rafmacalaba/datause-encoder-data huggingface dataset:
reasons noted above). The inclusion of a measure of length of illness (the number of months the person was ill for before he or she died), however, did not have any additional explanatory power over the mortality variables (Table 4, column 3).<sup>8</sup> As discussed in the previous section, the tests in Tables 3 and 4 are a joint test of the impact of an adult mortality shocks whether there is any impact of an adult mortality death _and_ whether there is risk-sharing within all split-offs of the same initial household. As in equation (3), we over-specify the earlier regressions with a variable describing the mortality of any prime age adult in the baseline survey belonging to the initial household. Under the null of full risksharing, consumption changes should not be affected by mortality shocks of a baseline member residing with the particular household, but should be significant for any mortality shock of a baseline member of the initial household. Table 5 presents the results.

To implement the test, the otherwise perfectly collinear initial household fixed effect will have to dropped, and replaced by a vector also including a series of baseline household characteristics. We include initial asset values, household demographics (males/females aged 0- 5, 6-15, 16-65 and 66+), characteristics of the head at baseline (number of years of education, sex, age and age squared) and the number of children of the head living outside the household. The regression also includes a full set of cluster fixed effects, (mean) age fixed effects, the mean individual baseline characteristics of those residing together in 2004 and agricultural shocks. Finally, to capture initial household conditions more fully, we also include household consumption at baseline. Since this variable is most obviously likely to be endogenous (including but not exclusively due to measurement error, thereby affecting both the dependent variable and the initial level of consumption), it is necessary to explore the use of IV estimation. Finding

> 8 The information on months ill before death was collected using a specific instrument probing for details on each mortality case.

18
_Cooperative Scenarios_ <!-- Start of picture text --> 13.00<br>12.00<br>11.00<br>10.00<br>9.00<br>8.00<br>7.00<br>6.00<br>2015 2020 2025 2030 2035 2040 2045 2050<br>BAU Unilateral climate policy w/o trade sanctions<br>Cooperative climate policy Unilateral climate policy, border tax on carbon content<br>Unilateral climate policy, Nordaus border tax<br>Gt CO2<br><!-- End of picture text --> Below we first discuss the FFDCs’ incentives to implement cooperative carbon prices, and then their incentives to diversify not only beyond fossil fuel exports but also beyond fuel-intensive industries. In each sub-subsection we first present results from a commonly observed myopic perspective, in which governments and economic agents make their decisions with the expectation that past trends will continue in future. Such backward-looking representation of decision making offers a conservative view on the incentives that would encourage FFDCs to diversify their wealth and cooperate on climate action. Second, we present the cumulative results over the period 20212050. We also identify scenarios that reconcile myopic and long-term perspectives and break the tragedy of the horizon.

# **3.1. Incentives to Cooperate on Climate Action**

“Tragedy of the Horizon” weakens FFDCs’ incentives to diversify early and cooperate on climate policies. Before LCT pioneers implement climate and trade policies, traditional diversification and noncooperative policies look like the most attractive strategies for FFDCs (figures 4 and 5). Both asset diversification and cooperation require upfront investments, which in the CGE framework crowd-out households’ disposable income in the short term, and yield delayed returns, hence initially decelerate growth of households’ consumption (figure 4) and economic output (figure 5). In this initial period the rates of growth of consumption and GDP with climate-friendly domestic policy choices are below those of BAU and below non-cooperative alternatives. Therefore, in the absence of external policy impacts the FFDCs would rather diversify into familiar heavy industries and delay cooperative climate policies. These results replicate observed behavior of most FFDCs thus far.

_Figure 4: Year-to-Year Consumption versus BAU in Fossil Fuel–Dependent Countries_ Optimal shortterm strategies: 11
# **1. Introduction**

It is becoming increasingly clear that developed and developing nations will need to take significant efforts to address the challenge of climate change. For example, the recent Report of the High Level Commission on Carbon Prices argues that all countries will need to take substantive action, including significant carbon prices. Moreover, 191 countries have submitted national climate commitments, or Nationally Determined Contributions, to the United Nations under the Paris Agreement on climate change.

In designing and evaluating policies to address climate change, governments must consider not just the global effects of such policies, but also concerns about how such policies will affect the welfare of their citizens, at the national level and at narrower, more local levels such as individual industries or regions. One of the key issues is the question of how policy will affect workers and labor markets. This is important because it represents an important channel for effects on economically vulnerable households and these effects on workers strongly influence the political viability of any policy choice.

How will workers be affected? Will those who work in carbon-intensive industries lose their jobs as a result of mitigation policy? What new job opportunities will be created in clean energy and related fields? How will the effects ripple through other industries that are not directly energy-related? These and many other related questions will play important roles as governments make climate policy decisions.

This study reviews what is known about how climate policy will affect workers and labor markets, evaluates where the major gaps are in that knowledge, and discusses promising directions for future knowledge work. Although we are primarily interested in the effects on developing nation labor markets, much of the existing research on labor market effects of climate policy focuses on developed economies. Therefore, we include that research, along with a discussion of what those developed economy studies might say about effects in developing economies.

We focus on the effects of climate mitigation policies, not the effects of the climate change itself. Damages from climate change will likely also affect labor markets 2
# Figure A2.2. Global financial markets A. Capital flows

<!-- Start of picture text --> Percent of GDP<br>50<br>40<br>30<br>20<br>10<br>0<br>1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

## C. Equity prices

<!-- Start of picture text --> Percent<br>60<br>40<br>20<br>0<br>-20<br>-40<br>1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

## E. Financial conditions index

## B. Credit

<!-- Start of picture text --> Percent<br>15<br>10<br>5<br>0<br>-5<br>1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

## D. House prices

<!-- Start of picture text --> Percent<br>12<br>8<br>4<br>0<br>-4<br>-8<br>1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

## F. Inflation

<!-- Start of picture text --> Percent Percent<br>2 16<br>1 12<br>0 8<br>-1 4<br>-2 0<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>Sources: Bank for International Settlements; Bloomberg; Haver Analytics; International Monetary Fund; Kose and<br>Terrones (2015); Organisation for Economic Co-operation and Development; World Bank.<br>Note: Equity prices, house prices, financial conditions index, and inflation are weighted by GDP in U.S. dollars.<br>Shaded areas indicate global recessions in 1975, 1982, 1991, and 2009, and global downturns in 1958, 1998, 2001, and<br>2012. World aggregates are not computed if data are available in less than 10 countries (in Panels A to D, and F)<br>and 4 countries (in Panel E).<br><!-- End of picture text --> 67
availability of administrative data sources is a key determinant of what is possible. Increasing access to tax data, even in summarized form, seems like a key goal if under-capturing of the top tail of the income distribution is to be solved.

# Challenges in Using Administrative Data

The discussion above has highlighted that administrative data can be used in several ways to solve challenges encountered in measuring the upper tail in household surveys. But these data also come with their own challenges, particularly in LMICs. In this section we discuss some of these challenges. Awareness of the challenges of using admin data is not new. Kuznets (1963:12), in comparing top income shares across countries produced by several different researchers, noted that “it may not be an exaggeration to say that we deal here not with data on the distribution of income by size but with estimates or judgments by courageous and ingenious scholars relating to size distribution of income in the country of their concern.” Haq (1964) lists several specific concerns in her analysis of income inequality in Pakistan using aggregated tax data. These concerns have subsequently been highlighted in recent research using admin data to better measure the top tail of the income distribution. These include the very small proportion of the population covered by tax data, which was one tenth of one percent in Pakistan at the time, changes in tax legislation that limited comparability over time (for example changes in the tax threshold), changes in efficiency of tax collection over time and the extent of the unregistered (i.e., informal) sector.

Other issues have also been highlighted in more recent research. Ravallion (2022) cautions that comparisons of surveys and national accounts data that show lower total incomes in surveys compared to national accounts could reflect the poor quality of national accounts rather than missing top incomes. Blanchet et. al (2022) note that in LMICs tax data is almost always in the form of aggregated tables rather than microdata, which limits what it can be used for and point out that even when microdata is available it contains few covariates, which can limit how the data can be used to improve estimates of the top tail from household surveys.

Kerr (2021) highlights that researchers using and comparing tax and survey data for South Africa created yearly earnings from household surveys obtained by multiplying monthly earnings by 12 for those employed at the time the survey was undertaken. This is very different conceptually to tax data on earnings in the last year, since the surveys exclude those employed at other points in the year, who are more likely to be low earners. Kerr (2021) show that, in the South African case, this error makes 21
VAXR is smaller than 1). In summary, the low VAX ratio of Chinese aggregate exports, as reported in the literature, hides substantial heterogeneity in VAX across firm ownership types and sizes.

Panel B of Table 2 shows the same set of estimates using the 2010 IO table. As reported, all but FIEs experienced an increase in VAX. The increase was particularly sharp for SOEs and SMEs. SOEs’ VAXR increased by about 47% while that of SMEs increased by about 27%. The significant increase in the VAXR of SOEs lends some support to the anecdote that the state sector has advanced their prominence in the Chinese economy in recent years, especially after the global financial crisis in 2008 when the Chinese central government implemented policies to stimulate the economy.

# (Insert Table 2 here)

The higher-than-unity VAXR of both SOEs and SMEs imply that many non-exporters from these two groups produce intermediate inputs and services that are embedded in Chinese exports. Table 2 reports the value of indirect exports. We find the following pecking order – SOEs have the highest share of indirect exports in VAX, followed by LPs and SMEs, with FIEs having the lowest share. Specifically, in 2007, about 80% of exports from SOEs are indirect (the numbers increased slightly in 2010). In other words, 80% of SOEs’ exports are values embedded in inputs used by firms that eventually export. For LPs and SMEs, the indirect export shares are about 72% and 63%, respectively. The indirect export share of SMEs increased significantly by 10 percentage points from 2007 to 2010, consistent with the hypothesis that small exporters could be financially constrained after the global finance crisis and less likely to engage in direct exporting. Once again, FIEs are very different from domestic firms and have a much lower share of indirect exports (about 46% in 2007, which decreased to 43% in 2010). Given the prevalence of FIEs in processing trade and the prevalence of intra-firm trade associated with vertical FDI, the low indirect export ratio is not surprising.

By splitting the IO table along the size and ownership type dimensions, we can also estimate the amount of indirect exports through different types of firms. As reported in Table 2, most of SOEs’ indirect exports are through non-SOEs. In particular, in 2007, FIEs account for over 40% (35/80) of SOEs’ indirect exports, which increased to over 55% in 2010. On the other hand, SMEs account for 25% of SOEs’ indirect exports in 2007, which declined to about 20% in 2010. Both LPs and SMEs also have high shares of indirect exports, but are both lower than that of SOEs. FIEs also play a more significant role in helping LPs to export indirectly, compared to SMEs. The role of SMEs in helping other firms export decreased from 2007 to 2010. For instance, when the SMEs’ indirect export share increased from 2007 to 2010, the role of other SMEs in facilitating their exports declined, with FIEs taking up most of 22
2 goods and factor markets.<sup>2</sup> In such conditions, attempts to identify a meaningful relationship between aggregate public and private capital are doomed to failure.

Another methodological pitfall plaguing most empirical studies of the macroeconomic impact of public investment is their failure to deal adequately with the non-stationarity of the variables of interest. In this vein, the large impact of public capital on real GDP found by some empirical studies using aggregate US data has been criticized as a spurious result that vanishes when the variables are expressed in first differences (Hulten and Schwab 1991). However, first-differencing is certainly not a solution, because it removes the long-run relationships that may be present in the data and that are precisely the main question at issue (Munnell, 1992).

This paper examines empirically the relationship between public and private investment in India. The heterogeneity of public capital is clearly visible in India, whose experience of the last four decades provides an extreme example of overexpansion of the public sector. In the context of a state-led, inwardoriented development strategy that lasted until the early 1990s, Indian public enterprises extended into virtually all sectors of the economy. Moreover, they were instrumental in generating increasingly large public deficits in the 1980s, which brought the country to the verge of a foreign exchange crisis in 1990/91 and eventually led to a radical reorientation of economic policy and the abandonment of the state-led growth model. From the policy viewpoint, the need for a substantial retrenchment of the public sector in India has underscored the importance of redefining public investment priorities in favor of those activities supportive of rapid private sector expansion.

Against this background, the paper revisits empirically the impact of public capital on private capital. The focus of the analysis is on the effects of the former on the profitability of the latter. This is the dimension that allows a meaningful distinction between public projects competitive, and complementary, with those of the private sector. The paper develops a simple analytical model embodying this distinction, and implements it using disaggregate public investment data for India. To avoid the methodological pitfalls of previous studies using aggregate time-series data, the empirical application follows a cointegration approach. In particular, because the available empirical sample is small, no attempt is made to model each of the different factors affecting the optimal private capital stock; instead, the analysis is based on a simpler conditional model, drawing from some recent results in the analysis of cointegrated systems. The consequences of this modeling strategy for the validity of the resulting parameter estimates are thoroughly explored.

2 Even for the U.S., Aschauer (I 989a) finds that 'core' infrastructure, comprising roads, water and sewage, is the type of public investment with strongest positive impact on private output.
and trade flows. Our approach here allows us to isolate Cambodia’s agreements specifically and estimate, through the “specific” component, the relationship between Cambodia’s trade agreements and trade flows, and then compare them to the “average” effect across all other agreements.

We estimate each gravity equation using the Poisson Pseudo Maximum Likelihood with High-Dimensional Fixed Effects (PPML HDFE) methodology (Correa, Guimarães, and Zylkin, 2020) that produces results robust to statistical separation and convergence issues and are corrected for potential biases arising from country-specific time trends and other parameters included in the model. More specifically, we measure trade at the country-pair level, so that the overall effect of GDP growth on trade is absorbed by the country-specific time trend variables included in the estimation. We also include country-pair fixed effects to—at least partially, if not mostly—control for cultural ties and political relationships.

# **_Data_**

Since the country-pair fixed effects and both importer and exporter time trends absorb the variation of the “usual” gravity variables (distance, gross domestic product, and other country-pair characteristics like common language, common border, and so on), our data consist of bilateral trade flows and trade agreement indicators from the Centre for Prospective Studies and International Information (CEPII) database (Abreha and Robertson 2023). The dataset includes over 1 million observations that cover 232 exporters, 179 importers, 262 Regional Trade Agreements (RTAs) over the 1990–2016 period.

# **_Results_**

Table 3.2 contains the gravity results. Again, the data cover all pair-wise trade flows for each year from 1996 to 2015. The impact compares the change in trade following the implementation of the agreement relative to countries without agreements (assuming that the trade agreements constitute an absorbing state in the sense that do not end before end of the sample period). Each row represents a specific trade agreement. The β<sup>k</sup> coefficients represent the estimated relationship between the agreement listed in the first column. The β<sup>l</sup> coefficients represent the average result of all other agreements excluding the specific agreement listed in the first column. The second pair of columns contain the same estimates, but correct for the incidental parameter bias resulting from having a large number of included controls. These corrections are potentially important because each estimation includes a fixed effect for each country pair and time trend terms for each importer and each exporter.

16 | P a g e
and limited ability to repay. It took creditors more than a decade to realize that serial rescheduling would not resolve the debt overhang.

# **4. Concluding remarks**

China’s overseas lending boom to developing countries is resulting in a growing number of defaults and debt restructurings. If history is any guide, multi-year debt workouts and serial restructurings lie ahead. Lack of transparency on the extent of indebtedness and on “hidden” debt restructurings makes it even more difficult to assess whether progress in restoring debt sustainability for the debtor countries is being accomplished. Filling in these knowledge gaps is a step in that direction.

# **References**

- Acker, Kevin, Deborah Brautigam, and Yufan Huang. 2020. Debt Relief with Chinese Characteristics. CARI Working Paper No. 2020/39.

- Acker, Kevin, Deborah Brautigam, and Yinxuan Wang. 2021. Global Debt Relief Dashboard: Tracking Chinese Debt Relief in the Covid-19 era. China Africa Research Initiative, Johns Hopkins University School of Advanced International Studies.

- Asonuma, Tamon, and Christoph Trebesch. 2016. Sovereign Debt Restructurings: Preemptive or Post-Default? _Journal of the European Economic Association,_ 14(1), 175-214.

- Asonuma, Tamon, Dirk Niepelt and Romain Ranciere. 2017. Sovereign Bond Prices, Haircuts and Maturity. IMF Working Paper No. 17/119.

- Bon, Gatien and Gong Cheng. 2020. China’s Debt Relief Actions Overseas and Macroeconomic Implications. EconomiX Working Papers No 2020-27.

- Bon, Gatien and Gong Cheng. 2021. China’s Overseas Sovereign Debt Relief Actions: What Insights Do Recent Cases Provide? _International Economics,_ 166, 23-41.

- Broner, Fernando, Tatiana Didier, Sergio L. Schmukler, Peter von Goetz. 2020. Bilateral International Investments: The Big Sur? World Bank Policy Research Working Paper No. 9501.

- Cerutti, Eugenio, Catherine Koch, and Swapan-Kumar Pradhan. 2020. Banking across borders: Are Chinese banks different? BIS Working Papers No. 892.

- Cheng, Gong, Javier Diaz-Cassou, and Aitor Erce. 2019. The macroeconomic effects of official debt restructurings. _Oxford Economic Papers_ , 71(2), 344-363.

- Custer, Samantha, Axel Dreher, Thai-Binh Elston, Andreas Fuchs, Siddhartha Ghose, Joyce Lin, Ammar Malik, Bradley P. Parks, Brooke Russell, Kyra Solomon, Austin Strange, Michael J. Tierney, Katherine Walsh, Lincoln Zaleski, and Sheng Zhang. 2021. _Tracking_

12
**Table 2: Characteristics of 21 to 58 year olds by RSE status**

||RSE<br>Selected<br>Worker|RSE<br>Unselected<br>Applicant|Males<br>RSE Non|Females<br>-applicant|
|---|---|---|---|---|
|Male|0.82|0.87|1|0|
|Age|35.9|35.9|35.3|34.6|
|Married/De-facto|0.78|0.78|0.75|0.87**|
|Have a child|0.61|0.63|0.64|0.72|
|Ever worked or studied in NZ|0.16|0.02***|0.02***|0.00*|
|Ever worked or studied overseas|0.21|0.04***|0.09***|0.00*|
|Literate in English|0.90|0.81*|0.74***|0.71*|
|Has primary schooling or less|0.64|0.66|0.64|0.72**|
|Has schooling past Form 4 (Year 10)|0.06|0.05|0.09|0.05|
|Years of Education|7.22|6.70|7.48|6.66|
|Ever held a paid job|0.48|0.36**|0.43|0.20***|
|Worked for pay in 2007|0.39|0.34|0.38|0.17***|
|Average hours worked in last week, if work|37.74|14.86***|36.80***|35.60|
|Wages last week (Vatu), if work|8710|6055|10970**|7761|
|Had a health compliant in last 6 months|0.05|0.15***|0.11|0.10*|
|Number of days of hard physical labor per week|3.62|3.74|3.81|3.41|
|Currently smokes|0.39|0.38|0.52|0.02|
|Has consumed kava or alcohol in last month|0.45|0.48|0.64***|0.04|
|**Sample Size**|201|97|316|447|

Notes:

*, **, and *** indicate significantly different at the 10, 5, and 1 percent levels respectively. T-tests for Unselected RSE applicants compare means to Selected RSE workers T-tests for Non-applicants compare means to RSE applicants of the same gender.

- 35 -