rafmacalaba/datause-encoder-data huggingface dataset:
-52-

gets R1 now and increases its period 2 debt by (D1-R1)(l+r ), where rL is the interest rate on what is rolled over. But this amount is paid if and if Y - YH. The debtor's period 2 obligation grows to D2 +(D -R )(l+r ), so that its optimal level of effort falls to e[YH-D +(D -R )(l+rL)-Y +RL. The 2 1<sup>-R</sup> 1<sup>)(+)Y+</sup> 2<sup>].Th</sup> creditor can expect to get R +w(e)[D2+(D -R )(l+rL)] + [l-ir(e)]R L since the creditor pays R1 now for sure and D2+(D -R )(l+r ) the next period with probability w(e[YH-D2+(D -R )(l+r )-YL+R2]) and RLwith remaining probability. Here the debt burden is at its maximum, but if income is high the creditor recovers the most.

# _Forgiveness_

Turn now to option (iii), forgiving the current shortfall. Again the creditor receives R for sure now, but receives D2, the original obligation, next period with probability lr[e(YH-D L YRL)]. Hence the period 2 debt 2 + 2)I ec h eid2db obligation and the consequent amount of effort on the part of the debtor to achieve a high income are somewhere in between what they are in the case of a rollover and a cancellation.6 In comparing options (i) and (iii), the creditor trades off the size of the period 2 obligation wich the likelihood that the obligation will be paid off. The higher the debt obligation, the less effort the debtor is likely to put into ensuring that circumstances will be such that it will pay.

6 L Note, by the way, that formally, through the choice of r , financing a postponement of repayment includes debt forgiveness (r - -100%) and cancellation (r -D2/(D -R )xlOO%) as special cases. We are thinking, however, of rollovers as occurring at either the actuarially fair rate or at least at the risk-free rate. A rollover at anything less than the actuarially fair rate involves an element of forgiveness.
- 877 C. Arkolakis and C. Walsh. Clean Growth. Technical Report w31615, National Bureau of Economic 878 Research, Cambridge, MA, Aug. 2023. URL `http://www.nber.org/papers/w31615.pdf` .

- 879 S. Armitage, N. Bakhtian, and A. Jaffe. Innovation Market Failures and the Design of New Climate 880 Policy Instruments. Technical Report w31622, National Bureau of Economic Research, Cambridge, 881 MA, Aug. 2023. URL `http://www.nber.org/papers/w31622.pdf` .

- 882 Asian Development Bank. Fossil Fuel Subsidies in Indonesia: Trends, Impacts, and Reforms. Report, 883 Asian Development Bank, Mandaluyong City, Philippines, Oct. 2015. URL `https://www.adb.org/` 884 `sites/default/files/publication/175444/fossil-fuel-subsidies-indonesia.pdf` .

- 885 A. Baranzini, J. C. J. M. Van Den Bergh, S. Carattini, R. B. Howarth, E. Padilla, and J. Roca. 886 Carbon pricing in climate policy: seven reasons, complementary instruments, and political economy 887 considerations. _WIREs Climate Change_ , 8(4):e462, July 2017. ISSN 1757-7780, 1757-7799. doi: 888 10.1002/wcc.462. URL `https://wires.onlinelibrary.wiley.com/doi/10.1002/wcc.462` .

- 889 M. Barnett. A Run on Oil? Climate Policy and Stranded Assets Risk. Working Paper, Arizona State 890 University W. P. School of Business, 2023. URL `https://www.ssrn.com/abstract=4346525` .

- 891 L. Barrage and W. Nordhaus. Policies, projections, and the social cost of carbon: Results from the 892 DICE-2023 model. _Proceedings of the National Academy of Sciences_ , 121(13):e2312030121, Mar. 893 2024. ISSN 0027-8424, 1091-6490. doi: 10.1073/pnas.2312030121. URL `https://pnas.org/doi/` 894 `10.1073/pnas.2312030121` .

- 895 A. M. Bauer, F. McIsaac, and S. Hallegatte. How Delayed Learning about Climate Uncertainty Impacts 896 Decarbonization Investment Strategies. Working Paper WPS10743, The World Bank, Washing897 ton DC, 2024a. URL `https://documents.worldbank.org/en/publication/documents-reports/` 898 `documentdetail/099829103282438373/idu1f2d86d77127091490d1a6df1dc342f15d10b` .

- 899 A. M. Bauer, C. Proistosescu, and G. Wagner. Carbon Dioxide as a Risky Asset. _Climatic Change_ , 900 177(5):72, May 2024b. ISSN 0165-0009, 1573-1480. doi: 10.1007/s10584-024-03724-3. URL `https:` 901 `//link.springer.com/10.1007/s10584-024-03724-3` .

- 902 M. D. Bauer and G. D. Rudebusch. The Rising Cost of Climate Change: Evidence from the Bond Mar903 ket. _Review of Economics and Statistics_ , 105(5):1255–1270, Sept. 2023. ISSN 0034-6535, 1530-9142. 904 doi: 10.1162/rest a 01109. URL `https://direct.mit.edu/rest/article/105/5/1255/107405/` 905 `The-Rising-Cost-of-Climate-Change-Evidence-from` .

46
<!-- Start of picture text --> Leo<br>150<br>140<br>130 A a)<br>120 Jd<br>f<br>Lo al eon<br>SSal —.P ‘4<br>100<br>Ps9 oiPPPob PPSy okFPgh FP© HGPAy PgA PPne WhPPA ADBPSD AOPrPA Ae A<br>—#— Brazil —e—High Income Countries<br>—— Latin America and Caribbean (excluding high income) —t Low Income Countries<br>—— Middle Income Countries<br><!-- End of picture text --> <!-- Start of picture text --> 4.0<br>3.0<br>2.0<br>10<br>ao<br>-10<br>2.0<br>1995-2002 2008-32010 2011-2018<br>meee Change in Productivity mum Change in Employment rate<br>Glas Change in Participation Rate Gls Change in Shareof Working Age Population<br>a= Annual Growth per capita Value Added<br><!-- End of picture text -->
**Table 1. Descriptive statistics of WEAI 2.0 pilot survey by country**

||Bangl<br>|adesh<br>|Uga<br>|nda<br>|
|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|
|Female|0.551|0.498|0.558|0.497|
|Age|43.3|14.0|45.8|17.7|
|_Highest level of education completed_|||||
|Less than primary|0.544|0.499|0.625|0.485|
|Primary|0.093|0.290|0.168|0.374|
|Secondary or above|0.361|0.481|0.207|0.406|
|Literate|0.551|0.498|0.582|0.494|
|Interviewed alone|0.744|0.437|0.970|0.172|
|Observations|39|9|3|28|

**Source:** Authors’ calculations based on WEAI 2.0 pilot data.

25
Policy Research Working Paper 10082

# **Abstract**

Over the past two decades, the causal relationship between climate change and migration has gained increasing prominence on the international political agenda. Despite recent advances in both conceptual frameworks and applied techniques, the empirical evidence does not provide clear-cut conclusions, mainly due to the intrinsic complexity of the phenomena of interest, the irreducible heterogeneity of the transmission mechanisms, some common misconceptions, and, in particular, the paucity of adequate data. This data-oriented review first summarizes the findings of the most recent empirical literature and identifies the main insights as well as the most important mediating channels and contextual factors. Then, it discusses open issues and assesses the main data gaps that currently prevent more robust quantifications. Finally, the paper highlights opportunities for exploring these research questions, exploiting the potential of the existing multi-topic and multi-purpose household survey data sets, such as those produced by the World Bank’s Living Standards Measurement Study. The paper focuses on the Living Standards Measurement Study– Integrated Surveys on Agriculture program to discuss potential improvements for integrating standard household surveys with additional modules and data sources.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at apaolantonio@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 1: Set of variables used for country-specific VARX* models

||non-US <br>domestic<br>**x**_it_|model (_i_ <br>foreign<br>**x**<sup>_∗_</sup><br>_it_|= US)<br>global<br>**d**_it_|US m<br>domestic<br>**x**_it_|odel (_i_=<br>foreign<br>**x**<sup>_∗_</sup><br>_it_|US)<br>global<br>**d**_it_|
|---|---|---|---|---|---|---|
|real GDP|_yit_|_y_<sup>_∗_</sup><br>_it_||_yit_|_y_<sup>_∗_</sup><br>_it_||
|infation rate|_πit_|_π_<sup>_∗_</sup><br>_it_||_πit_|_π_<sup>_∗_</sup><br>_it_||
|real equity price|_qit_<br>|_q_<sup>_∗_</sup><br>_it_<br>||_qit_<br>|||
|short-term interest rate|_ρ_<sup>_S_</sup><br>_it_<br>|_ρ_<sup>_S∗_</sup><br>_it_<br>||_ρ_<sup>_S_</sup><br>_it_<br>|||
|long-term interest rate|_ρ_<sup>_L_</sup><br>_it_|_ρ_<sup>_L∗_</sup><br>_it_||_ρ_<sup>_L_</sup><br>_it_|||
|real exchange rate|_eit −pit_||||_e_<sup>_∗_</sup><br>_it _<sup>_−p∗_</sup><br>_it_||
|oil price|||_p_<sup>_O_</sup><br>_t_<br>|_p_<sup>_O_</sup><br>_t_<br>|||
|metal price|||_p_<sup>_M_</sup><br>_t_<br>|_p_<sup>_M_</sup><br>_t_<br>|||
|agricultural price|||_p_<sup>_A_</sup><br>_t_|_p_<sup>_A_</sup><br>_t_|||

capture the influences from the international commodity market.<sup>11</sup> The dataset contains various types of countries. Twenty-six economies are divided into two types; the large-open economy and the small-open economies. The US is classified as the only large open economy. For this type, it is assumed that the foreign financial markets do not affect its economy. Also, the international commodity prices are included in the model representing its economy. See Dees, di Mauro, Pesaran, and Smith (2007) and CPRX(2011) for similar treatment. Although the instantaneous commodity prices are only included in the US model, other countries, such as China, can influence it through trade linkages but with a time lag. Thus in the US model, three commodity prices, i.e. _p_<sup>_O_</sup> _t_<sup>,</sup><sup>_pM_</sup> _t_ and _p_<sup>_A_</sup> _t_<sup>,are</sup> included in **x** US _,t_ , and foreign financial variables, i.e. _q_ US<sup>_∗_</sup> _,t_<sup>,</sup><sup>_ρS_</sup> US<sup>_∗_</sup> _,t_<sup>and</sup><sup>_ρL_</sup> US<sup>_∗_</sup> _,t_<sup>,are excluded from</sup> _eit_ = ln(E _it_ ), _ρ_<sup>_S_</sup> _it_<sup>= 0</sup><sup>_._25</sup><sup>_∗_ln(1 + R</sup> _it_<sup>_S/_100),and</sup><sup>_ρL_</sup> _it_<sup>= 0</sup><sup>_._25</sup><sup>_∗_ln(1 + R</sup> _it_<sup>_L/_100),where</sup> GDP _it_ = nominal GDP, in local currency CPI _it_ = consumer price index; base year is 2000 EQ _it_ = nominal equity price index E _it_ = exchange rate of country _i_ in terms of US dollars R<sup>_S_</sup> _it_ = short-term interest rate per annum R<sup>_L_</sup> _it_ = long-term interest rate per annum _yit_ is constructed as follows: 1) calculate the real GDP by GDP _it/_ CPI _it_ ; 2) from the 2006-2008 average of PPP-GDP, calculate the relative size of real GDPs for 2006-2008; 3) rescale the real GDP of each country, with US GDP as a baseline; 4) take the logarithm.

11It is possible to include the global variables for some countries, but exclude them for the other countries. Thus we added country index “ _i_ ” to its subscript.

11
<!-- Start of picture text --> Productivity Among Exitors and Stayers<br>1992 1994 1996 1998 2000 2002<br>year<br>Stayers Exitors<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Data source: EIA<br>(a) Number of Exiting firms (b) Average Productivity of Exitors vs Stayers<br>800<br>4.5<br>4<br>600<br>3.5<br>400 3<br>Labour Productivity<br>sum of ExitRevised 2.5<br>200 2<br>0<br><!-- End of picture text --> Figure 4: Firms Demography (Source: INEGI, Aguascalientes)

# 3.2 The Model

In order to explain the facts presented in the previous section, in particular the increase in the industrial inequality in a period of profound liberalization we develop a model with heterogenous firms based on neo-Schumpeterian growth models. The key intuition here is that liberalization can generate at the same time contrasting effects on the incentives to innovate. On one side, higher competition can hamper the innovative efforts of incumbent firms by reducing their expected profits.<sup>13</sup> At the same time, the liberalization may lead incumbent to increase their innovative efforts in order to “escape competition”. The synthesis provided by the neo-Schumpeterian growth models consists exactly in allowing these two mechanisms to coexists and interplay, and in this manner it provides us with the theoretical basis that could explain the stylised facts presented in Section 3.1.

Now we will briefly sketch our model, which builds on the work of Aghion, Burgess, Redding, and Zilibotti (2004) and Aghion, Bloom, Blundell, Griffith, and Howitt (2002). The principal innovation of our model is that we relax the assumption that the impact of increased competition on backward firms is invariably negative. Instead, we will allow also backward firms to be able to catch-up with the productive frontier, even if this will be less likely because of their initially disadvantaged condition. We characterize this model by discussing separately in each one of the following subsection (1) domestic production, (2) innovation decision, (3) foreign competition, (4) equilibrium innovation.

> 13This effect is referred to as “Schumpeterian effect” by Aghion, Bloom, Blundell, Griffith, and Howitt (2002)

9
Table I O Parameter estimates of expenditure per single persons and S, **s**

||Parameter|estimates||Contribution|to variance|explained|
|---|---|---|---|---|---|---|
|Independcent Variable|1988|1989|1992|1988|1989|1992|
|1) HcTprXsehold<br>size|-0.01*|-0.02*|-0.02*|4.7|7.2|13.5|
|2) Education of household head||||19.2|30.0|26.5|
|Some primary|0.03|0.03|-0.03||||
|Completion of primary|-0.06|0.12*|0.04||||
|Some middle|0.05|0.12*|0.0004||||
|Completion of middle|0.17*|0.27*|0.17*||||
|Some secondary|0.22*|0.33*|0.19*||||
|Completion of secondary|0.34*|0.57*|0.3 3*||||
|Higher education|0.52*|0.75*|0.60*||||
|3) Sector:|||||||
|Export farming|-0.11|0.05|-0.14*|_5.5_|3.2|11.1|
|Food crop farming|-0.06*|-0.06*|-0.10*|0.1|0.1|0.8|
|Nonfarm|-0.02|0.01|0.01|0.8|0.1|-0.2|
|Infornal|-0.04|0.01|-0.01|_-0.5_|-0.1|-0.5|
|4) Sex of household head<br>male= 1, female=0|0.03|0.14*<br>________|_-0.05*_<br>____|2.0|_5.7_|-2.1|
|5) Region:|||||||
|Western|0.40*|0.46*|0.08|10.6|5.8|-0.9|
|Central|-0.04|0.28*|0.20*|1.2|-1.9|1.0|
|Greater Accra|0.45*|0.49*|0.15*|25.3|17.4|3.4|
|Eastem|_0.15*_|0.30*|0.17*|-0.3|-1.3|-0.4|
|Volta|-0.01|0.16*|0.15*|_0.5_|-2.5|-0.5|
|Ashanti|0.14*|0.38*|0.28*|-1.8|-0.8|9.9|
|Brong-Ahafo|0.24*|0.70*|0.14*|0.7|17.6|-2.1|
|Northem|0.21*|0.19*|0.18*|-0.8|-2.2|-2.4|
|Upper Eastern|-0.06|-0.19*|0.34*|0.8|4.6|0.2|
|6) Experience ofhhd head|0.01*|0.02*|0.01 *|-20.2|-37.4|-11.1|
|7) (Experience of hhd head)'|-0.0002*|-0.0003*|-0.0001*|21.7|37.7|9.6|
|8) Employment ratio|_0.50*_|_0.45*_|0.46*|19.1|16.2|33.8|
|9) Female employed|-0.19*|-0.02*|-0.17*|_11.5_|_0.5_|10.5|
|**_R-square_**|**_0.26_**|**_0.28_**|**_0.18_**|_0.26_|**_0a28_**|**_0.17_**|

# _VI. Concluding remarks_

Previous work on the GLSS surveys have concentrated on the study of poverty incidence. This paper has sought to extend the analysis to a study of changes of the levels of household welfare (measured by expenditure per capita, per AEU and per single person) and their distribution over the period 1988-92, a period in which the structural adjustment programs had some tin'xe to work themselves out. We look at changes in different localities, and in different socio-economic groups within them. The more important results can be summarized as follows.

1. A study of different measures of inequality reveal that the most important changes in the degree of inequality took place at the lower end of the distribution. But the direction

_24_
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In a study of social reporting of 29 Islamic banks, Maali et al. (2006) find their charitable activities and employee-related issues to be moderately good. However, none of the Islamic banks report any activities related to environment. Similarly, interviews of 18 senior executives of Islamic financial institutions in the Gulf Cooperation Council (GCC) showed that corporate social responsibility is not a major concern for Islamic banks (Aribi and Arun 2012). The focus of their operations is on Shari’ah compliance, and none reported anything on environmental issues. In a study of 48 Islamic financial institutions from 19 countries, Sairally (2007) finds that though they did some corporate philanthropy, social responsibility was not an integral part of their business policy.

Evidence from Islamic capital markets also reveals similar patterns. The bulk of the Islamic investment screening used for the stock markets applies negative and exclusionary criteria. The focus of the screening is on legal prohibitions such as interest-based income and on prohibited sectors such as gambling, pornography, alcohol, etc. The current practice does not apply positive screening criteria, and commitment to socially-responsible business practices to support ESG goals seems to be negligible (BinMahfouz and Ahmed 2013). For example, issues such as employee rights, human rights, and environmentally-friendly production are not included in the contemporary Islamic investment decision-making processes (Wilson, 2004 and Forte and Miglietta, 2007).

The contribution of Islamic financial institutions to environmental and social goals would partly depend on how the broader goals ( _maqasid_ ) of Islamic law are conceptualized and implemented. One reason of the poor performance of Islamic banks in this regard is that the contemporary notions of social and environmental sustainability have not been incorporated in _maqasid_ and concepts related to Shari’ah compliance. Promoting _maqasid_ to include the broader environmental and social perspectives at the operational level would, therefore, require modifying the concept of Shari’ah based financing and endorsing the environmental and social goals as essential components of macro- _maqasid_ .

Some academic writings link the broader _maqasid_ to environmental and social goals. However, an authoritative Shari’ah body, for example the OIC-affiliated Islamic _Fiqh_ Academy, would have to recognize environmental, social and cultural issues as important elements of _maqasid al Shari’ah_ to have the industry recognize these issues as integral to Islamic banking operations.

# _Financial Institutions_

Integrating environmental and social goals into the operations of Islamic financial institutions would require moving from the current ESG _cultural reluctance_ phase (as defined above) prevalent in Islamic financial institutions to ESG _cultural grasp_ and then to ESG _cultural embedment_ phases. This would require, among other steps, actions by Boards of Directors to 25
**The land share value in capital stocks gives us a ratio of private capital to GDP, including land, of 2.72 for the 2003–2011 period, from a value of 2.23 without land.** Adding the ratio of stock of inventories to GDP, which was estimated with long-term national accounts series at an average of 1.47 over the 2003–2011 period, the private stock of capital, land, and inventories in relation to GDP (the ratio) comes to 4.19 for that same period. The stocks of inventories were constructed using long-term series data for changes in inventory for Mexico. The rates of investment on inventories reported for Mexico are comparatively high in the international context, averaging 4.2 percent of GDP over the 2003–2011 period. Further research is advisable to confirm and understand why Mexico has such a relatively high level of investment in increased inventories. As can be observed in the last column of Table 3, the ratio of capital, plus land and inventories, is quite stable for the period from 2003 to 2011.

**Table 3 Ratios of reproducible capital, land, and inventories to GDP at market** **<u>prices, 2003–2011</u>**

|Year|Total capital over|Private capital over|Private capital +<br>|Inventories over|Private capital +<br>land + inventories|
|---|---|---|---|---|---|
||GDP|GDP|land over GDP|GDP|<br>over GDP|
|2003|2.75|2.14|2.61|1.59|4.20|
|2004|2.75|2.13|2.60|1.56|4.16|
|2005|2.77|2.15|2.62|1.53|4.15|
|2006|2.76|2.14|2.61|1.47|4.07|
|2007|2.79|2.16|2.64|1.42|4.06|
|2008|2.89|2.22|2.71|1.41|4.12|
|2009|3.18|2.43|2.97|1.49|4.46|
|2010|3.11|2.37|2.89|1.41|4.30|
|2011|3.10|2.34|2.86|1.34|4.20|

_Source:_ SHCP; World Bank; UN.

**To calculate the rate of return on private capital stock, the first step is to adjust the net income earned by capital in the economy for the labor income adjusted for the share of self-employed labor income in mixed income.** Net income earned by private capital is the NDP at factor costs (GVA – depreciation) less the average adjusted labor income. Over the 2003–2011 period, this averaged 45.8 percent of GDP (see Table 4), or 52.9 percent of NDP.<sup>8</sup> Second, given the estimated total private capital (reproducible capital, land, and inventories), the rate of return on private capital averaged 10.9 percent over the period. Third, this rate of return includes all corporate income taxes, property taxes, and other indirect taxes paid out of value added, but it excludes any net indirect taxes collected in the product markets on the output of capital. When the share of net indirect taxes in the product markets is allocated to the value added earned by capital, the gross of indirect tax return to capital rises by an average of 0.6 percent, to a gross

> 8 NDP is about 86.5 percent of GDP for the 2003–2011 period.

17
# **Would Freeing Up World Trade Reduce Poverty and Inequality? The Vexed Role of Agricultural Distortions**

Kym Anderson University of Adelaide and CEPR <u>kym.anderson@delaide.edu.au</u> John Cockburn Laval University and PEP <u>jcoc@ecn.ulaval.ca</u> Will Martin World Bank <u>Wmartin1@worldbank.org</u>

**JEL codes** : D30, D58, D63, F13, O53, Q18

**Keywords:** Poverty, income inequality, price distortions, farm trade policy

**Author contact details** :

**Kym Anderson** School of Economics University of Adelaide Adelaide SA 5005, Australia Phone +61 8 8303 4712 Fax     +61 8 8223 1460 <u>kym.anderson@adelaide.edu.au</u>

This is a product of a World Bank research project on Distortions to Agricultural Incentives. The authors are grateful for helpful comments from workshop participants and referees, and for funding from World Bank Trust Funds provided by the governments of the Netherlands (BNPP) and the United Kingdom (DfID) and from the Australian Research Council. The views expressed are the authors’ alone and not necessarily those of the World Bank and its Executive Directors, nor the countries they represent, nor of the institutions providing funds for this research project.