rafmacalaba/datause-encoder-data huggingface dataset:
Given our use of US import trade data, we also exclude exports to North America (Canada, Mexico and the United States) for the construction of the instrument.

# **_ii. Instrument relevance_**

The first stage results in Table A3 in the Appendix suggest that our instrument, the diffusion of robotics among pre-existing foreign customers is a strong predictor for robot adoption of the home country. The first stage results of our instrumental variable, used throughout the paper, is reported in model 1. The size of the first-stage F statistic (115.8) demonstrates our instrument has strong predictive power for robot adoption in the home country, despite the inclusion of a range of restrictive country-product and country-year fixed effects. A 10% increase in robot use in export customers leads to a 7% increase in robot use amongst their foreign suppliers.

In models 2 to 6, we investigate alternative identification strategies that we not do not employ in the rest of the paper. In model 2, we calculate an alternative instrument, using robot diffusion in (initial) _developed_ country export partners. Trade connections to advanced markets may in principle have greater potential for technology spillovers, because of the wider variety or more sophisticated technologies used there. However, we do not find any additional predictive power of developed country export partners, over export partners more generally, and including this additional instrument halves the first stage F statistic.

In models 3 and 4, we find that our instrument works well as a predictor of robot adoption in both developed and developing countries. The instrument also predictors robot diffusion if we restrict the sample to developing (Model 3) and developed economies (Model 4) however the strength of the instruments are lower than in the pooled sample, reflected in the F statistics of 27.2 and 44.7 respectively. For either sub-sample we do not find any additional predictive power of developed export partners, over export partners more generally. Note we do not split the sample in our main regressions, but rather pool both country income groups, to maximize the strength of the instrument.

In models 5 and 6, we include alternative instruments reflecting robot diffusion in (initial) _import_ partners. As noted earlier, the FDI literature reveals extensive evidence of knowledge spillovers from multinational customers backwards to their suppliers, however there is far less evidence of the reverse. Our results mirror the findings of this literature. We do not find any evidence that 21
properties of the data would substantially complicate the analysis. Therefore I do not account for jointness. The specific regressions that are estimated in this paper are: where sg denotes the time allocated to school (over the past week) by child i of gender g at time _t. hg_ is the time spent in housework. mg _*_ is the time spent in child labor activities. _Q_<sup>_2_</sup> _,_ denotes health and sickness variables in householdj in which the individual i resides (these are discussed in detail below). **_Ok,_** captures cluster level economic shocks such as unemployment (also see below for details). X1,,Y , and **_Zk,_** are child (i), household (j) and community (k) level characteristics respectively.

A large proportion of boys and girls allocate time to housework and so there is little need to worry about the corners for that variable. However there are a number of problems specific to the schooling and labor variables in the Peru LSMS data. First, very few children were observed working for wages in the week prior to the survey (1.2% of the girls and 5.8% of the boys). This leaves one with too few non-zero observations to conduct econometric estimation on time allocated to wage work.)<sup>5</sup> A much larger proportion engages in work in the family enterprise19% of girls and 22% of boys. Thus I interpret _time allocated to self-employment only_ as child labor. Nevertheless, any estimation of time allocated to self-employment is still likely to suffer from the large number of zeros. I therefore conduct an estimation of the determinants of the _decision_ to work in self-employment. This allows me to use the zero observations of the

**_'5_** There are only about 80 boys and girls who report non-zero hours in wage work. Note that this is partly a function of the fact that I am limited to panel households common in the 1994 and 1997 data sets.

7
# [Figure 4 about here]

Figure 5 shows that more than half of all estimates focus on the interest rate pass-through to corporate lending rates (541 estimates). The greater focus on corporate lending rates may originate from greater allocations of bank loan books to this market segment.<sup>3</sup> If we compare the amount of loans outstanding in individual segments across countries, we find that corporate loans usually occupy a leading position among credit segments, followed by mortgage and consumer loans. Researchers also focus on the pass-through to the average lending rate across all categories. Other lending rates include rates on loans such as business, debt consolidation, and education.

# [Figure 5 about here]

As for the estimation methodology, the typical paper estimates a single-equation model in an error correction form (ECM) for a single country using aggregate data in monthly frequency (see figure 6). Specifically, the approach for estimating the interest rate pass-through is primarily based on a singleequation model (948 estimates). Simple reduced-form models are thus most popular for examining the relationship between the reference rate and bank lending rates. These models are mostly based on the error correction form (790 estimates) using estimation methodologies such as autoregressive distributed lag (ARDL), mean group (MG), and pooled mean group (PMG) estimators.

As for the data, the studies use mostly aggregate (macro level) data for the whole banking system in a single country. More recent studies have been increasingly using micro (bank level) data for better identification. For instance, Holmes et al. (2015) examine the interest rate pass-through in Colombia using data for 15 banking institutions. Holton and d’Acri (2015) analyze the pass-through for 188 Euro Area banks, and Havránek et al. (2016) test the pass-through based on data from 52 Czech banks. Typically, the studies use data for a single country (967 estimates). Only a few studies estimate the passthrough using cross-country panel data (Bernhofer and van Treeck, 2013; Leroy and Lucotte, 2015). One reason may be that the economic, social, and political environment is specific to each country and researchers try to avoid this source of heterogeneity. To increase power of the estimates and help

> 3 The ECB Statistics Bulletin: <u>http://sdw.ecb.europa.eu/servlet/desis?node=1000004045.</u>

10
and technical journal articles. In the “weighted” data these three variables are scaled by population, while the remaining nine variables are exactly the same as in the “unweighted” data. The scaling of these three variables does influence the optimal number of principal components required to rationalize the data. For brevity, we refer the reader to Chen and Gawande (2006) for details such as the factor loading matrices for the “weighted” data. The methods for estimating those matrices and then using them to estimate the factor scores are exactly the same as described in Section 3.

The main differences between the two data sets are that in the “weighted” with the ML method the optimal number of factors (according to the Bayes information criterion) is three, one less than for the “unweighted” data, while in PC analysis the optimal number of components is seven, one more than for the “unweighted” data. In the graphical analysis of the factor scores and rankings below, we differentiate the findings from “unweighted” and “weighted” data sets.

# **5. Analysis of the Factor Output**

The main objective of the factor score computations is to use them to describe how countries rank on the basis of these factors, and how those rankings have changed over the two periods. The authors’ working paper contains a more complete analysis for 20 underdeveloped, developing, emerging, oil-rich and industrialized economies. Here we discuss these results for countries.

Figure 1 for Albania has four panels in it. The panel on the top left depicts Albania’s rank vis-a-vis the other 120 countries in the sample on each of the six principal components. The spider chart on the top right depicts Albania’s rank on the four ML factors. The bottom row panel contains the weighted data counterparts to the top row. There are seven principal 30
# **Sources of Volatility in Small Economies**<sup>*****</sup>

Viktoria Hnatkovska<sup>†</sup> and Friederike Koehler-Geib<sup>‡</sup> Keywords: small states, volatility, business cycles, VAR, impulse responses, variance decompositions JEL Classification: E30, O11, O54

* This paper benefitted from useful comments from Carlos Vegh, Edgardo Favaro, Cesar Calderon, Norbert Fiess, Klaus Schmidt-Hebbel, Raimundo Soto, Emilia Skrok, Jan Gaska, Elena Ianchovichina, and participants at the Authors’ Workshop for the World Bank’s Regional Study for Latin America and the Caribbean on Fiscal Rules. The authors acknowledge financial support from the Office of the Regional Chief Economist for Latin America and the Caribbean.

> † University of British Columbia, 253-6000 Iona Dr., Vancouver, BC V6T1L4, Canada, Viktoriya.Hnatkovska@ubc.ca.

‡ Lead Economist and Program Leader, Central America Department, The World Bank, 1818 H Street Washington, DC 20433 USA; fkoehler@worldbank.org.
Table 7 summarizes the impact of the Uruguay Round on real GDP and exports for the APEC economies. The experiment is carried out in stages: first, the tariff and NTB cuts, and second the (additional) impact of subsidy reduction and MFA removal. Looking first at the tariff effects (columns 1-2), all economies gain from the Uruguay Round, with GDP increments ranging from .01 percent in the US to 1.33 percent for Korea & Taiwan. The pattern and magnitude of tariff cuts has a substantial impact on how much each country can expect to benefit from post-URA trading opportunities. Economies that have liberalized more are in a position to reap greater efficiency gains from reallocation of domestic resources than those that have acted less boldly. Among the developing economies, Indonesia, China, and the Philippines commited to relatively modest tariff reductions (4-8 percent) and correspondingly gain little in real GDP from the elimination of distortions. Total world exports grow by 2.1 percent, or around $48 billion. The biggest winners are again those economies that have offered up the largest tariff reductions, as the real exchange rate depreciation that results from substantial tariff cuts in Japan and Korea & Taiwan stimulates exports from these economies.

**Table 7: GDP and Export Growth from the Uruguay Round** (Percent change from base)

||URA Tari<br>Real GDP|ffs Only<br>Exports|Full URA Co<br>Real GDP|mmitments<br>Exports|Full URA & E<br>Real GDP|xternalities<br>Exports|
|---|---|---|---|---|---|---|
|Indonesia|0.08|0.07|1.60|3.14|3.40|5.92|
|Thailand|0.54|3.10|0.81|3.58|3.43|7.15|
|Philippines|0.21|0.98|1.74|3.37|2.47|2.54|
|Singapore & Malaysia|0.10|0.64|2.10|1.54|2.79|0.76|
|China|0.04|-0.05|0.61|1.81|0.97|1.61|
|Korea & Taiwan|1.33|2.43|1.45|2.97|1.78|3.42|
|Japan|0.66|5.32|0.68|5.49|1.03|5.91|
|United States|0.02|1.81|0.03|1.99|0.16|2.17|
|European Union|0.01|1.26|0.02|1.45|0.13|1.59|
|Total|-|2.14|-|2.56|-|2.82|

Notes: _Real GDP_ provides a production-based measure of economic activity. _Exports_ is the increase in total exports for each region.

When we include the subsidy reductions and MFA removal into the experiment (columns 3-4), the outcome is quite similar for the OECD economies, but much more favorable for the developing APEC economies, due to the stimulus provided to exports from elimination of MFA-related export taxes. GDP growth rises sharply in Indonesia and the Philippines to 1.6-1.7 percent, and global export expansion reaches 2.6 percent ($58 billion).

The results in columns 1-4 include no linkages or externalities from the greater openness and trade expansion that occurs as a result of the Uruguay Round implementation. This comparative static experiment does miss some potentially important dynamic feedbacks between trade and productivity which, if captured correctly, will likely increase the benefits accruing to economies committed to greater liberalization through the Uruguay Round. Columns 5-6 summarize the GDP and export implications of 21
<!-- Start of picture text --> Technological Classification of Exports<br>100%<br>50%<br>50%<br>70%<br>aox%<br>50%<br>40%<br>30%<br>20%<br>10%<br>O%<br> 222222222888 8 2<br>SHighTech OSlowlech © MedamTech © PremayProducs © Resource Based<br><!-- End of picture text -->
**Figure 5: Differences in poverty estimates calculated in this paper vs. PovcalNet estimates**

<!-- Start of picture text --> 0 20 40 60 80 0 20 40 60 80<br>$1.25 poverty headcount rate 2011 (PovcalNet, 2014) $1.25 poverty headcount rate 2011 (PovcalNet, 2014)<br>10<br>80<br>8<br>6<br>60 4<br>2<br>40 0<br>-2<br>-4<br>20<br>-6<br>-8<br>$1.25 poverty headcount rate 2011 (this paper)<br>0<br>Difference b/w headcount rates this paper vs PovcalNet -10<br><!-- End of picture text --> Imposing higher growth rates for lower percentiles means that some of them may end up with a final income above that of percentiles that were originally richer. It is then necessary to re-rank fractile groups before simulating another year of growth. The need to re-rank stems from the fact that we are not estimating the actual GIC between two re-ranked distributions of the same population over time. Instead, we impose growth in a ‘pseudo non-anonymous’ way by growing those groups _initially_ in the bottom 40% by a particular growth rate. Once the re-ranking is carried out, anonymity is restored and as a result some of the shared prosperity growth premium will have ‘leaked’ to the top 60% along with the fractile groups that were re-ranked upward. This also implies that the actual GICs we apply will look different from those plotted in the conceptual framework (Figure 3).

After the re-ranking, the same process is repeated over consecutive years until 2030, the point at which the annualized growth rate of the bottom 40% for the entire period is calculated (in an anonymous way). Precisely because of the re-ranking and the ‘leakage’ to the top 60%, this annualized growth rate is below the value of that was originally aimed at. In order to correct this we apply a slightly higher growth rate for the bottom 40% at every annual interval. We repeat this until the difference between mm the overall annualized growth rate of the bottom 40% and that of the mean equals the shared prosperity premium we report. mm Figure 6 shows the final simulated GIC for China from 2011 to 2030 under the shared prosperity premium of m = 2 % and 10 year historic growth. While the step function is clearly reflected in the shape of the GIC, a segment of the curve around the 40th percentile is sloped. This is a result of the 12
Table 4c: Summary statistics: community characteristics

|||**2011-201**|**2**||**2013-20**|**14**|
|---|---|---|---|---|---|---|
||(1)<br>Mean<br>enrolees|(2)<br>Mean non-<br>enrolees|(3)<br>Normalized<br>diferences|(4)<br>Mean<br>enrolees|(5)<br>Mean<br>enrolees|(6)<br>Normalized<br>diference|
|Type of land cover: forest|0.568|0.576|-0.012|0.533|0.555|-0.031|
|Type of land cover: rainforest|0.369|0.298|0.106|0.366|0.349|0.025|
|Type of land cover: bush/shrubs|0.063|0.126|-0.152|0.101|0.078|0.057|
|Community leader age|51.646|53.305|-0.103|51.711|51.472|0.016|
|Community leader is a man|0.961|0.954|0.026|0.965|0.968|-0.011|
|Community leader can read and write|0.927|0.967|-0.126|0.976|0.963|0.050|
|Community leader completed sec. school or above|0.252|0.384|-0.201|0.383|0.335|0.071|
|Community is connected to public electr. grid|0.898|0.887|0.024|0.944|0.908|0.097|
|# comm. members with rights to comm. land|150.456|178.026|-0.084|165.906|185.280|-0.052|
|# comm. members without rights to comm. land|209.568|168.861|0.049|298.606|202.294|0.077|
|1st/2nd livelihood: income gen. agric|0.471|0.510|-0.055|0.526|0.431|0.135|
|1st/2nd livelihood: subsistence agric|0.456|0.430|0.037|0.408|0.500|-0.131|
|1st/2nd livelihood: income gen. livestock rearing|0.471|0.523|-0.074|0.477|0.413|0.092|
|1st/2nd livelihood: labor in husbandry/forestry|0.199|0.132|0.127|0.157|0.138|0.038|
|Daily wage (pesos)|140.413|141.464|-0.015|138.868|131.367|0.116|
|PES payment per capita|6685.073|5365.926|0.118|5591.061|6030.615|-0.045|
|Obs|206|151|.|287|218|.|

> 1 The table shows means for enrolled and non-enrolled communities and normalized in means.

> 2 For control communities in the 2011-2012 cohort, payments per capita are those that would have been awarded had they been successful. For control communiites in the 2013-2014 cohort, CONAFOR did not calculate these payments, so we inputed the median payment per hectare for that state, year and modality, and multiplied it by the area they submitted.

37
<!-- Start of picture text --> 6 months 12 months 18 months<br>~ ‘S<br>* x \<br>sy SX LAY<br>N ~* \ SS \y<br>SANG ~ \ \ \<br>~ Wv ASK \As. WAY<br>o ANN \ N \\ . \ \, .<br>, \ . ‘I = ‘\ -_<br>0 2 4 6 8 10 2 4 6 8 1 0 2 4 6 8 1<br>Propensity score: Pr(Treat)<br>——— Treatment ——— Control ~~-~~~- 95%Cl<br><!-- End of picture text --> <!-- Start of picture text --> 6 months 12 months 18 months<br>/ 4<br>Lq f - vod<br>~. iit \7 / 4 /<br>>-- 14! ~-~-—-—~ 7 /<br>— ~ / --" \ / - ;<br>\ ; “——s \ fi<br>+: \oyvy \ \ /<br>V vw,<br>0 2 4 6 8 10 2 4 6 8 1 0 2 4 6 8 1<br>Propensity score: Pr(Treat)<br>——— Treatment ——— Control ~~-~~~- 95%Cl<br><!-- End of picture text --> <!-- Start of picture text --> 6 months 12 months 18 months<br>y . \ 7 \<br>eo L/I\\ “ \<br>5 Nee . \wne a vN<br>--~ ‘iL? -=--y\\ wets | i<br>——-)\ ~~ — \\r7 — \<br>Wv7 \N *\<br>\ \ \ \ \ \<br>\~ V7 \\\\ \ \ \ ‘ S\N\ ~<br>ANS - :<br>0 2 4 6 8 10 2 4 6 8 1 0 2 4 6 8 1<br>Propensity score: Pr(Treat)<br>——— Treatment ———— Control ~~-~~~- 95%Cl<br><!-- End of picture text -->

<!-- Start of picture text --> 6 months 12 months 18 months<br>\<br>= \ ~~S<br>NaN ~~. a \ AS \<br>o << =z — are — = =<br>_ \ Fiat - \ ~ ‘ \<br>Nv \ yo \ _—<br>oe \\ ~---<br>0 2 4 6 8 10 2 4 6 8 1 0 2 4 6 8 1<br>Propensity score: Pr(Treat)<br>——— Treatment ———— Control ~~-~~~- 95%Cl<br><!-- End of picture text -->
# **Appendix 5. Included studies**

- Andrew, A., O. Attanasio, R. Bernal, L.C. Sosa, S. Krutikova, and M. Rubio-Codina. 2019. _Preschool quality and child development ._ (No. w26191): National Bureau of Economic Research.

- Bailey, M.J., B.D. Timpe, and S. Sun. 2020. _Prep School for poor kids: The long-run impacts of Head Start on Human capital and economic self-sufficiency ._ (No. w28268): National Bureau of Economic Research.

- Baker, M., J. Gruber, and K. Milligan. 2008. "Universal child care, maternal labor supply, and family well-being." _Journal of Political Economy,_ 116 (4): 709-745.

- Bando, R., E. Näslund-Hadley, and P. Gertler. 2019. _Effect of inquiry and problem based pedagogy on learning: Evidence from 10 field experiments in four countries._ (No. w26280): National Bureau of Economic Research.

- Barnett, W.S., K. Jung, D.J. Yarosz, J. Thomas, A. Hornbeck, R. Stechuk, and S. Burns. 2008. "Educational effects of the Tools of the Mind curriculum: A randomized trial." _Early Childhood Research Quarterly_ 23 (3): 299-313.

- Berkes, J., A. Bouguen, D. Filmer, and T. Fukao. 2019. _Improving Preschool Provision and Encouraging Demand: Heterogeneous Impacts of a Large-Scale Program._ Washington D.C.: The World Bank Working Paper Series.

- Berlinski, S., S. Galiani, and M. Manacorda. 2008. "Giving children a better start: Preschool attendance and school-age profiles." _Journal of Public Economics_ 92 (5-6): 1416-1440.

- Berlinski, S., S. Galiani, and P. Gertler. 2009. "The effect of pre-primary education on primary school performance." _Journal of Public Economics_ 93 (1-2): 219-234.

- Bierman, K.L., C.E. Domitrovich, R.L. Nix, S.D. Gest, J.A. Welsh, M.T. Greenberg, C. Blair, K.E. Nelson, and S. Gill. 2008. "Promoting academic and social‐emotional school readiness: The Head Start REDI program." _Child Development_ 79 (6): 1802-181.

- Bitler, M.P., H.W. Hoynes, and T. Domina. 2014. _Experimental evidence on distributional effects of Head Start._ (No. w20434): National Bureau of Economic Research.

- Blair, C., and C.C. Raver. 2014. "Closing the achievement gap through modification of neurocognitive and neuroendocrine function: Results from a cluster randomized controlled trial of an innovative approach to the education of children in kindergarten." _PloS One_ 9 (11): e112393.

- Blimpo, M., P.M. Carneiro, P. Jervis, and T. Pugatch. 2019. _Improving access and quality in early childhood development programs: Experimental evidence from the Gambia._ Washington D.C.: World Bank Policy Research Working Paper, (8737).
Table 4: Estimation of general preferences

||_τ_ = 1|_τ_ = 2|ˆ_τ_ = 3|_τ_ = 4|
|---|---|---|---|---|
|_γ_|—|0.47|0.41<br>[0.080]|0.38|
|_β_|0.624|0.432|0.402|0.403|
||(0.131)|(0.0966)|(0.0939)<br>[0.0789]|(0.0949)|
|_α_|-0.0416|-0.0442|-0.0458|-0.0463|
||(0.0120)|(0.0124)|(0.0126)<br>[0.0148]|(0.0127)|
|_ω_|0.584|0.612|0.633|0.640|
||(0.193)|(0.199)|(0.203)<br>[0.216]|(0.203)|
|Efective-_F_|11.1|11.3|11.6|11.8|
|Over-id _p_-value|0.551|0.591|0.532|0.522|
|Month dummies|YES|YES|YES|YES|
|Observations|33,522|33,522|33,522|33,522|
|_S_(_γ, τ_)|20000|18929|18813|18823|

_Notes:_ Naive standard errors in parentheses clustered at household level (1,462 households); cluster bootstrapped standard errors in square brackets (for best fitting model). Dependent variable in all regressions is log monthly consumption. Estimation conditional on ( _γ, τ_ ) is by twostep GMM with additional instruments ∆ _pt−_ 1 and ∆log _pt−_ 1 _._ number of price lags beyond one does not appreciably add to the power of our first-stage.

While our procedure thus converges on _L_<sup>_∗_</sup> = 8 _,_ for robustness, we also report alternative differencing in columns 3 and 4 of Table 3, with little change in the results. Finally, to validate the importance of having experimental identification in this setting, we also report _p_ -values from an Arellano and Bond (1991) type AR(1) test in Table 3. Our rejection of zero firstorder serial correlation in the residuals suggests that dynamic panel methods, which use lags of consumption as instruments, may be invalid.<sup>21</sup>

# **4.3 Estimating** _γ_ **and** _τ_ **(step 4)**

Table 4 presents the results of our grid search over values of _γ ∈_ (0 _,_ 1) and _τ <_ 5 following the procedure outlined in section 4.1. Column 1 in Table 4 replicates the result in column 1 of Table

> 21With _L_ = 1, only the tests for 2nd and higher-order autocorrelation would be relevant, since first-order autocorrelation results mechanically by first-differencing the data. Given, however, that _L >_ 1 here, autocorrelation of first-order and higher is relevant to the validity of dynamic panel data methods.

25