rafmacalaba/datause-encoder-data huggingface dataset:
in some cases the increase is either not much in the last round or there is a decrease, while in other cases there appears to be a fluctuation in the effect of treatments over time.

The sixth set of hypotheses we test is the effect of time on fuel savings. Tests for differences in marginal effects across rounds show that they are statistically different. The magnitudes show an increase over time, except for the last round where there is some decline. This suggests that Mirt stove use requires learning and fuelwood savings results do not come immediately.

Few covariates are not found to be significant determinants of fuelwood savings, though gender, marital status, distance to road and average temperature are significant at least at the 10% level. We note that it is not surprising that few covariates are significant, because of our randomized treatment trial approach. Some of the district/woreda dummies are significant, suggesting variation in fuel savings across sites. This is also not surprising as there are differences in culture, fuel availability and agro-climatic conditions.

# _4.5 Emissions Reduction in CO2e_

We follow the method described in section 3 based on Lee et al. (2013) to calculate greenhouse gas emissions reductions from using Mirt instead of the traditional three-stone stove. As discussed above, we estimate that the Mirt stove on average saves 634 kilograms of fuelwood. To convert these into carbon emissions reductions we use a net calorific value of 15 MJ/kg of woody biomass (Hall et al. 1994) and 112 g of CO2 per MJ of fuelwood (UNFCCC, 2012). Considering the total woody biomass as non-renewable, 1.065 tons of CO2 would be saved per Mirt stove per year, which is about half of what Dresen et al. (2014) estimate.

If we instead use an 88% non-renewable biomass percentage, which is the CDM default value for Ethiopia (UNFCCC, 2012), we estimate 0.94 tons of CO2 saved per household per year. The CO2e corresponding to 9.4 million improved cookstoves, which is the official distribution goal of the Ethiopian government, assuming that all ICS distributed perform like the Mirt stove, would then be about 8.8 million tons of CO2e per year. Using the May 2015 California auction price of US$13.93 per ton of CO2,<sup>11</sup> these CO2e savings would be worth about US$123 million per year .

> 11 See <u>http://www.arb.ca.gov/cc/capandtrade/auction/may‐2015/summary_results_report.pdf downloaded May 29, 2015.</u>

**31 |** P a g e
# **4.  Impact on Health and Nutrition**

It is now well recognized that infrastructure may have a sizable impact on health outcomes in developing countries. As documented in the various microeconomic studies summarized by Brenneman and Kerf (2002)), access to safe water and sanitation helps to improve health, particularly among children. Recent surveys suggest that in some African cities, the death rate of children under five is about twice as high in slums (where water and sanitation services are poor, if not inexistent), compared to other urban communities. More formal studies by Behrman and Wolfe (1987), Lavy et al. (1996), Lee, Rosenzweig, and Pitt (1997), Newman et al. (2002), Leipziger et al. (2003), and Wagstaff and Claeson (2004, pp. 170-74) found that access to clean water and sanitation infrastructure helps to reduce infant mortality. In their study of Bolivia, for instance, Newman et al. (2002) found that investments in water systems led to declines in under-five mortality that were similar in size to those associated with health interventions. Greater access to clean water and sanitation also has a significant effect on the incidence of malaria, as documented by McCarthy, Wolf, and Wu (1999).

Access to electricity, by reducing the cost of boiling water, helps to improve hygiene and health as well. Availability of electricity is essential for the functioning of hospitals and the delivery of health services; vaccines, for instance, require continuous and reliable refrigeration to retain their effectiveness<sup>12</sup> . Getting access to clean energy for cooking in people's homes (as opposed to smoky traditional fuels, such as wood, crop residues, and charcoal) improves health outcomes, by reducing indoor air pollution and the incidence of respiratory illnesses (such as asthma and tuberculosis). According to World Bank estimates, more than half of the population in the developing world still relies on traditional biomass fuels, such as wood and charcoal, for cooking and heating (see Saghir (2005)). In Sub-Saharan Africa alone, the proportion cooking on biomass is over 90 percent. Traditional sources of energy represent serious health hazards; Warwick and Doig (2004) estimated that indoor air

> 12As noted by the World Health Organization  (2005, p. 36) lack of safe water and electricity has not only hampered the provision of heatlh services in poor countries but also raisd their cost.

18
<!-- Start of picture text --> Figure 4: Decline in Restaurant Figure 5: Decline in Movie Theater<br>Reservations vs. COVID Cases Revenues and Releases vs. COVID Cases<br>US Sweden March 30<br>-2 0 2 4 6<br>-5 0 5 10 Log(case per 1 million people)<br>Log(case per 1 million people)<br>Revenues, Y/Y percent Releases, level<br>No shutdown Shutdown<br>50 50<br>0 0<br>-50 -50<br>Restaurant reservations (Y/Y percent) Gross revenues and movie releases<br>-100<br>-100<br><!-- End of picture text --> Notes: U.S. Restaurant reservations against COVID incidence. Sweden: Movie releases and theater revenues against COVID incidence. See text for sources.

In both the cases of restaurant reservations in the United States and theater demand in Sweden, demand has fallen sharply and independent of NPIs. This suggests that, as in Wuhan, it is likely that release of NPIs will have little effect unless individuals are confident that the risk has diminished.

# **VI. Conclusion**

Several key findings thus emerge. First, clearly, the pattern of demobilization varies across countries with the political choices made. The United States and Japan have radically different degrees of demobilization.

Second, decreased mobility seems more driven by “voluntary” individual response to increased local and national COVID-19 case incidence, proxying for awareness or fear or social responsibility, rather than formal measures. For all except the poorest countries (LICs), the response of mobility with respect to cases is of similar orders of magnitude and can explain most of the reduction in mobility, dwarfing the effect of NPIs.

Third, that said, there is evidence that less affluent countries were also able to implement NPIs. LMICs and UMICs appear to have been able to engineer as much or more of a fall in mobility through NPIs as some HICs.

Fourth, our global data suggest that other measures beyond closing nonessential workplaces have important impacts-school closures, restrictions on internal mobility/shut-down of public transportation. Counterintuitively, public information campaigns appear to raise mobility- information on protective measures may make individuals feel more confident moving about.

13
variables of interest can be expressed in first-order terms.

Note that ( _θ_<sup>ˆ</sup> _t_<sup>_H−θ_ˆ</sup> _t_<sup>_F_)isafirstordertermandwassolvedinthefirst-ordersystem.</sup>

# **4 Quantitative Analysis**

This section explains the calibration and estimation procedure of the parameters, documents empirical and theoretical business cycle moments, and plots impulse responses to both the transitory and trend productivity shocks.

## **4.1 Calibrations**

I use a combination of calibrated and estimated parameters. The coefficients of risk aversion ( _σ_ ), consumption exponent ( _γ_ ) discount factor ( _β_ ), depreciation rate ( _δ_ ) and capital share ( _α_ ) are set as standards. I set both the convergence parameter ( _λ_ ) and the endogenous discount factor parameter ( _φ_ ) to 0.001. I set _g_ ̄ to the average growth rate of quarterly output from the data which is 1.0055 ( _i.e._ 0.55% per quarter). The investment cost _τ_ is calibrated so that the home asset holding _θ_ is 0.9 (about the level of home bias of U.S. investors in 1990-2000). I estimate the remaining structural parameters _ρz, σz, ρg, σg_ and _φ_ , using GMM estimation<sup>7</sup> .

Calibrated parameters are in table 1 below: I have the following parameters to estimate: _ρz, σz, ρg, σg_ and _φ_ . I also assume _σz_<sup>_H_=</sup> _σz_<sup>_F_sothatthemodelcanaccountforthedifferenceinU.S.andofG.6’soutputmo-</sup> ments. The six parameters are estimated by the GMM method, where the following theoretical moments std� _yyt_<sup>_H_</sup> _<u>t−</u>_<sup>_H_</sup> 1 �,std� _yyt_<sup>_F_</sup> _−t_<sup>_F_</sup> 1 �, std� _cc_<sup>_H_</sup> _t−_<sup>_H_</sup> _<u>t</u>_ 1 �,corr� _yc_<sup>_H_</sup> _<u>tt</u>_<sup>_H,_</sup><sup>_<u>y</u>_</sup> _yt_<sup>_H_</sup> _t_<sup>_F_</sup> �, corr� _yi_<sup>_H_</sup> _<u>tt</u>_<sup>_H,_</sup><sup>_<u>y</u>_</sup> _yt_<sup>_H_</sup> _t_<sup>_F_</sup> �,std� _yc_<sup>_H_</sup> _<u>tt</u>_<sup>_H_</sup> � are matched with the corresponding empirical moments. The estimated parameters are in table 2.

Historical quarterly data of U.S.’s GDP, consumption and investment are from the Bureau of Economic Analysis. Historical G.7’s PPP(Purchasing Power Parity) GDP

> 7See Burnside (1991) for a description and applications of the GMM methodology.

14
# **Appendix Table 3. ECOWAS Countries' Merchandise Exports**

||Level(in million d|ollars)|Share(inpercent|of total)|( <br>Average annual|In units indicated)<br>Direction of E|xports in 1974(inperc|ent)1/|
|---|---|---|---|---|---|---|---|---|
||1974|2004|1974|2004|percent change|ECOWAS|Other Africa|ROW|
|Total|12,630|46,991|100.0|100.0|4.5|3.1|0.9|96.0|
|Benin|34|349|0.3|0.7|8.1|19.9|1.0|79.0|
|Burkina Faso|36|371|0.3|0.8|8.0|47.2|0.2|52.7|
|Cape Verde|2|21|0.0|0.0|8.3|6.7|20.0|73.3|
|Cote d'Ivoire|1,213|6,545|9.6|13.9|5.8|9.4|3.9|86.7|
|Gambia|39|37|0.3|0.1|-0.2|1.7|0.0|98.3|
|Ghana|729|2,268|5.8|4.8|3.9|0.7|0.5|98.7|
|Guinea|112|560|0.9|1.2|5.5|0.1|7.8|92.1|
|Guinea-Bissau|3|115|0.0|0.2|13.1|1.4|1.9|96.7|
|Liberia|400|959|3.2|2.0|3.0|0.9|0.3|98.9|
|Mali|64|327|0.5|0.7|5.6|32.1|3.9|64.0|
|Niger|53|222|0.4|0.5|4.9|33.4|0.3|66.3|
|Nigeria|9,219|33,209|73.0|70.7|4.4|1.8|0.2|98.1|
|Senegal|391|1,269|3.1|2.7|4.0|10.3|7.2|82.5|
|Sierra Leone|146|185|1.2|0.4|0.8|2.0|0.0|98.0|
|Togo|188|554|1.5|1.2|3.7|2.3|0.3|97.4|
|Memorandum items:|||||||||
|WAEMU countries 2/|1,982|9,752|15.7|20.8|5.5|11.1|4.0|84.8|
|Non-WAEMU countries 3/|10,648|37,239|84.3|79.2|4.3|1.6|0.3|98.1|
|World exports|746,464|9,113,<br>972|...|...|8.7|1.7|3.3|95.0|

Sources: IMF Direction of Trade Statistics, World Economic Outlook database; and staff estimates.

1/ For Cape Verde and Guinea 1976 and 1981 respectively.

2/ Benin, Burkina Faso, Cote d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo 3/ Cape Verde, The Gambia, Ghana, Guinea, Liberia, Nigeria, and Sierra Leone 27
suggesting that the reforms also had intensive margin effects. Overall, the evidence suggests that increased competition between banks facilitated the provision of cheaper credit and better allocation of capital to new projects.

# **_E. Role of Large Banks and Foreign Banks_**

Differences in the organizational structure of banks can have important consequences for SME lending. The dominant view from the literature on large banks is that decentralized banks can act on soft information, allowing them to alleviate credit constraints for small businesses. For example, Sapienza (2002) shows that small firms are less likely to borrow from banks subsequent to mergers relative to firms borrowing from banks that have not merged. Using US data, Berger, Miller, Petersen, Rajan, and Stein (2005) show that small businesses in US regions with a majority of large banks were more likely to face credit constraints than firms located close to small, decentralized banks. Mian (2006) also shows that decentralized banks lend more to small firms in markets with weak contract enforceability. However, Canales and Nanda (2012) argue that there is a darker side to decentralized banks in concentrated markets where they can exploit their market power and cherry-pick clients or charge higher interest rates to small businesses.

Foreign banks have increasingly played an important role in domestic financial intermediation. Claessens and Van-Horen (2014) provide a comprehensive database of ownership information of 5,324 banks active in 137 countries over the period 1995–2009, and document a sharp increase in foreign bank ownership over this period affecting a large number of countries. They show that although the majority of foreign banks are from OECD home countries, the number of foreign 18
# monopsonists.

We do not know the 1993 picture yet, but for 1992 large loss-making firms accoumted for around 8% of employment in the enterprise sector (as measured by those submitting tax returns) and total loss-mnakers for nearly 40% of employment (or c22% of aggregate employment). These numbers are large. But 'exit' rules, as we indicate in more detail below, are still protracted and there appears to be a major bottleneck in the courts' ability to handle bankruptcies. The average lag between filing and conclusion of legal process appears to be in excess of two years.

What accounts for the losses ? There are several possibilities; the first is a possible negative relative price shock-cum-technological shock. Further, many of the major loss-makers appear to have very high prior exposure to CMlEA markets; this appears to hold for a significant proportion of the strategic companies, indicated in Table 3, where export revenues averaged over 40% of total sales in 1989. The second can be attributed to the high cost of borrowing facing firms. There appears to be little long term credit made available to firms by banks while short term borrowing is at real interest rates currently between 15-20%, deflating by producer prices. These problems are magnified of course in cases where fimns entered the transition under-capitalized. Viable fiuns appear to have started borrowing abroad.

# The firm sector, losses and financing

What follows is largely based on the 1992 balance sheet data, whose quality remains suspect. Nevertheless, aggregate numbers appear to be meaningful. What is more complex concerns the apparent separation of legal entities from productive units. Given that the dataset is primarily a tax record, this reflects the dissociation of assets and liabilities and is part of the shell game that significant numbers of firms play with the tax authorities and with the banking system. As such, the phenomenon can be seen as part of the broader question of the erosion of the tax base. The employment implications are rather more opaque.

For 1992 we find that nearly 50% of firms filing tax returns registered losses in excess of 0.1 billion forints; the sum of those losses accounted for 13% of GDP (HUF380 billion). The distributions are very similar for state firms and private ones; 50% make losses; roughly 50°/0 of their respective employments are in loss makers. Losses swamp the profitable entities. In short, at first inspection there seems to be litde that- separates out state from privatized firms.

8
# **Figure 8. Getting credit**

<!-- Start of picture text --> Rank Depth of credit information<br>index (0-6)<br>140 117 120 120 116<br>120 110 6 5<br>94<br>100 89 5 4<br>80 4 3 3 3<br>60 38 3 2 2<br>40 2 1<br>20 1<br>0 0<br>Private bureau<br>Strength of legal rights<br>coverage<br>index<br>Public registry coverage (% of adults) (0-10) 70 61<br>8 60<br>12 7<br>10 7 50<br>10<br>8 6<br>5 5 40<br>8<br>6 5<br>4 4 4 4 30<br>6 5 5 5 5<br>4<br>3 17 18<br>4 3 20<br>10<br>2 1 2 10 6 5 7<br>1<br>0 1 0<br>OIC GCC OPEC OECD OIC‐GCC Low income MENA developing countries 0 OIC GCC OPEC OECD OIC‐GCC Low income MENA developing countries<br><!-- End of picture text --> Source: Doing Business Report 2011

**In addition to the challenge of access to credit, SME in OIC countries also suffer from difficulties to start a business and enforce contracts, which further slow down the life cycle of SMEs and becomes obstacles for them to obtain financial investment.** Starting a new business requires 40 days and 8 procedures on average in OIC countries. The official fees for legal procedures and professional services required by law amount to 58% of income per capita, and the opportunity is only open to those business with a minimum capital equals to 122% of income per capita. On comparison, in OECD countries, it only takes 14 days and 6 procedures to

33
<!-- Start of picture text --> a. Changes in poverty rates and income, 1992-2014<br>0.10<br>0.05<br>Ba ee <i<br>gS2S8 0.00 ~<br>Bb23BB 0.05 z ve =e 4Bs.e<br>3:84 .<br>-0.10<br>-0.15<br>-0.04 0.00 0.04 0.08<br>Municipalities’ mean per capita income<br>(annualised growth, 1992-2014)<br><!-- End of picture text --> <!-- Start of picture text --> b. Changes in poverty rates and inequality, 1992-2014<br>0.10<br>0.05<br>BR 6 hh per. ee<br>gSsg8 0.00 o 2Peeseed a . <<br>Bbos8 B 0.05 ’ <<; arSSoe enbi=é<br>Be34<br>-0.10<br>-0.15<br>~0.02 -0.01 0.00 0.01 0.02 0.0:<br>Municipalities’ Gini coefficient<br>(annualised growth, 1992-2014)<br><!-- End of picture text -->
# **Table 1. Total Number of Listed Firms**

|||Full sample|||Full sample||Low- and|Middle-Incom|e Countries|Low- and|Middle-Incom|e Countries|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Stock Market Capitalization<br>(% of GDP)|0.073|||0.047|||0.026|||0.055|||
||(0.052)|||(0.037)|||(0.021)|||(0.034)|||
|Stock Market Total Value||0.078***|||0.086***|||0.090***|||0.092***||
|Traded (% of GDP)||(0.026)|||(0.028)|||(0.027)|||(0.028)||
|Stock Market Turnover|||0.020|||0.046*|||0.032|||0.020|
|Ratio|||(0.033)|||(0.026)|||(0.025)|||(0.024)|
|Foreign Direct Investment,|0.000|0.000|0.000|0.000|-0.001|0.000|-0.005|-0.010|-0.001|0.003|-0.013|-0.003|
|net inflows (% of GDP)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.009)|(0.009)|(0.009)|(0.008)|(0.009)|(0.007)|
|Political Stability and|-0.019|-0.074|-0.066||||0.011|0.012|-0.056||||
|Absence of Violence|(0.143)|(0.145)|(0.129)||||(0.0962)|(0.1)|(0.0914)||||
|||||-0.012|-0.016|-0.016||||0.033|0.022|0.031|
|Investment Profile||||(0.019)|(0.015)|(0.016)||||(0.025)|(0.025)|(0.025)|
||4.595***|5.069***|5.133***|4.940***|5.246***|5.300***|4.429***|4.852***|4.677***|4.134***|4.673***|4.554***|
|Constant|(0.217)|(0.068)|(0.091)|(0.284)|(0.192)|(0.160)|(0.124)|(0.084)|(0.096)|(0.303)|(0.248)|(0.232)|
|Country Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Time Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Observations|584|555|613|544|535|580|277|242|269|262|239|260|
|R-squared|0.036|0.02|0.007|0.021|0.022|0.011|0.089|0.131|0.076|0.123|0.145|0.084|

_Note:_ The table reports fixed effects panel regressions using 5 years of data. The outcome variable is total number of listed firms. FDI net flows as percentage of GDP, political stability and absence of violence/terrorism, as well as investment profile are used as controls. This analysis includes both country and year fixed effects. One-year lags (t-1) are applied to all the explanatory variables and controls. Markings *, ** and *** denote significance at 10%, 5% and 1%, respectively. Robust standard errors are presented in parentheses. All variables are described in Table A-7 and Table A-9 in the annex.

12
kinds of trade polices used in practice. Finally, we compare ...xed cost subsidies of various kinds in terms of their e¤ectiveness in promoting exports.

In our model, there are two sources of ...rm heterogeneity: ...rm speci...c productivity as in Melitz (2003), and ...rm and market speci...c demand shocks, which is motivated by the ...ndings in Demidova, Kee and Krishna (2012). They use a ...rm level data set on Bangladeshi garment producers and show that ...rms roughly follow the productivity hierarchy predicted in Melitz (2003), namely, that ...rms export to all markets that are easier than the toughest one they export to, and more productive ...rms export to tougher markets. However, there are a number of violators. While these violators are small in terms of their numbers, they are large in terms of their output. This fact can be rationalized by introducing ...rm and market speci...c demand shocks. Such shocks allow us to explain why, given its productivity, a ...rm may be very successful in one market but not the other.<sup>3</sup> We chose not to use the approach of Arkolakis (2010), who argues that ...rms have a choice of penetration costs that increase with the number of consumers ...rms want to access and decrease with the market size, which allows small exporters to exist - something that would be ruled out by large ...xed costs of entry. However, even with his approach, but without the presence of ...rm and market speci...c demand shocks, there would be a very strong positive correlation in the size of the ...rm’s market shares across export destinations, something we do not see in our data. To explain the data we need ...rm and market speci...c demand shocks, as postulated here. Such demand shocks do much of the work in ...tting the data, which is also consistent with the work of Roberts et al. (2012) and Eaton et al. (2011).

In addition to two dimensional heterogeneity, we also incorporate, albeit simply, various real world trade policies; tari¤s, preferences, rules of origin, and quotas into our model. We focus only on the partial equilibrium interaction between Bangladeshi ...rms and take the prices and actions of other ...rms operating in the EU and US as ...xed.

> 3Eaton, Kortum and Kramarz (2011) also postulate the existence of ...rm and market speci...c demand shocks. Kee and Krishna (2008) look at the patterns in the violations and what might explain them. Armenter and Koren (2014) assume heterogeneity on the ...xed cost side. They show that matching the share of exporters in a standard Melitz model to the data results in having exports per ...rm far larger than in the data. Fixed costs heterogeneity helps to reduce this mismatch and explain hierarchy violations.

4