rafmacalaba/datause-encoder-data huggingface dataset:
– – and are reported in columns _R2 R5_ (Table 2) and _G2 G5_ (Table 3). Regressions using – – the analogous market indicators are presented in columns _R2 R5_ (Table 2) and _G2 G5_ (Table 3), respectively.

We begin by discussing the random effects estimates, and focus initially on the regressions involving product diversification indicators only (Table 2, upper half). It can be seen that he coefficient on the product diversification variable is negative throughout the specifications we run, while the interaction terms carry positive coefficients. The coefficient on the trade openness variable is, for the most part, negative. The trade openness variable is mostly not (statistically) significant, while the diversification indicator is, aside from one exception, always significant at the 10 percent level or lower. The same holds—without exception—for the interaction term. This last finding alone suggests that the effect of trade openness on growth volatility is indeed conditioned by the degree of export diversification, a result that confirms our initial hypothesis.

In addition, the point estimates are economically significant: product diversification is bound by a range [ _−_ 12 _._ 18 _, −_ 1 _._ 21], and the interaction term has a range of [1 _._ 66 _,_ 15 _._ 78]. The total effect of openness on volatility is the sum of the coefficients on the openness variable and the product of the interaction term with a given level of diversification. Without further information on the level of diversification that we are considering, therefore, it is not possible to calculate the total effect that pertains to these respective variables. It is, however, illustrative to consider the effect of openness on volatility for a completely diversified economy ( _DIVi,t_ = 0) as opposed to a totally non-diversified economy ( _DIVi,t_ = 1). In these cases, the total effect of openness on volatility is the coefficient on the openness variable alone, versus the sum of the coefficients on the openness variable and interaction term. Since the coefficient of the latter dominates the former,<sup>11</sup> we can infer that openness does in fact reduce volatility in diversified economies, and it is in poorly diversified economies where openness has the opposite effect.

As discussed earlier, endogeneity is a potential concern for our estimates. We therefore treat the system GMM results in Table 3 as our favored benchmark since, as discussed in the previous section, these specifications explicitly account for possible reverse causality issues.

The results are qualitatively very similar to those reported in Table 2. The trade openness variable enters consistently with a negative sign, and only in the case of PC product is the coefficient positive. The interaction term is always positive and significant at the 10 percent level. The range of estimates consistent larger in magnitude compared

> 11With the exception of the PC product estimates; since this variables is not bound by [0 _,_ 1], the discussion does not apply to this specification.

16
|**6 digit HS**|**Description of the product line**|**MFN tariff**|
|---|---|---|
|**code**||**rate**|
|611610|Impregnated, coated or covered with plastics or rubber|56|
|670210|Of plastics|54|
|910191|Other :-- Electrically operated|54|
|910521|Wall clocks :-- Electrically operated|54|
|420212|Trunks, suit-cases, vanity-cases, executive-cases, brief-cases, school satchels|54|
|640219|Sports footwear :-- Other|54|
|910390|Other|53|
|620892|Other :-- Of man-made fibres|53|
|160220|Of liver of any animal|52|
|870322|Other vehicles, with spark-ignition internal combustion reciprocating piston eng|52|
|611511|Panty hose and tights :- Panty hose and tights :-- Of synthetic fibres, measurin|52|
|610469|Trousers, bib and brace overalls, breeches and shorts :-- Of other textile mater|51|
|160232|Of poultry of heading No. 01.05 :-- Of fowls of the species Gallus domesticus|51|
|420292|Other :-- With outer surface of plastic sheeting or of textile materials|51|
|170410|Chewing gum, whether or not sugar-coated|51|
|611520|Women's full-length or knee-length hosiery, measuring per single yarn less than|51|
|610230|Of man-made fibres|50|
|630229|Other bed linen, printed :-- Of other textile materials|50|
|20621|Of bovine animals, frozen :-- Tongues|50|
|291814|Carboxylic acids with alcohol function but without other oxygen function, their|50|
|640291|Other footwear :-- Covering the ankle|50|
|620510|Of wool or fine animal hair|50|
|160231|Of poultry of heading No. 01.05 :-- Of turkeys|49|
|20311|Fresh or chilled :-- Carcasses and half-carcasses|49|
|20312|Fresh or chilled :-- Hams, shoulders and cuts thereof, with bone in|49|
|852313|Magnetic tapes :-- Of a width exceeding 6.5 mm|48|
|620422|Ensembles :-- Of cotton|48|
|940380|Furniture of other materials, including cane, osier, bamboo or similar materials|48|
|610712|Underpants and briefs :-- Of man-made fibres|48|
|640299|Other footwear :-- Other|48|
|910591|Other :-- Electrically operated|46|
|620112|Overcoats, raincoats, car-coats, capes, cloaks and similar articles :-- Of cotto|46|
|610130|Of man-made fibres|46|
|430310|Articles of apparel and clothing accessories|46|
|610312|Suits :-- Of synthetic fibres|45|
|611720|Ties, bow ties and cravats|45|
|630299|Other :-- Of other textile materials|45|
|640199|Other footwear :-- Other|45|
|910529|Wall clocks :-- Other|45|
|20321|Frozen :-- Carcasses and half-carcasses|45|
|620423|Ensembles :-- Of synthetic fibres|45|
|847050|<br>Cash registers|45|
|640419|Footwear with outer soles of rubber or plastics :-- Other|44|
|420219|Trunks, suit-cases, vanity-cases, executive-cases, brief-cases, school satchels|44|
|620711|<br>Underpants and briefs :-- Of cotton|44|
|420239|Articles of a kind normally carried in the pocket or in the handbag :-- Other|44|
|570410|Tiles, having a maximum surface area of 0.3 mý|44|
|160239|<br>Of poultry of heading No. 01.05 :-- Other|44|
|620339|Jackets and blazers :-- Of other textile materials|44|
|180690|Other|43|
|961310|Pocket lighters, gas fuelled, non-refillable|43|
|20329|Frozen :-- Other|42|
|852311|Magnetic tapes :-- Of a width not exceeding4 mm|42|

39
Warren, R., Arnell, N., Nicholls, R., Levy, P. and Price, J. (2006) _Understanding the Regional Impacts of Climate Change_ . Research report prepared for the Stern Review on the economic of climate change. Tyndall Centre Working Paper No. 96. Tyndall Centre for Climate Change Research, Norwich, London.

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World Bank (1994) _Resettlement and Development: The Bankwide Review of Projects Involving Involuntary Resettlement 1986–1993_ . World Bank, Washington.

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Zetter, R., Griffiths, D., Sigona, N. and Hauser, M. (2002) _Survey on Policy and Practice Related to Refugee Integration_ . Report commissioned by European Refugee Fund Community Actions 2001/2. Conducted by School of Planning, Oxford Brookes University.

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62
are small partly because land distribution was based on family size (Ali et al. 2011), and those categories tend to have smaller households. The adult-equivalent land areas do not differ much across gender-disaggregated households or at the individual-level. There is an almost equitable distribution of farm areas held by women and men, because women and men in male-headed households are joint holders and women in female-headed households are exclusive holders.

Total area of landholding is less than 0.5 hectare for 58 percent of female-headed households and 65 percent of female-only households, compared to only about 40 percent of male-headed and dual-adult households (see Figure 3). This is concerning: A farm smaller than 0.5 ha is not large enough to support a sustainable livelihood (Holden and Tilahun 2020), and the per-hectare average tax rate is highest for this land size class (see Figure 1). Thus, not only are female-headed and female-only households disadvantaged because their farms are so small, but they also have to pay a higher per-hectare rate in taxes.

**Table 5. Average Area of Agricultural Land Held by Households and Individuals, Hectare**

||Male-<br>headed|**A.**<br>**Landho**<br>Female-<br>headed|**lding Households**<br>Dual adult|<br>Female-only|Male only|
|---|---|---|---|---|---|
|Total ag. land area (ha)|**0.92**|**0.68**|**0.92**|**0.53**|0.75|
|Adult equivalent farm area (ha)|0.23|0.27|0.23|0.27|0.42|
|_Number of landholding households_|1,740|556|1,923|303|70|
|||**B. Sex of L**|**andholder**|||
||Men|Women||||
|Individual-level ag. land area (ha)|0.44|0.43||||
|_Number of landholding respondents_|1,787|1,775||||

_Notes:_ In panel A, the household’s total agricultural landholding area is calculated by adding up the GPS-measured area of plots. Self-reported measured land area is used where the GPS measurements are missing. Because the area of land sharecropped out is missing from the survey, we use the median area of land rented in or sharecropped in at the zone level for parcels sharecropped out. These methods resulted in the land measurement being missing for 47 landholding households. In panel B, the area of individual agricultural land area is imputed by dividing the respondent’s parcel area by the number of landholders and summing up the individual’s apportioned area across the parcels. Tests of equality of means were conducted between (1) male- and female-headed households, (2) female-only and dual adult households, and (3) male and female respondents. Significant differences at p<0.05 are shown in bold. The analysis dropped the top and bottom 1% of farm area values. Also, we use household sampling weights.

**Figure 3. Area of Landholdings by Household Type, Percent**

<!-- Start of picture text --> Female-headed 58 22 14 7<br>Male-headed 38 29 22 11<br>Female-only 65 20 11 3<br>Dual adult 39 28 22 11<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br> <0.5 ha 0.5-0.99 ha 1-1.99 ha >2 ha<br><!-- End of picture text --> Note **<u>:</u>** <u>This figure shows the size of landholdings (in hectare) among landholding households.</u> Household sampling weights are used.

12
government in the past year. The survey was done in beginning March 2005, so toward the end of a fiscal year, and relates to the just passed fiscal year. School respondents were asked: “How much money did your school receive in the last year from the state government/SSA/parents/Gram Panchayat?” For the state government and for SSA, the question was asked for total funds and separately for funds received for maintenance, teaching and learning materials, and an “other” category.

The pattern of responses to these questions suggest that school respondents are not able to distinguish between different government sources of funds (the specific SSA program from other state government funds), nor between different categories of funding (for maintenance, teaching materials, etc.). How truly factual are these responses was also not verified. Hence, the survey data is not helpful in telling us about the actual size and source of funds available to the school from higher tiers of government. What it does tell us is that higher tiers of government are the only sources of funds—negligible number of schools cited receiving anything from parents and the Gram Panchayat—something perhaps already known to education departments in India.

We have tried to estimate the size of funds coming from the government, and find that it is in the tune of a few thousands of rupees, and close to what one might expect from SSA guidelines. For general school maintenance and equipment, most schools reported either receiving nothing from the SSA (59 percent) or between Rs 4000-7000 (30 percent), which is in the ballpark of SSA rules of allocation (See Appendix 1 for a table summarizing the SSA rules of allocation). For teacher assistance and learning materials, the majority of schools reported receiving nothing from the SSA (74 percent), while 15 percent reported receiving Rs. 500 per government teacher.<sup>8</sup> Six percent reported receiving between zero and 500, while 5 percent reported receiving more than Rs. 500 per teacher. When asked how much the _state_ government had provided the school for maintenance, 70 percent of schools reported receiving between Rs 4000 and Rs 7000, which, as above, is the expected range of SSA allocations. That is, the responses to what a school received from SSA and from the “state” are likely to be substitutes and not additive, and together

> 8 Para-teachers ( _Shiksha Mitras_ ) do not receive money for teaching and learning materials and were not figured in this calculation.

17
perspective. We conduct a robustness check using the comprehensive financial development index of Svirydzenka (2016) as an alternative measure of financial deepening.

We include several control variables in our regressions that might affect carbon emissions per dollar of GDP, broadly following De Haas and Popov (2023). We control for the level of economic development, proxied by GDP per capita. And since the relationship between economic development and carbon emissions can be non-linear owing to the so-called environmental Kuznets curve (Hu et al. 2018), we also include the squared term of GDP per capita. To account for country and market size, which might affect the attractiveness for innovative investments, we use population (De Haas and Popov, 2023). Finally, the relationship between financial deepening and CO2 emissions per dollar of GDP might be affected by financial crises, because banks might be retreating from more risky, innovative, and potentially less carbon-intensive investments (Hardy and Sever 2021). Furthermore, financial crises, which make access to capital more difficult, might discourage investments—including investments in environmentally friendly technologies (Jalles 2024). To account for the potential effect of crises, we also include a dummy variable for banking crises from Nguyen et al. (2022) among the vector of control variables. Overall, we limit the number of control variables to retain the largest possible sample of countries. To account for time-invariant country-specific factors that might influence CO2 emissions (i.e., renewable energy potential, fossil fuel reserves, urbanization), we include country fixed effects. And, to account for time-specific factors that might influence CO2 emissions in all countries during a particular year, we add time fixed effects.

We explore responses of CO2 emissions per dollar of GDP to financial deepening conditional on four variables capturing different dimensions of a country’s institutional environment. First, we use the _number of climate change laws_ from the Climate Change Laws of the World database of the Grantham Research Institute to proxy environmental regulation. Second, we use the _rule of law index_ taken from the World Governance Indicators (WGI) of the World Bank to capture the conditioning role of institutional quality. Third, we study whether the response varies in countries with more market-based versus bank-based financial systems. De Haas and Popov (2023) express this ‘financial structure’ as the ratio of the size of the equity market to the size of the entire financial 7
# **3.2. Early retirement and the generosity of the pension system**

Apart from **the** global significance of GDP per capita, the aggregate analysis identified a strong << European Effect >> for social transfers, total expenditure, and for social contributions. The CEC have a low income per capita, but seem to follow West-European patterns in social transfers, rather than take their place on the income line. This section tries to see whether this 'European specificity' can be explained (see Table 3 below) in a 'narrow sample' of OECD and CEC.

**Table 3. Social Transfers, Government Wages and Effective Dependency Ratios**

||._<br>.|DDO|GLABOUR|SOCWAGE|UNEMPLOY|R<sup>2</sup><br>F<br>Obs.|Missing/<br>Residuals|
|---|---|---|---|---|---|---|---|
|ADR||-0.25<br>(-445**)|-0.02<br>(-3.83**)||0.03<br>(4.80**)|60.3<br>10.1<br>24|None/<br>SIo. -0.2<br>Po. -0.2|
|ADR||-0.25<br>(-3.97**)|-0.02<br>(-3.57**)|-0.002<br>(-0.97)|0.03<br>(4.84**)|64.7<br>227|None/<br>PCR. -0.1|
||GDP$WB<br>(* OE-4)|DDO|GLABOUR|SOCWAGE<br>_ _ _**_**_<br>_ _<br>_|TRANSFER<br>_ _ _ _ _ _ _ _ _|R<sup>2</sup><br>F<br>O bs.|Missing/<br>Residuals|
|SOCEXP||-3.23<br>_(-1.51)_|-0.53<br>(-2.44*)|0.38<br>(5.16**)||62.6<br>10.0<br>22|None/<br>Cz.R.-10.8<br>Po. -8.0<br>Sbo. +8.2|
|SOCGDP||-2.33<br>(-2.90**)||0.28<br>(8.53**)||78.4<br>36.8<br>23|None/<br>Cz.R. -5.2<br>Po. -4.0|
|WAGESEXP|-6.32<br>(-3.31**)|1 1|1|1|-0.25<br>(-3.55**)<br>1<br>|61.7<br>15.3<br>1~~~|Slo./<br>Hu.. -6.6<br>~~~~~~~~~22<br>Cz.R. -8.3|

Note: In the regression for WAGESEXP, GDPHS as well as DUMMYI were significantly negative, the latter being accompanied with stronger income effect. ADR was marginally significant and positive. GINVGDP was significantly positive. Regressors GINVEXP, POPULATION and UNEMPLOY were nonsignificant. Regressors DUMMYI, GDPHS, POPULATION, INEQUAL, UNEMPLOY, ADR, GINVGDP, GINVEXP were never significant in the regressions for SOCEXP and SOCGDP. In ADR regressions INEQUAL, INVGDP, SOCGDP were never significant. DUMMYI was highly significant in combination with UNEMPLOY and GLABOUR, where GLABOUR turned out to be insignificant and R<sup>2</sup> was only 46.3.

See Appendix III for variable description and rules of presentation.

14
_Intertemporal Adjustment and Fiscal Policy in a Fixed Exchange Rate Regime_ Figure 1.5 The real exchange rate in Argentina, 1990–2001 Real Exchange Rate Index = 100 1990 <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br><!-- End of picture text --> Based on data from IMF.

Figure 1.6 The current account deficit in Argentina, 1990–2000 Millions of dollars <!-- Start of picture text --> 1800<br>900<br>0<br>-900<br>-1800<br>-2700<br>-3600<br>-4500<br>1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> Based on data from IMF.

7
**Figure 3: Supply and demand achievements**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>Chile Red Unidos Chile Red Unidos Chile Red Unidos Chile Red Unidos<br>Solidario Solidario Solidario Solidario<br>All achievements Demand and Supply Demand Achievements Supply Achievements<br>Achievements<br>in Achievements<br>Average Improvement<br><!-- End of picture text --> Note: only common indicators have been considered. Source: Administrative data provided by program staff.

# **Evidence from impact evaluations**

To the extent that improvements in indicators may be due to countrywide improvements that went beyond the specific interventions, it would be misleading to attribute the progress to the intervention. Rigorous impact evaluations with valid treatment and control groups can tell us causal impacts. Fortunately, both interventions have been evaluated to various extents. We summarize the findings of these evaluations, which broadly support the findings from the indicators analysis.

**_Red Unidos_ .** In 2012, the only rigorous impact evaluation of _Red Unidos_ was carried out, to measure impacts in the first two years of program implementation (Econometría Consultores et al 2012). The evaluation largely measures the impact of the Baseline and Family Plan since very few households had passed to the _Monitoring and Follow-up of Minimum Goals_ stage by the time of data collection.

While the administrative data show that beneficiaries increased their achievement of the welfare goals, the impact evaluation shows that factors other than early program activities may have played a key role in driving the observed results. The impact evaluation found 33
# Table of Contents

|1<br>Pr|eliminaries ...............................................................................................................3|
|---|---|
|2<br>Na|tural Resources, the Environment, and Trade: channels of interaction ............... 5|
|3<br>Di|rect Trade-Environment Linkages ......................................................................... 8|
|3.1|Trade-related Pressures on Natural Resources .................................................. 9|
|3.2|Transport-Related Emissions ............................................................................ 10|
|4<br>Po|llution Havens, Trade Leakages and Border tax adjustments ............................. 12|
|4.1|Pollution Havens? .............................................................................................. 12|
|4.2|Climate Change Mitigation, Leakages and Border tax Adjustments................. 16|
|5<br>Im|plementation Difficulties: Political Economy Considerations ............................ 18|
|5.1|Natural Resources .............................................................................................. 19|
|5.2|Climate change .................................................................................................. 20|
|6<br>Na<br>Multila|tural Resources and Environmental Goods and Services (EGS) in the<br>teral System .......................................................................................................... 21|
|6.1|Natural Resources .............................................................................................. 21|
|6.2|Environmental Goods and Services .................................................................. 22|
|7<br>Co|ncluding Remarks: Trade Policies for a Green Growth Strategy .........................25|
|Refere|nces ....................................................................................................................... 28|

2