rafmacalaba/datause-encoder-data huggingface dataset:
an exogenous government spending shock using a news variable that captures episodes that led to large military buildups in the U.S..<sup>8</sup> More recently, Owyang et al. (2013) use the same approach to estimate spending multipliers contingent on the state of the economy in Canada and in the U.S.. However, this approach is limited by the number of observations of the news variable for countries such as Canada, which rarely participate in large military operations. Romer and Romer (2010) use narrative records, such as Congressional reports and presidential speeches, to identify the main motivations for all major post-war tax policy changes in the U.S.. Although this approach is more appropriate for identifying exogenous changes in tax revenue, its implementation remains in practice more cumbersome.

Another common technique is the recursive approach. It consists of imposing the condition that government spending or tax revenue is not responsive to business cycle fluctuations for at least one quarter (this approach is used by Blanchard and Perotti (2002), Perotti (2004), Monacelli and Perotti (2010), Auerbach and Gorodnichenko (2012)). The logic behind this assumption is that fiscal variables need more than one quarter to adjust in response to unexpected changes in GDP. However, this assumption is often criticized because it imposes a systematic non response of fiscal variables to a business cycle shock.

# **2.2 The sign restrictions method**

I extend the methodology employed by Mountford and Uhlig (2009) in the case of a small open economy by assuming a block exogeneity in the VAR model. Sign restrictions are imposed on the IRFs of a set of variables to identify structural shocks. The advantage of this approach is that it does not assume a lack of contemporaneous effects of unexpected changes in GDP on fiscal variables. This strategy is also used to identify exogenous technology shocks (see, for example, Dedola and Neri (2007), Enders et al. (2011)) and monetary policy shocks (see Uhlig (2005), Mallick and Rafiq (2008)).

## **2.2.1 The sign restrictions assumptions**

Table 1 summarizes the minimal assumptions imposed to identify structural shocks. Following Mountford and Uhlig (2009), a _generic business cycle shock_ is defined as a shock that increases Canadian GDP, private consumption, private investment and net tax revenues for four quarters following the shock. Once the business cycle is identified, each fiscal policy shock is identified as following: a _net tax revenue shock_ is a shock that increases only the net tax revenue for four quarters following the shock and is orthogonal to the business cycle shock, while a _government spending shock_ is a shock that only increases government spending for four quarters following the shock and is orthogonal to the business cycle shock. Since I assume that Canada is a small open economy, U.S. GDP and oil prices do not respond to the shocks that hit the Canadian economy. The signs of

> 8These episodes correspond to the Korean war, the Vietnam war, the Carter-Reagan fiscal expansion and, more recently, the aftermath of the Sept. 11, 2001 terrorist attacks (see Ramey (2011)).

4
introduced. Our recommendation is to continue the attempts to collect panel data in developing countries, but to do so in a separate survey.

To be really useful, panel data collection would have to be extended over longer periods than two years, although this will of course raise difficulties and costs involved in finding the same households over subsequent years. If a country undertakes an integrated survey every four to five years and a lighter monitoring survey in the intervening years, a small survey panel could be conducted in parallel. Analytically it would be helpful if the monitoring sample and the panel sample are drawn from the same master sample, because this would allow panel results to be corrected for any loss of representativeness resulting from non-random attrition.

17
3D PRINTING AND TRADE 12 _Table 2: Effects of 3D printing on RCA in hearing aids_

|Sector control|(1)<br>HS Ch. 90 or<br>High-tech|(2)<br>HS Ch. 90|(3)<br>High-tech|(4)<br>HS Ch. 90 and<br>High-tech|(5)<br>HS Ch. 90 or<br>High-tech|(6)<br>HS Ch. 90|(7)<br>High-tech|(8)<br>HS Ch. 90 and<br>High-tech|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|log(exports)|log(exports)|log(exports)|log(exports)|log(exports)|log(exports)|log(exports)|log(exports)|
|(Hearing Aid 2007-)|0.562***|0.532***|0.545***|0.442***|||||
||(0.171)|(0.170)|(0.172)|(0.170)|||||
|(Hearing Aid 2007-)*(Dummy RCA 1995-2000 > 1)|0.230|0.225|0.213|0.167|||||
||(0.227)|(0.233)|(0.235)|(0.265)|||||
|(Hearing Aid 2007-)*(Dummy Low RCA 1995-2000 < 1)|||||0.551*|0.476*|0.544*|0.377|
||||||(0.289)|(0.283)|(0.291)|(0.277)|
|(Hearing Aid 2007-)*(Dummy High RCA 1995-2000 < 1)|||||0.566***|0.551***|0.545***|0.464**|
||||||(0.207)|(0.206)|(0.208)|(0.208)|
|(Hearing Aid 2007-)*(Dummy Low RCA 1995-2000 > 1)|||||0.981***|0.964***|0.952***|0.851***|
||||||(0.149)|(0.150)|(0.161)|(0.190)|
|(Hearing Aid 2007-)*(Dummy High RCA 1995-2000 > 1)|||||0.508***|0.448***|0.467***|0.248|
||||||(0.153)|(0.168)|(0.180)|(0.263)|
|Observations|476,248|234,932|360,007|118,678|476,248|234,932|360,007|118,678|
|R-squared|0.847|0.859|0.844|0.862|0.847|0.859|0.844|0.862|
|Sum of coefficients|.792***|.757***|.758***|.609***|2.606***|2.439***|2.508***|1.939***|
|Period|1995-2016|1995-2016|1995-2016|1995-2016|1995-2016|1995-2016|1995-2016|1995-2016|
|Country-Year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Country-Product FE|YES|YES|YES|YES|YES|YES|YES|YES|
|% Zeroes|.44|.4|.44|.37|.44|.4|.44|.37|

Note: Robust standard errors, clustered at the country-product level, are in parentheses. Countries with population over 5mln. *** p<0.01, ** p<0.05, * p<0.1
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**Table S9:** Attrition Analysis for Primary Round 4 Outcomes (Baseline Dropouts)

||Dependent|Variable: =1|Lr<br>if Conditiona|Ur<br>l Schoolgirl|
|---|---|---|---|---|
|_Outcomes_|Original|IPW|owe<br>Bound|ppe<br>Bound|
||(1)|(2)|(3)|(4)|
|Highest Grade Completed|0.621***<br>(0.125)|0.606***<br>(0.128)|0.616***<br>(0.125)|0.632***<br>(0.125)|
|Competencies Score (Standardized)|0.064|0.065|0.061|0.089|
||(0.057)|(0.057)|(0.057)|(0.056)|
|=1 if Ever Married|-0.107***|-0.108***|-0.112***|-0.106***|
||(0.032)|(0.033)|(0.032)|(0.032)|
|Age at First Marriage|0.431***<br>(0.155)|0.434***<br>(0.154)|0.418***<br>(0.156)|0.471***<br>(0.153)|
|=1 if Ever Pregnant|-0.040*|-0.041**|-0.044**|-0.040*|
||(0.021)|(0.021)|(0.021)|(0.021)|
|Number of Live Births|-0.147***|-0.145***|-0.153***|-0.127**|
||(0.054)|(0.055)|(0.053)|(0.052)|
|Age at First Birth|0.272*|0.328**|0.251|0.329**|
||(0.164)|(0.163)|(0.165)|(0.163)|
|= if HIV Positive|0.012|0.007|0.011|0.019|
||(0.026)|(0.026)|(0.026)|(0.026)|
|=1 if Anemic|0.039|0.041|0.038|0.045|
||(0.035)|(0.035)|(0.035)|(0.035)|
|Opportunity Cost of Time|-0.037|-0.041|-0.038|0.065|
||(0.079)|(0.079)|(0.079)|(0.052)|
|Typical Daily Wage in Last Three|-0.140**|-0.132*|-0.141**|-0.074|
|Months|(0.068)|(0.069)|(0.068)|(0.055)|
|Proportion of Hours Spent in Self-|-0.011|-0.012|-0.011|-0.007|
|Employment or Paid Work in Past|(0.009)|(0.009)|(0.009)|(0.008)|
|Super Index of Overall|-0.083|-0.073|-0.097|-0.065|
|Empowerment (Standardized)|(0.074)|(0.075)|(0.074)|(0.072)|
|Change in Subjective Wellbeing|-0.032|-0.050|-0.091|0.048|
|from Five Years Ago to Today<br>_Notes:_Regressions are OLS models wi<br>regressions are weighted to make them<br>EAs. Baseline values of the following va<br>analyses: age indicators, stratum indicat<br>indicator for never had sex, and whether<br>development of the testing instruments.<br>surveyed during the latest household sur<br>Parameter estimates statistically differe<br>confidence.|(0.232)<br>th robust stan<br>representative<br>riables are in<br>ors, househol<br>the responde<br>We restrict t<br>vey conducte<br>nt than zero a|(0.235)<br>dard errors cl<br>of the target<br>cluded as con<br>d asset index,<br>nt participate<br>he sample to r<br>d two years af<br>t 99% (***),|(0.232)<br>ustered at the<br>population in<br>trols in the re<br>highest grade<br>d in the pilot p<br>espondents w<br>ter the progra<br>95% (**), an|(0.226)<br>EA level. All<br>the study<br>gression<br>attended, an<br>hase of the<br>ho were<br>m (Round 4).<br>d 90% (*)|

89
for tailored policy initiatives targeting specic subpopulations. Male-dominated migration pushes females to give up wage-employment and increase labor supply in their own farms. Labor markets in rural Nepal tend to be incomplete and not fully integrated due to information asymmetry, lack of mobility, and lack of strong institutional implementations. Policy initiatives should be focused on these aspects to improve the rural wage labor markets thus allowing households to hire workers to replace those who migrate.

The rest of the paper is organized as follows. Section 2 provides background of out-bound migration from Nepal, a brief scenario of the current labor market in Nepal, and the motivation for the paper. Section 3 describes the data set used for the analysis, while the empirical strategy and identication are discussed in section 4. The ndings of the analysis are presented in section 5 and section 6 concludes the paper.

# 2 Background and Motivation

## 2.1 Emigration from and Remittances to Nepal

With 25.2% of its population earning less than US$ 1.25 per day (World Bank, 2014b), Nepal is one of the least-economically-developed nations in the world. However, with recent international labor treaties, Nepal has been experiencing large outows of migrants and hence, remittance inows from abroad. Figure (1) presents the historical international migration trend from Nepal. Close to 2 million Nepalese, 7.3% of the population, were living abroad during the census in 2011. This is a substantial increase compared to earlier decades. Only 3.2% (0.76 million), and 3.4% (0.66 million) of the population was living abroad in 2001 and 1991 respectively. The rise in numbers of Nepalese living abroad in the last decade is mainly due to low skilled employment related migration. The Foreign Employment Act of 2007 - which was designed to provide security, protect the welfare of migrants, provide migrants with education and training before leaving the country, and monitor the businesses that facilitate migration processes - along with the bilateral labor treaties that Nepal signed<sup>2</sup> have facilitated the migration process. The end of the Maoist insurgency, during which mobility within the country was severely restrained and government oces were destroyed, making

> 2Nepal signed major international labor treaties in last decade, with Qatar (2005), the United Arab Emirates and the Republic of Korea (2007), Bahrain (2008), Japan (2009), and Malaysia (started process in 2007), with the aim to protect migrants and facilitate the migration process.

4
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

||**V. Sector Performance**|||
|---|---|---|---|
|**Focus area**|**Indicator**|**Data Source (e.g.**<br>**UK)**|**Source/reference**<br>**(example)**|
|_? Technical and_<br>_physical performance_|**? Asset utilisation: system load factor**<br>**(electricity generation MWh / average**<br>**capacity MWx8760 as a percentage)**<br>? Labour productivity (net electricity<br>generation per employee MWh)<br>**? Plant availability factor (Load factor by**<br>**plant, i.e. electricity generation MWh /**<br>**average capacity MWx8760 ratio)**<br>**? T system losses - technical and non-**<br>**technical losses (MWh, %)**<br>**? D system losses – technical and non-**<br>**technical losses (MWh, %)**<br>? Electricity consumption per capita (KWh)<br>? Generation capacity per capita (MW)<br>? Reserve margin|Regulator’s<br>publications<br>Department of Trade<br>and Industry (DTI)<br>Questionnaire|Zhang (2002)<br>Steiner (2001)|
|_? Sector investments_|**? Domestic investments in G, T, D –**<br>**greenfield projects, privatised assets**<br>**(amounts)**<br>**? Foreign investments in G, T, D –**<br>**greenfield and privatised assets (amounts)**<br>**? Government investments in G, T, D**<br>**(amounts)**<br>? Foreign investments as % of total foreign<br>direct investments (FDI)|||
|_? Quality of service_|**? Reliability of service (number of**<br>**interruptions)**<br>**? Security of service (number of minutes lost**<br>**per customer)**|||
|_? Non-payments_|? Non-payment and foregone revenues<br>(amount, as % of total electricity delivered, as %<br>of total revenues|||
|_? System expansion_|? Generation capacity growth (MW, as % of<br>total)|||
|_? Prices_|**? End-user prices - residential, industrial,**<br>**commercial sectors with and without taxes**<br>**(price per KWh)**<br>**? Ratio of industrial to residential prices**|||

23
_Journal of Applied Econometrics 24_ , 209–44.

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Jayaratne, J., and P. E. Strahan. 1996. “The Finance-Growth Nexus: Evidence from 48
elicited priors take place. The data here are not very informative about the impact of the treatment on these outcomes, and so the posteriors place much more weight on the priors than the data. Two exceptions are the case of the literature priors, where our prior based on the literature has a wide distribution, reflecting the difficulty in estimating impacts on these outcomes; and the academic priors for impacts on export labor productivity, which are also quite wide. In these cases, a bit more updating takes place, since the priors are more diffuse.

Our overall index of export performance takes the average of standardized z-scores of all these different export outcomes, and provides a summary measure of the impact of the program on exporting. Panel H of Table 3 shows the overall impact is a statistically insignificant -0.012 standard deviations in 2019, and a statistically significant -0.112 standard deviation effect in 2020. That is, taken together, the results show the program actually reduced exporting. We thought that it might be hard for firms to understand impacts on a summary index in terms of standard deviations, so did not elicit priors from firms for this aggregate outcome. Figure 8 shows the Bayesian results using the literature, policymaker and academic priors. We see substantial updating of the priors towards the data, with the posterior intervals centering around zero in 2019 and around small negative impacts in 2020.

These findings of a reduction in exporting are consistent with the responses from directly asking firms about their export strategies in our follow-up survey. We asked firms whether they had attempted to export to a new destination, even if not yet successful, and the control group (56.6%) was more likely to have attempted exporting than the treatment group (48.3%). We also asked how the company’s focus on exports had changed in the pat 12 months, and 23.6% of the treatment group said they had become less focused on exports, compared to 18.1% of the control group. Treated firms are also more likely to now say their main growth focus is on the domestic market (50.6%) compared to control firms (43.4%). We analyse these and several other secondary outcomes and examine robustness to dropping particularly outlying firm strata in appendix E.

# **5.4 Bayesian Decision Analysis**

The Bayesian approach offers several additional possibilities for analysis to directly inform policy decisions. In this section we will consider and execute two of these options. First, we compute the probability that the average effects of the intervention pass a minimal costbenefit threshold, given the evidence. Second, as decisions may be based on more than just average effects, we derive and estimate the conditional distribution of individual treatment effects using additional priors on the correlation between firms’ potential outcomes, an analytic approach set out by Imbens and Rubin (2015).

24
To document more systematically the evidence of a middle-income trap for MENA relative to EAP, we use the non-parametric local-linear regression technique that give the mean and standard errors of the estimated growth rate of each region at each level of income:<sup>7</sup> estimated growth rate of each region at each level of income: <u>yi</u> i U = , f ~~�~~ U ∆log (yy) ,ii+10 −∆log (yy) ii,ii+10 ff yy ii ii�+ εεii where i and U are overlapping annualized decadal growth in GDP per capita (or TFP) of country ∆log (yy) ,ii+10 and of the U.S. between time ∆log (yy) ii,ii+10 and time , <u>yUi</u> is the country’s relative income per capita relative to the U.S. at time ii . We use GDP per capita derived from output-side real tt tt+ 10 yy ii GDP at chained PPPs and total factor productivity (TFP), both from Penn World Table 9.0. The tt regressions also include overlapping decade fixed-effect, f , to control for common global shocks. ii Note that we are agnostic about the form of the function <u>yi</u> , f . Unlike linear regression, a ~~�~~ U � <mark>nonparametric regression is agnostic about the functional form between the outcome and the explanatory</mark> ff yy ii ii <mark>variables and is therefore not subject to misspecification error. In our context, a non-parametric regression could capture sharp changes in growth rates as relative income rises, a key advantage for us to identify an income trap.</mark> For each region, the non-parametric regressions (with 100 bootstrap replications) help provide the average predicted values and confidence intervals of annualized decadal growth in GDP per capita at different levels of relative income. Average predicted relative growth in GDP per capita relative to the U.S. (and its 95% confidence interval) for MENA and EAP are visually shown in Panel A of Figure 3, while those of absolute growth in GDP per capita are shown in Panel B<sup>8</sup> . Their numerical values are reported in Appendix Table A1. The results for other regions are also reported in Appendix Figure A2, although not discussed in the text.

For EAP countries, both average relative and absolute growth in GDP do not significantly decline until the countries reach 60 percent of U.S. GDP per capita. At 50 percent or below, EAP economies maintain a stable growth rate at 4 to 4.5 percent (Panel B), or 2 to 2.5 percent higher than the U.S. (Panel A) indicating that these countries are catching up. In contrast, the growth performance of MENA countries is much weaker. Although starting at the same level of growth as EAP, around 4 percent, growth for MENA

> 7 The STATA command is _npregress kernel y x1 x2_ , where y is the dependent variable, and x1 and x2 are the explanatory variables. See Fan and Gijbels (1996) for a reference on local-linear regressions.

> 8 Note that we restrict the estimation at below 100 percent of U.S. income because we focus on the middle-income level. In addition, at above 100 percent of U.S. income, there are fewer observations making the estimations imprecise.

5
10 1986 and then rise again until 1988. Like the inequality measures, they fluctuate without a trend between 1989 and 1993, and then begin a sustained decline which lasts for the next eleven years.

**Figure 2: Poverty indices over time in Brazil using the Administrative Poverty Line, 1981-2004**

<!-- Start of picture text --> 0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1992 1993 1995 1996 1997 1998 1999 2001 2002 2003 2004<br>Years<br>Headcount Poverty Gap FGT(2)<br>FGT(a)<br><!-- End of picture text --> The poverty decline in 1986, which mirrors the enormous increase in mean and median incomes reported for that year in Table 1, deserves a word of explanation. These are the actual numbers from the PNAD survey, and they do not reflect any change in the questionnaire, reference period or survey design on that year. Despite considerable scrutiny from various authors, similar figures have been widely reported in the literature on Brazil, including Amadeo and Camargo (1997); Barros, Henriques and Mendonça (2000); and Ferreira and Litchfield (2000). The general view seems to be that this rise in mean incomes, and the corresponding decline in poverty, reflect the expansionary nature of the 1986 _Cruzado_ stabilization plan. GDP grew by 7.5%, and the FIESP industrial real wages by some 20% during that year, suggesting that _part_ of the increase in the survey mean and the decline in poverty are likely to have been real.

Nevertheless, the increase in the PNAD survey mean between 1985 and 1986 is of 46%, which is clearly inconsistent with the national accounts growth rate of 7.5%. Clearly, the _magnitudes_ of