rafmacalaba/data-use-mentions-extended huggingface dataset:
can be used to estimate demand elasticities and second order The estimation of labor income effects is harder due to lack of price variability at the household level. The simple idea of using unit values as a proxy for prices, which would deliver enough price variation, is problematic because of endogeneity of unit values, biases due to proxy variables, and measurement error. In this paper, I have proposed a method that uses unit values as measures of prices, but that is free from these problems. The method combines Deaton’s model of demand with an equation that describes the agricultural wage income of the household. By estimating the demand model together with the quality shading model, I was able to extract the right price signal from unit value data. These data can then be plugged into the wage equation to identify the relevant elasticities.

When a fraction of household income is allowed to be endogenous, there are additional effects to consider in the estimation of the demand parameters. First, there is the “profit effect”whereby a change in an agricultural price causes a change in household income and additional income effects in consumption. Second, the change in household income brings about further quality shading responses that affect the elasticities of demand. In this paper, I have derived formulas to correct for these factors.

The method was applied to the estimation of consumption and agricultural wage income responses to agricultural prices in rural Mexico. It was found that the corrections suggested in this paper can make a difference and should be preferred to a simpler model that uses average cluster unit values as regressors. Failing to control for endogeneity, biases, and measurement errors may lead to inconsistent estimates of the price elasticities and to an incorrect or misleading evaluation of policy changes. In addition, I have assessed the role of consumption and income responses in the evaluation of trade reforms. I have shown that the proposition that net consumers will be hurt by price increases whereas net producers will be benefited is only true in a static scenario. When consumers adjust quantities,
# **2 Data and Methodology**

## **2.1 Data Description and Variable Construction**

The variables we use for the U.S. economy include first a measure of U.S. monetary policy shocks (MP), as explained in the next section. We also control for domestic credit conditions and financial market liquidity by including the excess bond premium (EBP) from Gilchrist et al. (2021). Other U.S. variables include commodity prices (CP), consumer price index (P), industrial production index (IP), and the 10-year government bond yield (R10).<sup>3</sup> As our main focus is to evaluate the efficacy of capital controls, we begin considering which non-US (“foreign” hereafter) countries to include in the study. We do this based on the availability of data on capital controls. We use the overall capital restriction index (KC) by Fernández, Klein, Rebucci, Schindler & Uribe (2016) (FKRSU hereafter) in our main analysis. This measure covers 99 foreign countries over 1995-2019, ranging from 0-1, with 1 indicating the greatest restriction.<sup>4</sup> For each of these 99 countries, we collect exchange rate per USD (in logs and denoted as S) from the IMF’s International Financial Statistics (IFS) database. We obtain foreign interest rate (R<sup>_∗_</sup> ) and industrial production (IP<sup>_∗_</sup> ) from three data resources: IFS; Federal Reserve Economic Data (FRED); and Haver Analytics. We supplement interest rate data with the BIS policy rates, and also statistics published by countries’ central banks if possible. When multiple interest rate series are<sup>6</sup> available, we choose the one closest to the U.S. 3-month Treasury bill rate.<sup>5</sup
FE|Y|Y|Y|

_Source:_ Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. All estimates are of equation (1). The dependent variable in each regression is a binary dummy variable taking the value one if the individual female respondent was a victim of violence, defined as physical (I), sexual (II) or either (III) in the last twelve months. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. Details on the countries in samples in panel A and B, as well as the definition of equality in access to divorce, is given in Table S1.2. Differences in observations between columns (I) compared to (II) and (III) are due to information on sexual violence not being collected in all surveys (see Table S1.1).

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||Germany, and Switzerland in<br>2014.|energy costs. However, this effect is observed only for<br>19% of firms with the highest energy costs.|
|---|---|---|
|Rath et al. (2019)|The panel cointegration tests<br>and dynamic OLS are<br>applied to panel data of 36<br>countries for 1981-2013.|<br>Generally, the increase in renewable energy<br>consumption positively affects the TFP growth in the<br>long run, while the increase in fossil fuel consumption<br>has a mixed effect on TFP growth.|
|Zhang et al. (2019)|The modified fixed<br>investment model is applied<br>to data of 34000 Chinese<br>firms in 2013.|The environmental abatement crowds out innovation<br>investment and decreases firms’ productivity due to<br>financial constraints.|
|Palmer and Truong<br>(2017)|Data on 1020 technological<br>green products between 2007<br>and 2012 by 79 global firms<br>are used.|The relationship between technological green new<br>product introductions and firm profitability is positive.|
|Hrovatin et al. (2016)|The probit and bivariate<br>probit models are applied to<br>the panel data set of 848<br>Slovenian manufacturing<br>firms during 2005-2011.|Large firms with high energy costs tend to invest both<br>in EE and green technology to increase productivity<br>and to improve their competitive position in the<br>market. Non-environmental investments by the firms<br>do not crowd out green/clean technology investments.|
|Tugcu & Tiwari<br>(2016)|A panel bootstrap Granger<br>causality analysis of Konya<br>is used for BRICS countries<br>for 1992 to
We measure commodity shocks using a commodity terms of trade index following Gruss and Kebhaj (2019). Commodity-price shocks are calculated by taking the first differences of the log of the commodity terms of trade index, using historical fixed weights. The commodity terms of trade index is based on international prices of up to 45 individual commodities, constituting broad categories of energy, metals, food and beverages, and agricultural raw materials. External debt-financing shocks are calculated as the log difference of the US T-bill (10-year, long term) multiplied by the lagged external debt. The debt shock variable is included in a few specifications as it entails a drop in the number of observations due to availability of debt data.

We account for political institutions using data from the Worldwide Governance Indicators - WGI (Kauffman et al., 2010) following other studies in the literature (Antras and Chor, 2013; Grembi et al., 2016). In our estimations we include Voice and Accountability, Political Instability, Rule of Law, and Control of Corruption. Voice and Accountability is a measure of perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Political Instability measures perceptions of the likelihood of political instability and/or politically motivated violence. Rule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Control of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests.

We control for internal armed conflict using the UCDP/PRIO Armed Conflict Database updated by Pettersson and Öberg (2020). We follow a definition, similar to Abu Bader and Ianchovichina (
2020) note that border controls redirect migrants “into more perilous and deadly landscapes, where the likelihood of receiving humanitarian support and rescue is slim at best”. This increases the costs of migrating, _C_<sup>_S_</sup> and _C_<sup>_E_</sup> _._ At the same time costs are increasing, wealth levels in The Gambia are likely to have fallen as households need to use their assets to smooth consumption in wake of lower home earnings. Likewise the earnings and wealth of other community members who might be willing to lend money to help pay these costs is also likely to have fallen. More individuals will thus hit the budget constraint that prevents them from being able to migrate even if they would gain income from doing so.

> 8 Avdiu and Meyer (2021) report that remittances as reported in the balance of payments showed record growth in 2020, whereas household surveys show a decrease in remittances. In their surveys, between March and August 2020, 84.6 percent of households reported a decline of international remittances, and only 1.3 percent witnessed an increase. The increase in remittances in the macro data likely reflects a shift from informal to formal channels, whereas it is the decline in total remittances from formal and informal sources that should affect households in our model.

7
The table shows summary statistics of the SOFES survey for students without SOFES credit (i.e. the control group), and SOFES-recipients (the treated group). The survey is organized into seven blocks:

- (1) General information

- (2) University information

- (3) Educational attainment

- (4) Family information

- (5) Student’s employment and income information

- (6) Information on SOFES credit program

- (7) Performance of SOFES credit program

In the first block _general information_ is collected on the students’ age and gender. Students with and without SOFES credit are comparable in terms of average age and gender distribution.

> 10 We only include Bachelor-students in the analysis (too few observations of graduate students are available, so we excluded them). Extreme values of the percentage of repeated courses (>50%) are dropped.

10
.

Finally, we look at whether the school closure shock is either mitigated or accentuated by gender norms through the OLS estimation of equation 4: Variables are defined as in equation 3, with the addition of norms from the 2019 round of the FoB data set. In this analysis we split the sample by gender of the firm owner for ease of interpretation. It is worth noting that the control for GDP here helps control not only for pre-existing economic conditions, but also any effect that levels of national wealth might have on norms.

17
2018; Dix-Carneiro et al., 2021). Yet, these effects operate through competition. Our hypothesis is that an environment with low competition and protected firms is likely to hamper these effects. We test this hypothesis by estimating a modified version of Equation (5) using several proxies for the degree of competition or protection: indicator variables for higher than median versus lower than median Herfindahl index of concentration at the 3-digit sector level (based on firm employment shares) and indicator variables for state-owned firms (identified in the RAIS database) versus non-state-owned firms.<sup>42</sup> Table 8 shows that GFC firm shocks do not result in any downward employment or real wage adjustment for workers in sectors where few firms hold a large percentage of the market share nor for workers in state-owned firms, whereas significant negative impacts are found for workers in less concentrated sectors and in non-state-owned<sup>43</sup> All in all, Brazilian workers employed by firms facing less competition are more insulated from the GFC than workers employed by less protected firms. An alternative interpretation might be that this heterogeneity analysis reflects productivity rather than lack of competition. Indeed, higher concentration may reflect the existence of one or a few large firms and such firms tend to be more productive and have better access to credit which may enable them to better cope with negative shocks. In that case, our findings would suggest that more productive firms cope better with shocks and protect employment to a greater extent. This would imply that more productive firms capture larger shares of employment, which in turn could improve productivity in a cleansing sense. However, two elements dismiss this alternative interpretation. One the one hand, there is strong evidence that state-owned firms, while large, have lower rather than higher productivity compared to other firms (Shleifer, 1998; Harrison et al., 2019) and they are connected firms aiming to shield their market position (Colonnelli & Prem, 2022). On the other hand, unreported results show no employment insulation
IQMSC: Integrated Questionnaire for the Measurement of Social Capital; LSMS: Living Standards Measurement Survey; SCAT: Social Capital Assessment Tool _Note_ : Inevitably, some indicators in this table apply to two or more asset categories. For instance, access to communications infrastructure can be classified as an informational asset or a material asset. Where this is the case, they have been placed in just one category in the table. Where duplication of sources occurs, only one source is suggested.

64
shifted, or whether wages were affected by _SP_ . Fifth, most papers have focused on the earliest rollout periods, when the program covered only a small fraction of the population and found little effect of the program. This paper looks at the longer period from 2000 to 2009.

In this paper, we have data on almost 10 million individuals in rotating panels from 2000-09. Individual panel data allows for improved control of individual characteristics and assessing whether _SP_ affects labor market dynamics. It also allows us to test whether different subgroups of households or different individuals within the household are more sensitive to _SP_ . We can also control for the potential endogeneity of the employment decision based on the decisions of others in the household.

We find that _SP_ does impact the decisions of workers, generating a moderate increase in the share of workers in the informal sector. Exploiting variation both over time and across municipalities, the effects of introducing _SP_ results in a decline in the share of households with SS of 0.4 to 0.7 percentage points, depending how time effects are controlled for. The results indicate that much of the adjustment occurs within a few years of the introduction of the program, with the effects becoming relatively smaller as the intensity of the program expands. The effect is associated with a 3.1 percentage point reduction (a 20 percent decline) in the inflow of workers into formality rather than an increase of outflows from formality. Thus, while the aggregate net change in the level of formality is not that large, the proportional change in flows into formality is. There is also evidence of important income effects, with the introduction of _SP_ associated with fewer transitions out of unemployment and lower rates of entry into the labor force. Our results also suggest higher sensitivity in larger households, as well as among household headed by persons with less than secondary education completed, particularly if the head is female. They also suggest a higher response among individuals who are covered by SS through someone else, or among individuals in a household
lagged growth rates in the domestic exchange rate, , and allows studying asymmetric food price transmission: To facilitate a consistent interpretation across MENA countries, we derive estimates of the 6 and 12-month pass-through effects from cumulative impulse response functions.<sup>15</sup> The coefficients show the models‟ predicted adjustment of domestic food prices to changes in world food prices, controlling for the exchange rate and domestic factors. In most countries, the food price pass-through effects fade out after about one year. Our pass-through coefficients are, therefore, identical to those that can be directly obtained from equation (4).

When estimating the models, we use monthly data from December 1998 to mid-2011 for most countries, allowing for lags. The cumulative lag structure is chosen to minimize the Akaike Information Criterion (AIC) and by lag exclusion tests. The optimal lag length is found to be . In some country cases (Lebanon, Djibouti, Yemen, Syria, Iraq, Oman, Qatar, and the UAE) we use less lags because of the short time series. For these countries, the results are sensitive to outliers. Hence, we selectively employ impulse dummies to correct for extreme jumps in domestic food prices (i.e. due to religious holidays, specific national events, or weather effects). We also use time trends when significant to capture other unknown exogenous factors on domestic prices. Because we estimate the model in annual growth rates, we explicitly control for seasonal factors.

# **5. Empirical Results**

# **_Pass-Through Coefficients_**

The dynamics and the magnitude of the food price pass-through largely vary by country (Table 3). International food price shocks are a risk for many Arab countries despite food subsidies that are widely believed to shield consumers from increases in

> 15 Impulse response functions allow us to trace out the time path of the effect of structural food price shocks from the autoregressive model. A <mark>ccumulated impulse response functions compute the cumulative sum of the shock over time
A very recent effort to generate and collect indicators of how governments engage with the private sector to improve health in Africa (International Finance Corporation (IFC) 2011) provides a number of indicators that would be useful for our proposed measurement framework. Their assessment is broader in purpose and covers a range of domains (including the existence and nature of a policy framework and dialogue on private sector issues).

Two of the IFC’s domains are of particular relevance for our purposes. The first assesses the inclusion of the private sector in the national health information system—one of the most basic indicators of whether private providers are truly incorporated into a health system. They find that in 12 out of 45 countries, private providers are not mandated by law or regulation to provide health authorities with basic information such as births or deaths or health service utilization data. Out of the 33 countries that do have the mandate, the information reaches the ministry in only three. They go on to argue that in some cases requirements are onerous, which may explain the low rate of compliance.<sup>17</sup> These findings imply that both the lack of information policies and the inadequacy of those that do exist is weakening governance performance, as measured by the availability of basic information on private sector provision.

The second relevant domain in the IFC framework examines regulations governing the quality of private provision. This domain assesses whether a country sets requirements for registering a private clinic, has procedures for inspecting those clinics, and has standardized rules for operating a private clinic. It also assesses the quality of those rules and procedures, for

> 17 In the case of Burundi, for example, the requirement involves completing 27 forms per month.

29
of workers employed by firm _i_ in industry _j_ in country _k_ over the two years prior to the enterprise survey, as described above. represents

> 14 See, for example, Fisman and Love (2007) and Kroszner, Laeven, and Klingebiel (2007) for applications of this method, and Xu (2011) for summary of the use of this method in identifying the effects of business environment in general.

21
(Strauss et al. 2004, Frankenberg et al. 1999).

> 5 Countries with high infant mortality rates are those in the highest quartile of the distribution of under-five mortality rate scores (per 1,000 live births) for 151 developing countries  in the year 2007.  Similarly, countries with low female schooling are those in the lowest quartile of the distribution of the ratio of girls to boys in primary and secondary enrollment for 131 developing countries for the latest year between 2004 and 2007 (World Development Indicators 2009). Countries with decelerating growth identified by using World Economic Outlook 2009 projections and calculations by World Bank Staff.

17
De Beurs and Henebry, 2004) as well as economic globalization in driving land cover changes (Lambin and Meyfroidt, 2010), but these effects have not been studied empirically.

The goal of this paper is to address the above gaps by assessing the recent dynamics of cropland (i.e., measuring movements into and out of cropland) at the global scale and to causally assess the impacts of potential drivers and disentangle their respective contributions. To do that, we consider global cropland changes measured from space using the MODIS data product at a resolution of 500m * 500m over the 2
VSL. We use this computation to better understand the value of lives lost as a result of COVID19 over time. The VSL for Côte d’Ivoire is given by the following formula: With VSLCote d′Ivoire and VSLUSA, the VSL for Côte d’Ivoire and the United States, respectively. The GNI is the gross national income, and elasticity<sup>3</sup> is the degree of change in the VSL associated with a change in income.

Furthermore, age is considered because COVID-19 has had a disproportionately negative effect on elderly people. As a result, we estimate a constant value per statistical life year (VSLY) (Aldy & Viscusi, 2007). Following Robinson et al. (2017a), we divide the VSL by the discounted expected life years remaining for an individual at the average age of the population being studied. The VSLY, which is often called the value of a life year, measures how much people are willing to spend for a change in their life expectancy. The VSLY is given by the following formula: With _r_ and _L_ the discount rate and the remaining life expectancy, respectively.

We apply the standard 3% discount rate and the 5% discount rate that Haacker et al. (2020) advised for low- and lower-middle-income countries. Based on information from the WHO's Global Health Observatory Data Repository, we estimate that the remaining life expectancy is half of Côte d’Ivoire’s life expectancy at birth. The COVID-19 mortality costs are calculated by multiplying the VSLY values by the total number of deaths associated with COVID-19.

Lastly, we carried out a desk analysis (World Bank., 2020a, b, 2021, 2022) to comprehend the health policies put in place by policy makers to fight COVID-19, and we provide effective health policy recommendations to assist the policy makers in Côte d'Ivoire in better preventing, preparing for, and responding to future pandemics.

> 3 Using different income elasticity of the VSL values, we examine