rafmacalaba/datause-encoder-data huggingface dataset:
# **6. REFERENCES**

- Abraham, K. and S. Houseman, 1993. “Does Employment Protection Inhibit Labor Market Flexibility? Lessons from Germany, France, and Belgium”, NBER working paper W4390

- Abrahart, A., I. Kaur, and Z. Tzannatos.  2000.  “Government Employment and Active Labor Market Policies in MENA in a Comparative International Context.”  March, MDF3

- Acemoglu, D., S. Johnson, P. Querubin, and J. Robinson.  Forthcoming.  “When Does Policy Reform Work - The Case of Central Bank Independence.” _Brookings Papers on Economic Activity._

- Agenor, P.  2002.  “Why Crises are Bad for the Poor.” _Development Outreach_ .  World Bank Institute.

- Almeida, Rita and Emanuela Galasso.  2007. “Jump-starting self-employment? Evidence among welfare participants in Argentina.”  World Bank Policy Research Working Paper 4270.

- Auer, P., U. Efendioglu, J. Leschke.  2005. _Active labour market policies around the world._ International Labour Organization.

- Baily, M., C. Hulten, and D. Campbell.  1992.  “The Distribution of Productivity in Manufacturing Plants.” _Brookings Papers: Microeconomics_ :187–249.

- Barlevy, G.  2002.  “The sullying effect of recessions.” _Review of Economic Studies_ 69 (1): 65–96.

- <mark>Barlevy, G. 2003 “Credit market frictions and the allocation of resources over the business cycle.”</mark> _<mark>Journal of Monetary Economics</mark>_ <mark>50:1795–1818.</mark>

- Bassanini, A., and D. Nunziata.  2009.  “Job protection legislation and productivity growth in OECD countries.” _Economic Policy_ 24:349-402.

- Betcherman, G., K. Olivas, and A. Dar.  2004.  “Impacts of Active Labor Market Programs: New Evidence from Evaluations with Particular Attention to Developing and Transition Countries.”  World Bank Social Protection Discussion Paper 0402.

- Bidani, B., N. Blunch, C. Goh, and C. O’Leary.  2003.  “Evaluating Job Training in Two Chinese Cities.” Upjohn Institute Staff Working Paper 05-111.

- Blomquist, J., M. Verhoeven, J. Cordoba, C. Bouillon, and P. Moser.  2001.  “Social Safety Nets in Response to Crisis: Lessons and Guidelines from Asia and Latin America.”  Mimeo, World Bank.

- Bowlus, A.  1993.  “Job match quality over the business cycle.” _Panel Data and Labour Market Dynamics:_ 21-41.

- Braun, M. and L. DiGresia.  2003.  “Towards Effective Social Insurance in Latin America: The Importance of Countercyclical Fiscal Policy.” Seminar paper.

- Bresciani, F., D. Gilligan, G. Feder, T. Onchan, and H. Jacoby.  2002.  “Weathering the Strom: The Impact of the East Asian Crisis on Farm Households in Indonesia and Thailand.” _World Bank  Research Observer_ 17:1-20.

- Caballero, Ricardo J and Mohamad L Hammour.  1996.  “The Fundamental Transformation in Macroeconomics.”  American Economic Review 86(2):181-86.

16
**Table 2: Test of Parallel Trend: Effects of Fictitious Policy Implementation**

||**Wholes**<br>|**ale Margin**<br>|**Retai**<br>|**l Margin**<br>|
|---|---|---|---|---|
||Same Period<br>Prices|2 Weeks Lagged<br>Crude Price|Same Period<br>Prices|2 Weeks Lagged<br>Crude Price|
|Placebo Policy Dummy (PPD)|-0.946|1.868|-1.819|0.971|
||(-0.386)|(0.537)|(-0.798)|(0.301)|
|PPD * Palm Dummy|-1.399|-8.285|4.693|-1.085|
||(-0.324)|(-1.344)|(1.182)|(-0.191)|
|PPD* Palm Dummy * World Price|0.0209|0.126|-0.0732|0.0242|
||(0.380)|(1.576)|(-1.411)|(0.325)|
|Observations|626|606|626|606|

Notes: (1) ‘t’ statistics are in parenthesis. (2) Odd columns report estimates where the same period world market price of crude palm oil is used to calculate trading margins, and the even columns for the case when two weeks lagged crude price is used. (3) The regressions include year, quarter, product fixed effects, relative prices, a dummy for upswing in the world market, Ramadan dummy and fuel price (diesel).

**Table 3: Effects on Wholesale-World Margin**

||**Full S**|**ample**|**Restricte**|**d Sample**|**Matched**|**Sample**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(1)**|**(2)**|**(1)**|**(2)**|
|**Effects of Announcement**||**Panel**|**A (Same pe**|**riod crude p**|**rice)**||
|**Intercept**|**35.25****|**24.68***|**37.72****|**24.71***|**32.27****|**22.36**|
||(2.285)|(1.675)|(2.415)|(1.685)|(2.094)|(1.514)|
|**Slope**|**-0.312**|**-0.221**|**-0.311**|**-0.232**|**-0.312**|**-0.189**|
||(-1.629)|(-1.214)|(-1.605)|(-1.279)|(-1.630)|(-1.030)|
|**Effects of Implementation**|||||||
|**Intercept**|**25.62*****|**21.10*****|**27.52*****|**19.88*****|**25.95*****|**26.88*****|
||(7.503)|(5.591)|(7.857)|(4.934)|(7.435)|(6.603)|
|**Slope**|**-0.232*****|**-0.210*****|**-0.224*****|**-0.211*****|**-0.273*****|**-0.281*****|
||(-5.333)|(-4.445)|(-5.011)|(-4.301)|(-6.078)|(-5.385)|
|Observations|1,932|1,932|1,374|1,374|1,312|1,312|
|**Effects of Announcement**||**Panel**|**B (2 weeks la**|**gged crude**|**price)**||
|**Intercept**|**38.14****|**31.32***|**41.91****|**33.50***|**33.28****|**30.63***|
||(2.348)|(1.896)|(2.440)|(1.928)|(2.106)|(1.674)|
|**Slope**|**-0.355***|**-0.300**|**-0.376***|**-0.321**|**-0.320**|**-0.285**|
||(-1.744)|(-1.456)|(-1.752)|(-1.487)|(-1.617)|(-1.244)|
|**Effects of Implementation**|||||||
|**Intercept**|**23.08*****|**15.85*****|**22.92*****|**16.96*****|**24.80*****|**22.91*****|
||(6.983)|(4.123)|(7.103)|(4.326)|(7.198)|(4.521)|
|**Slope**|**-0.210*****|**-0.146*****|**-0.182*****|**-0.157*****|**-0.257*****|**-0.230*****|
||(-4.870)|(-3.076)|(-4.421)|(-3.296)|(-5.638)|(-3.499)|
|Observations|1,912|1,912|1,374|1,374|1,292|1,292|

Notes: (1) ‘t’ statistics are in parenthesis. All regressions include year, quarter and product fixed effects. (2) Slope denotes the slope of wholesale margin with respect to world crude oil price. (3) Regressions in even numbered columns include relative prices, a dummy for upswing in the world market, Ramadan dummy and fuel price (diesel).
We restrict our analysis to events as late as the year 2000, so that a minimum of 5 years of consumption data can still be used. As discussed in Ferreira, Hamilton, and Vincent (2008), green accounting theory refers to an infinite time horizon, and it has been shown that results become more valid with a longer time horizon; other studies have used a minimum of 10 years, with a preference for 20 years. In contrast, we use a minimum of 5 years, yet in order to maintain a large number of observations, we keep the time horizon flexible from a minimum of 5 to a maximum of 33 years of consumption changes observed. We do find this flexibility in terms of time horizon to adversely affect the analysis, which we discuss in the following. Given the data and adjustments done and a lack of net and genuine savings data for a number of countries, only 99 observations remained. Importantly, many of the low income countries with massive losses (such as the biggest event of St. Lucia in 1988) dropped out due to a lack of savings or consumption data, which is a constraint of the analysis to keep in mind. Table 4 quantitatively describes the key variables used for the analysis.

<u>Table 4: Descriptive statistics for the whole sample (N=99)</u>

||<br>**Minimum **|<br>**Maximum **|**Mean **|**Std. Deviation **|
|---|---|---|---|---|
|Time horizon(years)|5|33|15.1|8.18|
|Loss|-0.57|-0.01|-0.04|0.08|
|Gross savings|-0.04|0.56|0.17|0.12|
|Gross disaster savings|-0.25|0.54|0.13|0.14|
|Net savings|-0.14|0.45|0.08|0.11|
|Net disaster savings|-0.33|0.44|0.04|0.13|
|Genuine savings|-0.32|0.38|0.07|0.12|
|Genuine disaster<br>savings|-0.42|0.37|0.02|0.14|
|PV ∆Consumption|-0.44|1.04|0.18|0.24|

Losses exhibit a wide range from 1% to 57% of GNI per capita of the year of the event. The means of the (non-disaster adjusted) savings variables seem broadly of similar magnitude as the present value of consumption changes with means of 0.17, 0.08 and 0.07 of GNI as compared to the average consumption change of 0.18% of GNI per capita. The “disaster” adjustment to the savings indicators reduces the means and increases variability as measured by the standard deviation. To provide an idea for the distribution behind the summary statistics reported in Table 4, Figure 4 shows gross savings and gross disaster savings across the 99 cases. We observe that in the majority of cases 12
While such differences in average income may reinforce, at first blush, the perceptions of benefits to cadre status, it is important to realize that cadre and non-cadre households differ in important ways which may explain average differences in incomes. Table 3 shows that cadre households possess higher levels of human and physical capital, and this would lead us to expect differences in earnings, even apart from cadre status. After controlling for all of the observable household characteristics, and province-year fixed effects, cadre households earn an average of 109 Yuan of income more than noncadre households, or 14 percent higher earning of cadre households relative to non-cadre households (Table 4). Observable household characteristics explain 39 percent of the observed differences between cadre and non-cadre households.

In common with the cross-sectional studies of the returns to cadre status, our base OLS results do not account for the unobserved dimensions of ability and we may expect that the coefficient on cadre status is biased upward. Including household fixed effects, in the last column of Table 4, leads to a decline in earnings premiums of cadre households to 89.5 Yuan, which is 11 percent of the average per capita income of non-cadre households, and represents an 18 percent decline in the estimated earnings premium of cadre households.

Our estimates above estimate contribution of cadre status to household incomes with income estimated in levels, rather than in logs. Though we do this for the simple reason that some households may have negative incomes in some years, results are not substantively different when estimating models in logs (Table 5).<sup>18</sup> The coefficient on the

> 18Households reporting negative incomes typically have high gross incomes, but also high business-related expenses. Using log income as the dependant variable leads us to drop 294 household-year observations out of 123,867, or 0.24

17
**Figure 2: Post-Reform Cost Recovery Levels C1 and C3 in 17 Case Studies (24 utilities)**

<!-- Start of picture text --> a) Average Full-Cost Recovery Level in 2010-17 b) Average Operating Cost Recovery Level<br>on country level (C3, %) in 2010-17 on country level (C1, %)<br>Ukraine 2 out of 17 Colombia<br>Colombia ≥ 100 % Peru<br>Peru Uganda<br>Philippines Tajikistan<br>Uganda Kenya 9 out of 17<br>India - Odisha 10 out of 17 Ukraine ≥ 100 %<br>≥ 80%<br>Kenya<br>Philippines<br>Vietnam<br>Pakistan<br>Morocco<br>Vietnam<br>Pakistan<br>Egypt, Arab Rep.<br>Senegal<br>Tanzania India - Odisha 13 out of 17<br>Tajikistan Senegal ≥ 80%<br>Dominican Republic Tanzania<br>Egypt, Arab Rep. India - Andhra Pradesh<br>India - Rajasthan India - Rajasthan<br>India - Andhra Pradesh Dominican Republic<br>0% 20% 40% 60% 80% 100% 120% 0% 50% 100% 150% 200%<br><!-- End of picture text --> <!-- Start of picture text --> c) Average Full-Cost Recovery Level in d) Average Operating Cost Recovery Level in<br>2010-17 on utility level (C3, %) 2010-17 (C1, %)<br>Ukraine Colombia<br>3 out of 24<br>Colombia ≥ 100 % Peru (Luz del Sur)<br>Peru (Luz del Sur) Peru (Hidrandina)<br>Philippines (Meralco) Kenya<br>India - Odisha (WESCO) Uganda<br>Uganda Tajikistan 13 out of 23<br>Kenya 13 out of 24 Philippines (Meralco) ≥ 100 %<br>Vietnam<br>≥ 80% Ukraine<br>Philippines (Beneco)<br>Egypt, Arab Rep.<br>India - Odisha (CESCO)<br>Philippines (Beneco)<br>Peru (Hidrandina)<br>Vietnam<br>Pakistan (LESCO)<br>India - Odisha (WESCO)<br>Morocco<br>Tanzania India - Odisha (CESCO)<br>India - Andhra Pradesh (APEPDCL)<br>Senegal<br>Pakistan (LESCO)<br>Tajikistan<br>Pakistan (KE) Senegal 21 out of 23<br>Dominican Republic India - Andhra Pradesh (APSPDCL) ≥ 80%<br>India - Rajasthan (JVVN) Tanzania<br>Egypt, Arab Rep. India - Rajasthan (JVVN)<br>India - Rajasthan (JDVVN) India - Rajasthan (AVVN)<br>India - Andhra Pradesh (APEPDCL) India - Rajasthan (JDVVN)<br>India - Rajasthan (AVVN) Pakistan (KE)<br>India - Andhra Pradesh (APSPDCL) Dominican Republic<br>0% 50% 100% 150% 0% 100% 200%<br><!-- End of picture text --> _Source:_ World Bank staff.

> 25. From a financial perspective, which excludes many implicit and ‘hidden’ costs, the picture looks slightly better. Eleven out of 16 countries or states and 14 utilities out of 23 utilities are recovering their financial operating costs (A1; see Figure 3a and c). Eight out of 16 countries or states and 8 out of 23 utilities are meeting their operating and existing-capital expenditures from a financial perspective (see Figure 3b and d).

12
bias if we were to use non-participants as the comparators. To address this concern we use propensity score matching to select the comparison group from the set of non-participants.<sup>11</sup> Predicted values (the propensity scores) from the probit are used for matching.<sup>12</sup> Using a light survey instrument will no doubt leave biases in these estimators.<sup>13</sup> Given that the program is means tested it is tempting to include income as a predictor of participation in matching. The problem in doing so is that we would then be using the outcome variable (income net of DB) as one of the predictors for estimating impact on that same outcome variable! The results are only unbiased if it is assumed that there are no behavioral effects, which is what one is trying to test. It is not clear what one would then conclude from the results. In general, the direction of bias in the impact estimator cannot be determined.<sup>14</sup> To avoid this problem we should exclude income from the probit (using the regression in the Appendix), though then we run even higher risk that we have selection bias based on unobserved variables in the matching.

When we exclude income from the probit used to estimate the propensity scores we find that the mean income (net of DB) of participants is significantly lower than income of the matched comparison group of non-participants. Income minus DB receipts is 1417 Yuan lower for the DB participants (with a bootstrapped standard error of 270 Yuan using 100 replications) while mean DB receipts are 270 Yuan. It is not believable that receiving an extra 270 Yuan

> 11 On the theory of propensity score matching see Rosenbaum and Rubin (1983). For an application to a similar problem to the present one see Jalan and Ravallion (2003).

> 12 We match each treatment household with the five closest propensity scores.  We did not need to drop any observation from the treatment group.  The STATA program, _nnmatch_ , was used; results were checked against the program _Psmatch2_ .

> 13 For evidence on this point in the context of estimating behavioral responses to a cash transfer program in Argentina see Ravallion et al. (2005).

> 14 We will have underestimated the income net of DB for participants given that they attenuated their labor supply (or received less transfer income) but through the miss-matching we will probably have also underestimated the income of the comparison group (since we over-estimate the propensity scores for treatment units).

13
Non-Exporting firms

||Number||Year 2009|- 2010|(year t+1)||Firms|(2009)|
|---|---|---|---|---|---|---|---|---|
||of Layers|0|1|2|3|4|Number|Share*|
||0|**81.66**|15.75|2.23|0.32|0.05|80,628|35.42|
|year t|1|12.59|**73.64**|12.06|1.53|0.18|77,335|33.97|
|(2010)|2|2.81|16.67|**67.74**|11.59|1.19|41,815|18.37|
||3|1.17|3.46|18.42|**65.70**|11.26|18,960|8.33|
||4|0.63|0.77|3.46|16.80|**78.34**|8,904|3.91|
|Firms|Number|77,027|77,341|43,250|20,239|9,785|227,642|-|
|(2010)|Share*|33.84|33.97|19.00|8.89|4.30|-|100.00|
|New ex|porters frms||||||||
||Number||Year 2009|- 2010|(year t+1)||Firms|(2009)|
||of Layers|0|1|2|3|4|Number|Share*|
||0|**57.24**|28.97|9.66|1.38|2.76|145|8.02|
|year t|1|4.61|**70.72**|17.76|5.26|1.64|304|16.80|
|(2010)|2|1.30|6.74|**68.91**|17.88|5.18|386|21.34|
||3|-|0.50|13.03|**67.17**|19.30|399|22.06|
||4|-|-|0.70|7.30|**91.83**|575|31.79|
|Firms|Number|102|286|390|397|634|1809|-|
|(2010)|Share*|5.64|15.81|21.56|21.95|35.05|-|100.00|
|Continu|ing frms||||||||
||Number||Year 2009|- 2010|(year t+1)||Firms|(2009)|
||of Layers|0|1|2|3|4|Number|Share*|
||0|**75.21**|17.36|3.31|2.48|1.65|121|1.90|
|year t|1|9.28|**73.65**|13.77|2.10|1.20|334|5.24|
|(2010)|2|0.87|10.14|**69.13**|16.67|3.19|690|10.82|
||3|0.56|1.21|8.22|**70.84**|19.16|1,070|16.78|
||4|0.12|0.10|0.34|3.46|**95.99**|4,163|65.27|
|Firms|Number|139|354|629|1,027|4,229|6,378|-|
|(2010)|Share*|2.18|5.55|9.86|16.10|66.31|-|100.00|

Table 11: Distribution of layers at t+1 conditional on layers at t (by year) exporters are more likely to change their organization and add new layers.

Regarding continuing exporters, the share of firms that add new layers is much larger than firms that reduce them, which is similar to new exporters. Also, the majority of the firms have already 3 or 4 layers and among the former, almost 95% keep the same structure. Therefore, the structure of exporting firms is composed of more layers than non-exporting firms and new exporters are more likely to add layers of managers than non-exporting firms.<sup>48</sup> Figure 6 in section 6.2 of the appendix shows the dynamic of firms that become exporters regarding their average wage and hours hired.<sup>49</sup>

> 48What is interesting in these findings is that depending on adding layers or not, new exporters react differently in terms of how they distribute production knowledge within the firm when they expand and this can be observed by the average wage in different layers.

> 49Overall, we found that the prediction of their theory holds in most of the cases when the transition of the same firms that become exporters and add additional layers in the same year is compared. It means that the same firms that switch export status (from non-exporting to exporting) and add one layer usually pay lower wages for workers at previously existing layers.
more than women in the modern sector and 25.5 percent more than women in the traditional sector. We calibrate the average male and female wages to USD 1,686 in the modern sector and to USD 1,153 in the traditional sector. If women and men earned the same within sector, the value of traditional sector production was predicted to increase by 11.9 percent and modern sector production would increase by 2.1 percent. Women’s total contribution to GDP would rise to USD 1.28 billion, growing from 26.8 percent to 30.1 percent.

Gains would be even higher if the gender wage gap in Liberia replicated that of the OECD. While female-to-male earnings were similar between Liberia and Sub-Saharan Africa,<sup>25</sup> the pay-gap between men and women was 11.6 percent for employees in OECD countries (OECD, 2022). When we calibrated the average gender pay-gap found in the modern sector in Liberia from 25.5 percent to 11.6 percent, the GDP grew to USD 4.13 billion, or by 1.99 percent. Only the contribution of the female-modern sector was affected by this calibration, growing by 12.5 percent.

**Figure 4. Selected macro-economic changes due to complete convergence of the gender-pay gap (in USD, growth in %)**

<!-- Start of picture text --> $4,500 4,244 30%<br>25.5%<br> $4,000<br>25%<br> $3,500<br> $3,000 20%<br> $2,500<br>15%<br> $2,000<br> $1,500 1,278 10%<br>6.5%<br>958<br> $1,000 4.8% 813 736<br>3.3% 5%<br>465<br> $500<br>0.0% 0.0%<br> $- 0%<br>GDP Female Trad. Mod. Trad. Mod.<br>Female Male<br>Source: author’s calculations<br>Millions<br>Growth (in %)<br><!-- End of picture text --> If men and women were equally represented in each sector, GDP would increase by 5.3 percent or to USD 4.26 billion (Figure 5). The underrepresentation of women in the high-productive sector is substantial, as outlined in section 5.1. Equalizing the gender distribution within sectors would decrease the share of women in the traditional sector from 61 percent to 46.7 percent and increase the share of women in the modern sector from 39 percent to 53.3 percent. The number of women contributing to modernsector production would increase from 184,000 to 331,000 while the number of men would remain unchanged. Women’s total contribution to GDP would be USD 1.29 billion or 30.45 percent of GDP and 43.4 percent of the aggregate production of labor.

Allowing for modern and traditional sector wages to shift in response to an inflow of women to the modern sector led to a smaller estimated increase in GDP, of 2.48 percent, as compared to estimates when wage appreciation and depreciation were not accounted for. We parametrized the adjustment with

> 25 Estimates on this benchmark are available upon request to the authors.

19
26 Figure 11. Denmark, France and Ireland: Real Share Price Index (normalized). Source: IMFIFS.

<!-- Start of picture text --> =2<br>2 1 82 813 4 85 88<br><!-- End of picture text --> Expectations about the _pace of the disinflation_ play a significant role in the determination of both the real interest rate and real wages. For instance, if expectations turn to be pessimistic ex-post, we should observe an increase in both real wages and the interest rate. In turn, the behavior of the real wage and the real interest during stabilization explain much of output developments through their effect on supply.<sup>2</sup> ' We thus focused on real interest rates and tried to discover any major shift at the time of the stabilization that could distinguish Denmark from France and Ireland. The evidence obtained by looking at ex-post real interest rates is shown in figures 13 to 15.

21 It might be worth having in mind the case of the chronic inflation countries in Latin America. The evidence provided by Corbo (1985) and Ramos (1986) is consistent in that real interest rates fell in the Southern Cone stabilizations of the late 1970's. However, Kiguel and Liviatan report that this feature was not shared by the stabilization programs in the 1980's. They also mention that real interest rates rose to extremely high levels in the course of the stabilization program that started in Argentina and Israel in 1985.