rafmacalaba/datause-encoder-data huggingface dataset:
The third column indicates that the likelihood of participation in a PISA cycle is affected by a country’s _capacity for assessment_ , as indicated by its prior experience with TIMSS or national assessments. Participation in a regional assessment, however, is unrelated to participation in PISA. These odds are similar for all countries and non-OECD countries.

Table 7 presents the results for income level, region and assessment capacity considered simultaneously. In general, the size of the coefficients for most of the variables are smaller in the full models than in the models in which the variables are considered in separate groups. All odds are relative to countries with the following characteristics: low-income countries, not in the ECA region, not with prior assessment capacity. The first column presents the model that includes an indicator for OECD membership; as noted previously, OECD membership perfectly predicts PISA participation.

The level of economic development strongly increases the odds of PISA participation, with or without an OECD control; upper middle-income countries and high income countries are about 15 times more likely to participate in PISA as compared with low-income countries, and lower-middle income countries are about five times more likely to participate. Countries in the ECA region are about three times more likely to participate than countries outside the region. Coefficients for region and level of economic development are higher in the model that does not include a control for OECD membership.

Countries with prior experience of TIMSS are about five times more likely to participate in PISA, and countries with national assessments are about three times more likely to participate. Regional assessments are unrelated to PISA participation, in all models. The strong positive effect for having built capacity through TIMSS participation and the non-significance of regional assessment capacity do not differ between the two groups of countries.

**Table 7: Odds of participating in PISA, all countries and non‐OECD countries**

|In PISA|All countries<br>Odds Ratio<br>(std. error)|All countries<br>Odds Ratio<br>(std. error)|Non‐OECD countries<br>Odds ratio<br>(std. error)|
|---|---|---|---|
|Lower‐middle income|5.01 (2.47)**|4.91 (2.45) **|4.86 (2.48) **|
|Upper middle‐income|15.04 (7.35)***|20.46 (10.03) ***|15.60 (7.63) ***|
|High income|14.49 (7.21)***|39.22 (19.01) ***|13.19 (6.58) ***|
|ECA region|3.01 (0.68)***|6.07 (1.20) ****|3.07 (0.71) ***|
|In TIMSS|5.01 (1.04)***|5.43 (1.03) ***|5.36 (1.12) ***|
|In Regional|1.21 (0.41)|1.32 (0.3)|1.20 (0.41)|
|National|3.60 (0.76)***|6.03 (1.16) ***|3.41 (0.73) ***|
|OECD|104.90 (77.37)***|‐‐|‐‐|
|Constant|.006(.003)***|.004(.002)***|0.006(0.003)***|
|Observations|1269|1269|1081|
|LR chi2|809.07|678.58|256.04 ***|

13
Another, even more fundamental, price point for compensating host countries when implementing comprehensive carbon taxes could be defined as the “development support price”. Some countries may develop plans to implement their own comprehensive carbon taxes independent of knowledge about carbon tax support programs, and in principle could be willing to implement such taxes even without support. However, these countries might not have the capacity to develop and implement a carbon tax; or it may be politically difficult to do it on their own. The donor community might then want to provide the necessary funding to cover the host country’s policy costs in order to overcome such barriers. Such a “development support price” ought to be lower (or at least no higher) than either of the two price points derived here.

We will in section 2 discuss the ICP and OCP in the most basic cases. We will revert to more general discussion of the ICP in section 3 for our main case, where the host country’s NDC is not yet fulfilled. A more general discussion of OCP follows in section 4. Section 5 uses basic data on specific countries to calculate illustrative compensation amounts using our price formulas.

The concepts of “unconditional” and “conditional” NDCs are useful for understanding the issues considered in the paper. While these concepts are not addressed in the Paris Agreement (PA) text, they are used by many PA signatories in their NDC documents submitted to the UNFCCC. The “unconditional NDC” is the target that a given country aims to attain by itself, without outside finance or technical assistance. The “conditional NDC” is assumed to require outside help for its achievement, and it is always as or more ambitious than the unconditional NDC plan. Note that for support to a lower-income host country for carbon tax implementation to be meaningful, the host country’s unconditional NDC must be “real:” the target must at a minimum correspond to the host country’s “business-as-usual” (BAU) emissions rate.

# **2. Price points for carbon tax implementation: Basics**

# **2.1 The “incremental cost price” (ICP)**

We will now derive rules for financial support per unit of induced GHG mitigation by a donorsupported carbon tax implemented in the host country, required for the host country to accept this carbon tax. We start with considering the ICP in the most basic and simple case. To recapitulate, the ICP assumes that the host country can use the resulting mitigation, induced by the supported carbon tax, toward fulfilling its unconditional NDC under the PA.

5
# **2. Patterns of Housing Investment**

## **2.1 Housing Investment: An S-shaped trajectory**

We build a database of housing investment using the variable ‘o _wnership of dwelling, value added’_ in the National Account Statistics. The national accounts report housing investment by taking into account investment in dwellings by households, the government, and the private sector. By definition, this variable includes buildings that are used entirely or primarily as residences, including any associated structures, such as garages, and all permanent fixtures customarily installed in residences. Houseboats, barges, mobile homes and caravans used as principal residences of households are identified primarily as dwellings as well. As a part of investment in dwellings, costs of site clearance and preparation are also included.<sup>3</sup> However, there are a few limitations with these data. First, o _wnership of Dwellings_ does not include housing that has been built but is recorded in inventories. Second, the value is measured based on market prices but it uses cost in the absence of reference prices. Thus, actual value may differ from country to country.

The evidence from our data set points out that investments in housing pick up pace as countries approach middle incomes. In fact, housing investment follows an _S_ -shaped trajectory where low income economies have low income elasticities stemming from both demand and supply constraints. Rigidities in the supply of materials, organization of the construction industry, and nascent markets for land transactions constrain housing expansion in urban areas. On the demand side, when incomes are low at early stages of development, the claims of other consumption expenditures such as food tend to be stronger (Lakshmanan et al 1978, Regmi et al 2001). After lagging early in development, the income elasticity of housing expenditures is likely to increase during periods of rapid industrialization and then begins to decline at high levels of per capita incomes. Why? Rising incomes free up room for consumption of housing and related durables, and are typically associated with the building of technical and institutional capabilities that enable housing investment. Asset formation in housing provides a hedge against the erosion of savings driven by inflationary pressures. Finally, as countries approach high incomes, much of the housing and physical infrastructure is in place and residents instead demand many

> 3 Examples include products included in Central Product Classification (CPC) a class 5211, residential buildings and CPC group 387, prefabricated buildings, such as one- and two-dwelling buildings and other (System of National Accounts (SNA) Manual, 1993).

4
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obtain _real GDP_<sup>o</sup> _,_ then we need to use PPP<sup>o</sup> , which incorporates prices for exports and imports including both final goods and intermediate inputs.

In section 3 we show how PPP<sup>o</sup> can be computed using the Geary (1958)-Khamis (1970,1972) (GK) system, and further broken down into separate PPP’s for final expenditures, exports and imports. It should be emphasized that by working at the level of entire economies, rather than sectors, some of the difficulties with measuring output-based PPP’s are avoided. In particular, we find that while the international prices of intermediate inputs are used, the corresponding domestic prices of intermediates are not needed at all. In this sense, our use of trade data provides a _short cut_ to obtain output-based deflators for the entire economy.<sup>2</sup> In section 4, we provide an empirical application of our techniques. The data used for this illustration are from the 1996 ICP-PWT benchmark comparison, using 4-digit SITC export and import unit-values for 151 countries. With the normalization that world real GDP<sup>e</sup> equals world real GDP<sup>o</sup> , we find that one-third of the countries have real GDP<sup>e</sup> exceeding real GDP<sup>o</sup> , which means that they are less productive than indicated by the PWT and instead benefit from high terms of trade. Included in this group are Australia, New Zealand, Japan, North America and most countries in Europe, but also a set of developing countries that happen to benefit from high unit-values on selected export products. The remaining two-thirds of countries have real GDP<sup>e</sup> below real GDP<sup>o</sup> , which means that they are more productive than indicated by the PWT but have low terms of trade. That group has lower GDP per capita on average than the former group, but still includes some wealthy countries such as Australia, Hong Kong and Norway.

When we split our sample between oil and non-oil exporters, we find that the relationship between GDP and the terms of trade differs for the two sets of countries. Generally, there is a positive relationship for non-oil exporters: countries with higher per-capita GDP also have higher terms of trade. This terms-of-trade effect is driven mainly by an increase in export price levels, as import price levels are relatively stable. In contrast, we find no significant relationship between GDP per capita and the terms of trade for our set of 25 oil exporters. This appears to be

> 2  The International Comparisons of Output and Productivity (ICOP) project at the University of Groningen constructs real GDP by sector from the output side using the industry-of-origin approach. This requires comparison of sectoral output prices across countries (see van Ark and Timmer, 2001, for example). Sectoral real output can be aggregated to obtain real output GDP , but his has only been done for a limited number of countries so far. The short-cut proposed here, which uses international rather than domestic prices of intermediates, is much easier to implement for a larger set of countries.

4
**Table 7 Street Lighting in UAs**

Non Central City

|Urban Agglomeration|Centr|al City|Av<br>(Maximum|erage<br>, Minimum)|
|---|---|---|---|---|
||Number of Street<br>Lights|Households per<br>Street Light|Number of Street<br>Lights|Households per Street<br>Light|
|Hyderabad Urban<br>Agglomeration|18,138|36|6,368<br>(10,300, 1,465)|11<br>(44, 2)|
|Chennai Urban<br>Agglomeration|79,303|12|5,922<br>(1,7606,25)|199<br>(1,350,4)|
|Kolkata Urban<br>Agglomeration|78,354|12|3,408<br>(12,000, 55)|20<br>(181, 3)|
|Delhi Urban<br>Agglomeration|NA|NA|NA|NA|
|Pune Urban<br>Agglomeration|59,001|9|2,323<br>(4,000, 739)|82<br>(313, 4)|

**Source: Census of India, 2001, Authors’ Computations**

# **3.  Finances of Urban Local Bodies**

We analyze the finances of the ULBs by considering expenditures and revenues separately. The main sources of information on expenditures and revenues of ULBs are the Annual Administrative Reports, Budgets of the ULBs, the City Development Reports, and Environmental Status Reports, depending on availability at the city level. The smaller ULBs in India do not maintain systematic records of their finances. The data for this study were collected through extensive field visits to all the ULBs by circulating questionnaires 14
To compare the e¤ect of foreign and domestic linkages, we expand our ...nal sample to include establishments owned by domestic parents. Similar to Section 4, we perform a matching analysis to match each domestically owned subsidiary with a local stand-alone establishment from the same country and industry based on economic characteristics. The matched pairs share similar attributes such as lagged size, lagged labor productivity, age, and previous exporter status, except the status of domestic parent ownership. We then compare the performance of the matched pairs to establish the e¤ect of having domestic parents. As in the analysis of foreign owned subsidiaries, we identify the extent of production and ...nancial linkages between the establishments and their domestic parents and investigate how they a¤ect subsidiaries’performance during the crisis.

Table 8 reports the results. First, we ...nd in column (1) that compared to domestically owned subsidiaries, foreign owned subsidiaries exhibited a greater advantage over local counterfactuals. The performance gap between the latter two groups is 18 percentage points higher than the gap between domestically owned subsidiaries and their local stand-alone matches. In column (2), we allow the e¤ect of production and ...nancial linkages to vary between domestic and parent ...rms. The results show that while ...nancial linkages with domestic parents also played a stabilizing role during the crisis, the e¤ect of foreign ...nancial linkages was signi...cantly greater. Furthermore, only foreign production linkages are found to exert a signi...cant and positive e¤ect on establishments’resilience to the crisis; domestic production linkages did not signi...cantly a¤ect establishment performance.

## [Table 8 about here]

# 6.2 Network E¤ect

In addition to production and ...nancial linkages with foreign parent ...rms, MNC subsidiaries exhibit another distinct feature compared to domestic establishments. That is, most multinationals today operate a multilateral network. Having a larger multilateral network enables multinationals to diversify both product and ...nancial markets. In this subsection, we take into account this aspect of multinational ...rms and examine how a larger multinational network can potentially amplify the e¤ect of production and ...nancial linkages. To do so, we ...rst construct two variables to represent the number of countries in the MNC networks. First, we control for the total number of countries outside the subsidiary in which MNCs operate. Operation in a larger number of countries implies greater product market diversi...cation and potentially greater intra-...rm demand, lending subsidiaries stronger resilience to negative host-country demand shocks. Furthermore, a larger network outside the subsidiary country means that multinationals can o¤er subsidiaries internal capital ‡ows from more sources, mitigating subsidiaries’vulnerabilities to host-country ...nancial shocks.

20
out of the house, especially when given to women whose businesses were small to begin with.

The experiment allows us to test between three competing models of microenterprise investment and growth. The results are not consistent with either a standard Ramsey model or with a variation of this model that incorporates time-inconsistent preferences. To explain a large di¤erence in outcomes between cash and in-kind grants, we need a model with a lack of asset integration where the form in which capital comes a¤ects the extent to which it is invested in the business. We examine two possible causes of the di¤erence between in-kind and cash grants: self-control issues caused by time-inconsistent preferences, high discount rates, or lack of ability to save; and external pressure from others to share additional resources. We ...nd that the e¤ect of the cash treatment is signi...cantly more positive for individuals with the most self-control, whereas there is no evidence of treatment heterogeneity with respect to external pressure. This is consistent with the recent evidence in Spears (2009) who suggests that present-bias is a key constraint on microentrepreneurs expanding their businesses. But it contradicts results from Anderson and Baland (2002) for Kenya and Somville (2011) for Benin, whose evidence suggests that women seek to save outside the household in order to avoid contributing to household expenses, and ...ndings by Brune et al. (2011) in Malawi, who argue that the reason for the success of a saving commitment product is the desire to escape external pressure.

The remainder of the paper is structured as follows. In Section 2 we present the conceptual framework and testing strategy. Section 3 describes the experimental design and characteristics of our sample. Section 4 gives the basic experimental results, and explores heterogeneity by gender, treatment type, and randomization strata. Section 5 then asks what happens to the cash grants and what distinguishes the pro...table from less pro...table female businesses. Section 6 examines why the cash and in-kind treatments di¤er, and Section 7 concludes.

# 2 Conceptual framework and testing strategy

In this section we present a conceptual framework that guides our empirical analysis. The model is organized around two key assumptions which characterize the population of microenterprises we study: entrepreneurs cannot borrow and have to self ...nance (only 10 percent of our sample has ever had a formal loan); and they have di¤erent abilities and ability is a complement to capital. We ...rst present a model of capital accumulation without time inconsistency and derive testable predictions regarding the e¤ect of a capital grant. We then introduce time consistency 4
-16-

information as to the country-specific effects is available. Worldng with recursive substitution and the appropriate linear predictors, as we did in the previous case, the multivariate regression associated with regression equation (10) is the following, <!-- Start of picture text --> 7-to .t<br>Zt3 . 'A (11)<br>z~~~~~X.<br><!-- End of picture text --> H = _[B_ + +*where, 0 0 0 0 00 o0 0 0 0 0 0 (1+y)0' 0' 0 0 0 0 (1+y)<sup>2</sup> 0f' (0+y)0 0' 0 0 0 (1+y)<sup>3</sup> 0e **(i+y)**<sup>**2**</sup> **e, (i** +y)e' e' 0 0 (1+y)4Y0 (1+ y)<sup>3</sup> e (1+y)<sup>2</sup> 0' (I '-y)e0 0' 0
24

# Appendix 2 Complete List of Elasticity Estimates

|Elasticities of D|emand for Passenger Transport: A|U Studies|
|---|---|---|
|Mode|Market Demand Elasticities|Mode Choice Elasticities|
|_Air_<br>Vacation|1.11,1.52,0.4-1.68-1.98,1.48-1.92,<br>1.23-1.84-1.93-2.75-2.95-4.05,<br>1.4-1.6-2.0-2.7-3.3,<br>|0.3B|
||2.2-2.4-2.6-4.6||
|NonDracation|1.2-1.66-1 .S4-2.51-3.74-3.78-4.18,<br>0.08-0.36-0.48,1.15,0.65,0.90|0.18|
|Mixedc|0.44-1.81,0.67-0.78-0.91-1.28,|1.28-2.24-2.43-3.69-3.81-5.26,0.62,|
||0.76-0.84,1.39,0.53-1.00,1.12-1.28,<br>0.49-0.82-1.02-1.03-1.29-1.83,|0.26-0.38|
||1.85-2.09-2.91,0.7-1-1.5,2.82-4.61,<br>I.8-1.9||
|Rail|||
|Intercity|||
|Leisure|1.4|1.2|
|Business|0.7|0.57|
|Mixed"|0.11-0.14-0.34-0.48-0.6-0.62-<br>0.65-0.67-0.68-0.7-0.85-0.87-|0.32,0.86-1.14|
||1.03-1.18,0.74-0.9,0.37-0.4,||
||1.08-1.54,1.19-1.5,0.16-0.3||
|Intracity|||
|Peak|0.15|0.22-0.25|
|Ofr Peak|1.0||
|All Day-|0.12-0.23-0.44-0.49,1.8,0.3|0.08-0.29-0.44-0.57-0.75|
|<br>Automobile|||
|Peak|0.21-0.36,0.12-0.49|0.02-0.04-0.08-0.14-0.16-0.31-<br>0.550.88,0.16,0.04,0.7,0.16-0.18,<br>0.15-0.46-' 59-1.16-2.03-2.69,<br>0.32-0.47,|
|**Off**Peak|0.14-0.29,0.88,0.15-0.45,0.06-0.09,|0.16-0.34-0.79-0.96,0.96|
|All Day'|0.0-0.09-0.22-0.52,0.06-0.1-0.23-|0.12-0.26-0.38-0.62-0.97-1.26,|
||0.28,0.05-0.09-0.1-0.22-0.26-0.31|0.01-0.02,0.08,0.83|
effects of climate change such as drought. And, as in the past, civil conflicts and violence will pose by far the greatest danger to the continent’s economic performance.

# **References**

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- <mark>Christiaensen, Luc & Demery, Lionel & Kuhl, Jesper, 2011. "The (evolving) role of agriculture in poverty reduction--An empirical perspective," Journal of Development Economics, Elsevier,</mark> vol. 96(2), pages 239-254, November 2011.

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- <mark>Collier, Paul and Sanjay Pradhan, 1994, ‘Economic aspects of the Ugandan Transition to Peace”, in D. Bevan et. al, “Some Economic Consequences of the Transition from Civil War to Peace”, World</mark> Bank Policy Research Working Paper No. 1392.

- <mark>Loayza, Norman & Olaberria, Eduardo & Rigolini, Jamele & Christiaensen, Luc, 2009. "Natural disasters and growth - going beyond the averages," Policy Research Working Paper Series 4980, The</mark> World Bank.

- <mark>Karl Pauw, James Thurlow and Dirk van Seventer, 2010,  Droughts and floods in Malawi, No 962, IFPRI</mark> discussion papers from International Food Policy Research Institute (IFPRI).

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- <mark>van der Mensbrugghe, Dominique, 2013. “Modeling the Global Economy – Forward Looking Scenarios for Agriculture.” In P.B. Dixon and D.W. Jorgenson, eds.,</mark> _<mark>Handbook of Computable General Equilibrium Modeling.</mark>_ <mark>North Holland, Elsevier B.V., pp. 933-994. See also Van der Mensbrugghe (2011).</mark>

23
Figure 6-8. Wage Premiums by Skill Group Pre- and Post-TA <!-- Start of picture text --> 6.90231 -<br>1.18366<br>-2 21<br>HIGHLY SKILLED WORKERS WAGES AND ADOPTION<br>7.66017j<br>3.50962<br>-2 2<br>aim-<br>SKILLED AND SEMI-SKILLED WORKERS WAGES AND ADOPTION<br>8.05372<br>1.93777<br>-2 2<br>Uame<br>UNSKILLED WORKERS WAGES AND ADOPTION<br><!-- End of picture text -->