rafmacalaba/datause-encoder-data huggingface dataset:
In Argentina, DODO is the predominant contractual arrangement, with more than 85% of the branded gasoline stations operating under this arrangement while close to 10% are COCO outlets. In the United States 65% of the gasoline stations operate under COCO and CODO arrangements (being CODO much more common than<sup>COCO).</sup> Almost all the remaining 35% operate as DODO. Europe has a different distribution of contractual arrangements: 35% operate as COCO and just over _50%_ as DODO'<sup>8</sup> .

Why the data about contractual arrangements vary so much among countries? The answer has to be found in a cost-benefit analysis done by oil firms in each market.<sup>When</sup> oil companies face no legal constraints to the choice of contractual arrangement (for instance restrictions to vertical integration or limits to the duration of contracts),<sup>the</sup> optimal distribution of contractual arrangements depends on the ability of<sup>theoil</sup> company to solve a principal-agent problem. The theory (Tirole, 1988) says that an oil company chooses contracts with strong incentives but less direct control when retail outlets characteristics make unobservable effort by downstream agents (managers) important. Oil companies should trade off incentive power for more direct control<sup>when</sup> observable effort is relatively more important. The empirical implication of this theory is that in Argentina it is more difficult for an oil company to control its employees and so it is cheaper to operate a network of retail outlets with the DODO arrangement. It could also be the case that in Argentina the relation manager-customer has more weight when a consumer decides where to buy gasoline. In this country, a recurrent problem a consumer faces when buying gasoline is that gasoline could be adulterated. Thus, it is very important for the customer to trust the manager and this is more likely when the manager is the owner of the retail outlet because she bears the full cost of losing a customer.

The data clearly suggests that in Argentina the role played by the retail outlets operating under the DODO contractual arrangements is very important in the distribution network of oil companies. When an oil company decides how to allocate the contractual arrangements to maintain and expand its network, it not only takes into account the most efficient way to solve the principal-agent problem described in the previous paragraphs,<sup>it</sup> also incorporates in the decision-making process the competitive interaction in the

> 18 Sources: Secretariat of Energy, Argentina; Energy Information Administration, U.S; and RepsolYPF, Europe.

17
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-----== south africa (Ihs) — — — egypt (rhs) —— kenya (rhs)<br>—— morocco (rhs) —#— zimbabwe (rhs) —— tunisia (rhs)<br>2,000,000<br>1,500,000<br>1,000,000<br>500,000 ——<br>. eeaespoaierene—— 9OS—O—: :— fo 0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text -->
|**Table 5. Distribution of house**<br>|**holds by tariff brac**<br>c<br>|**kets and co**<br>onsumption<br>|**nsumptio**<br> percapita <br>|**n per capi**<br> quintiles<br>|**ta across**<br>|**quintiles**<br>|
|---|---|---|---|---|---|---|
|Brackets:KWh/Month|bottom|2|3|4|Top|Total|
|1-160|1|0|0|1|1|1|
|161-300|41|35|29|25|15|29|
|301-500|52|55|55|51|40|51|
|501-600|5|6|10|14|17|10|
|601-750|1|3|4|7|17|7|
|751-1000|0|0|1|2|8|2|
|>1000|0|0|0|0|2|0|
|Total|100|100|100|100|100|100|

Source: Authors’ calculations based on extrapolated HEIS 2010 data and official information. <u>Notes: Household quintiles based on spatially adjusted consumption per capita before the reform.</u>

# **3. Simulation of Subsidies Reform, Direct Impact**

All simulations in this paper are based on Jordan’s most recent Household Expenditures and Income Survey from 2010, a nationally representative survey which is used by the Department of Statistics (DOS) to produce official welfare aggregates and poverty estimates. Even though the reforms chosen for simulation were implemented in 2012, the analysis here refers to 2013.<sup>4</sup> Extrapolations between 2010 and 2013 are based on adjustments for economic growth (GDP per capita nominal) and inflation (CPI); also, household and population weights were updated to reflect population size in 2013.<sup>5</sup> Estimates of demand elasticity with respect to price are necessary in order to model consumer responses to price change. Given limitations of having only cross sectional household data with no variation in individual petroleum product prices across households, we used an own-price elasticity of -0.3 to simulate changes in quantities consumed.

## **3.1 Petroleum products**

Simulations for petroleum products are based on price changes largely mimicking the real reform that occurred in November 2012. The price of gasoline (Octane-90) rose by 14 percent, and diesel and kerosene prices increased by 33 percent. The price increases were meant to fully eliminate subsidies on these items. The highest increase in price was for LPG gas cylinders, whose unit price rose from JD6.5 to JD10, or by 53.8 percent. However, despite this large increase, LPG continued to be subsidized. In this section, we decided to simulate the full removal of petroleum subsidies and hence simulated for the full removal of LPG subsidies as well. This is the only difference of our simulation from real subsidies reform introduced in November 2012.

> 4 SUBSIM simulates short-term effects and November 2012 reforms were expected to kick in early in 2013.

> 5 GDP per capita growth and population size are taken from World Development Indicators (WDI), while CPI is based on official country numbers if different from WDI numbers. GDP per capita growth is used to inflate consumption, while CPI to inflate the poverty line. This procedure gives a poverty incidence of 13 percent for 2013 (lower than the official poverty estimate of 14.4 percent for 2010). Exact numbers used are shown in the annex.

9
Table 1 summarizes the results. Key findings include the following:<sup>2</sup>

- Seven MENA countries performed the best in their categories.  While several of these were hydrocarbon rich countries, some were not.  For example, the high achieving set includes the Arab Republic of Egypt and Jordan.  And Kuwait, which has been an oil rich country for many decades, had the worst performance relative to four comparators.

- Five MENA countries had the second best performance in their categories.  Yemen and Morocco are in this set though they are normally thought of as education underachievers within the region because their stock of education is not as high as that of other MENA countries.  The present methodology highlights the value of comparing relative to a starting point rather than comparing only at a given point in time.  While the latter shows prevailing gaps among countries, the former provides a better measure of policy effort and achievement over time.

**Table 1: Category wise performance of MENA countries for change in education stock (1970-2010)**

|Best in category|Amongbest two in category|
|---|---|
|Bahrain|Algeria|
|Egypt|Morocco|
|Iran|Saudi Arabia|
|Iraq|Tunisia|
|Jordan|Yemen|
|Libya||
|United Arab Emirates||

Table 2 takes another cut at the comparative performance question. It ranks MENA countries by how well they did in terms of percentage improvements in education stock during the past four decades. The table contains some remarkable results:

- Four MENA countries (Yemen, Libya, Egypt, and Morocco) are in the top 10 globally by this measure.  While this is due in part to the fact that the percentage change measure favors countries with low education stocks to begin with, the performance of these countries must be judged very impressive when combined with the information from Table 1 that they also outperformed many other countries with similarly low stocks of education in 1970.

- All but two MENA countries rank within the top half of the global distribution.  The two outliers are Qatar and Kuwait, which had relatively high stocks of education to begin with.

> 2 For Oman and Lebanon, which are not in the Barro-Lee dataset, other sources of information can be used to classify relative performance. Using percentage improvement in secondary school enrollment rates, for example, we find that Oman ranked best in its category while Lebanon failed to feature in the top three ranks in its group.

4
harvesters, and 10% for other services (Harris and Seid 2021). The lack of an exemption threshold for the _turnover tax_ and the high tax rate for most services make it particularly burdensome for microbusinesses, resulting in taxpayer dissatisfaction (Dixon, Freedman, and Yesegat 2019). In other countries, it is common to introduce a single small business tax or a presumptive tax regime, which replaces the VAT and other income taxes (Engelschalk and Loeprick 2016, IMF 2015).<sup>7</sup> In contrast, in Ethiopia, small and microbusinesses are subject to two taxes – the presumptive tax and the _turnover tax_ - with differing thresholds.<sup>8</sup>

# **3. Data and methodology**

# **_3.1 Data_**

The analysis uses two rounds of the Ethiopia Socioeconomic Survey (ESS) 2018/2019 and 2021/2022, a nationally representative panel survey, consisting of 6,770 households in 2018/2019 and 4,959 households in 2021/2022, although the Tigray region was excluded from the ESS 2021/2022.<sup>9</sup> The nonfarm enterprises in the sample are representative of those operated by the households. Period 1 is defined to be the ESS 2018/2019 and period 2 is the ESS 2021/2022. The panel survey includes a nonfarm enterprise module with questions on whether anyone and who in the household owns a nonfarm enterprise, the enterprise activity, the average monthly revenue, and whether the business is registered. It also includes a question on whether the enterprise paid taxes on income as well as the amount paid. The focus on Category C enterprises (with an annual turnover of less than 500,000 birr) resulted in 109 firms being excluded because their revenue exceeded the cut-off. The annual turnover is estimated by multiplying the self-reported average monthly revenue by the number of months of operation in a year.

The resulting sample consists of 1,523 nonfarm household enterprises in 2018/2019 and 1,374 enterprises in 2021/2022, which are disaggregated by sex of the owners. Female businesses are defined as those owned exclusively by women, while male businesses are those owned exclusively by men or jointly by women and men. Jointly-owned firms are classified as male firms because the presence of a male owner allows for easier entry into male-dominated sectors, which are more profitable (Alibhai et al., 2017). The estimates in the paper use household weights.

# **_3.2 Descriptive statistics: Gender and nonfarm enterprises_**

> 7 See Marchese (2021) for examples of these cases from Latin America.

> 8 The presumptive tax threshold at 500,000 birr is half the size of the VAT and turnover tax threshold at 1 million

> birr.

> 9 There were other ESS surveys that were conducted prior to the current presumptive tax schedule took effect in 2017 and are therefore excluded from the analysis.

6
- 15 -

occupation, mother's occupation, father's education and mother's education. Additional individual-level variables -- family size, home language, availability of reading materials in the home -- were too highly correlated with SES to be analyzed, although we report summary statistics on these variables as well.

# School Variables

29. Given the available data, measures were developed to indicate features of the school that have been found to be related to average achievement and withinschool achievement differentiation. The variables have been grouped into five categories: the social composition of the school, material and non-material inputs, academic emphasis, orderly environment and local control.

30. School social composition. Six social composition variables were created. Average family size (the percentage of families in the school with more than 5 children), dialect as home language (percentage of students from dialect-speaking family), English as home language (percentage of students from English-speaking family), average age (mean age of the class), average availability of reading materials in home (mean number of books in the home), and average SES.

31. Material and non-material inputs. The IEA data set includes a wealth of teacher and teaching variables, but relative few variables that measure actual inputs. Variables selected for analysis in this paper represent only a few of those available. They are: teacher education (whether or not the teacher has studied post-secondary science), teacher experience (number of years teaching experience), class size (number of students in science class), student time on "experiments or field work" (whether or not students spend more than half their
# Table IV.S

# Auto Parts Sample: Usefulness of Collective Marketing Support

||JE<br>Exporters|arl|yLate<br>Export|ers|IL<br>Exporters|arge<br>|
|---|---|---|---|---|---|---|
|1. Number of Finms<br>_<br>_|||7||6||
|2. Average Relative Importance of Support by Public<br>or Non Profit Agency for your Firm to Penetrate<br>Export Markets.||||||l|
|a. At First Export|2.4||1.8||2.6||
|b. In 1991|1.0||1.3||1.4||
|3. Proportion of Firms Receiving Service Provided by<br>such an Agency and its Relative Usefulness.|Ever<br>Used|**Usfl**<br>-|Ever<br>Used|Usfl<br> <br>-|Ever<br>Used|Usfl|
|Infonnation on Export Opportnities|0||.29|2.5|.33|2.5|
|Introduction of Buyers|.20|2.0|.29|1.5|.17|2.0|
|Participation in Domestic Trade Fair|.20|3.0|.14|3.0|.17|3.0|
|Participation in Overseas Trade Fair|0||.14|1.0|.17|1.0|
|Support of Exploratory Marketing Trips|0|=|.14|1.0|.17|IA)|
|Export - ImportAgency|0||.14|4.0|0|-|
|ObtainingForeign Standards|0||0|-|0|-|
|Overseas Distribution of Promotional<br>Material|.20|3.0|.14|2.0|.17|2.0|
|Joint Venture Technology Ties with Foreign Firm|0||.14|4.0|0||

Ever used = Average probability of ever having used. Usfi = Average usefulnes **s** core on a scale of 1 (low) to 5 (high).

Note: Early exporters began exporting before 1984. Late exporters began in 1984 or later.

62
**Table 7: Spatial Fixed Effects Estimates of the Impact of LTR on Female Access to Land**

|||**Full S**|**ample**||**Sample o**<br>**Cou**|**f Married**<br>**ples**|
|---|---|---|---|---|---|---|
||**Owns**<br>**land**|**Share of**<br>**land**|**Owns**<br>**land**|**Share of**<br>**land**|**Owns**<br>**land**|**Share of**<br>**land**|
|Treatment indicator|-0.030|2.706|-0.033|1.476|-0.080**|0.438|
||(-1.644)|(1.127)|(-1.633)|(0.788)|(-2.308)|(0.263)|
|Treatment X Female headship|||0.016<br>|6.309<br>|||
||||(0.600)|(1.276)|||
|Treatment X Marriage certificate|||||0.170***|4.355|
||||||(3.057)|(1.269)|
|Has marriage certificate|||||0.076***|3.179**|
||||||(2.775)|(1.974)|
|Number of years possessed|-0.002**|-0.030|-0.002**|-0.030|-0.001*|-0.079**|
||(-2.059)|(-0.726)|(-2.061)|(-0.731)|(-1.873)|(-2.151)|
|Parcel was purchased|0.003|-1.724|0.003|-1.829*|0.012|-1.646|
||(0.227)|(-1.535)|(0.203)|(-1.673)|(0.998)|(-1.545)|
|Parcel was inherited|0.017|-2.170|0.017|-2.306|0.019|-2.335|
||(1.063)|(-1.333)|(1.044)|(-1.458)|(1.372)|(-1.622)|
|Acquired through other means|0.013|8.148***|0.013|8.241***|0.044**|7.500***|
||(0.436)|(3.588)|(0.444)|(3.539)|(2.195)|(3.598)|
|Parcel size in hectares|0.001|-0.085|0.001|-0.065|0.005|0.025|
||(0.097)|(-0.162)|(0.104)|(-0.125)|(0.693)|(0.086)|
|Head's age|-0.001|0.103|-0.001|0.106|0.002*|0.246***|
||(-1.070)|(1.430)|(-1.056)|(1.483)|(1.663)|(2.762)|
|Female headed household|0.096***|29.886***|0.089***|27.317***|||
||(5.281)|(11.119)|(4.193)|(12.064)|||
|Head has at least primary education|0.006|-0.035|0.006|-0.017|0.009|-0.633|
||(0.388)|(-0.035)|(0.390)|(-0.017)|(0.628)|(-0.669)|
|Number of male children age 14 and less|0.047***|1.293***|0.047***|1.329***|0.010|-0.490|
||(6.250)|(2.930)|(6.288)|(3.028)|(1.499)|(-1.285)|
|No. of female children age 14 and less|0.046***|1.137**|0.046***|1.187**|0.007|-0.742|
||(5.926)|(2.389)|(5.915)|(2.445)|(1.060)|(-1.566)|
|Number of adult male age 15 to 60|0.020**|-1.294|0.020**|-1.265|-0.002|-1.370**|
||(2.112)|(-1.500)|(2.133)|(-1.504)|(-0.175)|(-2.018)|
|Number of adult female age 15 to 60|0.082***|2.190***|0.082***|2.140***|0.027*|0.807|
||(7.496)|(3.429)|(7.511)|(3.474)|(1.884)|(0.871)|
|Male members age 60 and above|-0.071**|-11.030***|-0.072**|-11.152***|-0.101***|-9.894***|
||(-1.984)|(-4.068)|(-1.973)|(-4.104)|(-2.765)|(-3.193)|
|Female members age 60 and above|0.317***|16.317***|0.317***|16.593***|0.129***|8.058***|
||(8.623)|(8.604)|(8.651)|(9.002)|(2.708)|(2.675)|
|Constant|-0.001|0.181|-0.001|0.188|0.023*|-3.495***|
||(-0.248)|(0.392)|(-0.245)|(0.403)|(1.939)|(-5.960)|
|Number of observations|6,225|6,219|6,225|6,219|4,609|4,608|
|R2|0.149|0.348|0.149|0.350|0.120|0.100|

**Note:** The geographical neighborhood of each household is defined at a distance of 1000 meters. Absolute value of t-statistics corrected for spatial dependence (1500 meters).* significant at 10%; ** significant at 5%; *** significant at 1%.

23
M. Regulatory substance- market entry index for Uganda and comparators, 2015

|**Regulatory substance**|**Kenya**|**Senegal**|**Tanzania**|**Uganda**|**International**<br>**benchmark**|
|---|---|---|---|---|---|
|**Market entry regulation**|100%|100%|100%|83%|77%|
|**Permitting new entrants**|100%|100%|100%|100%|90%|
|Responsible for monitoring compliance<br>with the terms of the license orpermit|||||88%|
|Authority to impose penalties for<br>violatinglicense orpermit terms|||||100%|
|Penalties are formally written and<br>publiclyavailable|||||80%|
|Provisions to force companies to<br>relinquish licenses or permits for<br>violation|||||100%|
|**PPA approvals**|100%|NAP|100%|67%|57%|
|Legally required to approve all power<br>sales contracts either directlyor indirectly|||||59%|
|Approve or refuse a proposed PPA in a<br>legallyspecifiedperiod of time||NAP|||60%|
|Authority over the process by which<br>utilities can select or procure power from<br>IPPs|||||50%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 55
response of incremental firms (those in the fifth decile) is zero. Nonetheless, our purpose is not to model the responsiveness of each size category, but rather to explore important differences in behavior between large and small firms. For that purpose, a cutoff that distinguishes large from small is useful, and the results in Table 4 show that it does not matter much whether one picks the first, second, third, or fourth decile when defining large firms.

The precision of estimates, however, is highest at the first decile of largest size, and partly for that reason, we prefer to use that definition. Furthermore, as we show in Appendix Table 3, there are a priori reasons to prefer the first decile as the definition of large firms. On average, the firms that choose to list issues abroad tend to be within the largest 12<sup>th</sup> percentile of firms. Those listed in the MSCI Index tend to be within the largest sixth percentile. These are two a priori indicators of firms that have access to foreign investors, one more selective than the other. Thus, we conclude that the firms in the top decile of firm size, which issue either abroad or in the domestic market, are large enough to be of interest to foreign investors.

# **V. Instrumental Variables Approach**

As discussed in the Introduction and in Section III, an increase in issuance activity could reflect an increase in foreign equity funding supply or domestic equity funding demand, or some combination of the two. This section analyzes the importance of the supply-side channel and whether it can explain the response, documented in the previous section, of the issuance activity of a large firms to equity inflows. To do so, it presents our instrumental variable (IV) regressions, which identify supply-side shocks affecting capital inflows.<sup>23</sup> We report our IV

> 23 The structure of our model combines aggregate country-level data in the first-stage regressions and firm-level data in the second-stage regressions. To account for most of the variation in the data, we use country and year fixed effects in the first-stage regressions and firm and country-year fixed effects in the second-stage regressions.

22
# Figure 4. Supply chain bottlenecks and trade integration

A. Supply chain pressures B. Impact of supply bottlenecks on goods trade and industrial production <!-- Start of picture text --> Standard deviations from average value<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> <!-- Start of picture text --> Index, 100 = December 2019<br>Trade: Counterfactual<br>120 Trade<br>Industrial production: Counterfactual<br>110 Industrial production<br>100<br>90<br>80<br>Dec-19 Feb-20 Apr-20 Jun-20 Aug-20 Oct-20 Dec-20 Feb-21 Apr-21 Jun-21 Aug-21 Oct-21<br><!-- End of picture text --> C. Global trade, tariffs, and Russia’s share in global oil production D. Foreign value added content of gross exports <!-- Start of picture text --> Share of GDP Percent Percent 1995 2008 2018<br>80 Trade 30 60<br>Tariff rate (RHS) 50<br>60 Russian Fed. share of oil production (RHS)<br>20 40<br>40 30<br>10 20<br>20<br>10<br>0 0 0<br>1950 1960 1970 1980 1990 2000 2010 2020 Advanced economies EMDEs<br>Sources: Benigno et al. (2022); BP Statistical Review; Organisation for Economic Co-operation and<br>Development; Federal Reserve Bank of New York; Penn World Tables; World Bank.<br>Note: EMDEs = emerging market and developing economies.<br><!-- End of picture text --> A. Chart shows the New York Federal Reserve’s Global Supply Chain Pressure Index on a monthly basis since 1998. The index is normalized such that a zero indicates that the index is at its average value, with positive/negative values representing how many standard deviations the index is above/below average. B. The effect of supply bottlenecks is derived from an ordinary least squares regression of global trade on the manufacturing PMI for new export orders, the manufacturing PMI for supplier delivery times, and two lags. Dotted lines show counterfactual scenarios derived by assuming that the PMI for supply delivery times (a proxy for supply bottlenecks) in January 2020-November 2021 had remained at its average 2019 level. Estimations are performed over the period from 2000-19.

C. Blue line shows global trade in percent of global GDP. Red line shows unweighted average tariffs for all products. Orange line shows oil production in the Russian Federation in percent of global oil production. Shaded area indicates cold war period of 1950-1990.

D. Chart shows the share of foreign value added content of gross exports in advanced economies and EMDEs, as defined in the OECD’s TiVA database.