rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Benchmark Calibration

||**Description**|**Parameter**|**Value**|**Source**|
|---|---|---|---|---|
|_Preferences_|Discount factor|_β_|0.99|BC, HP, GO|
||Consumption share|_μ_|0.34|HP|
||Intertemporal Substitution|1_/γ_|0.5|BC, HP, GO|
|_Trade_|Armington elasticity|1_/σ_|1.5|HP, GO|
||Armington aggregator weights|_ωi_=_{_1_,_2_}_, _ω_3|0.9, 0.5|HP, GO|
|_Technology_|Cost-share of labor in intermediate-goods production|_θ_|0.64|BC, HP|
||Depreciation rate of capital|_δ_|0.025|BC, HP|
||Cost of adjustment parameter|_η_|0.99|BC, GO|
|_Oil sector_|Oil share|_αo_|0.10|BC, GO|
||Elasticity of substitution _kl, o_|1_/ν_|0.09|BC, GO|
||Oil exporter’s share of oil output|_φ_<sup>_o_</sup>|0.5|BC, GO|
||Labor parameter|_α_|0.6|BC, GO|
|_Shocks_|Persistence of productivity shock|_ρn_|0.97|HP, GO|
||Correlation of productivity shocks across _i_=_{_1_,_2_}_|_χn_|0.025|HP|
||Persistence of energy efciency shock|_ρe_|0.70|II|
||Correlation of energy efciency shocks across _i_=_{_1_,_2_}_|_χe_|0.11|II|
||Persistence of non-U.S. oil supply shock|_ρ_<sup>_o_</sup><br>3|0.98|BC|
||Persistence of U.S. oil supply shock|_ρ_<sup>_o_</sup><br>1|0.91|II|

_Note:_ BC: Backus & Crucini (2000), HP: Heathcote & Perri (2002), GO: Gars & Olovsson (2017), II: indirect inference.

where **V** is a diagonal matrix with diagonal elements as the variances of Ω.

Figure 8 displays the empirical impulse responses, along with the response functions estimated using model simulated data. The persistence of U.S. oil supply shock is estimated as 0.91 and innovation standard deviation is 0.012. The persistence and correlation for U.S. energy efficiency is estimated as 0.7 and 0.11, respectively. I follow the standard assumption in the literature that the shock process is symmetric across countries. Thus, the estimated energy efficiency shock process is given as following where the standard deviation of innovations is 0.02.

# **V.3 Baseline Results**

In this section, I discuss the impulse responses to U.S. energy efficiency and U.S. oil supply shocks. In particular, I focus on understanding the mechanisms by which changes in U.S. oil production and consumption can affect the GDP of oil importing and oil exporting countries. Each shock has direct implications for either U.S. oil demand or U.S. oil supply and, thus, results in changes in oil price and, consequently, U.S. and international macroeconomic aggregates. Figure 9 shows the effects of a one-standard deviation improvement in U.S. energy efficiency. A positive shock to U.S.

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# **Box 1. Chile’s Economic Growth Experience in Comparison to Peru**

This box aims to compare the Chilean economic growth experience from 1991 (when Chile was at approximately at the same level of development as Peru in 2021) onwards. As Chile transitioned to high-income status, the historical drivers of growth were diverse, but were led by higher private investment, followed by higher TFP growth rates, and higher Human Capital growth rates, a demographic dividend that lasted for 22 years. Headline GDP growth and GDP per capita growth averaged 5.1% and 3.9%, respectively, throughout that process. Box Figure B1b shows that it took Chile 21 years to become a high-income economy.

<!-- Start of picture text --> Figure B1-a. Chile’s Economic Growth Figure B1-b. Chile’s path to High Income Status<br>Performance<br><!-- End of picture text --> <!-- Start of picture text --> 25,000<br>15 Chile (years from 1991)<br>HL GDP Growth HI Threshold 2022<br>20,000<br>10 GDP PC Growth<br>Peru years from 2021 (April<br>2023 WB MPO Estimate)<br>15,000<br>5<br>10,000<br>0<br>1991 1996 2001 2006 2011 2016 2021<br>5,000<br>-5<br>0<br>-10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22<br>Source: World Development Index Source: WDI, WB projections.<br><!-- End of picture text --> Growth drivers during this time were 1) investment-to-GDP ratio averaged 23% (private investment averaged 20.5% and public investment 2.5%), 2) average saving was 22.3% and Current Account Balance-to GDP ratio was, on average, -0.8%, 3) human capital growth averaged 0.6%, 4) TFP growth averaged 0.5%, 5) the growth of the working age-to-total population ratio (WATP) was around 0.35%, indicating a demographic dividend throughout this period and 6) total population growth declined from 1.65% to 1.05%.

# **3. Growth drivers in the business-as-usual baseline**

In this section we calibrate the LTGM-NR model to simulate Peru’s business-as-usual baseline over the 2023-50 period. To create a baseline for the Peruvian economy in the long run, we calibrate the future paths of the growth drivers such as investment, human capital, the growth of TFP in the non-resource and natural resource sectors,<sup>6</sup> as well as other key parameters. These assumptions are summarized in Table 1. We calibrate the LTGM-NR as the foundation of the analysis and add the LTGM-TFP extension and the LTGM-HC extension to simulate non-resource sector TFP growth and human capital growth, respectively. We use population growth projections (by age cohort) from the UN for demographic trends until 2050.

6 The resource sector is composed of the copper mining sector, which is around 8 percent of the total GDP and is expected to grow in the short run by around 2 percent due to the entrance of the Quellaveco mine. In the long run, the depletion in reserves will shrink the sector to 6 percent of GDP and grow below 1 percent on average.

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# Table 6: IV estimates of SHI effects on health and labor outcomes

||2SLS<br>Coef.|p-val|GMM<br>Coef.|p-val|KP LM|KPWald|HansenJ|
|---|---|---|---|---|---|---|---|
|Tot. health exp. per capita|_-0.154_|_0.05_|_-0.167_|_0.02_|0.00|12363|0.044|
|PYLL cancer of colon|_-0.179_|_0.08_|_-0.111_|_0.24_|0.00|11004|0.034|
|PYLL breast cancer|-0.263|0.06|-0.217|0.06|0.00|11004|0.188|
|PYLL cervical cancer|-0.059|0.72|-0.067|0.65|0.00|10938|0.179|
|PYLL diabetes|0.235|0.15|0.226|0.15|0.00|10999|0.904|
|PYLL ischemic heart disease|_-0.489_|_0.01_|_-0.551_|_0.00_|0.00|11119|0.076|
|PYLL cerebrovascular disease|0.245|0.06|0.216|0.07|0.00|11119|0.220|
|PYLL influenza pneumonia|_0.504_|_0.08_|_0.511_|_0.06_|0.00|11119|0.021|
|PYLL pregnancy|_0.953_|_0.00_|_0.944_|_0.00_|0.00|11797|0.010|
|PYLL perinatal causes|_0.285_|_0.21_|_0.286_|_0.16_|0.00|11119|0.059|
|Formal sector employment|-0.097|0.01|-0.084|0.01|0.00|8353|0.492|
|Employment|-0.053|0.14|-0.060|0.08|0.00|8298|0.413|

_Notes:_ Italicized numbers indicate estimates that are to be ignored because diagnostic tests have shown them to be irrelevant or unreliable. The reported IV point estimates are from 2SLS estimation, where the excluded instruments are the first lag of the SHI dummy, whether the country was a World War II Axis member or occupied by one or more of the Axis power, and a dummy indicating whether the country is an English-speaking country. P-values are reported for the Kleibergen-Paap LM and Hansen tests, whereas the values reported for the Kleibergen-Paap Wald tests of weak identification correspond to the estimated _rk_ Wald _F_ statistics; in our case, the critical value for the weak identification tests (tabulated by Stock and Yogo 2002, 10% maximal IV size) is 22.30. In the last column, the joint null hypothesis of the over-identification tests is that the instruments are uncorrelated with the error term and that the excluded instruments are correctly excluded from the main equation.
Table 3: Effects by Location in the Productivity Distribution

|DepVar: TFP Index|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Policy Restrictiveness in Services FDI|-1.113**<br>(0.554)||||||
|Restrictiveness*Top 25%|-1.005<br>(0.762)||||||
|Restrictiveness*Bottom 25%|1.169*<br>(0.659)||||||
|Bottom 25% Dummy|-0.168**|-0.0694***|-0.0419**|-0.0757|-0.0503***|-0.0595*|
||(0.0745)|(0.0229)|(0.0177)|(0.0605)|(0.0158)|(0.0356)|
|Top 25% Dummy|-0.00834|-0.104***|-0.0879**|-0.152**|-0.0709*|-0.0587|
||(0.0738)|(0.0326)|(0.0346)|(0.0595)|(0.0380)|(0.0525)|
|Policy Restrictiveness in Manufacturing FDI|-0.120|-0.123|-0.131|-0.0411|-0.223|-0.236|
||(0.807)|(0.748)|(0.737)|(0.763)|(0.736)|(0.680)|
|Tariffs on Inputs (Effective)|-0.00850*|-0.00816*|-0.00785|-0.00796|-0.00735|-0.0104**|
||(0.00487)|(0.00483)|(0.00492)|(0.00502)|(0.00482)|(0.00506)|
|% of Output by Foreign Firms in the Sector|-9.35e-05<br>(0.0004)|-0.000238<br>(0.0004)|-0.000267<br>(0.0004)|-0.000323<br>(0.0004)|-0.000217<br>(0.0004)|-0.000240<br>(0.0004)|
|Output Tariffs (Effective)|-0.000311|-0.000230|-0.000663|-0.00107|-0.00101|-0.000375|
||(0.00365)|(0.00360)|(0.00368)|(0.00366)|(0.00373)|(0.00373)|
|Communications Rest||-10.61<br>(7.940)|||||
|Rest Communications*Top25||-5.123<br>(12.93)|||||
|Rest Communications*Bottom25||20.51*<br>(11.97)|||||
|Construction Rest|||-4.484<br>(17.24)||||
|Rest Construction*Top25|||-32.72<br>(32.69)||||
|Rest Construction*Bottom25|||<br>2.109<br>(18.53)||||
|Distribution Rest||||-0.723<br>(0.872)|||
|Rest Distribution*Top25||||<br>0.830<br>(0.875)|||

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The thin diagonal in Figure 4 shows the line of “perfect equality” and serves as a useful reference. For two of the SFD programs – education, and waste water – the cumulative percentage of spending is below the line of perfect equality. This means, for instance, that the poorest 20% of villages (by population) receive less than 20% of SFD spending on education, or on waste water treatment. Spending on these programs thus favors the better-off over the poor.<sup>4</sup> All the other SFD programs have concentration curves above the line of perfect equality, meaning that spending is geared more towards the poor than the rich (at the village level). Spending on potable water projects, and to a lesser extent on road and health interventions, is relatively strongly geared toward poorer villages. While these effects are clear, they are not overwhelming; the poorest 30% of the population lives in villages that received 43% of SFD potable water spending, and 35% of SFD health and road spending. These reflect a pro-poor bias, but not a strong one.

Table 18 provides some further information on SFD targeting. For each SFD program, it shows the headcount poverty rate in villages with, and without, an SFD intervention. For instance, the poverty rate in villages and wards with SFD road projects was 21.8%, compared to 19.5% in areas without an SFD road project, a difference that is statistically significant (t=-5.03); the national poverty rate in 2004-05 was 19.6%. The numbers confirm that SFD education and wastewater treatment projects were not well targeted to villages with higher poverty rates, while road, health, and (especially) potable water projects were more successfully targeted to the poor. The poverty rate in villages and wards that received any SFD intervention was 21.6%, which is significantly higher than the 18.6% poverty rate in areas with no SFD project.

The numbers in Table 18 allow us to check the robustness of these findings. Let us suppose that there are six basic needs, as follows:

- (a) Access to drinking water on the property;

- (b) Having a toilet other than a simple pit latrine;

- (c) Primary-age children are enrolled in school;

- (d) The household head has at least three years of education and is employed; or at least one in every three household members is employed;

- (e) There are no more than three persons per room (excluding bathrooms); and

> 4 Public spending is “regressive” if it represents a smaller percentage of household income (or spending) for the poor than for the rich; this would be reflected in a concentration curve below the Lorenz curve, which is not the case even for SFD spending on education or waste water treatment.

_Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 32 of 36_
# I. **Overview**

A casual tourist can confirm that technologies often make their way from the developed world to the less developed countries (LDCs). However, despite the critical importance of technology diffusion for economic development, the evidence on many aspects of this process remains sketchy: What mechanisms most frequently transmit foreign technologies to LDC firms? Do these foreign technologies affect both productive efficiency and product quality in the recipient firms? Under what circumstances do firms pursue activities that give them access to foreign knowledge? This paper develops a new methodology for addressing these issues and applies the framework to plant-level panel data from Colombia, Mexico and Morocco.

# A. **The existing literature**

Our limited understanding of international knowledge diffusion does not derive from neglect of the topic. In the empirical economics literature alone, at least three different methodological approaches have been deployed. First, at the very micro level, a number of analysts have used case studies and qualitative surveys to generate descriptions of learning processes at individual firms (e.g., Hobday, 1995; Lall, 1987; Katz, 1987; Pack, 1987; Rhee, Ross-Larson and Pursell, 1984). As Pack (1999) observes, this literature provides invaluable details concerning firms' efforts to absorb technology. But it has to little to say quantitatively about the results of these efforts in terms of productive efficiency or product quality. There are some exceptions (e.g., Pack, 1987), but they are too few and based on such small samples that they provide little basis for generalization.

2
# **1 Introduction**

Will continuation of economic development increase pressure on the earth’s finite resources, or does the increase in income provide the basis for environmental improvement? This question asked by (Grossman and Krueger, 1995) and highlighted earlier in (World Bank, 1992) is critical to the design of sustainable development strategies but continues to remain at the center of debate. Evidence from past development suggests that increases in wealth and income occur simultaneously with a structural transformation process in which the composition of inputs and methods of production used in the economy gradually shift in favor of less destructive production. We revisit the empirical issue using panel data on environmental output and economic development for a large set of countries using a flexible kernel model that allows dependencies to vary smoothly throughout the data.

Interactions between the economic and natural systems are dominated by complex pathways, and the processes that produce the levels of environmental output are high dimensional. This is because environmental output is determined not only by the efficiency of production, which may improve nonlinearly across GDP, but also by the total production size, which varies across panels of countries (Stern et al., 1996; Stern, 2004). If the scale of the economy is large, minute changes in the efficiency of production can result in large differences in output levels. Therefore, if a panel is constructed that includes economies of widely different scales, the variance in environmental output levels can be expected to vary with the GDP levels of the countries. To cope with this, one should acknowledge in the model design that environmental output is de facto a result of both a _scale_ component, and a _technology_ component.

Many empirical approaches have tried to model degradation levels directly, not distinguishing between the role of _scale_ and a _technology_ separately, and therefore assume an unrealistic degree of homogeneity of the samples. Vollebergh et al. (2005) pay particular focus to homogeneity assumptions in their environmental output regressions, and conclude that correctly modeling heterogeneity is essential to prevent spurious correlation in reduced-form panel estimations. Other issues relate simply to inappropriately dealing with the serial dependence that results from misspecification and may bias inference. This has partly been addressed by adding control variables as in studies reviewed by (Stern, 1998), or by deploying fixed-effect approaches Stern (2004). Time series approaches are also widespread, for example (Perman and Stern, 2003; Stern, 2004) that claim that the error correction approach provides appropriate diagnostic statistics and specification tests 1
does not necessarily matter for infliction of violence. For instance, among all reported instances of violence by sibling or sibling-in-law we find that the perpetrator was not residing with the victim in the same household. Overall, the evidence is noteworthy and suggests that the 4P transfer helped in mitigating exposure to specific types and forms of violence rather than having an overarching impact on GBV exposure.

Further, we find a significant increase in help-seeking behavior among beneficiary women who faced any form of emotional violence compared to non-beneficiary women (refer to Table 7). These results provide pertinent evidence on how CCTs could be leveraged as an instrument not only to mitigate exposure to violence within households, but also encourage help-seeking and reporting behavior among survivors. We discuss potential mechanisms underlying these effects in Section 7. Additionally, estimates on the effect of CCT on individual acts of violence (i.e., slap, push, etc.) has been provided in Tables A1-A8 in the Appendix.

_Table 3: Effect of CCT on Incidence of Intimate Partner Violence (IPV)_

||(1) <br>Incidence of|(2)<br> <br>Incidence of sexual|(3)<br>Incidence of|(4)<br>Incidence of|
|---|---|---|---|---|
||physical IPV|IPV|emotional IPV|economic IPV|
|RD Effect|0.041|0.005|-0.077|0.014|
||(0.57)|(0.75)|(0.22)|(0.86)|
|FDR q-values|0.98|0.98|0.87|0.98|
|Observations|563|563|563|563|
|Control mean|0.175|0.050|0.186|0.211|
|P-values in paren|theses: * p<0.1 ** p|<0.05 *** p<0.01|||

Note: All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all IPV regressions in Tables 3 and 4.

_Table 4: Effect of CCT on Frequency of Exposure to Intimate Partner Violence (IPV)_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Frequency of|Frequency of|Frequency of|Frequency of|
||physical IPV|sexual IPV|emotional IPV|economic IPV|
|RD Effect|-0.002|0.019|-0.017|-0.052|
||(0.98)|(0.18)|(0.75)|(0.50)|
|FDR q-values|0.98|0.87|0.98|0.98|
|Observations|563|563|563|563|
|Control mean|0.075|0.025|0.125|0.164|
|P-values in parent|heses: * p<0.1 ** p<|0.05 *** p<0.01|||

_Note:_ All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all IPV regressions in Tables 3 and 4.

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90

**Figure 26: Retail gasoline and diesel prices in the United Arab Emirates**

<!-- Start of picture text --> 3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>Jul‐15 Aug‐15 Sep‐15 Oct‐15 Nov‐15 Dec‐15 Jan‐16<br>98 RON gasoline 95 RON gasoline 91 RON gasoline<br>Diesel, Abu Dhabi Diesel, Northern Emirates<br>Dh per liter<br><!-- End of picture text --> _Source_ : https://www.moenr.gov.ae/en/knowledge‐center/petrol‐prices.aspx. _Notes_ : US$1 = Dh 3.67. The price of diesel is nationally uniform under the new pricing policy.

# **República Bolivariana de Venezuela**

República Bolivariana de Venezuela last increased retail gasoline prices in July 1997 and the retail diesel price in April 1996. The gasoline prices remained the same from 1992 to September 1995, when the price of premium gasoline was tripled. The price of diesel fuel was frozen between 1992 and 1996. In real terms, fuel prices declined by 80 percent during the intervening period. The price increases of April 1996 were substantial: 330 percent for premium gasoline, 820 percent for regular gasoline, and 890 percent for diesel (IMF 1998). Gasoline prices were raised by 20 to 33 percent, depending on the grade, in July 1997. Fuel prices have remained frozen since, making prices in Venezuela the lowest in the world.

Large‐scale smuggling to Colombia, where prices are much higher, is an inevitable consequence of the governments’ fuel pricing policy. Upwards of 100 tankers’ worth of fuel is estimated to be entering Colombia illegally every day. According to the government, the smuggling has cost the Venezuelan government and its national oil company an additional US$8 billion a year. The government has introduced fuel rationing in the states bordering Colombia, and closure of the 2,200‐km border every night between 10 pm and 5 am, which was expanded to include air and sea transit (BMI 2015a).

The government in July 2015 increased fuel prices along the Colombian border to market prices to stem smuggling. However, even “market” prices are low when converted to other currencies at black‐market exchange rates. In August 2015, the government declared a state of emergency after three soldiers were injured in a clash with smugglers and closed the border indefinitely. A month after the first closure, the government stated that the border could remain closed for another six months (Colombia Reports 2015).
development practitioners have been faced with an acute lack of data and evidence on how to design and implement support policies. This lack of evidence is especially acute in developing countries. This paper addresses this gap by providing novel evidence using recently collected firm-level data covering more than 120,000 firms across 60 (mostly developing) countries.

The paper’s results show that there are significant gaps to be addressed in order to improve the reach, targeting, and effectiveness of policy support. Smaller firms, especially those owned by women in sectors such hospitality, are facing some of the largest declines in sales _and_ the most limited access to policy support – raising concerns about widening inequality. Similarly, the likelihood of receiving support for firms in poor countries is several times less than for similar firms in highincome countries. While governments appear to have prioritized minimizing exclusion concerns over strict targeting in the earlier stages of the pandemic, this has resulted in a large number of firms benefiting from public assistance without having experienced any adverse COVID-19 shock – an issue that will demand more attention as fiscal space becomes more constrained. Lastly, there is indicative evidence that some types of policies (i.e. liquidity injections direct or through credit and wage subsidies) have been successful in mitigating liquidity constraints and reducing layoffs – but much more rigorous analysis will be needed to provide more precise guidance to policy makers.

Going forward, we see four main avenues for future research. First, understanding better how firms manage to receive public support and the extent to which connections may explain access to public resources. Second, our results so far present some novel associations but we limited ourselves to mainly presenting conditional correlations. Future research, relying on additional data collection and stronger identification strategies, should address more carefully the question of the effect of receiving public support on subsequent firm results. Third, some of the policies being implemented may have important spillover effects, especially when targeted firms play an important role in supply chains and production, which will be an important area for future work to identify the systemic effects of the policies being enacted. Finally, going forward it will be important to address the issue of policy misallocation and the risks that policies being enacted may inhibit the prospects for recovery and future growth because of insufficient or incorrect targeting.

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