rafmacalaba/datause-encoder-data huggingface dataset:
|Tsunami|-0.173_∗∗∗_<br>(0.027)|0.607_∗_<br>(0.312)|-0.555_∗∗_<br>(0.273)|-0.025<br>(0.076)|-0.037<br>(0.157)||||||15,152|
|---|---|---|---|---|---|---|---|---|---|---|---|
|With Lag<br>Volcanic Eruptions_a_|-0.049<br>(0.041)|0.671_∗∗∗_<br>(0.156)|-0.186_∗∗_<br>(0.090)|0.144_∗∗∗_<br>(0.027)|-0.404_∗∗_<br>(0.162)|-0.032<br>(0.036)|0.529_∗∗∗_<br>(0.196)|-0.180_∗_<br>(0.100)|-0.030<br>(0.034)|-0.141<br>(0.175)|15,152|
|Earthquake|0.083<br>(0.193)|0.154<br>(1.543)|2.400_∗_<br>(1.411)|0.245<br>(0.220)|0.010<br>(0.911)|0.546_∗∗_<br>(0.254)|-4.006_∗∗_<br>(1.832)|5.058_∗∗_<br>(2.003)|0.473_∗∗∗_<br>(0.198)|1.810_∗_<br>(0.998)|15,152|
|Flood_a_|0.108_∗∗∗_<br>(0.030)|-0.307_∗∗_<br>(0.131)|-0.381_∗∗∗_<br>(0.105)|0.127_∗∗_<br>(0.059)|0.374_∗∗∗_<br>(0.095)|0.104_∗∗∗_<br>(0.038)|-0.426_∗∗_<br>(0.206)|-0.316_∗∗_<br>(0.154)|0.048<br>(0.042)|0.270<br>(0.171)|15,152|
|Tsunami|-0.285_∗∗∗_<br>(0.051)|0.726_∗∗∗_<br>(0.280)|-0.529_∗_<br>(0.290)|-0.045<br>(0.074)|-0.119<br>(0.154)||||||15,152<br>000.|
|Without Lag<br>Earthquake<br>Volcanic Eruptions_a_|0.092<br>-0.038<br>(0.170)<br>(0.047)|0.514<br>0.581_∗∗∗_<br>(1.423)<br>(0.178)|1.624<br>-0.134_∗∗_<br>(1.418)<br>(0.064)|0.208<br>0.151_∗∗∗_<br>(0.221)<br>(0.033)|-0.033<br>-0.380_∗∗_<br>(0.869)<br>(0.179)||||||15,152<br>15,152<br>nt at the 1 percent level.<br>t at the 5 percent level.<br> at the 10 percent level.<br>ts and standard errors multiplied by 1,|
|Flood_a_|0.094_∗∗∗_<br>(0.033)|-0.253_∗_<br>(0.136)|-0.327_∗∗∗_<br>(0.117)|0.124_∗∗_<br>(0.060)|0.331_∗∗∗_<br>(0.093)||||||15,152<br>_∗∗∗_Signifca<br>_∗∗_Signifcan<br>_∗_Signifcant <br>_a_ Coefcien|
|Sector|Agriculture|Education|General Administration|Health|Infrastructure|Agriculture Lag|Education Lag|General Administration Lag|Health Lag|Infrastructure Lag|Observations<br>_Notes:_|

22
Table 1: Balance tests for baseline covariates

||||Me|an<br>by<br>treat|ment|||||
|---|---|---|---|---|---|---|---|---|---|
||||Control|Treated|P-value|2023<br>Mexi|co<br>ENOE<br>Survey|2014<br>CR|EA<br>Survey|
|Control Variable|Mean|SD|N = 695|N = 1513|(Joint = .5)|Mean|SD|Mean|SD|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Years of business operation|4.21|5.32|3.98|4.32|0.099|9.73|10.3|2.14|2.65|
|Is a family business|0.510|0.500|0.498|0.516|0.592|||||
|Age|40.1|10.1|39.9|40.2|0.418|46.0|14.2|42.3|11.3|
|Married|0.555|0.497|0.522|0.570|0.055|0.594|0.491|||
|In State of Mexico or Mexico City|0.276|0.447|0.269|0.280|0.707|0.210|0.407|0.427|0.495|
|In Guatemala|0.075|0.264|0.079|0.073|0.998|||||
|Attended university|0.485|0.500|0.498|0.479|0.536|0.171|0.376|0.378|0.485|
|Household earnings > 8000|0.457|0.498|0.450|0.461|0.494|0.448|0.497|0.436|0.496|
|Sales in past month|7,824|15,096|7,733|7,866|0.796|||22,257|38,218|
|Profts in past month|1,966|3,617|1,983|1,958|0.740|||8,600|14,700|
|Any employees|0.585|0.493|0.588|0.583|0.663|0.273|0.445|0.400|0.490|
|Number of employees|1.52|2.40|1.43|1.56|0.224|0.544|2.75|0.968|6.68|
|Keeps written accounts|0.645|0.479|0.645|0.646|0.858|0.381|0.486|0.733|0.443|
|Index of marketing practices|0.378|0.211|0.367|0.383|0.036|||||
|Index of accounting practices|0.271|0.235|0.273|0.270|0.975|||||
|Index of planning practices|0.170|0.262|0.164|0.173|0.143|||||
|Food sector|0.320|0.466|0.344|0.309|0.037|||||
|Beauty sector|0.104|0.306|0.099|0.106|0.605|||||
|Handicrafts sector|0.101|0.302|0.095|0.104|0.433|||||
|Service sector|0.302|0.459|0.294|0.306|0.453|||0.298|0.458|
|Essential business|0.189|0.391|0.200|0.184|0.284|||||

**Notes:** Baseline characteristics of firms involved in the program shown in first five columns. Characteristics of a representative sample of Mexican female entrepreneurs shown in columns 6 and 7 are from the 2023 ENOE (National Survey of Occupation and Employment). Columns 8 and 9 show characteristics of CREA’s in-person training clients taken from a 2014 survey. Not all characteristics are available in these other surveys. P-value in Column (5) correspond to the effect of treatment on the baseline covariate, controlling for strata fixed effects.
**Figure 2a: Cyclical Comovement of  GDP and Exchange Rate– Brazil**

<!-- Start of picture text --> Brazil: NEER and GDP cycl. comp.<br>1995q1 2000q1 2005q1 2010q1 2015q1<br>Quarters<br>GDP NEER<br>.04 .6<br>.02 .4<br>.2<br>0<br>GDP NEER<br>0<br>-.02<br>-.2<br>-.04<br><!-- End of picture text -->

**Figure 2b Cyclical Comovement of  GDP and Exchange Rate – Japan**

<!-- Start of picture text --> Japan: NEER and GDP cycl. comp.<br>1980q1 1990q1 2000q1 2010q1<br>Quarters<br>GDP NEER<br>.04<br>.2<br>.02<br>.1<br>0<br>0<br>GDP NEER<br>-.02<br>-.1<br>-.04<br>-.06 -.2<br><!-- End of picture text --> Note: Left y-axis measures the cyclical component of quarterly real GDP; right y-axis measures the cyclical component of the nominal effective exchange rate (NEER). HP filter is used to extract the cyclical components.

7
# **6 Mechanisms**

Malawi’s PWP shifts household budget constraints out by providing paid work that is offset by reductions in other labor supply. Despite this, households offered PWP do not have better food security or use more agricultural inputs as a result of the program, and food security worsens among untreated households in PWP villages compared to their counterparts in villages with no program. We discuss, and when possible test, potential mechanisms for the lack of positive direct effects in any region, and for the negative spillovers in the Northern and Central regions.

## **6.1 Study design**

### **6.1.1 Low power**

Lack of statistical power is one possible explanation for a null main effect. It would be a more plausible explanation had we found positive but imprecisely estimated point estimates, however. Instead, we find predominantly negative effects that are not statistically different from zero. This is true at not only the national level but also within each region.

We calculate confidence intervals for the total effect on treated households using the delta method. In most cases, we can rule out meaningful positive effects of the program: nationally, the upper bound (at the 95 percent confidence level) of the improvement in food security for any of the eight individual indicators is always less than 0.2 standard deviations of the outcome in the control group. In the full sample, the standard deviation of the PCA index for food security is 2.018, and the confidence interval for the effect of the public works program on treated households is [-0.476, 0.087]. In the IHS3 subsample, the standard deviation in the control group is 2.051, and the confidence interval for the effect is [-0.337, 0.204].

By region, we can rule out effects greater than 0.16 SDs in the North, 0.21 SDs in the Center, and 0.24 SDs in the South. Thus, even moderate direct effects are outside the confidence intervals in each region. It does not appear that lack of statistical power explains failure to identify positive effects of the program.

### **6.1.2 Low take-up**

The second possibility is that household-level intervention, which chose households for inclusion in PWP at random, resulted in low take-up and therefore small intent-to-treat (ITT) estimates. The ITT estimates are not biased, but they are – by construction – smaller in absolute value than the treatment on the treated (TOT) effects. Since assignment to the treatment group increases MASAF participation, the TOT and ITT effects have the same 19
finance are not considered substitutes. In particular, firms that use external capital markets and hence are subject to their discipline could be expected to utilize resources more efficiently than firms that are more reliant on internal finance; this is also known as the "capital market pressure" hypothesis in the literature. Jensen (1986) argues that the constant scrutiny by the capital market is an efficiency-enhancing device that keeps managerial discretion to a minimum, best exemplified perhaps by leveraged buy-outs. In the extreme, there are firms that fund all investment out of internal finance alone as well as firms that use external finance consistently.

Samuel (1995) has investigated the relationship between returns on investment and the financing mix of firms. The evidence from the regressions supported the predictions of the cash flow theories in that firms that were more dependent on external finance attained higher rates of return on investments than firms that were more dependent on internal finance. To that extent, these results support the capital market pressure hypothesis and therefore implies that the overall resource allocation process is likely to be efficient. In other words, even though firms are primarily dependent on internal finance and therefore are not subject to the constant scrutiny of capital markets that would have come from a greater reliance on external finance, the attendant resource allocation process is none the less efficient. Again, from the perspective of developing countries, this result suggests that even though the stock market plays a limited role as a source of finance<sup>25</sup> , policy initiatives to reform the financial sector and develop capital markets can enhance the overall efficiency of the resource allocation process in the economy. However, there is a caveat to this conclusion. As noted earlier, while the informationtheoretic approach is based on the premise that managers maximize shareholder value, the 25 See Samuel (1996) and Cobham and Subramaniam (1995) for evidence regarding India.

25
**Table 2: Estimated Total Employed Workers in 2011 and 2030 in the Baselines and in the Age Structure Freeze Scenarios (in 1000s of workers)**

||**2011**||**203**<br>**A S**|**0**<br>**Diff**|**Percent**|
|---|---|---|---|---|---|
|||**Baseline**|**ge tructure**<br>**Freeze**|**erence**<br>**(III –II)**|**Difference (%)**<br>**(II/III)**|
||I|II|III|IV|V|
|Burkina Faso|1,908|3,486|3,169|317|10.0|
|Botswana|555|702|656|46|7.0|
|Côte d’Ivoire|3,850|6,189|5,803|385|6.6|
|Cameroon|5,621|9,587|8,787|800|9.1|
|Ethiopia|3,796|6,904|5,846|1,058|18.1|
|Ghana|7,965|12,176|11,316|860|7.6|
|Kenya|6,898|11,926|10,883|1,042|9.6|
|Madagascar|5,302|9,633|8,805|828|9.4|
|Mozambique|6,396|10,880|10,115|765|7.6|
|Malawi|3,618|6,574|6,076|497|8.2|
|Nigeria|24,923|42,591|41,457|1,134|2.7|
|Rwanda|2,761|5,017|4,403|614|13.9|
|Senegal|1,907|3,407|3,127|281|9.0|
|Tanzania|18,171|33,271|31,106|2,164|7.0|
|Uganda|8,148|16,332|14,694|1,638|11.1|
|Rest of SSA|55,597|95,949|90,881|5,068|5.6|
|South Africa|13,166|15,678|14,724|954|6.5|
|Zambia|4,020|7,833|7,359|474|6.4|
|Zimbabwe|3,629|6,594|5,513|1,081|19.6|
|Sub-Saharan<br>Africa|178,232|304,727|284,721|20,006|7.0|

Source: Authors’ estimates

# Two growth paths

In the spirit of Basu (2013) and Ravallion (2013), historical growth is used to provide guidance on possible future growth rates. As Table 3 indicates, growth was clearly very weak in the 1980s and 1990s in the region, while the region as a whole did much better in the 2000s, aside from Côte d’Ivoire, Togo, and Zimbabwe. The latter in particular experienced severe contractions in the economy between 2000 and 2009. Due to the substantial changes in performance of the region over time, two baselines – a ‘high growth’ baseline and a ‘low growth’ baseline – are considered and will allow the analysis to speak to the robustness of the demographic dividend.

The high growth baseline characterizes a world where Africa is assumed to continue the good policies and enabling environment that permitted sustained and high growth in the new millennium. The high growth baseline considers that all economies experience growth based on their average annual per capita growth from the 2000-09 period, except for China and some African countries. China’s population will almost cease growing by 2030, and expecting their real growth rate to remain at nearhistorical levels presents too unrealistic an upper bound. So China’s real GDP per capita is assumed to 12
24. The Gini coefficient of inequality based on pre-tax-benefit incomes is considerably higher than final equivalized disposable incomes—roughly 1.5 times in the case of Estonia to almost twice as high in Slovenia. Had it not been for the redistributive impact of taxes and public transfers, income inequality in EU countries would have been very high. Rather than low inherent (i.e. pre-tax) wage inequality _per se_ , it is the tax and benefit systems that help explain the relatively low income inequality typically observed in these countries.

**Chart 7. The role of public transfers in explaining differences in income inequality**

<!-- Start of picture text --> 0.36<br>Lithuania<br>0.34 Hungary<br>Poland<br>0.32<br>Estonia<br>0.30<br>0.28<br>Slovakia<br>0.26 Czech<br>0.24 Republic<br>Slovenia<br>0.22<br>0.20<br>1.52 1.55 1.70 1.70 1.75 1.95 1.97<br>Ratio of pre- to post-transfer Gini index<br>Gini index .<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on data from the 2006 EU Survey of Income and Living Conditions 25. Chart 7 illustrates the important role of public transfers in reducing income inequality. An inverse relationship is evident in the scatter plot of inequality of post-transfer disposable incomes vs. extent of redistribution taking place through the tax and benefit systems: not surprisingly, countries where governments actively redistribute incomes using the tax and benefit systems tend to have considerably lower inequality than those that do not. Thus, Slovenia and the Czech Republic have relatively low income inequality because of the strong redistributive role of taxes and benefits; by contrast, Baltic countries have higher inequality than other EU8 countries because of the smaller role of direct taxes and public transfers in redistributing incomes within these countries.

26. An interesting three-way classification of the EU8 countries emerges based on the relative roles played by their respective tax and benefit systems in reducing income inequality. The first group comprises Estonia and Lithuania,<sup>15</sup> with the lowest income redistribution among EU8 countries (similar in magnitude to the United Kingdom, Ireland, and Spain). The second group includes Slovakia, Poland, and Hungary (higher redistribution than in the Baltic countries, but a bit lower than in other continental European countries). Finally, the third group comprises the Czech Republic and Slovenia, where income redistribution through taxes and benefits is similar in magnitude to Sweden, Denmark, Finland, and the Netherlands.

> 15 Latvia likely belongs to the same group also, though as noted earlier comparable tax and benefit data are not available.

14
|**Table 7. Self-Reported Reasons to See Regulars and Engag**|**e in Sex Work**<br>(1)<br>|(2)<br>|
|---|---|---|
|**Panel A. Self-Reported Reasons**|Unprompted<sup>1</sup>|Prompted<sup>2</sup>|
|Self-Reported Reasons for Entering Sex Work<br>|||
|Love / Bodily Desire<br>|0.02<br>|0.41<br>|
|To Find a Husband|0.07|0.36|
|Income Premium|0.79|1.00|
|Encountered Adverse Shock|0.78|0.99|
|Self-Reported Reasons for Continuing Sex Work|||
|Love / Bodily Desire|0.04|0.58|
|To Find a Husband|0.12|0.61|
|Income Premium<br>|1.00|1.00|
|Regular Clients Provide Help with Shocks|0.21|0.97|
|Self-Reported Reasons for Seeing Regular Clients|||
|Love / Bodily Desire|0.33|0.86|
|To Find a Husband|0.31|0.78|
|They Pay More|0.09|0.71|
|They are Less Trouble|0.07|0.37|
|They are at Less Risk of HIV|0.01|0.02|
|It is More Enjoyable Spending Time with Regulars|0.07|0.29|
|They Can Help with Shocks|0.87|0.98|
|**Panel B. Information about Regular Clients**|(1)||
|Number of Regular Clients|2.37||
||(1.07)||
|Number of Regular Clients with a Good Chance to|0.73||
|Marry|(0.47)||
|Number of Regular Clients with no Chance to|1.32||
|Marry|(0.81)||
|Do You Expect Help from Regular Clients|1.00||
|when Shocks Occur?|||
|Do You Expect Help from Regular Clients Whom|1.00||
|You Have no Chance of Marrying when Shocks Occur?|||
||(1)|(2)|
||Self-Reported|Sources Used|
||for Ris|k Coping|
|**Panel C. Risk-Coping**|_Primary_|_Secondary_|
|Regular Clients|0.70|0.28|
|Friends / Family|0.13|0.45|
|Working More|0.10|0.12|
|Savings|0.07|0.14|
|Observations|99||

Notes: Figures are averages from a short survey conducted in April-May 2010 with a sample of 99 women. Standard deviations in parentheses. In Panel B, the omitted category is those clients with whom the woman has an intermediate chance of marrying. 1"Unprompted" responses are coded responses from open-ended questions. 2"Prompted" responses are responses when women were read a list of possible reasons, to which they could respond yes or no. The unprompted questions were always asked first.
**Table 6. Exporters Likely to Lose the Most from Kazakhstan’s Adoption of the CET**

|**Partner**|**Change in Import Value**<br>**(US$, millions)**|**Percentage in Total 2008**<br>**Kazakh Imports from Partner**|**Percentage drop in**<br>**Kazakh Import Value**<br>**Explained by Decrease in**<br>**Imports from Partner**|
|---|---|---|---|
|EU-27|-346.5|-4.3|-34.5|
|_Of which:_||||
|Germany|-103.5|-4.5|-10.3|
|France|-47.7|-6.5|-4.7|
|Italy|-30.5|-3.1|-3.0|
|Lithuania|-29.3|-10.9|-2.9|
|Poland|-23.2|-5.3|-2.3|
|Finland|-18.6|-3.3|-1.9|
|Austria|-14.0|-4.0|-1.4|
|Hungary|-13.3|-3.3|-1.3|
|Netherlands|-13.3|-2.7|-1.3|
|China|-439.3|-4.7|-43.7|
|United States|-48.6|-5.0|-4.8|
|Turkey|-40.5|-4.6|-4.0|
|United Arab Emirates|-23.4|-8.1|-2.3|
|Korea, Rep.|-20.8|-6.1|-2.1|
|Japan|-13.1|-6.9|-1.3|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 33
# **Table 3: Testing the significance of the trade channel in the transmission of the 2007-2008 crisis**

Table shows results from regressing local market returns expressed in US dollars. Estimations include time dummies and country fixed effects. Robust standard errors, clustered at the country level, are shown in parentheses. *, **,*** denote significance at 10, 5 and 1 percent significance level, respectively.

|Variables||Retur|ns in US$|||
|---|---|---|---|---|---|
|US Stock Returns*Exports to US/GDP *Pre-Lehman|-0.074<br>(0.089)|||||
|US Stock Returns*Exports to US/GDP *Post-Lehman|-0.166***<br>(0.057)|||||
|US Stock Returns*Total Exports /GDP *Pre-Lehman||-0.157**<br>(0.068)||||
|US Stock Returns*Total Exports to US/GDP *Post-Lehman||0.0087<br>(0.0510)||||
|US Stock Returns*(Exports+ Imports)/GDP *Pre-Lehman|||-0.119<br>(0.075)|||
|US Stock Returns*(Exports+ Imports)/GDP *Post-Lehman|||0.011<br>(0.050)|||
|US Stock Returns*Fuel Exports /Exports *Pre-Lehman||||0.095<br>(0.006)||
|US Stock Returns*Fuel Exports/ Exports*Post-Lehman||||0.040<br>(0.063)||
|US Stock Returns*Agricultural Exports/Exports *Pre-Lehman|||||-0.064<br>(0.071)|
|US Stock Returns*Agricultural Exports/Exports *Post-Lehman|||||0.031<br>(0.032)|
|F-test null Pre-Lehman interaction=Post-Lehman interaction|2.08|6.90|4.26|1.26|0.10|
|F-test (p-value)|0.154|0.010|0.042|0.264|0.747|
|Observations|1561|1605|1581|1621|1558|
|R-squared|0.526|0.515|0.516|0.538|0.533|
|Number of countries|75|77|76|78|75|

26
with economy-level measures of governance.

Table 5: Political Influence, Competition and Output

|Fewer than 20 competitors (Y=100)|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Pol. Infuence|0.357**|0.311**|0.274**|0.383***|0.366***|
||(0.136)|(0.124)|(0.104)|(0.108)|(0.0962)|
|Constant|31.34***|29.68***|31.79***|28.56***|29.43***|
||(6.122)|(5.482)|(4.528)|(4.330)|(4.723)|
|Observations|21,633|21,633|21,518|20,750|20,750|
|R-squared|0.193|0.195|0.284|0.361|0.361|
|Total Sales (log 2009 USD)||||||
|Pol. Infuence|0.0775***|0.0704***|0.0724***|0.0716***|0.0589***|
||(0.00540)|(0.00604)|(0.00704)|(0.00752)|(0.00646)|
|Constant|9.313***|9.428***|9.349***|9.381***|9.928***|
||(0.244)|(0.252)|(0.298)|(0.321)|(0.308)|
|Observations|20,687|20,687|20,570|19,796|19,796|
|R-squared|0.337|0.352|0.430|0.493|0.495|
|Sales per Worker (log 2009 USD)||||||
|Pol. Infuence|0.0234***|0.0215***|0.0234***|0.0215***|0.0152***|
||(0.00335)|(0.00444)|(0.00472)|(0.00460)|(0.00306)|
|Constant|9.144***|9.310***|9.246***|9.344***|9.610***|
||(0.150)|(0.170)|(0.182)|(0.180)|(0.124)|
|Observations|20,540|20,540|20,424|19,649|19,649|
|R-squared|0.414|0.418|0.496|0.557|0.557|
|Labor Costs to Sales Ratio (log)||||||
|Pol. Infuence|-0.0131***|-0.0132***|-0.0150***|-0.0146***|-0.0168***|
||(0.00293)|(0.00412)|(0.00464)|(0.00485)|(0.00317)|
|Constant|-1.318***|-1.328***|-1.273***|-1.288***|-1.192***|
||(0.130)|(0.174)|(0.182)|(0.203)|(0.159)|
|Observations|18,653|18,653|18,533|17,724|17,724|
|R-squared|0.176|0.177|0.289|0.380|0.381|
|**_X_** co-variates||✓|✓|✓||
|**_X_** interacted w/Pol. Infuence|||||✓|
|Location FEs|✓|✓|✓|by Sector|by Sector|
|Sector FEs|✓|✓|by Economy|by Location|by Location|

* _p <_ 0 _._ 10 ; ** _p <_ 0 _._ 05 ; *** _p <_ 0 _._ 01 OLS estimates. Robust standard errors, with two-way clustering at the 2-digit ISIC and region level in parentheses, per Cameron et al. (2011). Co-variates (not shown) include the log of age, the years of the manager’s experience, dummy variables for exporters, foreign-owned firms, and those part of a larger firm.

The results from Table 5 confirm general expectations. Higher levels of influence are associated, robustly, with a higher likelihood of having a low number of competitors. While this is only a rough indication of the restricted competition demanded by more influential firms, it is notably significant across all specifications, even when adjusting for sector-by-location fixed effects, which would partial out any unobserved confounders at that level, including those that are localized within an industry-location, a level at which we would expect influence to be important. Likewise, more influential firms, by our measure, command greater amounts of revenues across all specifications. Importantly, they also do so at lower levels of demanded input, either the number of workers, as shown in panel c., or the total estimated 28