rafmacalaba/datause-encoder-data huggingface dataset:
tives. Technical upgrading courses are aimed at workers seeking to update, deepen or complement the professional skills they have acquired in a specific occupation or field or work. In contrast, the other training categories are aimed at training a new generation of practitioners of a given trade or profession.

The resulting data set gathers this information for the years 2009 to 2012, a period characterized by high volatility of the real exchange rate relative to several important export destinations. To identify the effects of export participation on the share of workers receiving different types of training within firms, we first adopt a difference-in-differences approach using the full sample of manufacturing firms. We then restrict the attention to switchers—that is, firms that recorded export activity in a subset of the sample period— thereby exploiting solely variation in the timing of exporting across firms. Controlling for observed firm attributes, results from both these approaches suggest that export participation is positively associated with the share of workers who received technical upgrading, but show no effects on the share of workers who completed training in other training categories.

While the focus on switchers contributes to reduce biases associated with firm heterogeneity, one may still be concerned that unobserved firm-specific shocks (e.g. in management practices) might lead some firms to both enter export markets and seek to upgrade the technical skills of the workforce. To account for this concern, we take advantage of the fact that firms operating in different industries were exposed to movements in different currencies to generate plausibly exogenous variation in the timing of exporting at the firm-level. The IV results point once more to a positive effect of export participation on the share of workers receiving technical upgrading within firms, but again show no effects for the other training categories. For technical upgrading, the point estimates are positive across all occupational groups, but more precisely estimated among workers performing occupations that typically do not require post-compulsory formal education. While the data do not allow us to fully discriminate between the various theoretical channels discussed above, these results appear to suggest that one or more of these mechanisms played an important role in driving these effects.

3
**Appendix 3: Scatters among insurance variables, private credit and stock market turnover (all countries)**

<!-- Start of picture text --> Economic Growth and Life Insurance Economic Growth and Non-Life Insurance Economic Growth and Insurance Premiums<br>0.12 0.06 0.16<br>0.1 0.05 0.14<br>0.08 0.04 0.12<br>0.1<br>0.06<br>R 2 = 0.0119 0.03 R 2 = 0.0027 0.08 R 2 = 0.0099<br>0.04 0.02 0.06<br>0.02 0.04<br>0.01<br>0 0.02<br>-0.1 -0.05 0 0.05 0.1 0.15 0 0<br>-0.02 -0.1 -0.05 0 0.05 0.1 0.15 -0.1 -0.05 0 0.05 0.1 0.15<br>GDP per capita growth GDP per capita growth GDP per capita growth<br>Private Credit and Life Insurance Private Credit and Non-Life Insurance Private Credit and Insurance Premiums<br>0.10 0.10<br>0.09 0.050 R 2 = 0.3216 0.09<br>0.08 0.08<br>0.07 0.040 0.07<br>0.06 0.06<br>0.05 0.030 0.05<br>0.04 R 2 = 0.3545 0.020 0.04<br>0.03 0.03<br>0.02 0.010 0.02<br>0.01 0.01 R 2 = 0.4197<br>0.00 0.000 0.00<br>0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1<br>Private Credit to GDP Private Credit to GDP Private Credit to GDP<br>Stock Market Turnover and Life Insurance Stock Market Turnover and Non-Life Insurance Stock Market Turnover and Insurance Premiums<br>0.10 0.10<br>0.09 0.050 0.09<br>0.080.07 0.040 0.080.07<br>0.06 0.030 0.06<br>0.05 0.05<br>0.04 R 2 = 0.1214 0.020 0.04 R 2 = 0.1071<br>0.03 0.03<br>0.02 0.010 0.02<br>0.010.00 0.000 R 2 = 0.0153 0.010.00<br>0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1<br>Stock Market Turnover Stock Market Turnover Stock Market Turnover<br>to GDP<br>Life Premiums to GDP Non-Life Premiums to GDP Life and Non-Life Premiums<br>to GDP<br>Life Premiums to GDP<br>Non-Life Premiums to GDP Life and Non-Life Premiums<br>to GDP<br>Life Premiums to GDP<br>Non-Life Premiums to GDP Life and Non-Life Premiums<br><!-- End of picture text --> 21
and Hydrology ( _Insivumeh_ ). This system keeps records on daily and monthly rainfall and temperature from 1963 to 2013 for a grid of 73 weather stations scattered across the country.<sup>6</sup> However, many stations operated only for a short period of time. Consequently, in order to gauge more reliable estimates of historical rainfall patterns across geographic areas, we used information from the 39 stations that recorded weather data uninterruptedly from 1980 to 2010 (see Figure 2 for a detailed description of the coverage of the climate data).<sup>7</sup> Additionally, we constructed shock measures using a slightly larger subset of weather stations (42 stations with monthly rainfall data for the period 1990-2010) to check the consistency of both the treatment status assigned to each municipality (treated vs. comparison or alternatively high- vs. lowintensity rainfall due to Agatha) and the base empirical results resulting from a balanced panel of weather stations for the period 1980-2010.

We complement the precipitation data from _Insivumeh_ with weather records from 15 other stations owned by the Guatemala Sugarcane Association ( _Cengicaña_ ). These stations are geographically located in southern Guatemala, the area most affected by Agatha. Overall, the density of weather stations is larger in this part of the country. This is expected to increase the accuracy of rainfall measurement, something important considering that the south is more mountainous.. The average distance from the municipalities to the closest weather station in our final sample of analysis is 19 kilometers (kms) (s.d. = 12 kms). Finally, 327 municipalities in _Encovi_ were matched<sup>8</sup> to the closest weather station to determine their historical rainfall in the month of May and allocate the treatment.

# **4. Identification Strategy**

The identification of the causal effects of the 2010 Agatha storm on household welfare exploits the time and spatial variation in the trajectory and intensity of the shock across the Guatemalan territory in a D-D analysis. More specifically, our empirical strategy relies on the comparison, before and after the 2010 Agatha storm, of the outcomes of interest (for instance, per capita consumption or poverty incidence) between the more-affected (treated) and less- or

> 6 Daily rainfall and temperature data are patchy across stations in the registry so we use records on monthly averages which are more complete in the dataset.

> 7 Only one out of the 73 stations has been recurrently active during the whole period.

> 8 The algorithm to match a station to a municipality calculates the centroid (i.e. the average position of all the points in a shape) of the polygon that represents a municipality and finds the nearest weather station (linear distance controlling for the earth’s curvature). The maximum distance is 85 km and the minimum is less that 1km.

8
**Figure 3. Trends in the adoption of explicit deposit insurance by income level Figure 4. Trends in the adoption of explicit deposit insurance by income level**

# **Panel A: All income categories**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Year<br>High income Upper middle income Lower middle income Low income<br>1934 1962 1966 1969 1974 1977 1980 1983 1985 1987 1989 1992 1994 1996 1998 2000 2002<br>Number of countries<br><!-- End of picture text -->

**Panel B: High income category versus others**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Year<br>High income Middle and low income<br>1934 1962 1966 1969 1974 1977 1980 1983 1985 1987 1989 1992 1994 1996 1998 2000 2002<br>Number of countries<br><!-- End of picture text --> 28
At the same time, the capital adequacy ratio has experienced a slight, but important growth of 1.1 percentage point.

## **Chart 14. Annual credit growth of exposures from credit cards and overdrafts, before and after the NBRM measure**

<!-- Start of picture text --> 250.00% 25.0%<br>200.00%<br>20.0%<br>16.1%<br>150.00%<br>15.0% 15.0%<br>100.00%<br>58.69%<br>10.0%<br>50.00%<br>1.7% 2.3%<br>5.0%<br>0.00%<br>-50.00% 0.0%<br>Credit cards and overdrafts<br>Other exposures<br>Credit cards and overdrafts (after the measure)<br>Other exposures (after the measure)<br>CAR (rhs)<br><!-- End of picture text --> Source: NBRM

# - <u>Prevention of high credit growth risk</u>

The second measure that had reflected the credit growth of loans to households was introduced by the NBRM in June, 2008, with the adoption of the decision on the compulsory deposit with the NBRM. This decision defined the acceptable cumulative growth rates of loans to households with monthly dynamics for the rest of 2008, while in December 2008, the NBRM issued another Decision intended to cover the growth rates for 2009. With these two decisions, the annual growth rates of loans to households were projected at 39.8 percent, for the end of 2008 and 11.3 percent for the end of 2009.

Whenever a bank’s growth of loans to households was above the acceptable monthly rate, the bank was required to deposit with the NBRM an amount of funds equal to the achieved excess. The difference between the acceptable growth and the achieved growth constituted the compulsory deposit with the NBRM, on which NBRM paid an interest of 1 percent per annum.

This measure could not be regarded as a pure quantitative limitation of the credit growth. Banks were allowed to have higher credit growths than the rates prescribed with both decisions. However, the measure had made the higher credit growth more costly for the banks. The requirement for a compulsory deposit was an attempt to sterilize a certain amount of banks’ funds, which could not be used for other purposes, including lending activities to households. The measure had one additional intention—to prevent the possibility of a credit growth transfer, from the exposures from credit cards and overdrafts to other similar types of exposures to households (for example, consumer loans). The possibility for this transfer became visible with the introduction of the previous measure—the requirement for higher risk weights on exposures from credit cards and overdrafts.

19
# **Table 5: Correlates of adult mortality (ln): gender and education.**

||Sub-Sahara<br>(1)|n African countries<br>(2)|Countries<br>(3)|in other regions<br>(4)|
|---|---|---|---|---|
|Primary or more schooling, Male<sup>(a)</sup>|-0.022|-0.023|-0.137|-0.134|
||(0.042)<br>|(0.039)|(0.066)*|(0.059)*|
|Less than primary schooling, Female<sup>(a)</sup> <br>|-0.134<br>(0.041)**|-0.135<br>(0.037)**|-0.125<br>(0.065)|-0.121<br>(0.058)*|
|Primary or more schooling, Female<sup>(a)</sup>|-0.299<br>(0.042)**|-0.301<br>(0.039)**|-0.520<br>(0.066)**|-0.522<br>(0.059)**|
|Period dummies|Yes|Yes|Yes|Yes|
|Age dummies|Yes|Yes|Yes|Yes|
|Country FE|No|Yes|No|Yes|
|Observations|2407|2407|1007|1007|
|R-squared|0.31|0.43|0.31|0.45|

Note: Standard errors in parentheses. * significant at 5%; ** significant at 1%. Left out category is: (a) Less than primary schooling, Male.

49
The above policy elasticities should be considered with caution. The marginal impact of tariff reductions and trade facilitation would appear to be much stronger than for improved roads, since the estimated elasticities are greater in absolute value. Analysis of the standardized coefficients corresponding to the OLS estimates in column 1 tells a similar story: one standard deviation reductions in tariffs and export/import documents are associated with 0.1% and 0.2% increases in trade respectively, while similar innovations in _paved_ave_ and _paved_min_ are associated with 0.01% and 0.07% changes in trade. However, it would be unwise to draw policy conclusions from such an analysis. As is clear from the way the two paved roads variables are constructed, the relationship between bilateral road quality and the percent of paved roads in any given country is a complex one. It depends on overland transit routes, and on the quality of road infrastructure in transit countries. More important than the results from simply shocking either aggregate variable, is to trace through the impact of a change in individual country indices, allowing for transit effects.

We deal with these difficulties through simulations (cf. Wilson et al. 2005). We define four counterfactuals in terms of particular changes to national policies. Following this, we use our gravity model elasticities to estimate the resulting change in intraregional trade flows. We are conscious of the limits of this approach, in particular to the extent that it assumes parameter constancy across policy shifts and treats each policy change in isolation from the others. Our simulation results should therefore be taken as indicative of the orders of magnitude involved only. Given the scope of this research, our simulation results do not measure economic welfare, but focus exclusively on projected trade impacts. Nonetheless, comparison of results across simulations is likely to prove a useful tool in assessing different policy options.

We therefore identify two initial policy simulations:

- 19 -
of households allocated to each treatment with the households of the corresponding control groups. We test for differences in a wide range of variables including socioeconomic characteristics, dwelling facilities and materials, household assets, children’s situation (education, health, nutrition, cognitive development, childcare, and the relationship between family and school), and many variables directly or indirectly associated with handwashing habits. We obtained the variables reported through questionnaires, microbiological analysis of samples (drinking water and child stools), anthropometric measurements, and capillary blood sample of children (for anemia).

The province-level mass media treatment and control comparison used more than 242 variables; in 39 (16.1 percent) of them, we rejected the null hypothesis of mean equality at the 10 percent significance level. This proportion is slightly lower for the district-level intervention: for the community treatment sample, 30 of 242 (12.4 percent), and for the school treatment sample, 29 of 251 (11.5 percent) were unbalanced at conventional significance levels.<sup>39</sup> The larger proportion of statistically significant mean differences in the mass media treatment could have been expected given that the randomization was at province level for this intervention, unlike the district-level randomization within the same provinces for the community treatment samples. One would expect to find less significant differences among districts within the same group of provinces than among districts in different provinces.

The differences between treatment and control groups go in either direction without a clear pattern. The only apparent difference is that the control group for the mass media province treatment appears to perform better in terms of improved sanitation than the treatment group. However, the opposite occurs when comparing the school treatment sample.

> 39 The proportion of unbalanced variables is close to the standard of 10 percent usually acceptable in randomized experiments. It is important to note, however, that the variables measured are not independent (probably neither the balanced nor the unbalanced ones).

19
|||Play<br>Contribute 6|er 2<br>Contribute 2|
|---|---|---|---|
|||(_Stag_)|(_Hare_)|
|Player1|Contribute 6<br>(_Stag_)|4,4|-3,1|
||Contribute 2<br>(_Hare_)|1,-3|1,1|

Figure 1: Payoffs in the Stag Hunt in Rupees. Note: Payoffs are net changes relative to the endowment. Pure-strategy Nash equilibria are highlighted.

6 or 2 of his blue tokens into the bottom row of the first column. If he placed only 2 tokens, then he put the remaining 4 into his personal envelope. At the end of the first period, a messenger came and put orange tokens into the top row of the first column to indicate the partner’s action (either 6 or 2). The monitor then put blue tokens into the middle row of the first column to show the player his earnings from the period (either 10 or 3). Play then continued into the second period using the same format with the second column, and so on. The messengers never spoke to the players, and the monitor was instructed not to look inside the box (which had an opaque cover) when the player or messenger opened it, so that the monitor would never know the player’s actions and could not indicate approval or disapproval.

In the remainder of the paper, we will use the label “ _Stag_ ” to refer to the action “Contribute 6” and the label “ _Hare_ ” to refer to “Contribute 2.” We wish to emphasize, however, that the experiment was explained to the subjects using neutral language: invest 6 tokens or invest 2 tokens in the common pool.

After the tenth period, players received their payoffs in private and in cash. Each experimental session lasted about four hours. Mean earnings were 77 rupees, which was 77 percent of the maximum of 100 rupees and approximately 1.5 times the daily unskilled wage.

# **3 Results of the Stag Hunt**

## **3.1 Pair Outcomes**

We will now describe the outcome of the Stag Hunt experiment, starting with results at the level of pairs. In Figure 2, the top panel shows the distribution of outcomes by type of pair over all periods of a pairing, and the bottom panel shows the distribution of outcomes in 9
<u>Table 11. Openness, Technological Innovation and the Demand for Skills: Alternative Skills Definition</u>

||<br>Manufacturing|<br>Low Tech<br>Manufacturing|<br>High Tech<br>Manufacturing|
|---|---|---|---|
||(1)|(2)|(3)|
|Exporter|-0.106***|-0.0206|-0.230***|
||[0.0228]|[0.0312]|[0.0325]|
|Foreign Ownership|0.0148|0.00603|0.0465|
||[0.0243]|[0.0348]|[0.0342]|
|Technological Innnovation|0.0810***|0.105***|0.0523*|
||[0.0195]|[0.0271]|[0.0276]|
|Baseline Firm Characteristics?|Yes|Yes|Yes|
|Country-Industry-Size Fixed Effects?|Yes|Yes|Yes|
|Observations|6,865|3,916|2,949|
|Rsquared|0.26|0.22|0.18|

Source: Author's calculations are based on the _Enterprise Suveys_ (World Bank) Note: * significant at 10%; ** significant at 5%; *** significant at 1%. Dependent variable is logarithm of the share of skilled occupations (captured by managers, professionals and non-production workers). Columns (1) through (3) take the baseline specification (in column 5 of table 5) but run it separately for manufacturing sectors (column 1), manufacturing low technology sectors (column 2) and for the manufacturing high technology sectors (column 3). All the variables are defined in table A1 in the appendix.
10 the slowest growing (0.2) to the mean of the fastest growing quartile of countries (0.6) as depicted in Table I would have increased its growth rate by almost 1 percent per annum. Since the difference between the very fast and the very slow growers is about 5 percent (see Table I), the rise in LLY alone would eliminate 20 percent of this difference. This seems considerable, though only illustrative. These types of examples address neither causality nor how to achieve these changes in financial depth.

# E. Sensitivity analyses

The links between financial development and both growth and the sources of growth are robust to a number of sensitivity checks. These checks include altering the conditioning set of information, using sub-samples of countries and time periods, and examining the statistical properties of the error terms.

Using pooled cross-country, time-series data with data averaged over each decade, we get similar coefficient values with similar P-values to the results reported in Table V. Including variables such as population growth, changes in the terms of trade, the number of revolutions and coups, the number of assassinations, or an index of civil liberties also does not alter the conclusions. The results tend to hold on sub-samples of countries. Omitting OECD countries does not alter the conclusions. Omitting Sub-Saharan African countries (in the pooled decade analysis) weakens the significance of the partial correlation between LLY and GYP (the P-value falls to 0.09 because the standard error grows), but does not alter the results on the other three financial indicators. Similarly, including a dummy variable for countries in Sub-Saharan Africa and a dummy variable for countries in Latin America weakens the LLY results while not affecting the other financial indicator results. We also weighted countries differently. Using White's heteroskedastic consistent coefficient standard errors does not alter the conclusions, and omitting
|**Box 2. The Long Te**<br>This box describes<br>TFP growth. The d<br>physical infrastruc<br>constructed based<br>subcomponent ind<br>a). To measure th<br>growth rate is dec<br>Finally, the relatio<br>estimated as follo<br>T<br>TP gfgthY,t: an<br>IoIHxY,t−5: overall<br>ftfroH: TP oHlHo<br>θY: country effect.<br>δt: time effect.|**rm Growth Model - TFP Extension**<br>the LTGM-TFP extension (Kim and Loayza 2019) used to quantify the specific determinants to<br>eterminants of TFP are classified into five categories: innovation, education, market efficiency,<br>ture, and institutions (governance). For each category, a set of subcomponent indexes are<br>on the most relevant and available empirical indicators using the factor analysis. These<br>exes are combined into an overall TFP index using the principal component analysis (Table B2-<br>e relative contribution of the five main determinants of TFP growth, the variation of the TFP<br>omposed to that explained by each determinant across countries over the period 1985−2014.<br>nship between the overall determinant index and the change in the growth rate of TFP is<br>ws:<br>P gfgthY,t= β0+ β1ln (IoIHxY,t−5) + β3ln(ftfroH)Y,t−5+ θY+ δt+ εY,t <br>nualized TFP growth overt−5andt.<br>determinant index att−5.<br>(2011 = 1)|
|---|---|
|**Table B2-a. TFP de**<br>|**terminants and Related Indicators**|
|Innovation|R&D expenditure, public and private (percent of GDP)<br>Number of patents, residents, and nonresidents (per 100 people)<br>Number of scientificjournal articles(per 100people)|
|Education|Government expenditure on education (percent of GDP)<br>Proxy for primary education: secondary enrollment rate (gross percentage)<br>Proxy for secondary education: PISA score<br>Proxyfor tertiaryeducation: completed tertiaryeducation(percent ofpopulation 25+)|
|Market Efficiency|Goods market: Doing Business index.<br>Financial market: IMF Financial development index<br>Labor market: Minimum wage (percent of VA per worker), severance pay for redundancy dismissal<br>(weeks of salary),and share of women in wage employment in non-agricultural sectors|
|Infrastructure|Fixed telephone and mobile subscription (per capita)<br>Length of paved roads (kilometer per capita)<br>Electricity production (kilowatt per capita)<br>Access to improved water source (percent of population)<br>Access to improved sanitation facilities(percent ofpopulation)|
|Governance<br>Source: Kim and Loayza|Voice and accountability, corruption control, government effectiveness, political stability, regulatory<br>quality,and rule of law<br> (2019).|

# **4.1. Reforms to boost TFP growth in Peru**

Identifying ways to boost TFP growth is often challenging because TFP growth is a residual. While reforms to schooling and the investment climate (such as access to credit) are better understood, TFP is generally regarded as a residual component of growth representing economic efficiency and technological improvement. However, this loose and broad definition impedes a precise understanding of how to improve TFP growth.

14