rafmacalaba/datause-encoder-data huggingface dataset:
domestically and the volume of imports is taken from Petrobangla (2012). It is beyond the scope of this analysis to investigate the merits of the gas consumption forecast. Moreover, the demand for natural gas might have been suppressed due to the shortage of supply. However, an assessment of latent or suppressed demand for natural gas is a separate topic which is beyond the scope of this study.

**<u>Table 2: Evolution of gas price over time</u>**

|Year|Domestic Supply<br>(TJ)|Imports<br>(TJ)|Weighted average price of<br>gas (US$/GJ)|Ratio of import price<br>to weighted average<br>price|
|---|---|---|---|---|
|2018|1,179|237|3.60|2.37|
|2019|1,178|237|3.60|2.37|
|2020|1,244|161|3.29|2.60|
|2021|1,192|199|3.45|2.47|
|2022|1,043|337|4.05|2.10|
|2023|933|441|4.51|1.89|
|2024|820|556|5.00|1.71|
|2025|696|691|5.56|1.53|
|2026|630|772|5.87|1.45|
|2027|568|852|6.16|1.38|
|2028|548|893|6.28|1.36|
|2029|530|934|6.38|1.34|
|2030|488|1,005|6.59|1.29|

_Note: This uses prices for domestic and imported gas for 2015, which are 2.61 and 8.53 US$/GJ, respectively._ Table 2 also shows that the ratio of the import price to the weighted average price decreases over time as the volume of (higher priced) imported gas increases. Since Petrobangla is a stateowned enterprise, the additional revenue generated through the price increase accrues to the government. For example, increasing the 2018 price of domestically produced natural gas to import parity so that the weighted average price equals the import price of LNG ($8.51/GJ) would result in almost $7 billion of additional government revenue (with consumption assumed to be unchanged – a simplifying assumption), which is more than 3% of Bangladesh’s GDP in 2016. Since it seems unrealistic to expect the Government of Bangladesh to increase the consumer price of gas to 2.37 times its original value in one go, we assume that the increase will occur gradually. For this analysis, we assume Bangladesh will increase the consumer price of natural gas to1.53 times its starting value (i.e., by 53%) by 2025.

We consider four schemes (Table 3) to recycle the saved subsidy from the electricity sector (due to subsidy removal) and increased revenue from the natural gas sector (due to the price 13
# **MULTIDIMENSIONAL AUCTIONS FOR PUBLIC ENERGY EFFICIENCY**

**PROJECTS: EVIDENCE FROM THE JAPANESE ESCO MARKET**

Atsushi Iimi<sup>†</sup> Sustainable Development Department Africa Region The World Bank Key words: Multidimensional auction; Energy efficiency; Risk sharing. JEL classification: D44; D82; C36.

> † I would like to express my special thanks to the Jyukankyo Research Institute (JYURI) and the Japan Association of Energy Service Companies (JAESCO) for their collaboration in data collection. I am grateful to the Ministry of Economy, Trade and Industry (METI) and many seminar participants at the Institute of Developing Economies-Japan External Trade Organization (IDE-JETRO) and the Japan International Cooperation Agency (JICA) for their insightful comments on an earlier version of this paper. I also acknowledge Matias Herrera Dappe, Antonio Estache, Marianne Fay, Jas Singh, Luis Tineo and Yabei Zhang for their various suggestions.
# **Table 3: On  firms’ reporting access to credit as the main factor**

|||Reportin|g reduced a|ccess to Cr|edit as the m|ain factor|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Demand Sensitivity|-0.236||||-0.210|0.199|-0.313**|-0.213|
||(0.30)||||(0.32)|(0.35)|(0.14)|(0.16)|
|Dependence on External Finance||0.061|||0.421**|||0.189|
|||(0.07)|||(0.21)|||(0.15)|
|Cash Conversion Cycle|||-0.067|||-0.103||0.004|
||||(0.06)|||(0.07)||(0.06)|
|% of investment from own fund 20||||0.000|||0.000|-0.000|
|||||(0.00)|||(0.00)|(0.00)|
|Firm age||||||||-0.000<br>(0.00)|
|Small firm||||||||-0.028<br>(0.02)|
|Foreign owned||||||||-0.043*<br>(0.03)|
|Constant|0.049|0.021|0.151*|0.018|0.031|0.177**|0.027|0.083|
||(0.03)<br>|(0.04)<br>|(0.08)<br>|(0.02)<br>|(0.07)<br>|(0.08)<br>|(0.03)<br>|(0.08)<br>|
|Country fixed effects|√|√|√|√|√|√|√|√|
|Observations|964|803|580|910|406|425|598|211|
|R-squared|0.01|0.02|0.03|0.01|0.06|0.04|0.01|0.08|

Subsequently we run OLS regressions with country fixed effects, focusing on the remaining dependent variables, which are the change in firms’ capacity and employment. Capacity indicates the current utilization rate of all the resources at the time of the interview, as a percent of the maximum output possible using all resources available. Since we are not sure if the total resources remain unchanged between June 2009, and at the end of 2007, we take the results with caution. For example, in the case when a firm was hit by the crisis and sold some of its machinery, the total resources decline. This would inflate the utilization rate and distort our results.

Table 4 shows that the change in capacity is strongly influenced by demand sensitivity. The results are significant and very robust across specifications. Regarding the unconditional impact, for a 1 percent additional decline in a sector’s stock index after Sep 11, 2011, firms in that sector saw a 1.89 percent decline in capacity between June/July 2009 and at the end of 2007. The impact remains robust with other control variables (although the magnitude is somewhat reduced). Also quite interestingly, among credit factors, Cash Conversion Cycle now is strongly significantly correlated with the change in capacity. This implies that financial need for working capital matters for the firms’ operation. However, this is not robust: when we control for demand sensitivity, cash conversion cycle is no longer significant.

14
An important takeaway is that heterogeneity in the actual output levels (right), is primarily large around the income levels where output is also highest (around $4,000 for deforestation, $6,000 for pollution, and $8,000 for the carbon weight of a single dollar production value). This indicates that the theorized Environmental Kuznets tipping points are also the points at which an averaged result, such as obtained from a linear regression, provides the poorest indication of relationships at the individual country level. While a few general rules could be extracted from the marginal effects, such as the inequality, income and population density effects, the larger part of the environmental data seems to relate heterogeneously to economic variables.

# **4.3 Further results on average curvature**

<!-- Start of picture text --> Estimated standardized environmental degradation levels across income<br>100<br>Forest loss/ha/GDP p.c.<br>80 Pollution mcg/m3/GDP p.c.<br>Carbon kg/$/GDP p.c.<br>60<br>40<br>20<br>0<br>1100 1800 3000 4900 8100 13400 22000<br>Logarithmic GDP per Capita<br>Estimated standardized environmental degradation levels across income<br>100<br>Forest loss/ha<br>80 Pollution mcg/m3<br>Carbon kg/$<br>Carbon kg p.c.<br>60<br>40<br>20<br>0<br>1100 1800 3000 4900 8100 13400 22000<br>Logarithmic GDP per Capita<br>Normalized log intensity levels<br>Normalized degradation levels<br><!-- End of picture text --> Figure 4: Normalized predicted environmental output levels across income. Predictors are held at expectations conditional on GDP. The _R_<sup>2</sup> ’s from logarithmic GDP per capita to poverty, undernourishment, manufacturing, services and urban population shares are respectively 0 _._ 801, 0 _._ 633, 0 _._ 142, 0 _._ 573 and 0 _._ 739. The conditional expectations are plotted in fig. 9. Population density, income equality, and controls are held at their means.

23
2 International remittances refer to the money and goods that are transmitted to households by migrant workers working outside of their origin countries. At the start of the 21<sup>st</sup> Century these resource transfers represent one of the key issues in economic development. In 2004 official international remittances were estimated at $93 billion per year (Ratha, 2004),<sup>1</sup> making them about twice as large as the level of official aid-related flows to developing countries.

Despite the ever-increasing size of official international remittances, relatively little attention has been paid to the demographic, economic and financial determinants of these resource flows at the country level. The basic question here is simple: What causes different developing countries to receive different levels of remittances? What is the impact of such factors as the skill composition of migrants and the level of poverty in labor-sending countries on the amount of international remittances received? At the same time, how do economic, financial and political factors – like level of country income, interest rates and exchange rates -- affect the level of remittances received by countries?

This paper proposes to answer these, and similar, questions using a new data set composed of 76 developing countries. This data set includes all those labor-sending countries for which reasonable information on the skill composition of migrants, poverty, remittances and other financial and political factors could be assembled. The data set includes countries drawn from each major region of the developing world: Latin
Table 3: Effect on Female Turnout

||Treatment<br>vs control<br>households|Treated<br>clusters vs<br>control<br>clusters|Treated<br>household<br>s only vs<br>control<br>clusters|Control<br>households<br>in treated<br>clusters vs<br>households<br>in control<br>clusters|
|---|---|---|---|---|
||T-C|TN-CN|T-CN|CTN-CN|
||(1)|(2)|(3)|(4)|
|Panel A: Treatment|||||
|Treatment (T)|0.06|0.118|0.120*|0.121*|
||(0.045)|(0.073)|(0.071)|(0.062)|
|R-squared|0.18|0.19|0.19|0.21|
|Panel B: T1vs T2|||||
|Importance of voting (T1)|0.034|0.095|0.094|0.109|
||(0.052)|(0.077)|(0.075)|(0.070)|
|Importance of voting and secret balloting (T2)|0.093*|0.145*|0.152**|0.135*|
||(0.048)|(0.077)|(0.074)|(0.079)|
|R-squared|0.18|0.19|0.2|0.21|
|N. Observations|2,637|2,637|2,304|767|
|Mean dependent variable among CN|0.52|0.52|0.52|0.52|
|_P-value_(T1= T2)|0.223|0.308|0.229|0.752|
|_P-value_(F-test for joint significance of T1and T2)|0.146|0.159|0.106|0.152|

Note: The dependent variable takes the value 1 if a woman reports having voted in the February 2008 elections and had a verifiable ink mark on her thumb. The symbols *, **, *** represent significance at the 10, 5 and 1 percent respectively. All specifications include village fixed effects and the following controls: number of registered female voters, whether woman has a NIC or CNIC, level of schooling, age, zaat status, TV access, number of children between 0-5, total land owned, index of mobility and whether woman seeks advise from "Pir". Standard errors are reported in parentheses below the coefficient and are clustered at the geographic cluster level.
16 on real incomes. For these reasons, an economywide model with a sufficient level of disaggregation across sectors, factors of production, and households was developed and applied in the country studies of the OECD project.

The simulation models, explained in greater detail in the companion paper by Bourguignon, de Melo, and Suwa (1991) in this symposium, are of the open-economy variety described graphically above. The models have several sectors, distinguished by their degree of tradablity, and incorporate input-output linkages to better account for the net factoral intensity across sectors. Several types of labor with partial mobility across sectors and different wage-determination rules accommodate real wage rigidity and labor market segmentation. Factor incomes are mapped into households, which receive their incomes from several sources. Households have different expenditure patterns to account for the effects of adjustment on the purchasing power of real incomes. The models also include a government sector with current and capital expenditures and a financial sector that integrates portfolio choice by households and firms. These extensions of the basic framework presented here enable the models to trace the short-to-medium run effects of adjustment policies on income distribution across households."'

# 3. THE CASE STUDIES: A DIVERSITY OF INITIAL CONDITIONS AND SHOCKS

The country studies reveal a remarkable diversity in the patterns of adjustment. While all six countries had to retrench and adjust in the sense of bringing expenditure in line with income, two of them - Indonesia and Malaysia - continued to make progress on the distributional and poverty fronts, albeit at a slower pace than previously. And for two other countries in the group - CMte d'Ivoire and Ecuador - the verdict on adjustment was not yet in at the end of the period covered in the studies. Before presenting a comparative evaluation of the adjustment experience across this heterogeneous
_Blacks and mulattos are poorer than whites in Bahia_ . The poverty profile for 2001 reveals a large difference in the levels of well being among different groups. The P0 reveals that 42 and 44 percent of households headed by blacks and mulattos, respectively, are extremely poor compared to 33 percent of households headed by whites (Figure 5.1). During 1993-2001, poverty fell 8 percentage points among households headed by whites and 14 and 13 points, respectively, among those headed by blacks and mulattos, which is largely in line with the Northeast region as a whole.<sup>9</sup> <!-- Start of picture text --> Figure 5.1: Headcount Poverty by Race<br>Bahia 1993-2001<br>6 5 .0<br>6 0 .0 w h ite<br>b la c k *<br>5 5 .0<br>5 0 .0<br>4 5 .0<br>4 0 .0<br>3 5 .0<br>3 0 .0<br>2 5 .0<br>2 0 .0<br>1 9 9 3 1 9 9 5 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 1<br>Source: Author’s calculations based on PNAD.<br><!-- End of picture text --> However, there is little room for complacency. As Figure 5.2 shows, blacks and mulattos are still overrepresented in the low-income quintiles and underrepresented in the wealthiest quintile in Bahia in 2001.

**Figure 5.2:  Racial Distribution of Population** **<u>by Income Quintile in Bahia, 2001</u>**

<!-- Start of picture text --> 1 0 0 %<br>9 0 %<br>8 0 %<br>7 0 %<br>6 0 %<br>5 0 %<br>4 0 %<br>3 0 %<br>2 0 %<br>1 0 %<br>0 %<br>q 1 q 2 q 3 q 4 q 5<br>B la c k a n d P a rd o<br>W h ite<br>Source : PNAD 2001<br><!-- End of picture text -->

> 9 For a detailed description of the poverty profiles for Brazil and the Northeast, including Bahia 1981–99, see Fiess and Verner (2001).

21
# **Table S6.E: Results for the baseline specification with the geographical distance variable omitted**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|newprod|invdum|anyrd|it|pcf_tech|suprat|htrain|ftrain|
|supbuy|0.0567|-0.0451|0.112|-0.0429|0.104|-0.0188|0.107|-0.0208|
||(0.127)|(0.117)|(0.110)|(0.187)|(0.242)|(0.0332)|(0.103)|(0.129)|
|samesup|-0.0119|1.25e-05|0.0585|-0.0425|-0.00192|0.0159|0.0639|0.0296|
||(0.0394)|(0.0452)|(0.0417)|(0.0647)|(0.0681)|(0.0139)|(0.0456)|(0.0404)|
|samecus|0.0702|-0.0249|0.0189|0.0732|-0.0530|0.0175|0.0522|0.139**|
||(0.0586)|(0.0706)|(0.0697)|(0.0962)|(0.0980)|(0.0240)|(0.0736)|(0.0608)|
|same_sec|-0.0328*|-0.0562**|-0.0227|-0.0583**|-0.102***|-0.00262|-0.0189|-0.0318*|
||(0.0171)|(0.0260)|(0.0141)|(0.0277)|(0.0345)|(0.00494)|(0.0125)|(0.0176)|
|ad_fmage|-0.000527|-0.000762***|-0.000820|-0.00157|-0.00295*|-0.000115|8.61e-05|0.00207*|
||(0.000573)|(0.000273)|(0.00107)|(0.00131)|(0.00174)|(0.000326)|(0.000705)|(0.00113)|
|ad_meduc|0.00723|0.0138|-0.0130|0.0594**|0.0430*|-0.00449*|0.00625|0.00194|
||(0.00897)|(0.00949)|(0.00844)|(0.0281)|(0.0227)|(0.00233)|(0.0185)|(0.0159)|
|ad_mexper|-0.000720|0.000122|-0.000484|-0.000725|-0.00299*|0.000230|-0.00152*|-0.000829|
||(0.000899)|(0.000378)|(0.00133)|(0.00153)|(0.00171)|(0.000482)|(0.000843)|(0.000860)|
|ad_mfemale|0.000367|0.00108|-0.00157|0.0970**|0.0150|0.00841|0.0246|-0.00612|
||(0.0187)|(0.00591)|(0.0287)|(0.0459)|(0.0487)|(0.0116)|(0.0259)|(0.0177)|
|ad_lemp|0.00597|0.0218**|0.0298**|0.189***|0.120***|0.0109**|0.0866***|0.0979***|
||(0.00947)|(0.00904)|(0.0137)|(0.0264)|(0.0298)|(0.00490)|(0.0152)|(0.0142)|
|Constant|0.461***|0.470***|0.217***|0.354***|0.936***|0.163***|0.247***|0.206***|
||(0.0215)|(0.0191)|(0.0393)|(0.0585)|(0.0678)|(0.0129)|(0.0403)|(0.0382)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 23
years old into childcare (Fong and Lokshin 1999). Given that five-year-old children can also participate in preparatory classes by request of their parents under certain circumstances, we do not consider their mothers as part of the control group. To validate our results, we also consider women without children as an additional control group. Nevertheless, we believe that mothers of three- to four-year-old children might be a better control group as they might be more similar to mothers of six-year-old children on unobservable characteristics, and also might confront unobservable shocks in a more similar fashion.

Table 1 shows summary statistics for all three groups. Women of all three groups are in their early to mid-thirties and the share of migrants<sup>4</sup> is negligible. While nearly nine out of ten women whose youngest child is three, four, or six years old are married, only half of the women without children are married. The divorce rate is also larger for women without children. Similarly, nearly all women with little children indicate that their partner lives with them in the household, while only every second woman without children does so. These numbers imply that there are some significant differences between treated and untreated women when considering women without children as the control group. In line with these patterns of results, women without children also differ in their skill distribution when compared to the other two groups. A significantly lower share is low-skilled. Moreover, they live on average in smaller households, and a higher share of household members is employed but also inactive. Table 1 also reveals that women without children are more likely to be employed, belong on average to a slightly higher labor income decile, work more hours, and are less likely to work part-time. In addition, the share of women without children who seek employment is larger when compared to women whose youngest child is six, three, or four years old. See Online Appendix A for data and sample details.

The difference in employment shares of the three groups described in Table 1 also becomes evident in Figure 2 and 3. The left graph plots the employment rate of mothers with children aged six years old, mothers with children between three and four years old, and women

> 4The EU-LFS collects information on the country of birth of each respondent. We define a migrant as a person whose country of birth is not Romania.

9
Table A1: Robustness check using the subsample with tenure being 3 years and less—Job tenure

|||Log (tenur|e in years)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.083|-0.039|-0.040|-0.091<sup>_∗∗_</sup>|
||(0.065)|(0.054)|(0.041)|(0.040)|
|Schooing in years||-0.009|-0.010|-0.003|
|||(0.007)|(0.008)|(0.009)|
|Age at hire||0.001|-0.004|-0.001|
|||(0.006)|(0.006)|(0.006)|
|Father’s pri.school||0.080|-0.036|-0.065|
|||(0.082)|(0.072)|(0.075)|
|Mother’s pri.school||0.087|0.128<sup>_∗∗_</sup>|0.172<sup>_∗∗∗_</sup>|
|||(0.054)|(0.055)|(0.056)|
|Math z-score||-0.068<sup>_∗_</sup>|-0.015|0.022|
|||(0.038)|(0.027)|(0.020)|
|Language z-score||0.113<sup>_∗∗_</sup>|0.103<sup>_∗∗_</sup>|0.048|
|||(0.054)|(0.048)|(0.047)|
|Big 5 index z-score||0.014|-0.043|-0.085|
|||(0.051)|(0.068)|(0.066)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.015|0.063|0.041|0.048|
|N|1745|1744|1744|1744|

_Note_ . The dependent variable log( _tenure_ ) is constructed by treating 0 year of tenure as 0.5 year, 1 year as 1.5 year, and so forth. The other controls included are establishment-entry occupation fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

38
to changes in GDP, we have again a problem of reverse causality. As indicated in Figure 1, we identify 19 o¤setting changes (13 in industrial countries and 6 in developing ones). The proportion of o¤setting tax changes is roughly the same in industrial and developing countries, with 22 percent (or 13 out of 60) of total tax changes in industrial and 17 percent (or 6 out of 36) in developing countries.

In line with the discussion in RR, contemporaneous IMF, OECD, and news articles often explicitly identify policymakers’intentions in this regard. However, even when that link was not made explicit, it is appropriate to classify these type of changes as endogenous. Speci...cally, we found the following cases:

- In 4 cases, VAT rates were raised because government spending was increased. For example, after twelve years of armed con‡ict in El Salvador that ended in 1992, the new government of President Armando Calderon Sol increased the VAT rate from 10 percent to 13 percent on July 1, 1995 to help ...nance a “National Reconstruction Plan which will provide for the rehabilitation of damaged infrastructure [during the civil war] and the reintroduction of di¤erent segments of society into the economic mainstream,”as stated by Mr. Fernandez, the IMF o¢ cial at the time. In the same vein, Norway increased the VAT rate from 23 percent to 24 percent on January 1, 2001 to ensure no budgetary implications of an increase in spending on health and education.

- In 11 cases, VAT rates were raised because other non-VAT taxes were reduced. In most cases, these VAT hikes were implemented to o¤set reductions in personal, corporate, and labor taxes aimed at increasing competitiveness and labor supply. For example, on June 18, 1979, the government of recently-elected Prime Minister Margaret Thatcher increased the VAT rate by 7 percentage points (from 8 percent to 15 percent) as part of a major tax reform intended to partially o¤set the impact of large cuts to marginal income tax rates.

- In 4 cases, the VAT rate was reduced because the VAT base was broadened and/or VAT reduced rates were increased or eliminated. For example, on May 1, 2004, the Czech Republic reduced the standard VAT rate from 22 to 19 percent and shifted over 25 percent of the consumption basket of goods and services from the reduced to the standard VAT rate.

# 4.3 Exogenous tax changes

Following RR’s identi...cation strategy, exogenous tax changes are those not made in response to (i) current or prospective output di¤erent from normal or (ii) other factors likely to a¤ect output contemporaneously or in the near future. In other words, exogenous tax changes do not present a problem of reverse causality because, according to the historical narrative, they should be uncorrelated 15