result when using time series data for 115 countries to determine the elasticity of government size with respect to per capita GDP . Positive elasticities dominate but only slightly. Further, on the cross-section data, the estimations -- using constant international prices to avoid relative price bias -- suggest that, if anything, the government share of GDP falls as incomes rises.

While the original argument was that the public sector would expand both through the action of income -- as luxury consumption -- and through structural change -- the greater complexity of public tasks associated with an industrialising economy -- this lumps all government spending types together and, particularly in the recent period, ignores the fact that technical change might actually reduce the need for public

> _3_ Oxley (1994) uses data for Great Britain in the period 1870-1913 and finds strong support for a long run relationship between the government share and real or nominal income. Granger causality appears to run from income to public expenditure.

> Easterly and Rebelo (1993) use nominal government revenue to GDP, Oxley (1994) uses nominal and real government expenditure to GDP and Ram (1987) uses data on govermnent consumption to GDP in 1975 intemational prices.

14
and relatives (6.7 percent) directly about the job were used with intermediate frequency. On average, employers used two of the methods. For comparison, in the April 2013 survey, we asked those eligible for the subsidy program how they found employees hired in the two years after the subsidy period. We find similar patterns though the search intensity is somewhat higher, with employers using just over 2 methods. With the post-subsidy hires, employers were more likely to say they advertised (9.6 percent) and contacted former employees (17.2 percent), but referrals through networks of family, neighbors and friends remain the most common search methods by very large margins.

# **5 Results**

We first examine whether the wage subsidy changed the survival rates of firms, since, to the extent it did, we need to control for this in examining impacts on employment, profitability and sales. As noted above, our estimation aims to combine the advantages of combining multiple follow-up 15
|2.374|3.576|2.426***|1.225***|-1.201***|
|Repayment of loan in case of droughts but good harvest*|4.962|6.186|6.613|-1.224***|-1.651***|-0.427**|
|Rank of repayment of loan if not enough funds to repayall(1 to 3)*|2.486|1.907|1.607|0.579***|0.879***|0.300***|

Source: Household survey conducted in three districts in AP, India in 2007. An asterisk (*) indicates that the sample is restricted to those with more than one source of credit.

12
|(0.00154)|(0.00289)|
|Investment Profile|0.00549*|0.00590***|0.00625***|0.00697***|0.00768***|0.00719***|0.00783***|0.00809***|0.00613|
||(0.00325)|(0.00168)|(0.00132)|(0.00149)|(0.00158)|(0.00169)|(0.00141)|(0.00215)|(0.00404)|
|Trade Openness|-0.00655|-0.0149|-0.00947|-0.0101|-0.00517|-0.00296|0.00250|0.0107|0.0109|
|_(% of GDP, logs)_|(0.0262)|(0.0135)|(0.0106)|(0.0120)|(0.0127)|(0.0136)|(0.0114)|(0.0173)|(0.0325)|
|Financial Openness|-0.000649|0.00160|0.00283|0.00419|0.00446|0.00523|0.00568*|0.00542|0.00426|
|_(Chinn‐Ito Index)_|(0.00678)|(0.00351)|(0.00276)|(0.00310)|(0.00329)|(0.00352)|(0.00295)|(0.00449)|(0.00841)|
|_External Factors_||||||||||
|Foreign Growth|0.000959|0.00180|0.00140|0.000892|0.000208
worker is inferred to vary substantially across countries. Compared to Table 1, human gaps are noticeably higher for countries in the bottom part of the distribution. The _Success_ ratio increases up to 52.5%.

# **4.5 Imperfect Substitution**

All the approaches reviewed so far treat workers with different levels of human capital as perfect substitutes. This is an extreme assumption, as it implies that high- and low-skill labor supply the same type of inputs in the production process, the difference between the two groups being solely a matter of embedded productivity. Empirical studies (mostly with US data) provide evidence against this assumption, since the relative wage of skilled and unskilled workers appears to react to changes in their relative supply, as predicted by frameworks with imperfect subsitutability between the two (Katz and Murphy, 1992; Ciccone and Peri, 2005).

A recent line of work investigates the implications of relaxing this assumption for development accounting. Caselli and Ciccone (2013) show that allowing for imperfect substitutability between skill types necessarily reduces the contribution of human capital, when measured by educational attainment. Intuitively, under imperfect substitutability increasing the share of skilled (highly educated) workers has two effects: an increase of the share of the most productive workers (which increases output), and a decrease in the relative marginal productivity of skilled and unskilled workers (which decreases output). With perfect substitutability the second effect is not present, and therefore the results in Table 1 can be treated as un upper bound for the contribution of schooling quantity in development accounting.

However, imperfect substitutability has implications for the measurement of other dimensions of human capital (beyond schooling quantity). To illustrate this, I consider the simple case where the aggregate human capital stock is a combination of two types of workers, skilled and unskilled, so that human capital per worker can be written as where _lS,c_ and _lU,c_ are the shares of skilled and unskilled workers in the labor force, and _hS,c_ and _hU
# **5.1 Non-farm income as an exogenous transfer**

Under the hypothesis that non-farm income is an exogenous transfer, the Gini index can be decomposed into three parts: the share of each income component, the Gini index of each income component, and the Gini correlation of each income component. The first column of table 2 shows that the farm income is the largest component of the total income (58.6%), following by the non-farm income (31.5%). The second column shows that the Gini index of the distribution of the observed total income, including the contribution of non-farm activity, is 0.508; while that of the distribution of the farm income is 0.579. It suggests that, in the absence of non-farm activity, household income would have been 14.0% more unequal. In other words, the presence of nonfarm income reduces rural income inequality.

**<u>Table 2 Decomposition of Gini Index</u>**

||Percentage of<br>total income<br>_k_<br>_S_|Gini Index<br>_k_<br>_G_|Gini Correlation<br>_k_<br>_R_|Contribution to<br>inequality<br>0<br>_G_<br>_R_<br>_G_<br>_S_<br>_k_<br>_k_<br>_k_|
|---|---|---|---|---|
|Total income|100.0|0.508|1.00|100.0|
|Farm income|58.6|0.579|0.78|52.3|
|Agriculture|31.5|0.635|0.62|24.6|
|Livestock|16.0|0.729|0.45|10.3|
|Forestry and fishing|11.1|0.986
# **4. Quantifying the trade and income effects of the China-U.S. trade agreement**

In this section, we quantify the impact of import targets in the China-U.S. trade agreement on the trade and income of these two countries as well as on third countries under different scenarios.<sup>13</sup> The simulations are based on a global dynamic computable general equilibrium model, Linkage, which uses the GTAP database (Freund, Ferrantino, Maliszewska, and Ruta, 2018). The model tracks historical GDP growth, trade balances and investment up to 2019 and then projects the developments in the global economy up to 2025. The commitments of China to buy more U.S. goods and services as per the text of the agreement are aggregated to the CGE model sectors (Figure 3).<sup>14</sup>

**Figure 3** : Chinese imports of goods from the United States ($billion) in 2017-2019 and assumed targets under the China-U.S. agreement in 2020-2021.

<!-- Start of picture text --> Chinese imports of goods from US ($billion)<br> 60<br> 50<br> 40<br> 30<br> 20<br> 10<br> -<br>Agriculture Natural Food Textiles, Chemicals Metals Transport Electronic Machinery Other<br>resources wearing equip. equip. manuf.<br>apparel<br>2017 2018 2019 2020 2021<br><!-- End of picture text --> Source: WITS and authors’ assumptions based on China-U.S. trade agreement. Note: 2019 has been estimated based on Jan-Nov 2019 data.

13 The import targets in 2020 may be less feasible in light of the negative demand shock from the Coronavirus. As the focus of the note is on the trade agreement and the extent and duration of the Coronavirus shock is uncertain, the scenarios assume demand in 2020 is not affected by
- A man should have the last word in household decision-making about finances. ( _last word_ )

- Housewives should be paid for their household labor. ( _pay women_ )

- Women should have as much opportunity as men to take a break from household responsibilities to spend time with friends. ( _leisure_ )

- Men who take on more household tasks to support women pursuing careers are not very manly. ( _manly_ )

Responses to these items constitute the personal attitudes (PA) indicators. I also ask whether respondents believe that (1) most Moroccan women, (2) most Moroccan men, and (3) “people in your neighborhood” currently strongly agree, agree, disagree, or strongly disagree with the same set of statements. Responses to these items constitute the perceived norms indicators. To avoid systematic question order effects, whether respondents were asked about personal attitudes or perceived norms first was randomized using Qualtrics’ built-in randomization, as was the order in which respondents were asked about their perceptions of each reference group, and the order in which the individual survey items were presented within each grouping.

For each respondent, I generate an aggregate personal attitudes score and separate perceived norms scores with respect to each reference group (men, women, and neighbors). First, I re-code responses to the items such that 0 = strongly agree and 3 = strongly disagree when the statement as written is a conservative viewpoint, and such that 0 = strongly disagree and 3 = strongly agree when the statement as written is an egalitarian viewpoint. Higher numerical values then represent more egalitarian viewpoints for all items. The scores are the mean responses to survey items after this re-coding.<sup>4</sup> For each respondent, I also calculate a pooled perceived norm for each individual item taking the average of respondents’ perceived norms of men, women, and neighbors. From this, I calculate an overall _pooled perceived norms score_ which is the average pooled perceived norm across all eight survey items. The mean values of these scores
of seat km in medium or<br>smaller aircraft|**52.3**|15.7|29.6|39.3|23.3|48.6|
|Percent of seat km in newer aircraft|**90.8**|96.8|71.4|98.3|80.2|79.3|
|Registered carriers on EU blacklist|**0**|0|0|0|0|0|
|FAA/IASA Audit Status|**Fail**|Fail|No audit|No audit|No audit|No audit|
|Percent of carriers passing IATA/IOSA<br>Audit|**0**|0|28.6|50.0|11.1|33.3|

Source: Bofinger 2008. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data

# Water supply and sanitation

## **Achievements**

According to the UNICEF-WHO Joint Monitoring Program (2010 report), access to improved water in Côte d’Ivoirestarted from a relatively high base of 76 percent. Nevertheless, progress since that date has been slow, with the access to improved water increasing only slightly to 80 percent by 2008. While the overall envelope of those with access to improved water has not moved significantly, the level of service received by those with access has improved substantially. In particular, the percentage of the population with piped water on premises almost doubled, from 22 percent in 1990 to 40 percent in 2008 (table 7).

A relatively small percentage of the population of Côte d’Ivoire relies on surface water—less than 10 percent, compared with 37 percent in the low-income benchmark group and 13 percent in the middleincome benchmark group (table 8). As of 2006, access to piped water was about twice as high in Côte d’Ivoire as the low-income benchmark
<!-- Start of picture text --> Pakistan<br>ORBIS Database es 54 107 84<br>SOE Supplementary bal ave | [e 17 54 107<br>nly in SOE Supplementary [ in Both Database Would notbe identifiedin Orbis without SOE SupplementaryDE Onlyin Orbi<br>Total Domestic SOEs<br>Angola<br>ORBIS Database (ea 4 19 104<br>SOE Supplementary os 76 | [> 53 4 19<br>Only in SOE Supplementary DB in Both Database Would not be identifiedin Orbis withoutSOE Supplementary DB Onlyin Orb<br>Total Domestic SOEs<br><!-- End of picture text -->

<!-- Start of picture text --> Pakistan<br>Number of Domestic SOEs Employment in Domestic SOEs Revenuein Domestic SOEs<br>v.00 (Million USD)<br>262 522,675 ‘ 54,182<br>400,001 383,407 41,323<br>Angola<br>Number of Domestic SOEs Employmentin Domestic SOEs Revenuein Domestic SOEs<br>x x (Million USD)<br>180 x<br>a 4 - , 34,113 8.00 7,710<br>137% +<br>x 23,700 x<br>, , 4,100<br>| i<br><!-- End of picture text -->
industry-level data set, and the standard deviation equaled the standard deviation of literacy skills in the country’s original PIAAC data set. Second, PISA provides a household wealth index based on students’ assets at home. This wealth index was treated as the log of the individual endowment, α ; however, the mean and standard deviation of the wealth index was rescaled such that (1) the mean of was equal to the average capital stock per worker for the country, calculated from (kk) the industry-level data set, and (2) the mean-median difference matched the mean-median kk difference in the OECD’s Wealth Inequality Dataset (2021). Three countries did not have data in the OECD’s Wealth Inequality Dataset and as a result, the mean median wealth difference was imputed based on the relationship between this and the variance in the PISA wealth index for countries with data.

Two skills cost functions were estimated. The baseline skills cost function for 2012 was estimated as described using PISA 2012 data. A counterfactual skills cost function was also estimated for 2000 using the PISA 2000 data which was the earliest round of PISA. For this counterfactual skills cost function, the plausible values of reading skills were scaled using the same factors as used for the PISA 2012 data set; this results in a different mean and standard deviation which is used below to compare how the marginal effects of a carbon price increase differ between the two skills cost functions implied by the 2000 and 2012 PISA.

# **MODEL PREDICTIONS: MITGATING EFFECTS OF EDUCATION QUALITY**

With the above estimates, we use the model to predicts the marginal effects of an increase in the price of carbon on the endogenous variables, the total stock of skills, , output, , emissions, , and wage rate for skills, h as well as wage inequity. Wage inequity was defined as the difference HH YY EE in wages that children from wealthy and poorer households earn when they reach adulthood. In ww our model, the randomly distributed endowment, _k_
mark>Akyol, S., and N. Mocan (2020): “Education and Consanguineous Marriage”, NBER Working Paper 28212.</mark> Amin, S., J. Ahmed, J. Saha, I. Hossain, and E. Haque (2016): “Delaying child marriage through community-based skills-development programs for girls: Results from a randomized controlled study in rural Bangladesh,” New York and Dhaka: Population Council.

<mark>Amirapu, A, M N Asadullah, Z Wahhaj (2020), “Can child marriage laws change attitudes and behavior? Experimental evidence from an information intervention in Bangladesh”, Working Paper</mark> Andrabi, T., J. Das, and A. Khwaja (2017): “Report Cards: The Impact of Providing School and Child Test Scores on Educational Markets,” American Economic Review, vol. 107(6): 1535–1563.

Andrews, C., A. de Montesquiou, I. Arévalo Sánchez, P. Vasudeva Dutta, B. Varghese Paul, S. Samaranayake, J. Heisey, T. Clay, S., Chaudhary (2021): “The Potential to Scale,” The State of Economic Inclusion Report 2021, World Bank.

Angrist, J., D. Autor, and A. Pallais (2020): “Marginal Effects of Merit Aid for Low-Income Students,” <mark>NBER Working Paper No. 27834.</mark> 24
(Henderson et al., 2011, 2012) and at lower levels of aggregation (Harari and La Ferrara, 2013; Michalopoulos et al., 2014; Bundervoet et al., 2015; Alesina et al., 2016). Specifically, related to our paper the work of Bertinelli and Strobl (2013) and Elliott et al. (2015) have shown, respectively for the Caribbean and for China, that not only are night lights a reliable proxy of the economic damages brought about by natural disasters, but that it is night lights measured at lower levels of aggregation which provide the most reliable proxy. In section 6 we present our own tests of the relationship between night lights and economic activity at the subnational level in Mexico.

NOAA publicly provides composites in yearly frequency covering the 1992 to 2012 period. These satellite-year datasets have a spatial resolution of approximately 30 arc seconds, that is, each pixel roughly represents a one kilometer square cells. Each pixel has an associated digital number (DN) which represents the intensity of lights normalized across satellites in a scale ranging from 0 (no light) to 63 (maximum light). Given the operational lifespan of the satellites we will use data from the F16 satellite for the 2004 to 2009 period, and from the F18 satellite for the 2010 to 2012 period. Following Henderson et al. (2011) our subnational measure of night lights will be constructed by summing the DN across all pixels that are within the subnational boundary, dividing the sum by the area of the observed pixels, and then taking the log of the previous number. We will calculate this measure of brightness at both the state and municipal level, and refer to it from this point on as log night lights. The key outcome variable will be the change in log night lights between the year the disaster takes place and the following year.

In addition to the publicly available imagery, NOAA has produced, especially for this paper, monthly frequency composites for the 2004 to 2013 period. These
the actual spread of the disease and the severity of the pandemic. Misinformation could be a potential explanation, as Roozenbeek et al. (2020) find that individuals who distrust scientists are more susceptible to misinformation about the pandemic.

When introduced as a separate, independent regressor in equation (1), this “perception of high risk of infection” variable is associated with a 9 percentage points higher probability of accepting the vaccine (Table 3, column 3). The coefficients associated to the stringency index and the personal knowledge of a positive COVID-19 case in that same specification remain significant but decrease slightly in magnitude. A similar pattern is observed if risk perceptions are interacted with the stringency index (Table 3, column 4).

These results confirm the relevance of risk perceptions as highlighted by our analytical framework. Government policies and individual experience of the pandemic have an effect on vaccine acceptance through risk perceptions. However, the fact that both variables still have a considerable explanatory power suggests that this particular measure of risk perception does not capture fully the risk perceptions that individuals have in mind. Other dimensions of risks beyond individual infection may be driving vaccine acceptance.

# **5.c Trust and social descriptive norms**

Information and individual circumstances form risk perceptions, which are in turn mediated by trust and social norms as individuals decide on accepting the vaccine or not. The different specifications of Table 4 explore the role that mediating factors such as social descriptive norms (the number of people in the community that the individual believes are going to get the vaccine) or trust in government health authorities play in individual vaccine acceptance. Column 1 presents a simple specification in which both variables are included as independent regressors. Both are strongly and positively correlated with vaccine acceptance. The more individuals believe vaccine acceptance to be the prevalent social norm in their community, the higher their own willingness to get the vaccine. For every additional percentage point of believed acceptance in the community, individual vaccine 13
[10, 11] documented the gaps in malnutrition across consumption groups in that continent, while a new dataset [121 allows comparisons to be made across 48 countries of malnutrition differentials across quintiles of "assets".

This paper contributes to this growing empirical literature on poor-nonpoor inequalities in child malnutrition. It differs from the aforementioned studies in a number of respects. First, by using concentration indices and corresponding standard error estimates, it attempts to draw systematic conclusions about cross-country differences in malnutrition inequalities. In this respect, the paper is the analog for child malnutrition of the analysis of inequalities in infant and under-five mortality reported in Wagstaff [13]. The results in the present paper suggest that several countries have high inequalities on more than one indicator of malnutrition, while several have low levels of inequality. We also find, however, that in many of the pairwise comparisons between countries, the differences in inequality are not statistically significant. Second, we present some evidence-albeit very limited-on the issue of why some countries have higher levels of inequality in malnutrition than others. Specifically, we explore the possibility that inter-country differences in inequalities in malnutrition reflect inter-country differences in inequalities in consumption. Third, we emphasize the evidence on both inequalities _and_ population averages. We find some countries that do well on both dimensions, but find many that perform well on one dimension but badly on the other. We therefore go on to compute values of a summary index that captures how well a country does both on its average rate of malnutrition and on its inequality in malnutrition. We find some swapping of positions-