rafmacalaba/datause-encoder-data huggingface dataset:
known that rural households in low‐ and middle‐income countries that cook with biogas or other non‐biomass fuels often also continue to use firewood (Arnold et al., 2006; Cooke et al, 2008).<sup>4</sup> Thus, this method is sure to over‐estimate the actual reduction in firewood use from adoption of an alternative fuel/cooking technology package.

The second method used in the literature is direct, relying on surveys of households that have started to use a technology and asking them how much they reduced their firewood consumption (Katuwal and Bohara (2009); Masera et al., 2007; Thakuri, 2009). A particularly relevant paper is Christiansen and Heltberg (2014), who evaluate a large biogas program in China using survey data. Though the results are not fully conclusive due to the nature of their data, they find strong indications that users are satisfied with the technology and that biogas adoption reduces firewood use and collection time. We use a different direct method in Section 3 of this paper, relying on comparisons between adopter and non‐adopter households, which do not rely on respondents’ ability to recall past use.

In Section 4, we go on to calculate the greenhouse gas implications of those reductions using emission factors for various pollutants emitted from traditional wood stoves and 100‐year Global Warming Potentials from the scientific literature. This allows us to map firewood reductions into carbon market offsets to which households adopting biogas might be entitled. With this information and the offset price of CO2e emissions reductions, we can evaluate to what extent carbon offsets could subsidize biogas in Nepal and similarly situated countries.

# **3. The Effect of Biogas Adoption on Firewood Consumption**

To estimate the effect of biogas adoption on firewood use, we use data from the third round of the Nepal Living Standards Survey (NLSS‐III), which is managed by the Central Bureau of Statistics of the Government of Nepal (GON) and sponsored by the World Bank and GON. NLSS‐III is a nationally representative survey of 5,988

> 4 For empirical support of such “fuel stacking” behavior, see Heltberg, 2004; 2005; Leach (1992), Davis (1998) and Campbell et al. (2003).

5
|Non-OECD economy|The composition of<br>government spending<br>in your country (1=is<br>wasteful, 7=provides<br>necessary goods and<br>services not provided<br>by the market)|Government decisions<br>on the procurement of<br>advanced technology<br>products are based on<br>(1=price alone,<br>7=technology and<br>encouraging<br>innovation)|When deciding upon<br>policies and contracts,<br>government officials<br>(1=usually favor well-<br>connected firms and<br>individuals, 7=are<br>neutral among firms<br>and individuals)|New governments<br>honor the contractual<br>commitments and<br>obligations of previous<br>regimes (1=not true,<br>7=true)|How commonly do<br>firms in your industry<br>give irregular extra<br>payments or bribes<br>connected with public<br>contracts/investment<br>projects (1=common,<br>7=never)|
|---|---|---|---|---|---|
|Peru|2.8|2.7|2.9|3.9|4.2|
|Philippines|2.8|3.4|2.7|4.8|3.0|
|Romania|2.5|3.3|3.7|3.2|4.1|
|Russia|2.5|3.6|3.7|4.0|3.8|
|Singapore|_5.9_|_5.7_|_5.1_|_6.3_|_6.4_|
|Slovenia|3.3|4.2|3.5|4.5|4.1|
|South Africa|3.8|3.9|3.1|_5.6_|4.4|
|Sri Lanka|2.6|3.5|2.7|4.1|4.3|
|Taiwan|_4.3_|_5.1_|3.9|3.6|_5.6_|
|Thailand|_4.3_|4.0|3.5|4.9|3.7|
|Trinidad and Tobago|_4.1_|4.0|2.7|4.8|4.2|
|Ukraine|2.1|3.9|2.1|2.5|2.9|
|Uruguay|2.9|3.8|3.6|5.4|4.4|
|Venezuela|2.2|3.1|2.6|3.9|3.5|
|Vietnam|3.1|4.0|3.0|3.7|3.5|
|Zimbabwe|1.4|3.3|1.9|4.1|2.5|
|Mean for these developing countries|3.1|3.6|3.0|4.3|3.9|
|Mean for the OECD economies|4.0|4.4|4.2|5.5|5.3|

Source: _The Global Competititiveness Report, 2001-2002_ . Interview responses to questions above by business people familiar with a given economy. Notes: _number in italics_ signifies the reported value is less than the average for those developing countries reported here signifies the reported value is more than the average for the OECD countries surveyed 13
Table 9a Regression Results for ln(Green)

|Intercept<br>_____|InPCGNP<br>_____|lnICPGDP|InPOP|ln(Shoro<br>of<br>agriculture<br>in<br>GDP)<br>P|In(Pop.<br>density)<br>_-_______|Adjusted<br>RI<br>_______nG|
|---|---|---|---|---|---|---|
|3.31<br>(25.55)|0.16<br>(8.66)||||_....|0.71|
|2.60<br>(12.29)||0.23<br>(8.65)||||0.71|
|2.75<br>(4.69)||0.20<br>(3.85)|-0.11<br>(-1.31)|_0.06_<br>(0.93)|0.09<br>(1.32)|0 64|
|3.27<br>(11.11)|. .|0.17<br>|<br>(5.38)|-0.16<br>(2.19)|_<br>_|0.09<br>(1.34)|0.73|

# Table 9b

Regression Results for ln(Brown)

|Intercp|lnPCGNP<br>________|lnICPGDP<br>____<br>____|ln FOP<br>_______|ln(Urban<br>/total<br>popula-<br>tion)|ln(Popu-<br>lation<br>density)|in<br>(Manuf.<br>share<br>of GDP)|ln(Life<br>expect-<br>ancy)<br>_<br>_<br>_<br>_|Adj Rz|
|---|---|---|---|---|---|---|---|---|
|3.81<br>(24.25)|0.21<br>(9.7S)|||||||0.76|
|2.73<br>(12.40|)|0.32<br>(11.75)||||||0.82|
|3.91<br>(2.63)||0.20<br>(2.27)|-0.19<br>(1.98)|0.14<br>(1.46)|0.06<br>(2.30)|0.16<br>(2.04)|-0.34<br>(-0.67)|0.82|
|2.94<br>(8.02)|.|0.16<br>_ !2.65)|-0.20<br>(2.20)|0.14<br>(1.46)|0.06<br>(2.25)|0.15<br>(1.95)|.|0.83|

Table 9c Green/Brown Impacts of ICPGDP, FOP and Regulatory Efficiency

|vYariable||ntercept|ln ICPGDP|ln FOP|tlnRTB|ln LSJ|Adj R<sup>2</sup>|
|---|---|---|---|---|---|---|
|ln(Green)<br>____________|3.84<br>1(9.37)|0.03<br>(0.52)|-0.17<br>(1.93)|0.39<br>j(3|.37)|0.93|
|ln(Brown)|3.95<br>(9.44)<br>1|0.09<br>(2.69)|-0.07<br>-<br>(1.09)|0.36<br>(4.20)|0.14<br>(1.07)|0.98<br>_<br>J|

21
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relying on reductions in public sector investment will have high and permanent negative output effects through the mechanisms demonstrated. These are in addition to any output effects that may arise because of short-run macroeconomic problems, which are not covered here.

IV. OUTPUT GROWTH AND EXTERNAL BALANCE: CAN THEY BE RECONCILED? A. Looking Ahead: External Constraints. Fiscal Consistency and the Prospects for Sustainable Growth The main issue now is, of course, can the successful performance of the past six years be repeated in the future? Can Turkey sustain a reasonable growth rate within the limits set by creditworthiness constraints? If so, what should the public sector do to bring this about?

To answer this question, one first needs to have some idea of what exactly these creditworthiness constraints imply. How much can Turkey borrow without bringing its creditworthiness in jeopardy? The answer naturally depends on the target real growth rate in Turkey itself and on anticipated growth rates in trading partners; an approach to ouarntify all these factors has been developed by Daniel Cohen (1985, 1987) and is applied to Turkey in Section IV.B.

Once the feasible current account deficit has been decided upon, a matching internal adjustment program needs to be set up. An internal adjustment program consists of a set of policies that will bring about a fiscal deficit and a private savings surplus over investment just enough to match the external current account target. The challenge is to design this package in such a way that total investment, private and public, will be high enough to allow output to grow at its target rate. This involves once again a two stage design. First how much should the public sector contribute to the required improvement in the surplus of aggregate savings over investment? The ChhibberDisk/chhibber-Turkey Paper/ac: 07-8-88: ic
Table 5. Impacts of Refugee Camps on Adult and Household Welfare

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_A. Wealth_|||||
||Wealth<br>Index|Service<br>Access|Adequate<br>Shelter<br>Materials|Share<br>of Total<br>Assets|
|SID|-0.103|-0.012|0.001|0.006|
||(0.147)|(0.016)|(0.009)|(0.006)|
|FDR q-values|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.108|0.048|0.036|0.057|
|Observations|9,264|13,701|13,701|13,701|
|_B. Income and Ex_|_penditures_||||
||Total|Total|Non-food|Food|
||Income|Expenditures|Expenditures|Expenditures|
||(Log)|(Log)|(Log)|(Log)|
|SID|-0.031|-0.020|-0.021|-0.020|
||(0.051)|(0.037)|(0.038)|(0.047)|
|FDR q-values|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.132|0.122|0.111|0.081|
|Observations|9,068|9,436|9,376|9,430|
|_C. Food Consump_|_tion_||||
||Food<br>|Self-Produced|Humanitarian|Gift or Barter|
||Variety<br>Consumption<br>(Share of Products)|Food<br>(Share of products)|Food Aid<br>(Share of Products)|Food<br>(Share of Products)|
|SID|0.010|-0.009|0.083***|-0.005|
||(0.005)|(0.025)|(0.008)|(0.003)|
|FDR q-values|[0.101]|[0.225]|[0.001]|[0.101]|
|R-squared|0.014|0.022|0.086|0.004|
|Observations|9,438|9,438|9,438|9,438|
|_D. Assistance_|||||
||Received<br>Vouchers or<br>Cash and In-Kind|Received<br>Food<br>|Received<br>Assistance to<br>Access|Received<br>Assistance for<br>|
||Assistance|in Kind|Basic Services|Household Items|
|SID|0.037|0.055**|0.014|0.106**|
||(0.052)|(0.017)|(0.013)|(0.028)|
|FDR q-values|[0.345]|[0.036]|[0.294]|[0.036]|
|R-squared|0.021|0.028|0.010|0.127|
|Observations|1,005|3,017|5,679|3,156|
|Outcome Level|Household|Household|Household|Household|

_Notes:_ The table presents results from equation 3. SID represents standardized values of the inverse weighted distance from the respondent’s household location to the camps. Dependent variables are available only for the 2019 wave and are explained in detail in Appendix B. Panel A depicts the wealth dimension, which includes a wealth index and its three main dimensions: service access, shelter materials, and share of total assets. Panel B presents the income and expenditures dimensions, including total household income, food expenditures, and non-food expenditures. Panel C presents the average number of distinct food products consumed by households over the past seven days and the specific way in which the households acquired those food products. Panel D shows the assistance dimensions, with indicator variables set to one indicating the source of assistance. Estimates control for individual characteristics including male=1, age, household size in 2019, household head=1, married=1, and household income sufficiency in 2017 (corresponding to retrospective information before the Rohingya influx). Clustered standard errors at the _upazila_ (subdistrict) district are presented in parentheses, and False Discovery Rate (FDR) q-values are reported in brackets. Significance levels:<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> 46at 5%,<sup>_∗_</sup> at 10%.
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# RELATIVE EXCISE TAX BURDENS

It is impossible to coinpile data on excise taxes relative to prices for developing countries, other than for a very small sample. Thus as a second best solution, data are presented in Table 2 for the OECD countries, as compiled by the U.S. Congressional Budget Office, with data for 1988.

The almost universal assumption is that excise taxes, by adding to the cost of producing the particular items, will be reflected in higher prices, under usual competitive conditions, and thus will be borne in relation to consumer spending on the items. This is of course an oversimplification, because of the possibility of shift in factor prices as demand for the tax products fail, particularly in a relatively open economy, as demonstrated by G. A. Hughes with respect to Thailand.<sup>1</sup> Widespread smuggling and black markets (not reached by the tax), for example, may prevent full shifting, as will some price controls. Import licensing, which leads to monopoly pricing, may result in less than full shifting.<sup>2</sup> But nevertheless it is the only reasonable workable assumption.

Empirical studies of distribution of excise tax burden by income class have been made in a number of countries. Many of these were sunmmarized by Charles McLure and Wayne

> 1G. A. Hughes, "A New Method for Estimating the Effects of Fuel Taxes: An Application to Thailand," World Bank Economic Review 1 (1986) 65-101. Note also A. Lans Bovenberg, Indirect Taxation in DevelopinQ Countries: A General Eauilibrium Approach, International Monetary Funa Staff Papers 34 (June 1987) 333-73.

2A good discussion is presented in the paper by Anwar Shah and John Whalley, An Alternative View of Tax Incidence Analysis for DeveloDina Countries, NBER Working Paper 3375 (Cambridge, Mass.: National Bureau of Economic Research), June 1990.
# **<u>Abstract:</u>**

We test competing theories of capital structure choices using firm-level data on firm borrowings. The majority of firms in the dataset are privately owned, young, micro, or small and medium sized enterprises (SME) firms concentrated in the service sector. In general, the financing pattern of firms is low leverage ratios and, in particular, low levels of intermediated financing and long-term financing. Average firm growth rates decreased dring the five years of the sample period. Average profitability growth ratios are also negative across age and sectors and large firms have the highest negative profit growth rate. Statistical tests find a positive firm size effect on financial intermediation. Larger firms have higher leverage ratios (both short term and long term), including higher use of trade credit. There is also a negative influence of profitability on leverage ratios (more profitable firms use less external financing), which supports the “pecking order” theory that in environments with greater asymmetric information (such as weaker credit information) firms prefer to use internal or inter-firm financing. Finally, firms operating in a competitive environment have higher leverage ratios. For instance, young, small firms are the most active employment generators in the Polish economy. We find overall that SMEs seem to be very active in creating jobs in recent years. This suggests that a new type of firm is emerging that is more market and profit-oriented. But at the same time, these firms appear to have financial constraints that impede their growth. Improvements in the business environment, such as better credit and registry information, could help promote growth in this sector.

2
- Do migrant families spend more on health and education?

- Are migrants’ children healthier and more likely to remain at school (instead of, say, working)?

- What is the gender impact, do daughters in migrant households go to school?

- How do existing government programs impact migration, and how does migration affects those programs?

This note argues that while most of these questions have intrinsic worth, some of them are of lesser value when posed in isolation, particularly if one is interested in the broad poverty-reducing impact of migration. The principal question in most operational research should rather be a general one – _what is the impact of migration on poverty and/or human capital?_ – which accurately reflects the complexity of the relationships involved.

## **3. ANALYTICAL CHALLENGES IDENTIFYING IMPACT**

Analytical work on the impact of migration is made more complex by three sets of challenges. The first group of issues is related to data and definitions. The second group relates to the intrinsic endogeneity and selectivity involved in decisions surrounding migration. Finally, the third group of problems arises due to the multiple indirect socioeconomic effects of migration. These issues, discussed below in more detail, have implications for the formulation of appropriate research questions and the design of methodologies.

# **3.1. Data and definitions**

As regards data, the minimum requirement is to have a variable in the survey indicating whether a household has a migrant. Information on the amount of remittances is helpful but not essential. These two variables constitute a migration “module” in a typical household survey. However, several World Bank’s Living Standard Measurement Surveys (LSMS) pay more attention to migration issues.

Obtaining other, non-survey data, such as administrative statistics on socioeconomic characteristics of villages/regions can, as discussed later, significantly contribute to the explanatory power of research, by helping to control for “other factors” and to construct alternative identification strategies.

Panel data can enormously improve the situation. By taking differences one can address many biases that arise due to omitted variables (including unobservable individual characteristics), selection or endogeneity.

If one has control over the design of the survey, then (without going into too much detail) useful ideas are: i) oversampling migrants; ii) introducing retrospective questions about pre-migration characteristics (income, work history) to enable construction of better counterfactuals; iii) collecting information on migration locations within destination countries; iv) surveying both areas of origin and areas of destination.

4
role that FDI plays on GVC integration by providing foreign capital and technical know-how. Research has demonstrated that this high degree of complementarity between GVCs and FDI is enhanced by policies, such as those included in deep PTAs, providing support to the entry and upgrading in GVCs.<sup>7</sup>

# 2. Related literature

The economic effects of PTAs have been thoroughly studied in the literature. Researchers have looked at their impact on trade flows, investment, growth and welfare (see (World Trade Organization, 2011) and (Limão, 2016) for recent surveys of the literature on PTAs). Our paper relates to the strand that studies the role of trade agreements in increasing cross-border investments through different channels. One potential channel is driven by the complementarity between trade and investment that is based on the production structure of multinational enterprises.<sup>8</sup> A core argument is that by reducing trade barriers PTAs facilitate the exchange of inputs (tangible or not) and therefore stimulate efficiency-seeking investments along the value chain. An important implication is that the trade creation and trade diversion effects of PTAs may translate into FDI relocation, (Baltagi , Egger, & Pfaffermayr, 2008) and (Tintelnot, 2017) provide empirical and counterfactual evidence for this mechanism.

Another channel linking PTAs with FDI comes from the liberalization of services, investment and other behind-the-border disciplines. (Dee & Gali, 2005) show that PTAs in general have a significant impact on investment flows through their non-trade disciplines. (Büthe & Milner, 2008) offer a political economy perspective and argue

> 7 See the studies on FDI spillovers in the context of GVCs (Amendolagine, et al. 2017) and (Farole and Winkler 2014) .

> 8 The relationship between trade and FDI is complex and both  “tariff jumping” substitutability and “value chain” complementarity motives are supported by the theoretical analysis. (Markusen & Maskus, A Unified Approach to Intra-Industry Trade and Foreign Direct Investment, 2002) provide a comprehensive framework to analyze and understand these mechanisms. While (Fontagné 1999) brings empirical evidence for the dominance of the complementarity relationship.

5
Relative income rather than total income may affect satisfaction, and if ignored, may result in biased estimates (Borah _et al._ , 2018). We include dummy variables to indicate the relative position of the household in the reference group`s distribution of household income quartiles (Appendix A, Table A.8). The reference group is determined for each year and consists of individuals living in households with a similar size in the same primary sampling units. To ensure stability, we only consider the number of households in the reference group as having 10 or more households. We report estimates of the scale parameters for the POL model only, since the variable used to define the reference groups is largely time-invariant, especially at the primary sampling unit level. Controlling for the reference group decreases the child scale parameter to 0.05 in the POL model but does not change the baseline elasticity. More importantly, we still obtain the earlier result that an additional child has a smaller effect compared to an additional adult (Table 5, row 2).<sup>21</sup> We also analyze the other satisfaction variables in the RLMS as alternative dependent variables for the subjective wealth variables, which are satisfaction with one’s life and satisfaction with one’s economic conditions. The estimated coefficients on household income and household size are still statistically significant as expected (Appendix A, Table A.9). To save space, we only report the scale parameters derived from the regressions for life satisfaction (Table 5, row 3). The estimation results of the BUC model are robust with the adult scale parameter of about 0.6 and child scale parameter about 0.04.<sup>22</sup> As a check on the total household income variable, we generate a new total household income by summing all the net incomes reported by household members (Appendix A, Table A.10). Yet, the estimated scale parameters of 0.3 for adults and 0.06 for children obtained from the BUC

> 21 The full regression results are shown in Table 1.

> 22 The adult scale parameter is still high when using satisfaction with economic conditions (0.8), but the child scale parameter decreases to 0.02.  The POL model also similarly provides a higher elasticity for adults (0.8), as well as for children (0.1) (Table 5, row 3).

19