rafmacalaba/datause-encoder-data huggingface dataset:
<u>Figure 3c: Impact of Unilateral Emissions Reductions by Industrial Countries on India’s Manufacturing Ouptut (% change: relative to 2005 for emissions reductions; and relative to BAU in 2020 for output)</u> <!-- Start of picture text --> 6%<br>4%<br>2%<br>Reduction in emissions<br>0%<br>NBTA17<br>5% 10% 15% 20% 25% 30% 35% 40%<br>BTADU<br>‐2% BTADE<br>BTAFU<br>‐4%<br>‐6%<br>manufacturing<br>Change in output for energy‐intensive‐<br><!-- End of picture text --> The impact on China and India is shown in Figures 3b and 3c. The impact on manufacturing output remains relatively muted when no trade policy actions are taken (NBTA17) or when the trade policy action is based on domestic carbon content (BTADU and BTADE). But actions based on the carbon content of imports and applied to all merchandise imports lead to a dramatic decline in exports, with the magnitude depending on the extent of unilateral emissions reduction by industrial countries (BTAFU).

# **VI. Implications for International Trade Rules**

From a purely trade perspective, the best outcome would be to have no scope for carbonbased border tax adjustment. However, as we have noted in the introduction, unconstrained border tax adjustments are already under consideration and enjoy a certain measure of support, including from the WTO. It may, therefore, be useful to identify the least undesirable alternative which our results suggest is border tax adjustments based on the carbon content of domestic production and applied symmetrically to imports and exports.

How would the different alternatives relate to existing trade rules? Recall that current WTO rules and jurisprudence are not settled. If indirect taxes on inputs such as carbon/energy that are consumed in production can be subject to border tax adjustment, then it would seem that taxes based on both domestic and foreign carbon content would be permissible. If, on the other hand, taxes on consumed inputs cannot be subject to border tax adjustment, then it would seem that neither basis for border adjustments would be permissible. Of course, both bases for applying border taxes could be justified by the environmental exceptions provisions of Article XX (GATT, 1994), but that avenue itself is untested and uncertain.

18
tion) are displaced by immigration. Every 10 immigrants displace 3 natives with at most primary education (an elasticity of -0.21) in a given industry-region. All other groups of natives experience a positive demand shock due to immigration. An additional 10 immigrants results in the employment of 1.6 natives with lower secondary education (an elasticity of 0.12), 3.1 natives for who have completed secondary education (an elasticity of 0.07), 0.4 with some form of vocational training (an elasticity of 0.03), and the impact on natives with a university degree is even smaller and not statistically significant.

OLS and IV estimates exhibit the same inverse u-shaped pattern. Interpreting the difference in sign and magnitude of OLS and IV estimates as suggestive about the location decisions of immigrants, we find that (primarily very low-skilled) immigrants are less likely to locate in industry-regions that experience negative shocks to the demand for very lowskilled native labor (OLS is upward biased). They are, however, more likely to locate in industry-regions that experience negative demand shocks for medium skilled native labor (OLS is downward biased). Finally, their location decisions are only very weakly correlated with demand shocks for high-skilled native labor (OLS unbiased).

An advantage of being agnostic about how immigrants and natives of different education levels interact is that our estimates can simply be interpreted as local average treatment effects. Moreover, we can unambiguously conclude that immigrants, given their composition, decrease the demand for native workers with at most primary education, and increase the demand for all other types of workers. However, to use our estimates to calculate wage effects at the national level requires estimating the underlying structural parameters. These are presented in Panel C of Table 8. For those with at most primary education the elasticity of substitution is greater than the elasticity of la42
Of course, the development impact of a reallocation of funds from military spending to development initiatives would strongly depend upon improvements in Sudan’s policies and institutions. Several studies have found a statistically significant positive relationship between government health spending and MDG health outcomes (such as, reduction of under-five mortality and maternal mortality ratio) for countries with good governance and institutions; on the other hand, the relationship appears not significant for poorlygoverned countries.<sup>32</sup> Sudan’s current levels of governance do not offer much hope for improved development outcomes as a consequence of a reduction of military expenditures: the country scores 2.5 in the World Bank’s Country Policy and Institutional Assessment (CPIA) index (ranging from a minimum of 1 to a maximum of 6) and 1 in the Political Risk Services Group’s index of bureaucratic quality (ranging from a minimum of 1 to a maximum of 6). However, if the democratic political reforms envisioned above actually take place, it would be reasonable to expect a general improvement in governance standards.

For the following two scenarios, we assume improvements in quality of bureaucracy and institutions. We take an extremely conservative approach, assuming that 25% of total unexplained military expenditures of the average from the four models in Appendix Table 1 ($ 219 million) is used for public health expenditures. According to the World Development Indicators (2007) public health expenditure in Sudan is only 1.4% of GDP, whereas military expenditures are 3.7% of GDP. Shifting the $219 million from military expenditure would result in a 33.5% increase in public health expenditure. 32 See, for example, World Bank (2004) and Rajkumar and Swaroop (2002).

40
Longitudinal observations—in which the same households are surveyed repeatedly to form a panel—are needed to distinguish the two types of poverty. Such data are still rather rare. Yet, even with panel data, explaining transient poverty can be difficult. For example, the transient component could also be the result of highly idiosyncratic random variables (including Measurement errors) and, hence, difficult to explain using conventional survey data. Systematic factors in transient poverty may well be buried in considerable "noise".

The paper addresses these questions using a new panel data set for post-reform rural China. Our main aim here is to quantify the extent and nature of transient poverty in this setting. We measure transient poverty by the contribution to expected poverty of consumption variability over time. We focus on consumption because that is likely to be a better welfare indicator for assessing poverty, particularly when incomes vary over time in reasonably predictable ways.* The next section outlines our approach, and the sections following that address the three questions above in that order. Our conclusions can be found in section 6.

# 2 Measuring and modelling transient poverty , 2.1 Decomposing poverty measures into "transient" and "chronic" components

Let (¥15¥j2+--+s¥jp) be household i’s (positive) consumption stream over D dates. Consumptions have been normalized for differences in demographics and prices, such thaty, is an agreed money metric of household welfare. Let P(y,,,¥,.1-.-s¥,p) be an aggregate intertemporal poverty measure for household i. (We are more explicit about this measure below.) ? On the arguments for basing poverty measures on consumption rather than income see Slesnick (1993) and Ravallion (1994). There is a large literature on consumption smoothing; for a recent discussion see Deaton (1992).

2
WPS 4235

# **A Framework for Thinking about Enterprise Formalization Policies in Developing Countries**

## **Thomas Kenyon tkenyon@worldbank.org**

### **Abstract**

What policies encourage firms to become ‘formal’? The standard approach emphasizes reducing the costs of compliance with government regulation. This is unlikely to be sufficient. Instead we need to understand compliance as a function not only of firm-level costs and benefits but also in terms of the interaction between the firm and its competitors and between the firm and the state. This paper emphasizes the coordination and credibility issues involved in promoting formalization and discusses possible institutional solutions, among them business associations that make the benefits of membership dependent on compliance, information sharing arrangements among government agencies and improvements in the quality of public management.

## World Bank Policy Research Working Paper 4235, May 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._
trust and association generated in the work place spill over into more peaceful group interactions at the aggregate level (Delhey et al. 2011, Kilroy 2012).

_Jobs and subjective wellbeing._ Another increasingly widely investigated dimension of social development in the recent policy literature is Subjective Wellbeing (SWB, OECD 2011:55ff, Dolan and Metcalfe 2012). Earlier psychological research suggests that job satisfaction and financial security related to regular employment can be an important source of wellbeing (for an overview see Diener et al. 1999: 293). These psychological outcomes also typically emerge as important correlates of other dimensions of social cohesion in individual and cross-national research. For example personal self-esteem, trust, and social capital are usually strongly associated with SWB in international survey data (Helliwell and Putnam 2004, Kahneman 1999, see also Larsen 2007, Rothstein and Uslaner 2005).

From the perspective of this review SWB is particularly useful for summarizing the non-economic costs of uneven participation in the labor market. In high income countries there is already a large literature that documents often considerable differences in life satisfaction between the employed, the unemployed, and people in temporary jobs. One of the earliest studies on the subject found that the experience of joblessness in Britain ‘depressed well-being more than any other single characteristic, including important negative ones such as divorce and separation’ (Clark and Oswald 1994). Subsequent research also shows that these effects are typically robust to the inclusion of controls for income. This supports the often noted claim that unemployment also affects individual wellbeing through non-monetary channels, such as the loss of positive psychological rewards from work or the sense of exclusion from work-related interactions (for overviews and evidence see Darity and Goldsmith 1996, Di Tella et al 2001, 2003, Frey 2008). There are also fairly robust indications for this part of the world that the causal link flows from unemployment to subjective wellbeing. For instance, research carried out on German panel data suggests that the same individuals experience considerable reductions in life satisfaction when they lose their job (Winkelmann and Winkelmann 1998, Frey 2008: 47). Dawson and Veliziotis (2013) who use a similar panel design for the UK, find large difference in self-reported well-being between permanent and temporary employees that appear to be explained by less satisfaction with job security for the latter group.

19
# **The Influence of COVID-19 on Young Women’s Labor Market Aspirations and Expectations in India**<sup>∗</sup>

S Anukriti Catalina Herrera-Almanza The World Bank<sup>†</sup> University of Illinois at Urbana-Champaign<sup>‡</sup> Sophie Ochmann The World Bank<sup>§</sup> _Keywords_ : COVID-19, Pandemic, India, Gender, Job Search _JEL codes_ : J20, O12, D83

> ∗We are grateful to Gaurav Chiplunkar, Isis Gaddis, Rob Garlick, Erin Kelley, David McKenzie, Jayati Sethi, Ambika Sharma, Shobhana Sosale, and Emcet Tas for their support at various stages of this project. Rocio Valdebenito and Kunal Datt provided excellent research assistance. This project received funding from the World Bank’s Knowledge for Change Program and from the South Asia Gender Innovation Lab. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> †Development Research Group. `sanukriti@worldbank.org` .

> ‡Department of Agricultural and Consumer Economics. `cataher@illinois.edu` .

> § `sophie.ochmann@gmail.com` .
33

|**Region**|**Country**|**Source**|**Year**|**% all**<br>**house-**<br>**holds**<br>**using LPG**|**% urban**<br>**house-**<br>**holds**<br>**using LPG**|**% rural**<br>**house-**<br>**holds**<br>**using LPG**|**% using**<br>**solid fuels**|
|---|---|---|---|---|---|---|---|
||Côte d’Ivoire|MICS3|2006|14|—|—|86|
||Ethiopia|WHO|2005|0.05|0.4|0.0|95|
||Gabon|WHO|2006|68|80|23|27|
||Gambia, The|MICS3|2005–06|2.5|4.6|0.6|91|
||Ghana|MICS3|2006|10|20|2.5|86|
||Guinea|WHO|2005|0.1|0.3|0.03|98|
||Guinea-Bissau|MICS3|2006|0.2|0.4|0.05|98|
||Kenya|HH surveys|2005–06|3.5|12|0.7|83|
||Lesotho|WHO|2005|19|57|10|69|
||Liberia|WHO|2006|0.07|0.2|0|99|
||Madagascar|WHO|2004|0.8|2.4|0.4|99|
||Malawi|MICS3|2006|0|0|0|99|
||Mali|WHO|2006|0.4|1.4|0.04|98|
||Mauritania|MICS3|2007|36|—|—|62|
||Mauritius|WHO|2003|95|95|95|1.3|
||Mozambique|WHO|2003|1.4|4.9|0.01|97|
||Namibia|WHO|2006|2.5|4.2|1.0|56|
||Niger|WHO|2006|0.7|3.4|0.1|99|
||Nigeria|MICS3|2007|0.5|1.2|0.1|75|
||Rwanda|WHO|2005|0.02|0.1|0|99|
||São Tomé and<br>Principe|MICS3|2006|23|32|11|76|
||Senegal|WHO|2006|41|74|12|56|
||Sierra Leone|WHO|2007|0.3|0.1|0|99|
||Somalia|MICS3|2006|0.03|0.1|0|100|
||South Africa|Statistics SA|2005–06|2.6|—|—|15|
||Swaziland|WHO|2003|12|24|7.2|46|
||Tanzania|WHO|2007|0.2|0.8|0|97|
||Togo|MICS3|2006|1.2|2.6|0.2|98|
||Uganda|HH surveys|2005–06|0.2|0.7|0|98|
||Zambia|WHO|2007|0.01|0.02|0|85|
||Zimbabwe|WHO|2003|0.5|1.5|0.08|67|

_Sources:_ WHO 2011; UNICEF various years for MICS3; World Bank various years for DHS; Bacon, Bhattacharya, and Kojima 2010 for HH (household) surveys; and Statistics South Africa 2008 for Statistics SA. _Notes:_ ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MNA = Middle East and North Africa; SSA = Sub-Saharan Africa; — = not available; + = no distinction made between natural gas and LPG, and hence the answer may apply to either fuel; * = total use across fuels greater than 100 percent, indicating that the question asked which fuels were used for cooking.
However, Sala-i-Martin (1997) points out that the extreme bounds test is too strict a test especially in the presence of multicollinearity. Instead, he suggests that rather than focusing on extreme bounds, one should focus on the entire distribution of the estimators, specifically the fraction of the cumulative distribution lying on each side of zero, CDF (0). If CDF (0) is >0.95, then the variable is considered to be robust. The cumulative distribution function itself is calculated from the weighted mean and weighted standard deviation of the parameter with the integrated likelihood of each model being used as weights. So under the assumption that the distribution of the estimates of bz across models is normal, for each of the M models, we compute the integrated likelihood Lj. From this, the weighted mean and weighted standard deviation, which are used as parameters in the cumulative distribution function, are calculated where the weights used are proportional to the integrated likelihoods. See Sala-i-Martin (1997) for more details.

Table 6 replicates our analysis using EBA and the Sala-i-Martin (1997) specification for Common Law, Latitude and Ethnic Fractionalization at the 5% level and for Latitude and Ethnic Fractionalization at the 1% level since in the latter case, only these two variables are found to be significant in predicting Property Rights when used by themselves. Panels A1 and A2 present the extreme bounds for the variable at the 5% significance level (bzj ±2szj) and 1% significance levels (bzj ±3szj) respectively and the corresponding conditioning information set.

At the 5% level, the extreme lower bound for Ethnic Fractionalization is -0.447 and is attained when we include Common Law along with Ethnic Fractionalization in the Property Rights regression. The upper extreme bound for Ethnic Fractionalization is -0.031 and is attained when we include both Latitude and Catholic Religion along with 28
- Graff Zivin, J., Hsiang, S. M., & Neidell, M. (2018). Temperature and human capital in the short and long run. _Journal of the Association of Environmental and Resource Economists_ , _5_ (1), 77-105.

- Graff Zivin, J., Song, Y., Tang, Q., & Zhang, P. (2020). Temperature and high-stakes cognitive performance: Evidence from the national college entrance examination in China. _Journal of Environmental Economics and Management_ , _104_ , 102365.

- Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., Vogt-Schilb, A. (2016). _Shock waves: Managing the impacts of climate change on poverty_ . Washington, DC: World Bank.

- <mark>Hallegatte, S., Vogt-Schilb, A., Rozenberg, J., Bangalore, M., & Beaudet, C. (2020). From poverty to disaster and back: A review of the literature.</mark> _<mark>Economics of Disasters and Climate Change</mark>_ <mark>,</mark> _<mark>4</mark>_ <mark>(1), 223–247.</mark>

- Heilmann, K., Kahn, M. E., & Tang, C. K. (2021). The urban crime and heat gradient in high and low poverty areas. _Journal of Public Economics_ , _197_ , 104408.

- <mark>Hertel, T. W., & Rosch, S. D. (2010). Climate change, agriculture, and poverty.</mark> _<mark>Applied Economic Perspectives and Policy</mark>_ <mark>,</mark> _<mark>32</mark>_ <mark>(3), 355-385.</mark>

- Hsiang, S. M. (2010). Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. _Proceedings of the National Academy of Sciences_ , _107_ (35), 15367-15372.

- Hsiang, S., Oliva, P., & Walker, R. (2019). The distribution of environmental damages. _Review of Environmental Economics and Policy, 13_ (1), 83-103.

- Iizumi, T., & Sakai, T. (2020). The global dataset of historical yields for major crops 1981– 2016. _Scientific Data_ , _7_ (1), 1–7.

- Intergovernmental Panel on Climate Change. (2018). _Global warming of 1.5° C: An IPCC special report on the impacts of global warming of 1.5° C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty_ . Intergovernmental Panel on Climate Change. In Masson-Delmotte _et al._ (eds.) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−24.

- Intergovernmental Panel on Climate Change (IPCC). (2021). “Technical Summary.” In _Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change_ . In Masson-

24
lowest rates were observed in the Utena (13.6 percent) and Taurage (13.7) regions. By all standards the spread is very limited. There is probably more variation at lower territorial levels, but given the small size of the country it is questionable whether they form separate labor markets. In this context, improved labor mobility seems to be of limited importance as an equilibrating mechanism. While higher geographical labor mobility could be still desirable for better allocation of labor, it cannot be expected to significantly lower unemployment. In other words, even if labor mobility is low, it does not seem to be an important source of labor market rigidities in Lithuania.

**Table 4 Unemployment Rate by Region, 2000**

||Region|Unemployment rate|
|---|---|---|
|Nationalaverage||15.4|
|Alytus||16.3|
|Kaunas||15.0|
|Klaipeda||14.4|
|Marijampole||15.5|
|Panavezys||14.6|
|Siauliai||18.4|
|Taurage||13.7|
|Telsiai||14.0|
|Utena||13.6|
|Vilnius||15.7|

_Source Laborforce, employment and unemployment (survey data) 1997-2000, Statistics Lithuania, Vilnius 2001._ Overall, the central labor market problem in Lithuania is the low level of utilization of labor resources, which means that the output level is lower than potential. Unemployment is high an of long-duration, and labor force participation is low, especially among young and older workers. As a result, the overall employment rate is low by OECD standards. Despite economic growth which Lithuania has enjoyed since the mid 1990s, the labor market slack has not diminished. One possible reason is a strong increase in real wages, which has far outpaced productivity growth, thus leading to an increase in labor costs.

10
9 caseflow and outpatient expenditures, a high degree of specialization raised average costs." More recent work on specialization and hospital costs [cf eg Cowing and Holtmann (1983), Grannemann, Brown and Pauly (1986),<sup>Vita</sup> (1990)] has built on the literature on economies of scope that emerged in the 1980s lcf eg Baumol, Panzar and Willig (1982)]. Economies of scope are said to exist if the joint output of a single organization is greater than the output that could be achieved by several separate organizations each producing one product but together employing the same amount of input. In this context, inpatient and outpatient care might be treated as two different products, and one might treat different inpatient casetypes as different products. An implication of economies of scope is that production costs can be reduced by producing<sup>products</sup> jointly, rather than specializing. Thus in the two-product case (the generalization to three or more products is straightforward), economies of scope can be measured as Sc _=_ (C(Y,,O)+C(O,Y2 )-C(YI*Y2) _)/C(YI,Y 2 )I_ where C(y,-,0) and C(O,y2;) are the costs that would be incurred if y,* units of y, and y; units of y2 were produced separately, and C(y*,y2<sup>) is</sup> the cost that would be incurred if the same quantities of y, and Y2<sup>were</sup> to be produced jointly. With economies of scope, the joint cost is less than the sum of the individual costs, so that Sc>O. With diseconomies of scope, Sc<O. If both products have positive marginal costs,<sup>it can</sup> be shown that Sc<l.

In order to establish whether economies of scope exist, and if<sup>so</sup> to what extent, it is necessary to estimate a multiproduct cost function. Moreover, the functional form must yield plausible<sup>estimates</sup> of the costs that would obtain if only one product were produced at any one time. In principle this is straightforward. Eq (1) might, for example, be generalized to a multiproduct setting by treating y as a vector rather than a scalar. In practice cost functions are often specified in a way that prejudges the issue of economies of scope. Suppose, for example, that the estimated cost function involves<sup>cost per</sup> case expressed as a linear function of a casemix vector'2 (9) C/N =- 0 _+ Vjip,_ + e, where C/N is cost per admission and p, is the proportion of cases falling into the ith casemix category (there being n categories altogether). Multiplying through by N gives the total cost<sup>counterpart</sup> of eq (9), namely ". Cf. Barer (198Z), who obtained a negative but insignificant coefficient in his equations.

**12.** Such an equation was used by Feldstein (1967).