rafmacalaba/datause-encoder-data huggingface dataset:
methodology to address the input prices bias stemming from unobserved plant-level input prices, henceforth, TFPQ à la De Loecker et al..

_Estimation:_ We employ the standard specification in the literature where i represents the set of logged values of productivity measures of plant _i_ at time _t_ : wages, i ; TFPR, t t i ; and TFPQ, t t i , using both the Foster et al. and à la De Loecker et al. methods, price, i ; marginal yy ww cost, m i ; and mark-ups, i . d is the log of population density in location ( ) where plant ( ) is located tt at time ( ). For each country included in our study, we adopt a comparable spatial scale used in the literature: ll(ii)ii μμ dd ll ii analysis in Chile and Colombia is at municipality level, while in Ethiopia it is carried out at the towns-level. In tt Indonesia, we use district as the primary spatial unit of analysis.<sup>11</sup> i includes plant-level controls, such as plant age and size (that is, the number of workers i , included in wage and price regressions only). are pair-wise XX 4-digit-industry-year fixed effects to help factor in any sectoral or time trend. Base estimations pool the jjii ll TTFF observations (OLS), and standard errors are clustered at the level of the spatial unit. Later we will exploit the cross-sectional variation to attempt to control imperfectly for firm selection.

Both quantity and quality effects described in the previous section can lead to inconsistent estimates. Though previous studies suggest that attempting to remedy this endogeneity does not drastically change the elasticity magnitudes (see meta-analysis by Grover, Lall and Timmis, 2021; Duranton 2016 for Colombia), we follow the literature by: (i) employing geological variables such as land fertility (Combes et al. 2010), land suitability for the construction of tall buildings (Rosenthal and Strange 2008; Combes et al. 2010) as instruments<sup>12</sup> and (ii) including location or plant fixed effects to capture any unobserved attributes that may have attracted more establishments to a given city (Henderson 2003).

In addition, cities may attract more educated workers or productive firms in large locations and several studies report lower estimates once controlling for observed skills (e.g., see Glaeser and Mare, 2001 for the US and

> 11 Typically, research uses administrative regions or agglomerations of contiguous and economically integrated spatial units. For example, metropolitan areas in the United States, microregions in Brazil, districts in India and prefecture cities in China (see, e.g., Chauvin et al., 2017).

> 12 Studies also instrument using historical measures of density (Ciccone and Hall 1996) and its validity relies on the past populations being uncorrelated with unobserved drivers of contemporaneous wages conditional on the other controls. While a good case can be made for this (Combes et al., 2010), it is by no means decisive. In either case, we are unable to use this instrument because for the four countries studied in this paper, we did not consistently have a comparable measure of past density.

8
(1999) per capita income (purchasing power adjusted) and average annual growth (19992005) should capture any tendency for (subjective) assessments of ERM to be inferred in part from observed economic performance. The likelihood of reforms in tax policy and administration could also be related to economic performance; e.g. if tax reforms are sometimes motivated by crisis as reflected in slow growth. Income may also proxy for structural variables (e.g. agricultural, manufacturing and service share of GNI, or urbanization<sup>3</sup> ) that potentially could influence tax revenues or policies. The coefficient estimates for income and growth will reflect the net impact of these various influences on ERM.

We control for the initial level of democracy, and for its change over the period, using the well-known Freedom House ratings on political freedom. Democratic institutions could create more pressure on elected officials to produce fair and efficient tax legislation, and on tax officials to collect revenue in more transparent and honest ways. However, it is conceivable that electoral majorities could vote to narrow the tax base and exploit a small set of revenue sources. Organized producer groups in democracies may also lobby for exemptions and other special treatment. The net impact of political freedoms on ERM is therefore ambiguous.

Aid, one of the key independent variables, is potentially endogenous, as donors may direct either more or less aid toward reforming countries. In fact, the purpose of the CPIA ratings is to allocate the World Bank’s IDA funds, which account for about 10% of all ODA. The premise of this “performance-based allocation” system is that aid is more effective in more favorable policy environments, as argued in World Bank (1998). This

> 3 When those variables are included in the regressions they are not significant, and have little or no impact on results for the sovereign rents variables.

10
# Table 2 MaLawi: Central Goverrnent Fiscal Operations (Budgetary revenues and expenditure), 1974-88 a/

||||As a pe|rcent of GDP a|t market pri|ces|||Debt S|ervicing|
|---|---|---|---|---|---|---|---|---|---|---|
|Year|(1)<br>Interest<br>Expenditur<br>(%)|(2)<br>Non-interes<br>e Current<br>Expenditure<br>(X)|(3)<br>t Current<br>Expenditure<br>1 bI<br>(%)|(4)<br>Tax Revenue<br>s<br>(X)|(5)<br>Non-Tax<br>Revenue<br>(Z)|(6)<br>Current<br>Revenue<br>t%)|(7)<br>Current<br>Deficit<br>(X)|(8)<br>Overalt<br>Budget<br>Deficit<br>c/<br>(X)|(9)<br>Interest/<br>Current<br>Expenditure<br>I<br>a/<br>(X)|(10)<br>Interest<br>+<br>Amortization/<br> <br>Current<br>Expenditure<br>II d/<br>(%)|
|Pre-study:|||||||||||
|1973|1.1|15.8|16.9|12.0|5.4|17.4|+0.5|7.9|6.4|17.0|
|1974|1.1|15.9|16.0|11.7|5.4|17.1|+1.1|7.8|7.1|18.8|
|1975|1.0|14.9|15.9|12.6|4.4|17.0|+1.0|12.2|6.4|17.2|
|1976|1.5|13.8|15.3|11.8|3.7|15.5|+0.2|7.7|10.0|16.8|
|1977|1.0|13.9|14.9|12.3|3.6|15.9|+1.1|8.5|0.7|16.0|
|1978|1.9|15.6|17.5|15.2|3.3|18.5|+1.0|12.4|10.6|14.4|
|1979|2.6|17.7|20.3|16.8|5.1|21.9|+1.6|13.9|12.9|21.1|
|1980|3.7|17.0|20.7|16.6|3.2|19.8|-0.8|15.8|18.0|27.4|
|1981|5.5|19.1|24.7|16.2|3.8|20.0|-4.7|15.5|22.4|34.1|
|1982|5.1|17.2|22.4|16.7|2.9|19.6|-2.7|12.5|22.9|27.9|
|1983|4.2|17.7|21.9|16.7|3.3|20.0|-1.9|10.1|19.3|26.0|
|1984|7.1|17.3|24.4|17.4|3.3|20.7|-3.7|8.8|28.9|37.8|
|Post-study:|||||||||||
|1985|5.8|20.8|26.6|19.1|3.5|22.6|-4.0|8.7|21.7|35.8|
|1986|8.2|21.8|30.0|17.8|4.6|22.4|-7.5|13.9|27.2|37.9|
|1987|7.7|18.7|26.4|16.4|4.8|21.2|-5.3|9.9|29.3|40.2|
|1988|5.8|20.0|25.6|18.1|3.4|21.5|-4.1|7.6|22.0|39.0|

a/ Budget data are in fiscal years (FY) and GDP in Calendar Years (CY)

- _b/_ Current Expenditure I Excludes Amortization.

_ci_ Budget Deficit before Grants d/ Current Expenditure II Includ.es Amortization.

Source: Tax Poticy for Malawi, World Bank, November 1985. Post study data are drawn from MaLawi Country Economic Memorandum (#8140-MAI). This source includes minor revisions to eartier data
**Alternative Estimation Technique** The first robustness check is to estimate the relationship between the quality of institutions and pattern of export using a different estimation technique. Arguing that the use of log(export) as the regressand results in biased estimators, Silva & Tenreyro (2006) suggest the use of Poisson regression with the level of export – instead of log(export) – as the LHS variable. As the results in Table 11 show, our results are robust to the use of this alternative technique.

Table 11: Robustness Check Using Poisson Regression

|Dependent||_Exportijst |_|
|---|---|---|
|Variable:|_Exportijst_|_Export >_0|
|Rule of law|-0.184<sup>_∗∗_</sup>|-0.179<sup>_∗_</sup>|
|Rule of law x Inst-Int|0.757<sup>_∗∗∗_</sup>|0.752<sup>_∗∗∗_</sup>|
|Rule of law x Inst-Int x OECD|-0.809<sup>_∗∗∗_</sup>|-0.798<sup>_∗∗∗_</sup>|
|lnH|-0.414<sup>_∗_</sup>|-0.315|
|lnH x H-Int|0.090|0.089|
|lnH x H-Int x OECD|0.021|0.021|
|lnK|-0.740<sup>_∗∗∗_</sup>|-0.710<sup>_∗∗∗_</sup>|
|lnK x K-Int|-1.581<sup>_∗∗_</sup>|-1.600<sup>_∗∗_</sup>|
|lnK x K-Int x OECD|-0.433<sup>_∗∗∗_</sup>|-0.424<sup>_∗∗∗_</sup>|
|NR|0.003|0.009|
|NR x OECD|-0.007|-0.015<sup>_∗_</sup>|
|NR x N-Int|-0.039<sup>_∗∗∗_</sup>|-0.038<sup>_∗∗∗_</sup>|
|NR x N-Int x NOECD|-0.001|-0.001|
|lnESTABLISH|0.029|0.031|
|lnGDPE|2.213<sup>_∗∗∗_</sup>|1.958<sup>_∗∗∗_</sup>|
|lnGDPI|0.320<sup>_∗∗∗_</sup>|0.588<sup>_∗∗∗_</sup>|
|lnDIST|-0.474<sup>_∗∗∗_</sup>|-0.802<sup>_∗∗∗_</sup>|
|No. of Observations|1627008|727969|
|Pseudo-R<sup>2</sup>|0.801|0.804|
|_χ_<sup>2</sup><br>|113941.162|124502.742|
|Prob _> χ_<sup>2</sup>|0.000|0.000|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Each model includes exporter, imp-, orter, industry and year fixed effects. Robust standard errors reported.

# **6 Conclusion**

Although the effects of institutions on the trade pattern are now extensively studied, little is known about the exact mechanisms through which this effect operates. In this paper, we disentangle the influence of institutions on trade into two margins: the probability of exporting (extensive-margin) and the volume of export conditional on exporting (intensive-margin).

Our findings both complement and extend the existing empirical literature on institutions and trade flows. Using highly disaggregated product-level and firm-level trade data, we confirm that institutional quality does have a robust and significant effect on international trade patterns. Specifically, we find that countries with better institutional quality export products that are intensive in contracts while those with poor institutions specialize in institutionally less-intensive products. This finding is robust to the use of alternative measures of institutional quality as well as different estimation techniques. Nonetheless, the role of institutional quality does not supersede that of factor endowments in general and that of human capital in particular.

24
Figure 9: Comparison of Predictions from Different Simulations <!-- Start of picture text --> 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Year<br>Actual Pred_70 Pred_80 Pred_90<br>40000<br>30000<br>20000<br>10000<br>0<br><!-- End of picture text --> Note (1) Each line represents the actual or the predicted path of GDP per capita at different starting date of simulation according to the LTGM using the prior data.

Note (2) ”Actual”: Actual GDP per capita, ”Pred ~~7~~ 0”: Predicted GDP per capita in the year 1970, ”Pred ~~8~~ 0”: Predicted GDP per capita in the year 1980, ”Pred ~~9~~ 0”: Predicted GDP per capita in the year 1990.

24
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- [16] J rgen Carling. “Migration in the age of involuntary immobility: Theoretical reflections and Cape Verdean experiences”. In: _Journal of ethnic and migration studies_ 28.1 (2002), pp. 5–42.

- [17] Hein De Haas. “A theory of migration: the aspirations-capabilities framework”. In: _Comparative Migration Studies_ 9.1 (2021), pp. 1–35.

- [18] Cristina Cattaneo et al. “Human migration in the era of climate change”. In: _Review of Environmental Economics and Policy_ 13.2 (2019), pp. 189–206.

- [19] Ingrid Boas et al. “Climate mobilities: Migration, im/mobilities and mobility regimes in a changing climate”. In: _Journal of Ethnic and Migration Studies_ (2022), pp. 1–15.

- [20] Parag Mahajan and Dean Yang. “Taken by storm: Hurricanes, migrant networks, and US immigration”. In: _American Economic Journal: Applied Economics_ 12.2 (2020), pp. 250– 77.

- [21] Pin Sun. _Asymmetric Migration Response: An Application to Welfare Analysis in Climate Change_ . Tech. rep. Pennsylvania State University, 2023.

- [22] Narayan Sastry and Jesse Gregory. “The location of displaced New Orleans residents in the year after Hurricane Katrina”. In: _Demography_ 51.3 (2014), pp. 753–775.

- [23] Angel L Griego et al. “Social vulnerability, disaster assistance, and recovery: A populationbased study of Hurricane Harvey in Greater Houston, Texas”. In: _International Journal of Disaster Risk Reduction_ 51 (2020), p. 101766.

- [24] Coline Bodenreider et al. “Assessment of social, economic, and geographic vulnerability pre-and post-Hurricane Harvey in Houston, Texas”. In: _Environmental Justice_ 12.4 (2019), pp. 182–193.

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17
The growth rates are unequal among the domestic assets also. The states' debts renegotiation, which appears on items 1.3 and 1.4, is responsible for slightly more than half of this increase (55.20%). The Central Bank's credits to financial institutions, which include the Proer (the private banks' bailout program), also played a significant role: 17.04% TABLE 111.9. ASSETS ACCUMULATION IN R$: 1995-1998

||Dec/94|Dec/98|Variation|Percent<br>Variation|
|---|---|---|---|---|
|1. Domestic|73,478.00|216,332.39|142,854.39|194.42%|
|1.1. FAT|10,125.00|27,878.83|17,753.83|175.35%|
|1.2. CB's credits to financial institulions|20,561.00|48,490.18|27,929.18|135.84%|
|1.3. Federal Government's credits<br>(Law 8727 / 93)|0.00|3,849.51|3,849.51||
|1.4. Debt Renegotiations with the sltates|0.00|86,612.46|86,612.46||
|1.5. Others|42,792.00|49,501.41|6,709.41|15.68%|
|2. Foreign Reserves|32,829.88|53,854.84|21,024.96|64.04%|
|TOTAL|106,307.88|270,187.23|163,879.35|154.16%|

# **_I1.3 Repayment of other kinds offeteralpublic debt_**

As shown on Table 111.3, the other debts suffered, in nominal terms, a net increase. Therefore, if the other debts had rernained the same, the federal public debt would have increased even more in nominal terms. Table 111.10 shows the other kinds of debt variation in this period. Once more, the domestic components growth is faster than the foreign one in progress.

23
population), as shown in Figure 2b. Other major changes include cost-of-living adjustments (COLA) in 2007 as well as the expiration of the ARRA in 2013. Figure 4a plots the changes in SNAP benefits per population across these four events. The Farm Bill and the ARRA contribute large and dispersed changes across states, whereas the 2007 COLA and 2013 ARRA expiration contribute limited and negligible variation.

We also use the SNAP Quality Control (QC) Survey, which contains detailed demographic, economic, and program-eligibility information for a nationally representative sample of approximately 50,000 SNAP households. This data set is collected by the USDA FNS and generated from monthly quality-control reviews of SNAP cases that are conducted by state SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for the state’s SNAP caseload. We get the formula from the FNS to impute synthetic benefit amounts. That is, we analyze the algorithms used by the FNS to audit benefit amounts and construct the QC data, and use the same algorithms to build the simulated instrument. The formula we use generates identical benefits to those in the QC data for 98% of households.<sup>14</sup>

# **2.2 The SNAP Formula and Instrumentation Strategy**

The federal SNAP formula is designed to ensure a floor of spending power on what the government considers sufficient nutrition for households in poverty. Every year, the USDA prepares a monthly dietary budget called the “Thrifty Food Plan” for a typical four-person household adjusting for changes in the Consumer Price Index (CPI). In October, the USDA updates the SNAP-benefit parameters based on the size of this budget, which becomes the maximum level of benefit a four-person household can receive from SNAP. The annual formula adjusts maximum levels for households with different numbers of family members based on a sliding scale. SNAP may update these parameters in months other than October at the discretion of the Congress. For example, the 2009 ARRA included a provision to add 13.6% to the Thrifty Food Plan budget, increasing the maximum benefit level for a single-member household to $ 200, and for a household of seven or more members to $ 150 per person. We show how maximum benefits changed over time in Appendix Figure O2a and how the maximum benefits per recipient decrease as household size increases in Appendix Figure O2b.

Because the federal formula’s intent is to ensure a floor of spending power on food, the benefit amount falls as a given household’s estimated ability to contribute to food spending

> 14We do not code in the Supplemental Security Income Combined Application Projects (SSI-CAP) adjustments, which affect only 2% of the national sample of participants, yet are extremely complex and render the costs of coding prohibitive.

7
<!-- Start of picture text --> 2000 Rural: Poverty & Basic Needs<br>poverty-children's education-sanitation<br>omaia<br>~<br>NS<br>J<br>fj<br>Naver es /<br>~, 2<br>ooo~ ee<br>———— below_poverty_line --------- education<br>—-—-- sanitation<br>population: approx. 36997000<br><!-- End of picture text --> <!-- Start of picture text --> 2011 Rural: Poverty & Basic Needs<br>poverty-children's education-sanitation<br>aee<br>ot<br>\<br>oa |<br>y /uf<br>‘—_ 2 jv<br>me: vA<br>¥eeff<br>SS<br>———— below_poverty line --------- education<br>—"—"7 Sanitation<br>population: approx. 48711600<br><!-- End of picture text -->

<!-- Start of picture text --> 2000 Urban: Poverty & Basic Needs<br>poverty-children's education-sanitation<br>a<br>m4<br>A<br>’ #<br>a<br>==~ sentationbelow_poverty_line ---===--- education |<br>population: approx. 5468700<br><!-- End of picture text -->

<!-- Start of picture text --> 2011 Urban: Poverty & Basic Needs<br>poverty-children's education-sanitation<br>. oe<br>ane<br>7 f<br>* —,<br>‘ /<br>\, if<br>povertyline --------- education<br>—-:—- sanitationbelow<br>population: approx. 7526900<br><!-- End of picture text -->
36

# E. **Complementing an OSF with Market-Based Hedging**

To the extent that an OSF or general revenue stabilization fund is desired, it can be usefully complemented by market-based risk management tools. One possibility is to use market-based instruments such as options to hedge against the occurrence of price<sup>spikes or</sup> sharp drops. Given the presence of fat tails in the oil price distribution (see Figure 4) it may be efficient to hedge using market-based instruments (especially instruments designed to remove the effects of spikes, such as options) instead of holding low-yielding assets in reserves. To the extent desired, the OSF could be used to manage any remaining interperiod risk or more<sup>general</sup> government revenues risk.

<!-- Start of picture text --> Histogram Tia Juana Light<br>Weekly prices<br>45-<br>40 -<br>35 -<br>30<br>25-<br>C7<br>a) 2<br>L 2<br>UA.<br>15<br>10<br>5<br>12 13. 5 15 16 5 19 19.-5 21 22. 5 24 25. 5 27 29. 5 30 31. 5 33 34. 5 36 37. 5<br>Pr I ce<br><!-- End of picture text -->

**Flgure** 4: **Price Histogram:** 1983-1991
|Travel time|_Own_|-0.20|-0.38|-0.66|-0.95|-1.27|-1.51|
|---|---|---|---|---|---|---|---|
|(equations 3b)|_Cross-bus/walk_|0.03|0.07|0.24|0.78|2.10|4.49|

# **4. Considering policies that reduce aggregate fuel use and emissions**

The model ignored some important aspects because data were lacking. Although mode switching is treated, the total trips, and the trip distance by mode are fixed. Car fuel economy is uniform in the population and although fuel costs are a function of speed, the cost of car acquisition or nonfuel variable costs are not treated. Still, the model has enough detail to help us learn about how pricing policies affect congestion and emissions.

In Figure 1 where _e=_ 1, fuel and emissions per car km are high when speed is low, but each falls rapidly with speed, making relatively flat bottoms and rising again at higher speeds. Congestion mitigation policies would yield a high return in megacities where the speed is very low. A policy may improve emissions per car km (the intensive margin), but if car trips are increased by the policy (the extensive margin), then emissions also increase. We now examine this in the case of four policies treatable in the model.

(i) _Building more_ roads: Increasing road capacity _Z_ , reduces congestion. But as the speed increases, walkers switch to car and bus. The higher speed reduces fuel and emissions per car km. Whether aggregate fuel or emissions are reduced depends on the elasticity of car trips with respect to travel time. If this elasticity is sufficiently high (low), then trips by car and aggregate fuel and emissions increase (decrease). The elasticity shown in Table 3 is low enough that a 20% increase in base capacity cuts emissions by 0.95%.

(ii) _Subsidizing public transit_ : A lower monetary cost of bus induces switching to bus and a higher car speed, but if a lot more trips switched to bus from walking than from cars, then speed can decrease. What happens to the aggregate fuel and emissions depends on the elasticity of the trips by bus with respect to the monetary cost of bus. In the present model, by the I.I.A.

13
17 went toward the purchase of medical services from the private sector in 1994, when the reform was launched (MOH reports and data).

* Incentives created by the cost reimbursement system, for physicians to choose to hospitalize patients for interventions that could be provided on an outpatient basis, and for hospitals to use high-cost interventions when lower-cost treatments would be sufficient. Not surprisingly, cost reimbursement also created incentives for overbilling, especially given expected and actual arrears on the part of the MOH.

. In the public hospitals, eroded public-sector wages and compressed pay scales. These created incentives for public hospital staff to absent themselves from their positions, and seek employment in the private sector in order to supplement their income.

* Weak incentives and meager means for hospitals to gather and use information that would improve their performance, and an even weaker regulatory capacity at the level of the Ministry of Health to oversee the operation of public hospitals.

* No consumer protection policies, and therefore weak incentives on the part of hospitals to ensure that they were satisfying community needs and equity considerations. Despite the possibility of government reimbursement, poor patients have difficulty accessing private hospital services, and when they do receive care, they are often asked for significant co-payments. Those who were most politically connected benefited most from the cost reimbursement system.

# _4.1 The declared objectives of the reform_

As part of the effort to restructure the public health sector, a law was drafted to corporatize public hospitals by granting them a degree of fiscal and managerial autonomy