rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 2: Random Effects GLS Regression Results

|Rate of return to university education|Coefficient|SE|
|---|---|---|
|Economic growth|-0.679*|0.269|
|Economic growth-squared|-0.058*|0.031|
|Constant|24.790**|6.891|
|R squared within|0.02||
|R-squared between|0.81||
|R-squared overall|0.14||
|Number of observations|33||

Notes: (1) *p<0.1 and ** p<0.05. (2) Includes cluster-robust standard errors.

The interpretation of the random effects GLS regression coefficients is tricky since they include both the within-entity and between-entity effects. In the case of three-country data, the results represent the average effect of economic growth over returns to education when the returns change across time and between countries by one unit. Thus, we cannot use the coefficients for projections. Nevertheless, the fixed effects GLS results provide statistical evidence confirming the negative relationship between economic crises and the rate of return to university education.

For projections, we use the data from the crisis years of the 1990s and 2000s. Based on the simple analyses of percentage changes in the three countries, we can project that returns to university education will increase by 25 percent to 33 percent in the three countries during COVID-19. As mentioned earlier, the emerging data from the United States and Europe during COVID-19 provide further support for Schultz’s thesis: the unemployment rate for those with university degrees rose less than the unemployment rate for those without university degrees (Berube and Bateman 2020; Lund et al. 2020; Fuchs-Schundeln et al. 2020). These patterns are likely to be even stronger in low- and middle-income countries because of the larger differences in technology education provided in universities versus secondary schools. That is, university educated workers in low- and middle-income countries
**Table 3: Average prices by** **<u>quarter</u>**

||**Quarter 1**<br>**(Fall)**|**Quarter 2**<br>**(Winter)**<br>_Full S_|**Quarter 3**<br>**(Spring)**<br>_ample_|<br>**Quarter 4**<br>**(Summer)**|
|---|---|---|---|---|
|Price of domestic wheat flour|18.09|23.51|34.19|36.51|
|Price of vegetable oil|64.81|76.93|88.90|91.70|
|Price of domestic rice|33.93|33.99|46.16|55.29|
|Price of lamb|182.34|186.20|189.28|180.27|
|Price of milk|23.44|25.66|27.23|30.75|
|Price of kerosene|43.15|45.78|46.83|55.48|

Note: Estimated population weighted means. Prices in Afghani per kilogram or liter (kerosene). _Source:_ NRVA 2007/08

**Table 4. Population statistics by** **<u>quarter</u>**

||**Quarter 1**<br>**(Fall)**|**Quarter 2**<br>**(Winter)**<br>_Full S_|**Quarter 3**<br>**(Spring)**<br>_ample_|<br>**Quarter 4**<br>**(Summer)**|
shocks ( _Aijt_ ), since this is a perfect competition model and all rms face the same price. We treat the sector-level prot shock _bjt_ as a single random variable, and we assume that _Aijt_ and _bjt_ follow two independent time-invariant rst-order Markov processes. As in Cooper and Haltiwanger (2006), we assume that the prot shock _bjt_ has two states, _high_ and _low_ .

The two assumptions on materialsthat materials transform into output in xed proportions and that rms source materials from their own sector onlyare key to keep the estimation tractable. Sourcing only from own-sector implies that rms consider only own-sector state variables in the dynamic optimization problem; whereas the combination of the two assumptions on materials results on sector-level shocks entering multiplicatively into prots and therefore being treated as a single state variable. This is not the case in a Cobb-Douglas or CES production function case. We further assume that technology parameters are the same across sectors, so that all sectors can be treated symmetrically in the estimation. For suciently aggregate sectors such as ours, evidence from input-output tables shows that most input-output sourcing does occur within each sector. Calculations from IO tables from Argentina for the year 1997 show that materials sourced from own-sector account for 75 percent of all materials for food and beverages, 59 percent for textiles, 80 percent for metals, 26 percent for minerals, and 70 percent for other manufacturing industries. To assess the xed proportions technology assumption, we present an alternative specication with a Cobb-Douglas technology in on-line Appendix B. The qualitative conclusions of the paper remain unchanged.

Given the rm-level predetermined capital and the idiosyncratic productivity shock ( _K, A_ ), as well as the sector-level prot shock and wage ( _b, w_ ), rms choose labor to maximize instantaneous prots. From the static prot maximization problem we obtain rm-level labor demand, output supply, and indirect instantaneous prots. We denote the indirect instantaneous prot function with _π_ ( _Kijt, Aijt, bjt, wjt_ ) . Let _μjt_ denote
the general understanding of the effects of classical measurement error is to introduce noise, not bias, meaning we would expect a product suffering from classical measurement error to produce fewer, not more, significant _p_ -values. For ERA5 temperature measurements, the picture is somewhat more muddled. The descriptive evidence for temperature from ERA5 does not reveal a larger variance, which a common indicator of classical measurement error. As with rainfall from ERA5, that the large number of significant _p_ -values produced by temperature metrics from ERA5 is the opposite of what one expects from classical measurement error, where noise introduces imprecision in coefficient estimates. Yet, neither of the descriptive statistics for temperature from ERA5 demonstrate a tendency to under- or over-report temperatures, relative to the other products, as ERA5 does for rainfall.

This leaves us with one of two options: either ERA5 is accurate and the other five precipitation products mismeasure precipitation or, alternatively, ERA5 suffers from non-classical measurement error, overestimating precipitation and thereby biasing regressions results so that rainfall appears more correlated with outcomes than it actually is in reality. Similarly, either ERA5 is accurate and MERRA-2 and CPC suffer from measurement error or MERRA-2 and CPC accurately report the objective fact of temperature, while ERA5 suffers from non-classical measurement error that results in biased coefficients that are more strongly correlated with outcomes than they should be. If the latter is true for either rainfall or temperature, then we cannot determine if those 36
Bonds of 1944 (Plan B)|<br>252;253|Plan A: 1;<br>Plan B: 0.5|11/1943|10/1945|
|Chile|4.5% Bonds of 1887|316|<br>1.5-3% Mixed Bonds of 1948|343|1|03/1948|03/1953|
|Chile|5% Loan of 1911 (1st<br>series)|322|1.5-3% Mixed Bonds of 1948|343|1|03/1948|03/1953|
|Chile|<br>6% Loan of 1926|330|1.5-3% Mixed Bonds of 1948|343|1|03/1948|03/1953|
|Chile|6% Bonds of 1926|348|1.5-3% of 1948|349|1|03/1948|12/1948|
|Chile|6% Bonds of 1927|350|1.5-3% of 1948|349|1|03/1948|12/1948|

A11
8, 2015.

The use of market exchange rates for converting GNI to a common currency is also felt to be sub-optimal. The common suggestion is to use purchasing power parities (PPP); some argued that, at least for a period of time, there is a need for both a PPP and US dollar based GNI per capita classification.

Users also voiced concerns about the volatility of the classification—in other words, an undesirable characteristic of a country classification system is that reclassifications occur too frequently. Suggestions to address this were put forward, including restricting classification changes until a country has been in the new category for a fixed time period, or above a set percentage of the threshold, or the use of moving averages of the GNI per capita estimates. There were also proposals for the publication of aggregates with a much closer link to the Bank’s operational activities. For example, measures could be based on fragile and conflict-affected 12
determinants in _X_ by forecasting their future values using univariate forecasting models and assuming that they evolve following past trends. For the 20 countries in Latin America and Caribbean we cite results from Loayza et al. (2005). Next we discuss the panel data, the estimation methodology, and the forecasting models.

# _3.1 Data_

Loayza _et al_ . (2005) estimate cross-country regression (4) using cross-country, time-series panel data, spanning the period 1961-99. The following variables are part of their panel.

Growth per capita (d _lny)_ is the log difference of real GDP per capita, represented in 1995 PPP-adjusted US dollars and constructed using Summers and Heston (1991) and World Bank (2002). For the calculation of a period’s growth rate, the base corresponds to the final year of the previous period. Initial per capita GDP ( _lny_ ) is the initial value of the ratio of total GDP to total population<sup>15</sup> and is computed using data in Summers and Heston (1991) and the World Bank (2002). Initial output gap ( _lny-lny_<sup>_T_</sup> ) is the difference between the log of actual per capita GDP and the log of potential (trend) per capita GDP at the start of the period. The trend per capita GDP is identified using the Baxter-King filter.

The regression includes the following steady state growth determinants in _X’_ (equation 4): education, financial depth, trade openness, government burden, public infrastructure, and governance.

Education is measured as the ratio of total secondary enrollment to the population of the age group that officially corresponds to that level of education. This “flow” variable – used as proxy for human capital in Barro (1991), Mankiw, Romer and Weil (1992), and Easterly (2001) – captures more closely current education policies and human capital investment than “stock” measures related with the educational attainment of the adult

> 15 GDP is
indirectly. However, after 2010, indirect connections grew at a much faster pace due to strong network effects. By the end of our sample period, 36% of prefecture pairs were linked indirectly, compared to just 2.6% connected directly. In summary, the substantial network effects from indirect HSR connections became increasingly prominent in the late 2010s. In the subsequent analysis, if two prefecture-level cities are linked either directly or through transfers on HSR lines, we consider them to have an established HSR connection.

# **3 Estimating DVAR Based on the Export Revenue Function**

This section introduces a novel methodology to estimate DVAR in exports, by directly estimating the share of imports used for exports from firm’s export revenue function. This method extends the applicability of DVAR estimation as in Kee and Tang (2016), but without requiring the matching of customs transactions to firm census data.

# **3.1 Conceptual Framework**

Let’s start with the export revenue function of an exporting firm, who use both domestic materials ( _M_<sup>_D_</sup> ) and imported materials ( _M_<sup>_W_</sup> ), combined with labor ( _L_ ) and capital ( _K_ ) to produce output for exports and domestic sales. For each year _t_ , the firm’s total export revenue _EXPit_ comprises the following components specific for export: export profits ( _πit_ ), cost of labor ( _wtLit_ ), cost of capital ( _rtKit_ ), cost of domestic materials � _p_<sup>_D_</sup> _t_<sup>_M D_</sup> _it_ �, and cost of imported materials � _p_<sup>_W_</sup> _t_<sup>_M W_</sup> _it_ �: From customs transaction data, we observe the total value of firms imports, _IMPit_ . Let’s define 0 _≤ κ ≤_ 1 equals the share of imports that is used for exports, such that _p_<
the daughters receive significantly less educational investment in rural India; the daughter dummy is negative and statistically significant at the 10 percent or lower levels, implying that _θ_<sup>ˆ</sup> 0<sup>_d<θ_ˆ</sup> 0<sup>_s_.Theevidenceisrobustacrossalternativedatasets(IHDS2005andNSS</sup> 1995) and consistent with existing evidence in the context of India (Azam and Kingdon (2013)).<sup>27</sup> The evidence on the marginal effect of father’s higher education in Table T3.IR also suggests a smaller effect for girls; the interaction of the daughter dummy with father’s schooling is negative, and significant at the 10 percent level in 5 out of 8 cases. However, the evidence is weaker than that in urban India, because only one of the estimates is significant at the 5 percent level, and the magnitude of the interaction effect is also smaller.<sup>28</sup> Moreover, the interaction is significant only in 4 cases when we use standard errors clustered at the district level (see Table A3.IR in online appendix).

Putting together the estimates of investment equation with those from the intergenerational mobility equation, we have the following binary relations in rural India: Since the CEF is concave for both sons and daughters, we cannot reject the null hypothesis that _δ_ 5 = 0. In what follows, the discussion assumes that this restriction holds.

# **Implications of the Investment Equation Estimates**

We first consider the evidence on the slope of the investment function. Given the uncertainty regarding the evidence discussed above ( _θ_<sup>ˆ</sup> 1<sup>_d_≦</sup><sup>_θ_ˆ</sup> 1<sup>_s_), the inference regarding gender bias</sup> depends critically on one’s interpretation. If one interprets the evidence as a lower slope for the girls (i.e., _θ_<
2018; Rios-Avila, 2019):

> 11 For the countries and years selected, income is expressed in the units of national currencies that were in use at the time of the data collection. To directly compare real amounts across countries and over time, we apply the 2017 PPP deflators available via the LIS remote execution system to convert LIS monetary values into 2017 US$ PPPs. Furthermore, to maintain consistency with the set of methodological practices adopted by the LIS staff to construct - - the “Inequality and Poverty Key Figures” (https://www.lisdatacenter.org/data <u>access/key figures/), the focus is placed</u> on what is called “equivalized income”, i.e. the household disposable income divided by the square root of household size to adjust for differences in household demographics. We also exclude all households where disposable income is missing or exactly equal to zero and use person-level adjusted weights (the product of household weights and the number of household members) when generating estimates for the total population. Finally, before proceeding to an estimation of RIF-regression models, data have been bottom-coded at 1 percent of equivalized mean income and topcoded at 10 times the median of non-equivalized income.

> 12 The most popular Gini coefficient to measure income inequality has not been used as the income micro-data are previously transformed to the log-scale in the empirical application.

11
First, we run separated regressions by different levels of household wealth, which, it is generally agreed, influences the extent to which maternal mental health affects children (Aizer et al 2016, Lovejoy 2000; Shonkoff and Phillips 2000). For our analysis, we compared the vocabulary of children living with mothers with different mental health levels within the upper or lower half of the wealth distribution. Using data from the 2002 round, we estimated three indexes: Housing quality,<sup>13</sup> consumer durables,<sup>14</sup> and services indexes.<sup>15</sup> Then, we created a wealth index for each household of our sample that consisted of the average of the three first ones mentioned above. Using the wealth index distribution, we separated our sample by the median of the wealth index distribution. Results are presented in Table V. Our estimations indicate that 1 standard deviation of maternal mental health issues reduces vocabulary by 0.58 to 0.63 standard deviation of children living in less wealthy households (columns 4-6). This is around 0.08 standard deviation more than the impacts in the total sample. The effects of maternal mental health on vocabulary of children living in wealthier households is not statistically significant.

These results are an important contribution to the evidence of intergenerational transmission of poverty. Poor households are less able to protect themselves from external shocks, such as crop or livestock losses, which then increases stress levels for household heads. In poor families where there is a pregnant woman or with a child younger than 1 year old, our results indicate that the negative shock translates into a reduction of the child’s cognitive skills in the short term. Given that the development of these skills during early childhood is the foundation of future ones (Case and Paxson 2008; Cunha and Heckman 2007; Schady 2011; Powell and Diamond 2012), this negative effect can have long-lasting impacts in terms of human capital accumulation, which is in line with
contribution to household decision-making (see Oyediran and Isiugo-Abanihe 2005).

> 3 Reasons include: arguing with him, refusing sex, burning the food, neglecting the children, or going out without telling him.

> 4 Armed Conflict Location & Event Data Project (ACLED). https://www.acleddata.com/, accessed March 10, 2018.

> 5 BH events in the ACLED are defined as any incidents including riots, clashes, protests, riots or battles where BH is an actor.

4
are plotted in Figure 2. Over the period as a whole, all three poverty measures fell for both lines, although the declines were quantitatively modest for such a long period. The proportional decline in poverty incidence (according to the Administrative Poverty Line) from 0.296 to 0.222 is of exactly 25%. This contrasts, for instance, with a poverty reduction of 62% (from 0.418 in 1975 to 0.157 in 1992) in

> 13 In fact, this was done for the nine metropolitan areas (Belém, Fortaleza, Recife, Salvador, Belo Horizonte, Rio de Janeiro, São Paulo, Curitiba and Porto Alegre), as well as Brasília and Goiânia, using the 1987 expenditure survey - Pesquisa de Orçamentos Familiares (POF). For the other urban and rural areas, conversion factors were borrowed from an earlier work by Fava (1984), which was based on the most recent available data for these areas, namely the 1975 Estudo Nacional da Despesa Familiar (ENDEF). These were updated to 1990 prices using the INPC price index.

> 14 For an alternative approach to dealing with regional differences in the cost of living, using a regional price index defined for a fixed basket, see Ferreira et. al. (2003).

> 15 'The poor' amongst whom she computes non-food expenditures are those who, according to information recorded in the POF, were unable to meet _minimum_ caloric requirements as specified by FAO.
and wages of four-digit-level occupations in Brazil, Chile, Mexico, and Peru—–four countries that have household survey data with a homogeneous occupation classification during the 2000s (288 occupations in Brazil, 210 in Chile, 184 in Mexico, and 113 in Peru). Each panel shows the changes (in percentage) in employment over time for detailed occupations that are ranked by skill percentile in the base year.<sup>11</sup> Since the sum of the shares must equal one in the baseline and end years, the total change in the shares depicted is zero. The figure also shows the log changes in the wages of each skill percentile.

With the exception of Chile, figure 6 shows no sign of job polarization. In Chile, employment growth was concentrated among high-skill and, to a lesser extent, low-skill occupations, while there was a contraction in employment in the middle of the distribution. The other three countries had modest growth of similar magnitude among occupations in the middle- to high-wage range. Only occupations below the 30th percentile in the skill distribution display employment losses. If anything, there are mild signs of skill-biased technical change in the changes in occupational structure. However, compared with the United States during the 1980s and 1990s, growth among high-skill occupations in the four countries was modest. Using census data, Maloney and Molina (2016) study changes in occupations in 21 developing countries, including Brazil, the Dominican Republic, Ecuador, El Salvador, Mexico, Nicaragua, Panama, and Peru. Their findings are consistent with those observed in the household surveys. They report no significant decline in occupations that are easily codifiable, such as operators and assemblers. Moreover, elementary occupations appeared to be in decline, and high-skilled occupations such as professionals and technicians exhibited small positive employment growth.

> 11Skill content here is approximated by average wages in the base year. The figure looks very similar if instead occupations are ranked by the average years of education in the base year; see Messina and Silva
individuals obtain property principally purchases. Within each pathway, gender gaps can emerge from an interaction between households, markets and social norms or institutions (World Bank 2011).

We begin with the first pathway, marriage, by reviewing how basic institutional rights to property ownership sometimes change for women upon marriage. According to the 2020 Women, Business and the Law (WBL) database, married women face legal restrictions on property ownership in 19 out of 190 countries for which data are available. While property rights of unmarried women are no longer included as a separate indicator in the 2020 WBL, earlier versions of the data showed that unmarried women typically have the same rights as unmarried men (Gaddis et al 2018). This demonstrates that, from a legal perspective, discriminatory provisions often do not apply to all women but to married women specifically, whose legal status changes, sometimes profoundly, upon marriage (Hallward-Driemeier and Hasan 2013).

Today’s property rights of women in marriage often have historical roots. British common law was particularly unfavorable to women, owing to the doctrine of ‘couverture’, whereby a woman’s legal status was subsumed by her husband upon marriage. Roman and Islamic legal traditions generally allowed married women to retain their legal personality (Deere and Doss 2006). Nowadays, women’s and men’s ability to own property during marriage and after its dissolution is governed by marital property regimes and other laws specifying how nonmonetary contributions – to the marriage for example, childcare or other unpaid domestic work from a stay-at-home spouse – are taken into consideration for the distribution of property between spouses. The most common

> 1 Some studies do not support this notion. Akresh et al (2016) find that (conditional) cash transfers given to women in rural Burkina Faso improve routine health checkups of children by a similar magnitude as transfers given to men.

4
*(sweet beverage<br>consumption, previous day)<br>**Unhealthy food consumption**<br>(sentinel unhealthy foods, previous<br>day)|NO VALIDATED METRIC<br>Average number of times per<br>day adolescents reported<br>consuming...<br>- carbonated soft drinks<br>- fruit<br>- vegetables<br>- fast food<br>Prevalence of at least weekly<br>fast food consumption|**Global Dietary Recommendat**<br>combined with MDD (for nutrie<br>2020<br>-<br>NCD-Protect score<br>-<br>NCD-Risk score<br>**Global Diet Quality Score (GD**<br>-<br>GDQS+<br>-<br>GDQS-<br>**Sentinel food group consumpt**<br>meat, processed meat, sweet foo<br>**-Sugar-sweetened drink consu**<br>**-Zero vegetable or fruit consu**|**ions Score (GDRS)**to be used<br>nt adequacy) per Herforth et al.<br>**QS)**<br>**ion**- whole grains, unprocessed red<br>ds<br>**mption**<br>**mption**|

**Key:** light blue shading indicates measures particularly relevant for energy-(in)sufficiency; no shading indicates measures particularly relevant for micronutrient (in)adequacy; and orange shading indicates measures particularly relevant for (un)healthy diets

**Note:** there are key gaps in measures for children 24-59 months and children in primary school grades (ages 5-9), as well as for older adults (age 50+ years) in LMIC. For children under the age of 6 months, the recommendation is that they