rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: Counterfactuals: Welfare Implications

|Policy:|Cash transfers|Universal voucher|Employment-based|Income-targeted|Quality-based|Public provision|
|---|---|---|---|---|---|---|
|Acronym:|CT|UV|voucher<br>EV|voucher<br>IV|voucher<br>QV|PP|
|Panel a. Percent of households that gain or lose|||||||
|Percent of households that gain|||||||
|All households|100.00|56.17|47.77|29.60|49.69|27.31|
|Married mothers, college|100.00|54.85|48.55|29.74|48.67|30.34|
|Married mothers, high school|99.99|54.64|45.84|23.01|48.13|21.09|
|Married mothers, incomplete high school|100.00|63.03|53.95|16.35|55.49|16.43|
|Single mothers|100.00|60.25|48.25|46.18|53.43|36.21|
|Percent of households that lose|0.00|1.18|2.14|4.64|2.17|4.56|
|Percent of households with no welfare change|0.00|42.65|50.09|65.76|48.14|68.13|
|Panel b. Percent of winning households with an increase in:|||||||
|Consumption|95.93|65.87|74.60|82.97|68.93|92.37|
|Idiosyncratic preferences for child care provider|12.61|60.52|48.49|21.72|49.27|9.01|
|Child development|13.05|50.03|42.99|16.84|44.73|12.54|
|Mother’s leisure|15.64|35.61|31.42|13.12|39.31|9.09|
|Father’s leisure|5.74|37.28|32.32|9.38|33.69|7.60|
|Panel c. Avg. welfare change|||||||
|Avg. dollars/day|44.24|16.15|10.72|1.55|11.80|0.43|
|Avg. dollars/day as a % of baseline household income|||||||
|All households|19.39|7.08|4.70|0.68|5.17|0.19|
|Married mothers, college|12.67|5.22|3.64|-0.02|3.74|0.06|
|Married mothers, high school|22.01|7.54|5.16|0.55|5.74|0.37|
|Married mothers, incomplete high school|28.82|9.60|7.25|0.83|7.65|0.44|
|Single mothers|73.56|23.59|12.30|9.12|16.36|0.45|
|Panel d. Fiscal Costs|||||||
|Total fscal costs (dollars per day)|36,000|16,857|12,204|2,376|14,013|0.00|
|Fiscal cost per child ($/day)|40.00|18.73|13.56|2.64|15.57|0.00|
|Implicit Tax Rate (%)|18.40|4.62|2.55|0.09|3.37|0.00|

Note: For a given policy, welfare changes are computed as compensating variation relative to the baseline. _Winning households_ are those that experience a positive welfare change (i.e., a welfare gain). _Total fiscal costs_ is the sum of cash transfers or vouchers taken up by households. _Implicit Tax Rate_ is equal to (total fiscal cost / total household income)*100 in the policy equilibrium.

51
and/or his/her spouse regardless of when they left, especially if the mother is absent or no longer alive. These methods were used in various Albania Living Standards Measurement Surveys (INSTAT, 2002, 2005; Carletto and Azzarri, 2007).

In all of these techniques, the critical issue is double-counting of migrants, especially those who can be claimed as members in other households’ rosters. The problem of double-counting is even more acute if household rosters are further extended to include any former household member irrespective of their relationship to the household head. Constructing the list based on clearly defined familial relationships, such as for children or siblings, renders the identification and recall of potential migrants simpler and more accurate, and the sample more demographically representative.

# **_Collecting information to assess the impact of migration_**

Data collection needs to be designed according to objectives. Since migration, by its very definition, is a selective process, any analysis needs to control for the determinants of migration and collect the necessary information for identification of both migrant (treatment) and non-migrant (control) individuals and their households. In addition, data on pre-migration conditions are needed. Assuming a migrant is identified based on departure within a year, the pre-migration timing corresponds to the prior year. For longer reference periods, ideally one would want to collect information for each single year as the factors affecting migration are likely to have changed over time (Bilsborrow et al., 1997).

17
_Source: World Bank Private Participation in Infrastructure Database._

**Optimal service provision approaches require an enabling environment to materialize.** It is a good sign that the private sector is playing a bigger role in power generation. But governments across the region need to set the conditions for an even bigger role of the private sector in a service such as power generation, which is better suited for liberalization, and shift the public resources and efforts toward other services where the public sector has the comparative advantage. In that sense, South Asian governments should aim for a transparent, well designed, and implemented regulatory framework for both public and private operators of infrastructure services to attract private investment in line with the best organizational form for each service.

# **8. Decentralization of Infrastructure**<sup>**32**</sup>

**Decentralization means to distribute the administrative powers or functions of a central authority over a less concentrated area.** As stated by the World Development Report (2004):<sup>33</sup> “Decentralization can be a powerful tool for moving decision making closer to those affected by it. Doing so can strengthen the links and accountability between policymakers and citizens—local governments are potentially more accountable to local demands. It can also strengthen them between policymakers and providers—local governments are potentially more able to monitor providers. But local governments should not be romanticized. Like national governments they are vulnerable to capture—and this might be easier for local elites on a local scale.” Ultimately, decentralization is a tool for improving service delivery to the smallest units of society—households and individuals.

**The degree of decision making at the local level determines the type of administrative decentralization.** For example, de-concentration refers to the process of dispersing responsibility of the central government to its regional offices—often a mere shift of responsibilities from the capital offices to the regional ones. Delegation however, enables local governments to perform on behalf of the central government, sharing decision-making and administration responsibilities. However, the most complete form of decentralization is devolution, which transfers authority for decision-making, finance, and management to local government.  Local governments elect their own mayors, raise their own revenues, and have independent investment powers.

## **8.1 Conditions for Successful Decentralization**

**The experience on decentralization and infrastructure service delivery is mixed.** The varying results of different decentralization attempts point to its underlying essence. The outcome of decentralization is highly contingent on the type of infrastructure and the existing institutional, political, and economic environment in a given country. Decentralization is also a dynamic process that takes time to be fully integrated in any given society. That said, there are several features that characterize successful

> 32 This section is based on Andres, Biller, and Schwartz (2013).

> 33 World Bank (2004).

29
Table 7. Impacts of Proximity to Refugee Camps on Health Outcomes

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_A. Likelihood of Symp _|_toms of Viral Di_|_seases_|||
|Indicator Variables|Diarrhea<br>last month|Fever<br>last month|Cough<br>last month|—|
|SID|0.021*|0.037**|0.027**|–|
||(0.014)|(0.011)|(0.007)|–|
|FDR q-values|[0.062]|[0.019]|[0.019]|–|
|R-squared|0.013|0.017|0.025|–|
|Observations|1,831|3,341|2,187|–|
|_B. Type of Health Prov_|_ider_||||
|Indicator Variables|UN|NGO|Privately run|Religious group|
|SID|0.002|0.017**|-0.016|-0.000|
||(0.001)|(0.004)|(0.010)|(0.001)|
|FDR q-values|[0.323]|[0.021]|[0.323]|[0.483]|
|R-squared|0.001|0.026|0.017|0.006|
|Observations|6,916|6,916|6,916|6,916|
|_C. Perceptions of Heal_|_th Services_||||
||Improved|Improved|Improved|Improved|
|Indicator Variables|overall|health care|health care|health care|
||health care|treatments|safety|accessibility|
|SID|0.003|0.012|0.014|0.001|
||(0.006)|(0.008)|(0.010)|(0.007)|
|FDR q-values|[0.846]|[0.846]|[0.846]|[0.846]|
|R-squared|0.003|0.006|0.004|0.005|
|Observations|4,301|2,925|2,358|3,074|
|Outcome Level|Individual|Individual|Individual|Individual|

_Notes:_ The table presents results from equation 3. SID represents standardized values of the inverse weighted distance from the respondent’s household location to refugee camps. Dependent variables from Panels A and B are available for the 2019 and 2023 waves, while Panel C variables are available only for the 2019 wave. Detailed explanations for the construction of dependent variables are provided in Appendix B. Panel A includes indicator variables for (i) Diarrhea, (ii) Fever, and (iii) Cough reported by respondents in the last month. Panel B indicates the entity running the health care provider visited by respondents in the last month. Panel C includes indicators for (i) Improved overall health care compared to before July 2017, (ii) Better treatment by staff and users, (iii) Safer health care, and (iv) Easier access to health care. Estimates control for individual characteristics including male=1, age, household size in 2019, household head=1, married=1, and household income sufficiency in 2017. Wave fixed effects are included and clustered standard errors at the _upazila_ (subdistrict) level are presented in parentheses, and False Discovery Rate (FDR) q-values are reported in brackets. Significance levels:<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%,<sup>_∗_</sup> at 10%.

48
16 52. Some local/provincial governments are given power to tax motor fuel and retain the revenue. In Indonesia, provincial government is, in theory, allowed to levy fuel tax, but this power is not used. Bahl and Linn (forthcoming) surveyed the local government use of motor fuel tax in the 1970s and 1980s. They found fuel tax was levied by a few large urban governments in LDCs.

_53._ A . A major attraction of motor fuel taxation is ease of administration. Since there are relatively few distribution points, and few distributors, the liable taxpayer is easily identified and policed. In Indonesia, for example, the nationally owned oil company (Pertamina), assesses and collects all domestic tax on motor fuel. The situation is similar in Nepal. Tax on motor fuel in Ghana is paid by a small number of distributors. The tax is based on their supply from the National Petroleum Company, which is the sole importer. In Yugoslavia, the tax is administered by a few petrol companies, that own and operate petrol 'tations. Input and output prices remain centrally determined. Fuel price is centrally regulated in Indch, where the government collects the tax at its source as part of import or ex-factory prices. Siace input and output prices are usually fixed, collection cost is borne by distributors or factories. As the compliance controls are straightforward, collection cost is not high. Due (1988, p. 79), notes that inspectors at refineries are not necessary. In Bolivia, collection cost is estimated to be less than 1 percent of collected fuel tax revenue.

54. Evasion. avoidance and revenue potential. Problems with evasion or avoidance of - motor fuel tax in the eight sample countries are rare all report a collection rate of 100 percent.<sup>2</sup> ' But a 100 percent collection rate means only that the total billed amount is collected. This implies that as far as tax collection is concerned, evasion is not a problem. Neither is assessment a problem, since taxpayers and petroleum suppliers are easily identified, and assessment methods are straightforward.

_55._ The bigger issues in transport tax administration are whether the fuel tax base is assessed fully and whether legal tax avoidance occurs. The evasion of motor fuel tax through smuggling is discussed later.

56. The first gap between revenue potential and revenue collection lies within the legal definition of the tax base, that is, the level at which market price is defined. Among LDCs, any commonality of practice in fuel pricing is the placement of diesel and kerosene below border prices and the placement of gasoline at a higher level. One recent World Bank study in Tanzania estimates that, on average, diesel and kerosene are set at 25 to 40 percent below international border prices, and that gasoline, on average, is set at 50 percent above. The ideal

> 2/ An **inteeting exception** is Chad **where the** government failed to pay for **its** deliveries and the oil companies **responded** by withholding their **tax** payments. 4
# **Table 7(b): Eigen vector of the variables entering into the respective cointegrating vector**

**beta (scaled on diagonal; cointegrating vectors in columns**

|rm2|1.0000|-0.47695|165.25|21.856|-6.0917|
|---|---|---|---|---|---|
|rgdp|-1.2264|1.0000|-259.85|-30.087|8.5706|
|INFL|0.013214|0.024104|1.0000|-1.2279|-0.086809|
|Interestrt|0.37234|0.00087224|-17.559|1.0000|-1.3580|
|DISC|-0.35275|-0.074257|7.6098|-0.41313|1.0000|
|Constant|1.7232|-2.6453|695.39|71.806|-15.638|

|**alpha**<br>||||||
|---|---|---|---|---|---|
|rm2|-0.088507|0.18350|-0.00024601|0.0010006|0.0032835|
|rgdp|-0.038323|0.082962|0.00037222|0.0013230|-0.00055115|
|INFL|7.6654|-5.7450|-0.0025741|0.014953|0.67741|
|Interestrt|-0.83995|-0.20789|0.0060341|-0.030286|0.047328|
|DISC|0.77753|1.3702|0.0060106|-0.026798|0.028332|

48
<!-- Start of picture text --> "Grand Total<br>Sub-Saharan Africa<br>= s 2s at | | North America.<br>AMiddle East & North Africa<br>“Latin America & Caribbean<br>; ; ; E *Europe & Central Asia<br>®East Asia & Pacific<br>* 7 *—t ' Australia<br>1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->
14 Second, the growth of China’s manufacturing sector has also made intensive use of commodities, particularly hard commodities and energy. To a considerable extent this is reflected in the metals and minerals-intensity of China’s rapidly-growing manufactured exports which comprised the bulk of exports between 1990 and 2006 (Figure 3).

As a result of these combined factors, the elasticity of demand for energy and metals grew rapidly between the 1990s and the 2000s, and for key resource inputs such as coal, pig iron, crude steel and rolled steel, comfortably exceed a value of one (Table 8). That is, for example, every 1 percent increase in GDP saw a more than 2 percent increase in the demand for rolled steel.

**Table 8: Elasticity of Energy Consumption and Metal Production in China**

|||**(1991**<br>|**-2005)**<br>|||
|---|---|---|---|---|---|
|**Period**|**Coal**|**Crude Oil**|**Pig Iron**|**Crude**<br>**Steel **|**Rolled**<br>**Steel **|
|**1991-1995**|0.441|0.569|0.900|0.614|0.958|
|**2001-2005**|1.105|0.832|2.222|2.340|2.545|

Source: Selected from Zhang and Zheng (2008).

**Figure 3: China’s Metal and Minerals Intensive Exports in Total Manufactures Exports (1990-2006)**

<!-- Start of picture text --> 1,000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006<br>Total Manufactures Metals Intensive Products<br>$ Billions<br><!-- End of picture text --> Source: Farooki (2009), from COMTRADE data accessed via WITS in November 2008. The listing of metals-intensive sectors is available in Farooki (2009; Annex 1).

With regard to agricultural inputs, a key component of demand at low per capita incomes is that for food products. Studies of urban consumers in China show that the income elasticity of demand for food falls from almost unity (0.96) at household incomes around Yuan2,500 ($375) p.a., to 0.4 for household incomes of Yuan7,500 ($1,125) and to 0.33 for household incomes of Yuan10,000 ($1,500).<sup>2</sup> Thus, even though incomes are growing (and the income elasticity of demand for food is falling), there is considerable scope for sustained demand for food, particularly as in 2007, around 75 percent of Chinese households had an annual income of less than

> 2 Adapted from Gale and Huang (2007)
reduction. Brambilla's (2005) policy simulations suggest that after equalizing tariffs between the two countries, Argentine consumers gained 393 dollars per vehicle sold, whereas their Brazilian peers lost 204 per vehicle. Tovar (2012) finds sizeable welfare gains of $3,000 per vehicle from trade liberalization but concludes that 95 percent of these welfare gains for consumers came from expanded product variety, not from price changes. Unlike our study, none of these papers accounts for the environmental costs and benefits of a trade policy.

# 2. Data and Policy Context

This section describes the data sources, provides key characteristics of Colombia’s new vehicle market, and outlines the major policy changes affecting hybrid and electric vehicles during the data period.

## 2.1 Data

The primary data set used to estimate the model parameters and conduct counterfactual simulations consists of quarterly observations of new vehicles in Colombia between 2015 and 2020. For each vehicle, we observe new registrations, prices, and attributes. This subsection describes the construction of the data set.

Data on vehicle registrations and attributes from IHSMarkit are the primary data source. A vehicle is defined by a unique model name, trim, engine size, and fuel type. Each quarter, there are typically about 30,000 vehicles with positive registrations. The definition of a vehicle is highly specific and distinguishes between vehicles sold under the same model’s name, but which have different fuel types (such as gasoline or hybrid), engine size (such as 4-cylinder or 6- cylinder), or trim level (such as the “standard” or “sport” trim). For example, a gasoline-powered Mazda 3 Standard trim with a 1.6-liter engine is a unique vehicle. This level of detail is comparable to some recent papers on Europe (Grigolon, Reynaert, and Verboven, 2018) or the United States (e.g., Leard, Linn, and Zhou 2021), and it is unusual for papers on low- or middleincome countries. As Section 4 explains in more detail, disaggregation helps identify preference parameters.

7
The explanatory variables vary by specification, but time and country fixed effects are included in every specification, so are the log of the initial GDP, education, investment, government consumption, inflation, democracy, trade openness and some measure of natural resource rents.

# **4 Data**

**Table 1** – Description of variables

|**Variable**|**Description**|**Source**|
|---|---|---|
|Dependent variable|Growth in per capita GDP, 5-year averages|PWT|
|Log of initial GDP|Log of initial GDP per capita measured at start of each period|PWT|
|Education|Share of tertiary educated in working age population|IIASA|
|Log initial GDP * Education|Interaction between education variable and initial GDP||
|Openness|Share of exports plus imports in GDP, fltered for its relation to<br>log(area) and log(population), 5-year average|PWT|
|Investment|Share of investment in GDP, 5-year average|PWT|
|Government consumption|Government consumption as share of GDP, 5-year average|PWT|
|Democracy|Democracy index (0 least, 10 most democratic), lagged by 5 years|Polity IV|
|Infation|Infation rates, 5-year average|WDI|
|Natural resource rents|Natural resource rents as share of GDP, 5-year averages|WDI|
|Oil rents|Oil rents as share of GDP, 5-year averages|WDI|
|Mineral rents|Mineral rents as share of GDP, 5-year averages|WDI|
|Forest rents|Forest rents as share of GDP, 5-year averages|WDI|
|Non-oil rents|Natural resource rents excluding oil as share of GDP, 5-year averages|WDI|
|Oil rents, 1st quartile|Oil rents if oil rents belong to the lowest quartile in corresponding<br>period, 0 otherwise (i.e. interaction of the oil rents variable with a<br>dummy variable indicating the frst quartile)||
|Oil rents, 2nd quartile|Oil rents if oil rents belong to the 2nd quartile in corresponding period,<br>0 otherwise||
|Oil rents, 3rd quartile|Oil rents if oil rents belong to the 3rd quartile in corresponding period,<br>0 otherwise||
|Oil rents, 4th quartile|Oil rents if oil rents belong to the highest quartile in corresponding<br>period, 0 otherwise<br>Note: The Variables oilrent<br>~~0~~025, oilrent<br>~~2~~550, oilrent<br>5075 and<br>oilrent<br>~~7~~500 add up to the variable oilrent||

PWT corresponds to the Penn World Tables (Heston, Summers, and Aten, 2012) IIASA corresponds to the IIASA-VID dateset on educational attainment (Lutz, Goujon, K.C., and Sanderson, 2007) Polity IV corresponds to the Polity IV dataset obtained from Teorell, Charron, Samanni, Holmberg, and Rothstein (2011) WDI corresponds to the World Development Indicators (2013) 6
Table 10: Simulation Results - Short Run Effects (Percent Change)

||Change in|Wages, _β_|= 0_._90||
|---|---|---|---|---|
||**_Aggr/Cons_**|**_Manuf_**|**_NonTraded_**|**_Traded_**|
|_White Collar_|6.42|-17.00|6.42|12.29|
|_Tech/Sales_|6.42|-17.00|6.42|12.29|
|_Service_|6.42|-17.00|6.42|12.29|
|_Craft_|6.42|-17.00|6.42|12.29|
|_Laborer_|6.42|-17.00|6.42|12.29|

Change in Welfare, No-College, _<u>β</u>_ = 0 _._ 90

||**_Aggr/Cons_**|**_Manuf_**|**_NonTraded_**|**_Traded_**|
|---|---|---|---|---|
|_White Collar_|2.40|-1.70|2.28|3.39|
|_Tech/Sales_|2.03|-1.25|2.17|2.92|
|_Service_|1.65|-1.13|1.90|2.26|
|_Craft_|1.89|-2.64|1.82|2.52|
|_Laborer_|1.70|-2.30|1.61|2.42|

Change in Welfare, College, _<u>β</u>_ = 0 _._ 90

||**_Aggr/Cons_**|**_Manuf_**|**_NonTraded_**|**_Traded_**|
|---|---|---|---|---|
|_White Collar_|2.46|-2.43|2.45|3.46|
|_Tech/Sales_|2.33|-1.76|2.34|3.23|
|_Service_|2.02|-1.39|2.16|2.48|
|_Craft_|2.24|-1.43|2.25|2.72|
|_Laborer_|2.21|-0.13|2.20|2.61|

30
# **Appendix C: Mathematical Appendix**

For all estimates included in the meta-analysis, the goal is to estimate the standardized effect size, again drawing on Borenstein et al. (2009): using some estimate of the raw mean difference between treatment and control groups, _D_ , as well as its combined standard deviation for treatment and control groups, _Spooled_ . All studies report _D_ directly, however, _Spooled_ is commonly not reported. Almost all studies we review instead report the standard error of _D, SED._ Where this is the case, if we assume that the standard deviations of the two groups are the same, then the variance of D is: where _n1_ and _n2_ are the sample sizes in the two groups. The standard error of _D_ is then the square root of _V_ .

Combining Equation A1 and Equation A2, we derive our equation for _Spooled_ , the within-groups standard deviation, pooled across treatment and control groups: 54