rafmacalaba/datause-encoder-data huggingface dataset:
unsurprising to the extent that the news literature uses utilization-adjusted technology rather than labor productivity, which aims to remove some business cycle variation from the data. In addition, the maximized shock is considered orthogonal from period 1 technology innovations.<sup>17</sup> A second key difference is that our shock is not orthogonalized to contemporaneous innovations in productivity.

# **6 Applications to Emerging Markets**

In the United States, data is of relatively good quality, and volatility is relatively low. In emerging markets and developing economies (EMDEs), macroeconomic variables of interest are more volatile, and also often considered to be subject to larger business cycle fluctuations than advanced economies (Neumeyer and Perri (2005), Garc ́ıa-Cicco et al. (2010)). EMDEs are therefore ideal candidates to demonstrate differences across the proposed SVAR identifications.

EMDE data availability is limited. Few EMDEs publish quarterly data on hoursworked or employment before 2000. Instead, we prioritize data _span_ over data frequency, given our identifications focus on the persistence of shocks. Using annual data from the World Bank’s World Development Indicators (WDI), The Conference Board, and the Penn World Table 9.1, we estimate four-variable VARs consisting of productivity (output per employee), employment, the share of consumption in output, and the share of investment in output.

For illustrative purposes, we show results for three major EMDEs below, with time horizons restricted by data availability.

- Brazil - 1988-2017

- Indonesia - 1961-2017

- South Africa - 1961-2017

The first key finding is that the technology shock in the Max-Share identification always captures the largest proportion of forecast error variance in these EMDEs, particularly in the early stages of the estimation (Figure 13). This is consistent with previous simulation results showing that volatile but less persistent shocks can bias the results of the Max-Share identification. Both the Spectral and NAMS approaches are less influenced by these shocks and demonstrate a lower share of explained forecast-error variance initially (note that ’periods’ are now expressed in years rather than quarters). We note that in some cases the long-run restriction displays a lower share of FEVD than the other approaches (notably in Brazil), but in simulations has shown a tendency to underestimate the FEVD share relative to the true shock (see Figure 7).

> 17The news literature follows a similar procedure to our identification. The notable differences are that utilization-adjusted technology of Basu et al. (2006) is used in place of labor productivity in the VAR, and the shock that maximizes the FEVD of technology over the long-run is orthogonalized from contemporaneous innovations in technology. In a similar approach, Beaudry and Portier (2006) applies the long-run restriction identification and orthogonalizes the news shock from contemporaneous innovations in technology.

31
26 Table 6: Matching estimates of HCFP impact on out-of-pocket spending and utilization

||Outcome<br>HCFP ben|among<br>eficiaries|Outcome<br>nonbenef|among<br>iciaries|Differe<br>without m|nces<br>atching|Differenc<br>matching: p<br>score w|es via<br>ropensity<br>eights|Differe<br>ke|nces via<br>rnel wei|matching:<br>ghts<br>%|
|---|---|---|---|---|---|---|---|---|---|---|---|
||coeff.|t-stat|coeff.|t-stat|coeff.|t-stat|coeff.|t-stat|coeff.|t-stat|<br>change|
|Total out-of-pocket payments|138.013|20.56|231.602|26.80|-93.589|-7.58|18.340|2.04|-6.249|-0.62|-2.7%|
|Catastrophic (>10% nonfood consumption)|0.315|46.13|0.340|63.37|-0.025|-2.84|-0.033|-2.28|-0.027|-2.25|-7.9%|
|Outpatient expenses|51.001|10.29|73.342|14.40|-22.342|-2.93|12.837|1.94|5.805|0.80|7.9%|
|Inpatient expenses|65.050|6.21|102.626|6.21|-37.576|-1.64|26.522|2.01|18.115|1.25|17.7%|
|Outpatient expenses per visit|74.418|10.29|107.496|14.43|-33.078|-3.00|5.511|0.62|-1.211|-0.14|-1.1%|
|Inpatient expenses per admission|563.825|6.07|954.132|8.58|-390.307|-2.51|169.231|1.49|18.861|0.15|2.0%|
|Non-prescribed medicine expenses|32.066|26.05|46.653|41.45|-14.587|-8.37|2.446|1.32|-3.476|-1.47|-7.5%|
|Medical equipment expenses|1.020|9.32|2.383|16.09|-1.363|-6.49|0.124|0.90|-0.059|-0.48|-2.5%|
|# outpatient visits|0.834|23.13|0.809|32.11|0.025|0.58|0.224|4.18|0.168|3.20|20.8%|
|# inpatient spells|0.126|11.49|0.104|19.77|0.022|2.05|0.036|2.27|0.047|3.49|45.2%|
|Outpatient visits Yes|0.300|44.48|0.284|55.58|0.016|1.90|0.046|3.46|0.045|4.25|15.8%|
|Inpatient spells Yes|0.087|20.95|0.076|25.36|0.011|2.09|0.011|1.22|0.021|3.42|27.6%|
|Village station Yes|0.012|7.39|0.01|8.99|0.001|0.74|-0.003|-0.54|0.004|1.69|40.0%|
|Commune center Yes|0.174|31.21|0.099|29.31|0.075|12.17|0.033|2.77|0.048|5.55|48.5%|
|Regional clinic Yes|0.015|8.37|0.016|11.13|-0.001|-0.32|0.000|-0.01|0.001|0.21|6.3%|
|District hospital Yes|0.083|20.46|0.073|24.82|0.010|1.98|0.026|3.77|0.022|3.83|30.1%|
|Provincial hospital Yes|0.038|13.49|0.046|19.37|-0.008|-2.15|0.021|5.02|0.019|4.78|41.3%|
|Central hospital Yes|0.005|4.59|0.012|9.60|-0.007|-4.07|0.001|0.59|0.000|-0.24|0.0%|
|Other govt hospital Yes|0.002|3.32|0.003|4.91|-0.001|-0.71|0.001|0.68|0.000|0.00|0.0%|
|Private provider Yes|0.057|16.80|0.107|30.58|-0.050|-9.43|-0.010|-1.65|-0.021|-3.64|-19.6%|
|Traditional healer Yes|0.007|5.85|0.01|8.93|-0.003|-1.58|0.000|0.21|0.001|0.31|10.0%|

Note: Expenditures are in thousands of VND, except in case of catastrophic out-of-pocket payments which is a share of the relevant subsample.
# Figure 3. Trade during global recessions

## A. Global goods trade B. Global services trade

<!-- Start of picture text --> Index, t=100 Index, t=100<br>140 1975 1982 1991 2009 2020 140 Global financial crisis 2020 recession<br>120 120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>t-3 t-2 t-1 t t+1 t+2 t-3 t-2 t-1 t t+1 t+2<br>Year Year<br><!-- End of picture text --> Source: World Bank.

A B. Figures show annual levels of goods and services trade in the run-up to and aftermath of past recessions and in 2020. t refers to the year before the recession.
67

## Table 111.11

# Distribution of Prices Paid by Method for Jamaica and Thailand (1984 U.S. Dollars)

|Price Rangesa|Condom|IUD|Met<br>Injection|hod<br>Pill|Male<br>Sterili-<br>zation|Female<br>Sterili-<br>zation|
|---|---|---|---|---|---|---|
|Jamaica|||||||
|Sample Size|(77)|(43)|(194)|(303)|N/A|N/A|
|Free|74.0|93.0|72.2|18.2|N/A|N/A|
|<.20|22.1|0.0|3.1|24.1|N/A|N/A|
|$.21-.50|3.9|0.0|15.5|32.7|N/A|N/A|
|S.51-2.00|0.0|7.0|0.0|18.2|N/A|N/A|
|> $2.00|0.0|0.0|9.3|6.9|N/A|N/A|
|Thailand|||||||
|Sample Size|(40)|(49)|<br>(101)|(298)|(36)|(343)|
|Free|30.0|65.3|12.9|33.6|75.0|31.2|
|<$.47|50.0|0.0|<br>3.0*|24.2|0.0|0.0|
|$.48-$1.10|0.0|10.2|31.7|29.5|0.0|0.3*|
|$1.11-$4.26|20.0|8.2|50.5|12.8|5.6|3.8|
|> $4.26|0.0|16.3|2.0*|0.0|19.4|64.7|
|Total|100.0|100.0|<br>100.0|100.0|100.0|100.0|

# Source: Akin (1984).

a. See Table 111.9 for exchange rates used for conversion. ***** Fewer than S cases.

**T. able 111.11**
19 such listings, respectively (table 5.9).<sup>65</sup> Malawi’s small exchange in southern Africa also had two secondary listings from within the subregion, and the Zambian and Sudanese exchanges each had one. The cross-border listings on the southern African exchanges have been primarily in the banking, retail, and mining sectors, however. Indeed, aside from Kenya Airways, there have not yet been cross-border equity listings by companies providing infrastructure services on the national exchanges covered in this study.

**Table 5.9 Cross-listings and overseas listings by companies in focus countries at end-2006**

|Country|Market cap of<br>infrastructure<br>companies as share of<br>total market cap of<br>listed companies (%)|Tally of African<br>domiciled firms cross-<br>/dual listed on other<br>exchanges in the SSA<br>region|Tally of African<br>domiciled firms listed<br>on international<br>exchanges|
|---|---|---|---|
|BRVM total|47|1|0|
|of which:||||
|Benin|0|—|—|
|Burkina Faso|0|—|—|
|Côte d’Ivoire|10|—|—|
|Niger|0|—|—|
|Senegal|100|—|—|
|Cameroon|0|0|0|
|Cape Verde|0|0|0|
|Ghana|0|1|1|
|Kenya|18|3|0|
|Malawi|0|2|1|
|Mozambique|0|0|0|
|Namibia|0|19|2|
|Nigeria|0.4|1|0|
|South Africa|8|22|21|
|Sudan|50|1|1|
|Tanzania|25|3|0|
|Uganda|33|3|0|
|Zambia|0|1|1|

Source: Equity markets data are sourced from national/regional securities exchanges and/or national regulatory authorities.

Note: At the end of 2006, there were no organized equity markets in Chad, Democratic Republic of the Congo, Ethiopia, Lesotho, Madagascar, and Rwanda.Data for the eight-member country BRVM. Côte d’Ivoire, Benin, Burkina Faso, Niger and Senegal are all members of the BRVM, headquartered in Côte d’Ivoire but with trading floors in each member country. Companies domiciled in Côte d’Ivoire, Benin, Burkina Faso, Niger and Senegal have issued shares on the BRVM. — = Not available.

A N$500 million, 13-year, rand-denominated corporate bond issued in August 2007 by Namibia’s electricity utility, Nampower, was the first infrastructure-finance bond to be dual listed on two SubSaharan exchanges: the Bond Exchange of South Africa, where it was able to access South Africa’s large 65 However, the relatively large number of primary as well as dual listings by southern African firms on the Johannesburg Stock Exchange have created uncertainties regarding possible shifts in capital flows toward Johannesburg within the South African Development Community.

59
<!-- Start of picture text --> Share of Lending Devoted to Agriculture<br>0.25<br>A<br>G Stanbic<br>0.2<br>R<br>I<br>UCB<br>C 0.15<br>UCB+Stanbic<br>S 0.1<br>(Virtual)<br>H<br>A Sector<br>0.05<br>R<br>E<br>0<br>1996 1998 2000 2002 2004<br><!-- End of picture text -->

**Figure 5**

The discussion of agricultural lending and bank performance could be relevant for Stanbic because it has created a business development unit that originates SME loans, especially agricultural loans to growers of commercial crops who have long-standing relationships with multinational buyers. The success of that unit is likely reflected in the gradual increase in Stanbic’s share of agricultural lending from almost nothing at the time of privatization to almost 4 percent as of the end of 2004. At the same time, however, conversations with Stanbic management indicate that much of the new lending supports the marketing and sales efforts of larger agribusiness firms rather than small or mediumsized farms.

The even-numbered regressions in Table 5 help put the 4 percent figure in perspective.<sup>24</sup> Foreign banks typically devote 7 percent less of their portfolios to agriculture than domestic banks, a result which is significant in Models 2 and 4. This effect must be added to the coefficient for “Stanbic prior to privatization” to gauge Stanbic’s overall standing with respect to agricultural lending. For example, in Model 2, Stanbic’s preprivatization coefficient is -.021, which is significant at the 1 percent level. When added to the foreign coefficient, this implies that Stanbic devoted 9.4 percent less

> 24 Conversations with Bank of Uganda staff suggest that not all agricultural activities are subsumed within the agricultural lending variable in our dataset. This too might contribute to the relatively low level of lending to that sector indicated in Figure 5.

18
# **Exhibit 4b: Evidence from individual country studies (II/II)**

|**Study**|**Brief description**|**Time**<br>**period**|**Key findings**|**Implications for**<br>**market vs.**<br>**intervention**<br>**question**|**Implications for centre vs.**<br>**region question**|
|---|---|---|---|---|---|
|De La<br>Fuente<br>(2003)|• Impact of cohesion<br>policy in Spain||• EU investment in infrastructure post accession<br>makes it more distributive, with a positive effect<br>on convergence<br>• EU Community Support Framework seen as a<br>large positive shock, that over a period of 7<br>years raises aggregate infrastructure, other<br>capital stock and years of training<br>• For objective 1 regions as a whole, the CSF<br>adds 6.9 percentage points to output and 3.4<br>points to employment in 2000|• EU support<br>found to be<br>positive|• NA|
|Garcia-<br>Mila and<br>McGuire<br>(2001)|• Evaluated impact of<br>central government<br>and EU grants on 17<br>regions of Spain by<br>evaluating economic<br>performance before<br>and after<br>implementation of<br>grant programs|1977-<br>1981<br>and<br>1989-<br>1992|• Grants are generally redistributive and targeted<br>to stimulate economic activity in the poorer<br>regions<br>• However, they have not been effective at<br>stimulating private investment or improving<br>overall economies of the poorer regions<br>• Difference in GDP growth rates between<br>recipient and non-recipient regions between the<br>two time periods not statistically significant<br>• Non-recipient group experiences larger<br>increase in private investment per capita<br>between the two time periods|• EU support<br>seen to be<br>ineffective,<br>other factors<br>lead to greater<br>private<br>investment in<br>non-recipient<br>group|• NA|

**Evidence for market         Evidence for interventionist policy         Evidence for decentralized approach Evidence for centralized approach**

33
3. Repalletization: bananas on large pallets have to be put on smaller pallets because of this lack of standardization (scenario 3).

# **Table 1: Transport, Handling and Pallet Costs of the Banana Chain from Ecuador to Europe**

(in USD per carton)

||**Benchmark:**<br>**ISO chain**|**Combined Chain:**<br>**ISO pallet repalletized**<br>**to an EURO pallet**|
|---|---|---|
|**Inefficiency 1: Cost of pallets**|**0.36**|**0.76**|
|**Inefficiency 2: transport, handling & storage**|||
|Cost from packing house in Ecuador to<br>Europeanport|2.41|2.41|
|Cold storage andhandling portofdischarge|0.47|0.52|
|Transport toripeningfacility andhandling|1.33|1.49|
|Transport from ripening facility and handling to<br>the client|0.25|0.31|
|**Total cost of transport, handling and storage**|**4.46**|**4.72**|
|**Inefficiency 3: Repalletization from ISO to**<br>**EURO pallet at the port of discharge**|**NA**|**0.33**|
|**Total transport costs (price of pallet, handling,**<br>**transport, repalletization & storage)**|**4.82**|**5.82**|

Source: Veenstra et al. (2000) adapted from authors. All costs per carton in USD. ISO chain is the transport chain that uses an ISO pallet 1200*1000 which is the pallet size used mainly in the UK and the Netherlands.

The study concludes that having two different pallet sizes results in a total inefficiency that leads to an additional dollar per carton, which equals 21% total transport costs per carton of bananas exported from Ecuador to Europe (see Table 2). This increase is due to an increase in the cost of pallets of $0.40, an increase in transport, handling and storage costs of $0.26 and the additional handling costs of repalletizing cartons from a 1200 mm * 1000 mm pallet to a 1200 mm * 800 mm Europallet that adds $0.33 per carton of bananas exported from Ecuador to Europe. As the case of bananas shows, the multiplicity of pallet sizes increases trade costs significantly – 21% in the case of Ecuadorian bananas. In the case of banana exports from Ecuador to the EU, the combined chain inefficiency would increase logistics costs by 27 million annually<sup>10</sup> .

> 10 In 2000, Ecuador exported 3.993 million tons. Out of this volume, 17% is exported to the EU or 678975 tonnes. Containers are loaded with 14.9 tonnes of bananas (20 pallets of 30 55-pound boxes). Consequently, Ecuador exports annually 45358 containers. If we assume that all bananas are exported through a combined pallet chain, the inefficient pallet costs are 1 USD per box. Therefore, the pallets costs linked to pallets inefficiencies are equal to 600 USD per container (600 boxes) or 27 million for the annual exported volumes to the EU.

8
- Jacob, B. A. and Lefgren, L. (2003). Are idle hands the devil’s workshop? incapacitation, concentration, and juvenile crime. _American economic review_ , 93(5):1560–1577.

- Kahneman, D. (2011). _Thinking, fast and slow_ . Macmillan. New York and London.

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- Keng, S.-L., Smoski, M. J., and Robins, C. J. (2011). Effects of mindfulness on psychological health: A review of empirical studies. _Clinical psychology review_ , 31(6):1041–1056.

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- behaviors: A systematic review and meta-analysis. _Journal of youth and adolescence_ , 44(3):616–636.

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- Levine, P. B. and Zimmerman, D. J. (2010). _Targeting investments in children: Fighting poverty when resources are limited_ . University of Chicago Press.

- Linley, P. A., Maltby, J., Wood, A. M., Joseph, S., Harrington, S., Peterson, C., Park, N., and Seligman, M. E. (2007). Character strengths in the united kingdom: The via inventory of strengths. _Personality and individual differences_ , 43(2):341–351.

- Lira, B., O’Brien, J. M., Peña, P. A., Galla, B. M., D’Mello, S., Yeager, D. S., Defnet, A., Kautz, T., Munkacsy, K., and Duckworth, A. L. (2022). Large studies reveal how reference bias limits policy applications of self-report measures. _Scientific Reports_ , 12(1):19189.

42
**Table 6: Proportion of labor productivity FEVD explained at horizon t in each**

|Identifcation|**Max-Share**|**Long-Run**|**Spectral**|**Limited Spectral**|**NAMS**|
|---|---|---|---|---|---|
|**Levels: t=1**|0.45|NA|0.31|0.44|0.29|
|**Levels: t=20**|0.89|NA|0.81|0.89|0.69|
|**Levels: t=40**|0.93|NA|0.84|0.92|0.83|
|**Diferences: t=1**|0.99|0.16|0.36|0.35|0.13|
|**Diferences: t=20**|0.40|0.56|0.92|0.92|0.11|
|**Diferences: t=40**|0.32|0.64|0.94|0.94|0.13|

The growth rate of US productivity exhibits a wide range of frequencies driving its spectral density, unlike the level, suggesting an estimation of the VAR with productivity in log-differences will also be informative.

# **_Spectral identifications best capture technology shocks with persistent effects on productivity growth_**

When estimating the VARs in differences (Figure 12), the Spectral approaches show a more persistent IRF than when estimated on the level of productivity, and a similar share of forecast error variance explained of productivity. This demonstrates similar circumstances to the model-based scenario which simulated data in the case of technology taking the form of a unit-root plus stochastic trend growth specification (Section (3.4). The differenced Spectral VARs are therefore likely producing the least biased IRFs for the response of productivity to technology.

# **_Only the Spectral identifications produce consistent estimates when estimating on data in levels and differences_**

Comparing the impulse responses across the two specifications (productivity estimated in levels versus differences), shows that only the spectral estimators produce consistent impulse responses in each case (the long-run identification is always estimated in differences). In response to a positive technology shock, productivity, consumption, and investment rise, while hours, inflation, and the long rate fall. The confidence bands around the spectral estimates are also tighter than their counterparts. While the Max-Share impulses are mainly consistent, we see differences in the impulse of hours and long rates: both hours and the long rate are negative with productivity in levels and positive when estimated in differences.

NAMS produces contradictory or non-informative impulse responses across all variables except for consumption and investment when estimated on labor productivity in differences. Additionally, the accompanying wide confidence bands of the difference specification precludes us from drawing any concrete conclusions and again manifest themselves in the very low explained FEVD (Table 6). This is to be expected, given the impact of a technology shock on productivity _growth_ after 10 years is understandably small and uncertain.

29
as a 250% increase in women employed in women-owned plants (including owners) swamps an otherwise robust 40% increase in men employed in women-owned plants.<sup>16</sup>

# _Dynamics Patterns of Entry and State-Level Election Timing_

Figure 5 provides a dynamic analysis of the entrepreneurship patterns to see if they possess a timing that links them to the implementation of political reservations. The estimations continue with the estimation approach of Table 2, but we introduce four indicator variables for the timing of the elections: 1-3 years before the state’s elections, 0-1 years after the elections, 2-4 years after the elections, and 5+ years after the elections. Coefficients on these variables are relative to the period four or more years before the state’s elections and non-adopting states. The solid lines are the coefficient estimates and the dashed lines give the 90% confidence bands. The dependent variable in Panel A is the log count of new women-owned businesses by state-industry-year; the dependent variable in Panel B is similarly defined for male-owned entrants. Estimations cluster standard errors at the state level and include state-industry and industry-year fixed effects. Appendix Table 3 reports the base estimates graphed in Figure 5.

The coefficient pattern for women-owned businesses is encouraging for our estimation design. There is no evidence of abnormal patterns for states in the 1-3 years before the elections were held, and the entry increase remains slight in the year of the political reform or immediately afterwards. This null result connects to the lack of pre-trends noted in the prior section, and it extends that earlier work to look specifically at pre-activity immediately before each state’s elections (rather than, for example, the trends over states from 1989 to 1994).

Thereafter, we observe a heightened entry rate for 2-4 and 5+ years after the reform. This entry rate is statistically significant for the 2-4 year period and falls just short of conventional significance levels for 5+ years. The lower panel shows no response for male-owned entrants other than a dip in pre-period. While our three data surveys do not have sufficient power to estimate year-by-year interactions with state-level clustering of standard errors, these estimates

> 16 The share of male employees in male-owned firms is stable at 97% during the period. This extreme and growing segregation of the employment bases by the gender of business owner is a particularly important topic for future research.

21