rafmacalaba/data-use-mentions-extended huggingface dataset:
.78-2.49|0.22-0.96|0.74-2.87|

Source: Authors' calculations.

Our results are consistent with recent findings of lower elasticities for macro or aggregate elasticities than those of micro elasticity studies of specific sectors or commodities. Because a cointegration equation corresponds to a long-term equilibrium, the results are comparable to the range of 1.75 to 2.25 for the long-term elasticity estimates of Boehm, Levchenko, and Pandalai-Nayar (2023), especially for the high-income group. Past results that do not correct for nonstationarity issues in time-series estimation will also be spuriously high. The survey and analysis by Bajzik et al. (2020), including many past studies at sectoral levels, show a broader range in magnitude. Plausible explanations mentioned before include aggregated imports, exports, and domestic goods are composites of many goods; developing countries are less diversified, and the composition of these goods tends to differ; and the external balance of payments constraint at the macro level could limit substitution possibilities. Finally, this study provides countryspecific estimates of trade elasticities for many countries, 128 developing countries and 63 high-income countries. Not only were estimates for developing countries lacking in the literature, but estimates of the export supply side were also lacking.

# V. Conclusion

This paper tries to fill a lacuna in the literature. While the Armington elasticity has been estimated at the sectoral level for a number of (mostly) developed countries, the equivalent elasticity in the widely used aggregate, 1-2-3 model of developing countries has typically been assumed. We provide empirical estimates of the import and export elasticities of the 1-2-3 model for 191 countries. Using data from 1970-2019 and the Vector Error Correction model as the dominant technique, we derive robust estimates that also square with intuition. Elasticities for high-income countries are generally greater than one, averaging around 1.4, while those of lower-income countries are below one, averaging around 0.65. Not only do these estimates confirm

> 22 Argentina became high-income in 2017.

18
choices would allow policymakers to expand the existing set of policy alternatives in considering the optimal design of a given program. While this is true in principle, in order for the two approaches to complement each other in the context of a specific program, the consistency of the results produced by the two approaches would be an important consideration. The concerns about the validity of assumptions that underlie the _ex ante_ behavioral models make it all the more important that their predictions are checked for consistency with the results of _ex post_ experimental evaluations. Any evidence to suggest that _ex ante_ approaches yield results that are at least broadly consistent with those from methodologically sound experimental evaluations would strengthen the case for using such approaches to complement _ex post_ evaluations of similar programs in future, where the relative ease of employing _ex ante_ approaches can be particularly useful in evaluating alternative design options before a program is implemented.

Our paper represents an attempt to generate evidence on the consistency between the two approaches in the context of CCT programs by comparing _ex ante_ predictions with results of _ex post_ experimental evaluations, following the suggestion of Bourguignon and Ferreira (2003). Specifically, we compare the school enrollment, poverty and inequality impacts of a CCT program generated _ex ante_ , using the behavioral model suggested by BFL, with the corresponding impacts estimated _ex post_ from a randomized experiment involving the same program. The exercise is conducted for a number of CCT programs, of which the results of two exercises are presented in detail.

The comparison presented in this paper is a validation test of the strongest hypothesis of the BFL model: that the cross-sectional income effects estimated _ex ante_ with a representative household sample would coincide with the _ex post_ estimates of income effects generated by the program. The _ex ante_ simulation of the impact of CCTs on enrolment rate and poverty is conducted using the same baseline surveys that are used for the experimental evaluations of _PROGRESA_ in Mexico and _Bono de Desarrollo Humano_ (henceforth BDH) in Ecuador. The full set of _ex ante_ predictions are then compared with the highly credible and accepted
## **Figure A7.2: Comparison of Colombian World Management Survey Management Score to Other Countries**

<!-- Start of picture text --> United States 3.308<br>Japan 3.230<br>Germany 3.210<br>Sweden 3.188<br>Canada 3.142<br>Great Britain 3.033<br>France 3.015<br>Australia 2.997<br>Italy 2.978<br>Mexico 2.899<br>Poland 2.887<br>Singapore 2.861<br>New Zealand 2.851<br>Northern Ireland 2.839<br>Portugal 2.826<br>Republic of Ireland 2.762<br>Chile 2.752<br>Spain 2.748<br>Greece 2.720<br>China 2.712<br>Turkey 2.706<br>Argentina 2.699<br>Brazil 2.684 Africa<br>India 2.611<br>Vietnam 2.608<br>Asia<br>Colombia 2.578<br>Kenya 2.549<br>Nigeria 2.516 Oceania<br>Nicaragua 2.397<br>Myanmar 2.372 Europe<br>Zambia 2.316<br>Tanzania 2.254<br>Latin America<br>Ghana 2.225<br>Ethiopia 2.221<br>Mozambique 2.027 North America<br>1.5 2 2.5 3 3.5<br>Average Management Scores, Manufacturing<br><!-- End of picture text --> Source: World Management Surveys, Nick Bloom.

# **WMS 2016 Data Collection**

In September 2016, we asked _Innovations for Poverty Action_ (IPA) to conduct a second round of the _World Management Survey (WMS)_ . The LSE provided support in training the
Che et al., 2020; Hayakawa and Mukunoki, 2021a).

We contribute to two sets of literature. The first covers studies on how firms’ participation in GVCs played a role in the impact of the Covid-19 pandemic. A growing body of literature has analyzed the effects that the pandemic had on different firms (Ayres and Raveendranathan, 2021; Cirera et al., 2021; Bruhn et al., 2021; Bricongne et al., 2012; Brucal et al., 2021; de Lucio et al., 2022) and the role of GVCs as a potential transmitter of the Covid-19 induced consequences (Bonadio et al., 2021; Egger and Zhu, 2022; Baqaee and Farhi, 2020; Gerschel et al., 2020; Heise et al., 2020; Chakrabati et al., 2021; Lafrogne-Joussier et al., 2022; Rose et al., 2021; Arenas et al., 2022). Most scholars agree that the initial impact was stronger on international firms (Constantinescu et al., 2022; Pimenta et al., 2021; Borino et al., 2021) and characteristics like high imports or connections to China made firms worse off (Lafrogne-Joussier et al., 2022; Meier et al., 2020). However, authors also argue that internationally trading firms were more resilient and recovered faster (Constantinescu et al., 2022; Borino et al., 2021; Hyun et al., 2020; de Lucio et al., 2022). Our paper contributes to this literature by providing novel evidence of various performance indicators of GVC firms compared to non-GVC firms following Covid-19 up to a year and a half into the pandemic, especially since GVC data is usually available with a lag.

Second, we contribute to the literature on the role of trade policy when such a shock takes place, particularly non-tariff measures. Until now, only a few studies have focused on Covid-19 and trade policy, mainly looking at the trade policy responses to the pandemic (
Now, let us turn to interpreting results from estimating Equation (6), which explains variations in city-industry specific premiums. Estimated coefficients are provided Table 3. We have organized the findings around the relative importance of the business environment and agglomeration economies/ clustering. Columns 1, 2, and 3 in Table 4 provide results for low, medium and high technology manufacturing firms respectively.

# **_Business Environment_**

_Labor regulations_ : We find that across industries, the coefficient for labor regulation is negative and significant, which implies that cities located in states that have passed pro labor regulations have lower attractiveness in terms of location decisions of manufacturing firms. These effects are stronger for high technology manufacturing. Our findings are consistent with those from Dollar, Iarossi and Mengistae (2002), who survey 1000 manufacturing establishments from 10 Indian states and find that that managers would be willing to reduce their work force by 16-17 percent if there was greater labor market flexibility, indicating the negative impact of labor regulation on firm productivity. The negative impact of labor regulations on formal sector manufacturing is also seen in cross-country evidence. For example, Botero et. al. (2004) study labor regulations in 85 countries and find that heavier regulation of labor is associated with a larger unofficial economy.

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<mark>Mbiti, I., Muralidharan, K., Romero, M., Schipper, Y., Manda, C., & Rajani, R. (2019). Inputs, incentives, and complementarities in education: Experimental evidence from Tanzania.</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>,</mark> _<mark>134</mark>_ <mark>(3), 1627-1673.</mark> Mexico News Daily. (2019). Oaxaca teachers declare strike action, erect blockades. January 29.

<u>https://mexiconewsdaily.com/news/oaxaca-teachers-declare-strike-action-erect-blockades/</u> (retrieved March 20, 2020) Mincer, J. (1974). Schooling, Experience, and Earnings. Human Behavior & Social Institutions No. 2.

Mizala, A., & Ñopo, H. (2016). Measuring the relative pay of school teachers in Latin America 1997– 2007. _International Journal of Educational Development, 47_ , 20-32.

<mark>Muralidharan, K., & Sundararaman, V. (2011). Teacher performance pay: Experimental evidence from India.</mark> _<mark>Journal of political Economy</mark>_ <mark>,</mark> _<mark>119</mark>_ <mark>(1), 39-77.</mark> <mark>Muralidharan, K., & Sundararaman, V. (2013).</mark> _<mark>Contract teachers: Experimental evidence from India</mark>_ <mark>. NBER Working Paper No. 19440.</mark> Mwenda, D. B., & Mgomezulu, V. Y. (2018). Impact of Monetary Incentives on Teacher Retention in and Attraction to Rural Primary Schools: Case of the Rural Allowance in Salima District of Malawi. African Educational Research Journal, 6
value 1 in period _t_ after the event and 0 otherwise. Since the time units differ between the CP and PoI data, we set _t_ as 1 month for the household consumption regression and four months (equivalent to one wave) for the individual-level time usage regression. For example, in the household-level data (CP data), _Time_ = 0 corresponds to 1 month before the event; in the individual-level data (PoI data), _Time_ = 0 corresponds to 1 wave (equivalent to four months) before the event. The omitted base period (or _Time_ = 0) is the wave (or month) of the survey before the start of the program. We then pool the preand post-treatment periods together to estimate In both equations (6) and (7), _αi_ are individual (or household) fixed effects that control for all time-invariant characteristics of an individual (or a household). _αt_ represents the time-fixed effects, encompassing month and year-fixed effects in the regression that examines changes in household consumption. In the analysis of PoI data, _αt_ comprises wave and year-fixed effects. Appendix Table D2 provides a detailed definition for _Timet_ and _Postt_ . To account for the impact of the pandemic on people’s behavior, we also control for average daily confirmed COVID-19 cases as a share of the population. Because the treatment is at the state level, we cluster standard errors at the state level for statistical inference. Our parameters of interest are _βt_ and _β_ .

# **G Treatment Effect on Expenditure**

We use both the CMIE CPHS data and the Delhi survey to isolate the effect of treatment on transportation expenditure.

## **G.1 Evidence from The CPHS Sample**

To examine the of the _Pink_ on _household_ we impact _Slip Program_ monthly expenses, plot the estimated coefficient _β_<sup>ˆ</sup> _t_ from the event-study specification (6) in Figure C1 and detect no pre-trends. However, after implementation, we see a substantial decrease in household expenditure on transportation in
and model types commonly used by economists. Further details on the specifics of each remote sensing product are provided below with the goal of providing economists with direction to a data set that meets the requirements of their analysis.

### **A.1.1 Africa Rainfall Climatology version 2 (ARC2)**

ARC2 is a merged gauge data and remote sensing product that provides daily rainfall outputs for the African continent. The data set, produced by the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) provides improvements over ARC1 and a longer length of record compared to the rainfall estimate (RFE), the operational data set of USAID’s Famine Early Warning Systems Network (FEWSNET) program. Inputs are Global Telecommunications System (GTS) rain gauge data over Africa, geostationary Meteosat infrared (IR) imagery, and polar-orbiting microwave Special Sensor Microwave Image (SSM/I) and Advanced Microwave Sounding Unit (AMSU-B).

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production.

This data is organized by industry according to the Vietnamese Standard Industrial Classification (VSIC)<sup>11</sup> . Data on exports from Vietnam comes from the Vietnam Trade Database. This data was also organized according to the VSIC.

> 11 The Vietnamese equivalent of ISIC is the Vietnamese Standards Industrial Classification (VSIC) which is identical to ISIC Rev. 3. ISIC – refers to the International Standard Industrial Classification. It has

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cases, our results indicate that the impact of Fonden is not permanent, and that its dynamics can be characterized by a three phase process: setup for reconstruction, reconstruction, and catch up by municipalities not covered by Fonden.

# **5.3 The dynamic impact of Fonden on construction employment**

Next we turn to quarterly employment data. These data allow us to provide direct evidence of the impact of Fonden on local economic activity, as well as to document the time path of reconstruction work. While we find no evidence of an overall effect on employment, see figure 7, we do find that Fonden has a considerable impact on construction employment. Specifically, figures 8a to 8f plot the mean growth in construction employment, at different points in the post disaster period, relative to rainfall millimeters (mm) to the threshold. Specifically, the six outcome variables are the log difference in construction employment between the quarter the disaster takes place and the next six quarters, in one quarter steps.

In spite of the coverage of the LFS only allowing us to use half of the previous sample, we find that the time path of growth in construction employment is consistent with our previous findings. In particular, in the very short run, we find no evidence of differential growth in employment at threshold. However, at 6 months or more, we begin to observe a differential effect at the threshold. This effect appears to peak, in a very clear jump of roughly 25% 12 months after a disaster has taken place.

As in the previous case, we provide a more detailed account of the dynamic impact of Fonden on employment growth by sequentially estimating equations 1 to 3 for each of the six quarters following a disaster (3 to 18 months). Each plotted coefficient corresponds to a separate OLS regression. In all regressions, it is assumed that function _g_ ( _·_ ) is linear, and the sample is restricted to the set of observations that fall within the average optimal bandwidth.

Figure 9a
and DSD level education, age (to capture cohort e¤ects), and district ...xed e¤ects. In a recent paper, Lucas (2010) shows that the cohorts that were exposed to severe malaria in the late 1930s had lower schooling, and malaria eradication had had signi...cant positive e¤ects on the schooling of post-eradication cohorts in Sri Lanka.<sup>8</sup> To check whether the potential long term negative e¤ects of early-age malaria exposure invalidate our results, we perform robustness checks with di¤erent subsamples, excluding cohorts born before 1947 (the start of a nationwide eradication campaign), 1950 and 1960 respectively.<sup>9</sup> Also, note that the long-term

> 8 Note that our identi...cation strategy does not use malaria variations across cohorts.

> 9 Lucas (2010) de...nes the cohorts born before 1947 as exposed to historical malaria prevalence over the period 1937-1941. We also use historical malaria data for the period 1937-1941 (average).

5
the 6.2 percent of women, suggests a transfer of nearly 5,000 FDJ, when the income gains are only about 3,000 FDJ.

The program has no effect on husbands’ income transfers to their wives. Three-quarters of husbands participating in the program report giving money to their wives in at least one of the three weekly surveys at midline (not shown). The weighted rate of intra-household transfer from husbands to wives is, therefore, 57 percent. Moreover, women report receiving nearly 90 percent of their husbands’ mean labor income. This is a surprising finding but, we think, reflects the fact that women do most of the purchasing and preparation of food and other consumables, and these are poor households. When taking this large portion into consideration and in light of the fact that women rarely transfer their income to their husbands, these findings show that female beneficiaries obtained a significant net increase in income through their participation in the public works program. Not surprisingly, given

> 21 A caveat for these findings is that the magnitudes of the different effects do not add up to the observed total increase in labor income. Obtaining accurate estimates of expenditures is methodologically less challenging than estimating savings. Respondents often have no incentive to provide precise reports about their savings, which is a sensitive topic and less apparent to the rest of the community. These methodological aspects, together with our endline results, suggest that a reduction in household indebtedness occurred, leading us to conjecture that the participants mostly saved their increased income.

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operating expenses, donated goods and services, loan write-offs, loan loss reserves and loan loss provisioning. In addition, the MIX reclassifies some long-term liabilities as equity, and reverses any interest income accrued on non-performing loans. We further adjust the data to reflect ideas consistent with economic definitions of profit.

The MIX Market presents a calculation of profitability: i.e., the financial self-sufficiency (FSS) ratio. This notion of financial self-sufficiency is meant to indicate whether an organization can continue operations without external donor funding, but the FSS ratio falls short of accounting for inputs at their opportunity costs. The MIX Market reports that they make a costof-funds adjustment to account for the impact of “soft loans.” The MIX Market calculates “the difference between what the MFI actually paid in interest on its subsidized liabilities and what it would have paid at market terms.” To do that, the MIX Market uses data for shadow interest rates from the IMF’s _International Financial Statistics_ database, using the country’s deposit rate as the benchmark.<sup>6</sup> Yaron (1994) and Shreiner and Yaron (2001) argue that this adjustment is inadequate and that the FSS thus over-states financial self-sufficiency. The deposit rate provides a benchmark for the cost of borrowing by microfinance banks that is too low: The interest rate spread (the difference between the interest rate charged by banks to private sector customers when lending and the interest rate that the private sector offers to its depositors) is generally over 5 percentage points. (2014 World Bank data, for example, show that the interest rate spread for low income countries as a group was 11.2 percentage points and 6.4 percentage points for middle income countries as a whole.)<sup>7</sup> Moreover, many institutions, are not legally able to collect deposits, and even those

> 6 From MIX Market, “Benchmarks Methodology”

> http://www.themix.org/sites/default/files
studies of labor survey data find that 0 < λ< ,1 which Bound and Krueger (1991) call meanreverting measurement error.

The estimator of the response coefficient with the error-ridden dependent variable is: showing that in the special case of classical errors there is no bias in the response coefficient. But if measurement errors negatively co-vary with true values (0 < λ< )1 the estimated response coefficient will be attenuated. Hence, knowing what type of measurement error afflicts consumption data is likely to be important for empirical research in poor countries. For example, many studies of program impacts use household consumption as an outcome measure (Khandker, 2005). Treatment effects may be greatly understated by these studies if measurement errors in household consumption are negatively correlated with true values.<sup>6</sup> Consider next the case of no error in the dependent variable (or just white noise error) * while the observed value for the independent variable, _x_ , is related to the true value by: The estimator of the response coefficient in equation (1) is then: With classical measurement error, where λ= ,1 the rescaling of the response coefficient is the familiar attenuation in proportion to the explanatory variable’s ‘reliability ratio’ (the variance 2 2 2 in the true data relative to that in the mis-measured data, σ _x_ /[ σ _x_ + σ _v_ ]). But with mean-

> 6 Understated treatment effects are likely even in panel studies. Validation of panel labor surveys shows that just as errors in reported earnings levels negatively co-vary with true values, so too do errors in reported changes in earnings negatively co-vary with true changes, causing understated estimates of growth-related processes (Gibson and Kim, 2010). Validation studies for panel consumption surveys appear to be a long way off.

5
varies, I only include those that are precise to the village or subregion location and exclude those that are only recorded at the regional level.

LRA attacks are recorded using a binary variable for whether or not a particular community was attacked by rebels in 2004. Insecurity is a binary variable for whether “any section of the community found it difficult to cultivate their land in 2004 because of insecurity.”<sup>14</sup> Variables for the distance from each community nearest to rebel attacks in each year are created from the NUS and the ACLED data sets. Specifically, these measure the distance (in arc degrees) from community _i_ to the nearest attacked community (excluding community _i_ ). For the NUS data, these are created for the rebel attacks in 2004, 1999, and 1992. The ACLED data contain information on LRA attacks for each year from 1997 to 2003.

On average, communities were relatively close to attacks by the LRA as the average distance varied between 0.20 and 0.90 decimal degrees (approximately 22 and 100 kilometers, respectively). For the closest communities, this was as low as approximately 3.6 kilometers. Moreover, close to one third of the sample communities were attacked in 2004. A similar number of communities reported being insecure.

Consumption is measured as the natural log of per capita annual total household consumption.<sup>15,16</sup> Livestock holding are aggregated into tropical livestock units<sup>17</sup> (TLU). Household members are defined as all household members who have lived in the house 6 months

> 14. The full text of the questions as well as full descriptive statistics of the variables are provided the appendix (tables S.1–S.3).

> 15. The variable is calculated by summing the following consumption sections: food consumption (purchased, consumption of home production, and free), non-durable goods and frequently purchased services (including rent), semi-durable and durable goods