rafmacalaba/datause-encoder-data huggingface dataset:
protected from flooding. For a description and robustness results for different calibrations, see examples in Burns, Jooste, and Schwerhoff (2021).

### 3.9.1 Accounting for the opportunity costs of adaptation investments

The investment into adaptation capital needs to be considered in the calculation of GDP and government expenses. On the demand side, the standard GDP and government spending identities are modified to account for spending on adaptation capital as follows: When running simulations, the economy-wide effects of adaptation investments will depend strongly on how the government finances adaptation investments. The government can finance these investments through higher deficits, higher taxes, reduced expenditure in any of its expenditure categories, or a combination of these.

# 4. Simulation properties

PAKMod can be used in standard forecasting, policy analysis and fiscal sustainability analyses. Its additional climate features mean that in addition to the normal macroeconomic indicators that are generated (growth, inflation, current account, fiscal account, and unemployment outcomes), the model also will generate climate outcomes, including carbon emissions produced, air pollution, and associated reductions in labor productivity, health, capital destruction and agricultural production. These results are generated whether the focus of the analysis is generating a long-term baseline, analyzing a policy change that has nothing to do with climate, or analyzing the economic impacts of alternative global climate scenarios and different climate policies.

## 4.1 The emissions module

This section presents model properties in response to climate change. The following subsection discusses the baseline and presents the outputs of the model for several standard climate-change scenarios. This includes results from the imposition of a simple carbon-tax under three scenarios. In scenario 1 the government uses the additional revenues to reduce debt (i.e. as a savings device), in scenario 2 the government imposes a revenue neutral carbon policy via a reduction in income taxes and in scenario 3 the government uses the revenue from the carbon tax as a transfer to households.

### 4.1.1 The baseline

To evaluate the model’s climate properties, its outputs are compared with those coming from various Integrated Assessment Models<sup>28</sup> (IAMs). This requires both creating a baseline or “business as usual” (BAU) scenario, which will serve as a reference for various policy scenarios. To anchor the baseline a set

> 28 Integrated Assessment Models (IAMs) are integrated climate-macro models, which are constantly developed by entire teams of researchers over long time horizons (for more see https://www.iamcdocumentation.eu/). They also form an important basis for the assessments of the Intergovernmental Panel on Climate Change (IPCC). We can thus use their baseline scenarios as a benchmark for our baseline. In this subsection we will thus discuss what the benchmark baselines look like and then how to approach the baseline.

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# 5. MODELLINGi THE IMPACT OF FISCAL POLICY

A comprehensive analysis of fiscal policy requires an economy-wide model. In this section, building on the estimates already presented and on previous work by Faini, Porter and van Wijnber gen (1989), we present a simple macroeconometrlc model which will then provide the basis for an evaluation of fiscal policy In Morocco.

A full presentation oF the model goes beyond the scope of this paper and can already be found in Faini et al. (1989). In what fo.ows we only present a stylized description of the main features of the model. The latter is based on a simple variant of the aggregate supply-aggregate demand open economy framework. Noticeable features of the model include the emphasis on supply behaviour, the modelling of import demand under rationing and the analysis of both external and domestic debt dynamics. The presentation is organised by economic agents (firms, households, goverment, etc.) rather than by the more usual approach based on a distinction between markets (goods, labour, and money markets). This should permit a more critical evaluation oF the microeconomic foundations of the model.

# _a)_ a macroeconomic model

Table 6 can used as a guide to the maln relationships comprised In the model. The first block of equations focusses on the firm. The approach used to specify the behaviour of the representatlve firms was described in the previous paragraph when analysing the investment decision. In eq. 1 in the Table investment (I) is a Function of expected output (Ye), the cost oF capital (c) and the availability of credit (B ). The cost of capital (eq. 2) is a function of tax (,u and r) and depreciatfon (8) parameters as well as oF the ILnterest rate r and the price of investment goods, p. In turn p (eq. 3) is equal to a weighted average of the price of domestic (pI ) and imported capital goods, with the latter depending on the real exchange rate (X, defined as the ratio of foreign to domestic prices) and on the tariff rate on domestic capital goods (r .). The specification of expected output is taken from eq. 11 in the textm'After choosing total investment, the firm decides how to allocate it between domestic and foreign capital goods as a Function of their relative prices and of the extent of quantitative restrictions on
World Bank, 1990. _World Development Report 1990: Poverty_ . Washington, DC: World Bank. <u>https://doi.org/10.1596/0-1952-0851-X</u>

- ———. 2018. _Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle_ . Washington, DC: The World Bank. http://www.worldbank.org/en/publication/poverty-and- <u>shared-prosperity.</u>

- ———. 2020a. _Poverty and Shared Prosperity 2020: Reversals of Fortune_ . Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-1602-4

- ———. 2020b. _Purchasing Power Parities and the Size of World Economies: Results from the 2017 International Comparison Program_ . World Bank. https://doi.org/10.1596/978-1-4648- <u>1530-0</u>

- ———. Forthcoming. _Poverty and Shared Prosperity 2022: Toward an Inclusive Recovery_ . Washington, DC: World Bank.

- Vleminckx, Koen, and Tim Smeeding. 2001. _Child Well-Being, Child Poverty and Child Policy in Modern Nations: What Do We Know?_ 1st edition. Bristol, UK: Policy Press.

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Working Conditions Survey (2005). This way, we have 51 to 95 observations on informal employees for six countries, 100 to 200 observations for eleven countries, 200 to 350 observations for five countries, and 500 to 1000 observations for four countries. Only for Sweden the number of observations (31) is insufficient and the results should be interpreted with care. The samples of formal employees and those of informal self-employed are large enough for all countries.

We find systematic differences between the formal and informal employees in terms of gender, age, educational attainment, occupation, economic sector, and establishment size. Some of these differences are country-specific or just more pronounced in some countries than in the others. Many of the comparisons are given also for informal self-employed, which appear to be quite different from informal employees in most respects. These findings might be of interest from the perspective of integrating informal labor. Moreover, some of them also contribute to the ̳exit vs. exclusion‘ literature about prevailing reasons for working informally (see Maloney, 1999; Maloney, 2004; Djankov et al., 2003; Hanousek and Palda, 2003; Perry _et al._ 2007, Williams and Renooy, 2008; Loayza, Servén and Sugawara, 2009; Pfau-Effinger _,_ 2009; Williams, 2009; Schneider, Buehn and Montenegro, 2010). Although heterogeneous nature of self-employment is now well understood in principle, the empirical base in European context remains scarce.

To further inform this debate, Section 3.2 compares formal and informal workers in terms of belonging to groups which are known to be exposed to social exclusion or discrimination (minorities, first and second generation immigrants) or are associated with past (and maybe future) work in the formal sector (past and current union membership); we also look at perceived discrimination. This analysis sheds some light on the worker mobility between formal and informal sector, thus complementing country-specific studies (Packard, 2000; Bosch et al., 2007; Bosch and Maloney, 2010; Nikolovova et al., 2010; see also Le (1990, Section 4.1) for a survey of earlier studies). Section 3.3 compares household income of informal and formal employees.

# **3.1 Key personal characteristics and job profile**

**_Gender._** Figure 4 shows that in nine European countries (Latvia, Bulgaria, Poland, Estonia, Ukraine and Hungary in the East; Finland and Norway in the North; and UK in the West) proportion of males is much higher among the shadow sector employees than in the formal economy. The opposite situation is found in Sweden, Italy, Spain, and Switzerland. In the rest of the countries, shadow and formal employees do not differ substantially in terms of

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- Feyrer, J., D. Politi, and D. N. Weil (2017). The Cognitive Effects of Micronutrient Deficiency: Evidence from Salt Iodization in the United States. _Journal of the European Economic Association 15_ (2), 355–387.

- Figlio, D., J. Guryan, K. Karbownik, and J. Roth (2014, December). The Effects of Poor Neonatal Health on Children’s Cognitive Development. _American Economic Review 104_ (12), 3921–3955.

- Fiszbein, A. and N. Schady (2009). _Conditional Cash Transfers: Reducing Present and Future Poverty_ . The International Bank for Reconstruction and Development/The World Bank.

- Gertler, P. J., S. W. Martinez, and M. Rubio-Codina (2012, January). Investing cash transfers to raise long-term living standards. _American Economic Journal: Applied Economics 4_ (1), 164–92.

- Glewwe, P. and E. A. Miguel (2007). The impact of child health and nutrition on education in less developed countries. _Handbook of development economics 4_ , 3561–3606.

- Glewwe, P. and K. Muralidharan (2016, June). _Improving Education Outcomes in Developing Countries_ , Volume 5 of _Handbook of the Economics of Education_ , Chapter 0, pp. 653–743. Elsevier.

- Heady, C. (2003, February). The Effect of Child Labor on Learning Achievement. _World Development 31_ (2), 385–398.

- Heckman, J. and F. Cunha (2007, May). The Technology of Skill Formation. _American Economic Review 97_ (2), 31–47.

- INEE (2014). Panorama educativo de méxico. indicadores del sistema educativo nacional 2013 educación b’asica y media superior. Technical report.

- Maluccio, J. A., J. Hoddinott, J. R. Behrman, R. Martorell, A. R. Quisumbing, and A. D. Stein (2009, April). The Impact of Improving Nutrition During Early Childhood on Education among Guatemalan Adults. _Economic Journal 119_ (537), 734–763.

- McKenzie, D. and H. Rapoport (2011, October). Can migration reduce educational attainment? Evidence from Mexico. _Journal of Population Economics 24_ (4), 1331–1358.

- Millán, T. M., K. Macours, J. A. Maluccio, and L. Tejerina (2020). Experimental long-term effects of early-childhood and school-age exposure to a conditional cash transfer program. _Journal of Development Economics 143_ , 102385.

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To understand the process of industrial location and concentration, it is important to first analyze the location decisions of firms in particular industries. The location decision of the individual firm may be influenced by several factors. These include (a) availability of infrastructure, and the external economies provided by localization and urbanization, i.e., the "economic geography", (b) local wages, taxes, subsidies, and incentives, i.e., the "political economy", and (c) history, being "accidental". Here we focus on the economic geography characteristics. We develop and estimate an economic model to assess the impacts of region specific characteristics on location choices of firms in well defined industries. For the empirical application, we use micro level establishment data for Indian industry to examine the contribution of regional characteristics on location choices. Our concept of regional characteristics extends beyond its natural geography. Rather than focusing on inherent characteristics such as climate and physical distance to the coast and market areas, we analyze the economic geography of the region. Economic geography characteristics include two elements: _market access,_ represented by the transport network linking a location to market centers; and _spatial externalities,_ represented by the local presence of buyers and suppliers to facilitate inter-industry transfers, the local presence of firms in the same industry to facilitate intra-industry transfers, and the diversity of the local industrial base. Drawing on testable hypotheses from the New Economic Geography (NEG) literature, this analysis provides the micro-foundation for understanding whether a region's economic geography influences location decisions at the firm level. Only by first explaining these decisions, will it be possible to build a general framework for evaluating the overall spatial distribution of economic activity and employment.

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_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_

# _Workers_

African countries have made major strides in increasing educational attainment. Since 1990, the annual change in primary enrollment has been well above the rate of population growth – nearly 5 percent between 1998 and 2002. Secondary enrollment has increased even faster but from a lower base (see Figure 3).

# **Figure 3**

<!-- Start of picture text --> Gross secondary enrollment ratio<br>100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Latin America & East Asia & Pacific South Asia Sub-Saharan Africa<br>Caribbean<br>Source: WDI (2007) 2000 2005<br>Percent<br><!-- End of picture text --> As a result, the supply of graduates with completed primary and some secondary education is increasing every year. Table 6 reports that countries such as Cameroon and Namibia have been able to shift their education distribution so that about 40 percent of their labor force has 10-12 years of education. Countries such as Uganda, who started later owning to a long civil war, were still able by 2002 to provide 70 percent of the labor force with at least 6-9 years of education, and another 8 percent with 10 and above. Unfortunately, rivals in South and East Asia have been able to increase education levels faster, in part because they have lower population growth. The educational distribution of the entire labor force (at the turn of the century) in Africa is quite similar to that of Asian countries 20 or 30 years earlier.<sup>19</sup> INSERT TABLE 6

> 19 See Psacharopoulos and Arriagada (1992).

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Policy Research Working Paper 10798

# **Abstract**

There was substantial growth in the resource sector in Papua New Guinea during the last resource boom, increased revenue collection by the government associated with that growth, and significant increases in international assistance, all which might have translated into improved well-being outcomes across the country. For a better understanding of whether these changes improved household-level outcomes, this paper updates estimates of key well-being outcomes in the country. The analysis imputes monetary poverty status using nonmonetary indicators in the 2016 –18 Demographic and Health Survey and estimates the World Bank’s Multidimensional Poverty Measure. Despite the country’s significant growth since 2009, monetary poverty and access to several essential services hardly changed, which stands in stark contrast to the substantial improvement across the rest of the world and other comparison regions over the same period. Combined, the results illustrate that it is possible that very little of resource-led growth trickles down to the population and that the link between macroeconomic and microeconomic outcomes is more tenuous in Papua New Guinea than found in other resource-intensive settings.

This paper is a product of the Poverty and Equity Global Practice. 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 standon3@worldbank.org or dnaidoo@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
We find fairly convincing evidence from the Muravyev, Schafer and Talavera (2007) that women in this region (and data set) are less likely than men to get financing from a bank, and they pay higher interests rates when they do. We also find that women who need a loan are less likely to get a loan than men who need a loan. Our analysis also shows that firms that do have access to capital are larger in scale. Hence, we conclude that women in ECA are capital constrained.

To what extent does the capital constraint affect their scale of operations? We show that female firms that are constrained in terms of capital are operating at higher returns to scale than female firms that are unconstrained in terms of capital. Hence, if the capital constraint were lifted they should grow to a more profitable scale. We also find that even though they are capital constrained, women tend to use capital and labor in the same proportions (similar K/L) as men. Hence, we surmise that in response to the constraint on capital, women adjust their scale rather than their factor proportions. Similarly, we found very little effect of access to finance on total factor productivity of either male or female-owned firms. We conclude that access to capital can partially explain the smaller scale but does not seem to affect the efficiency. Firms are adjusting their input to reflect the constraint, which perhaps could be attributed to their managerial skills, technological differences, regulatory environment (e.g., flexibility of labor market enables them to lay off workers easily), which would be useful areas to research in the future.

A separate exploratory analysis of firms in the EU and Non-EU member countries indicates that the patterns found for the entire ECA are similar in some respects and not others. Whereas the overall finding of gender differences in scale and profits is the same, we find that the gender gap in TFP is significant only in the Non-EU countries and not in the EU countries. We do find that women and men are operating at inefficiently small scale in both sets of countries but there is some evidence that women in Non-EU countries fare better than men with access to finance.

There is a stream in the literature that argues that women choose to run smaller businesses because they have limited time outside of home production (see e.g., Dolton and Makepeace, 1986). However, we find women do increase their scale if given more access to capital and they do so at the same rate as men in the ECA region as a whole.

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# **6. Conclusions**

An emerging literature building on Rauch’s (1999) paper has demonstrated some unique characteristics of trade in differentiated products. This paper contributes to the literature on differentiated products by postulating that such products may be subject to greater tariff evasion due to the difficulties associated with assessing the quality and thus the price of such products, which creates greater scope for tariff evasion on the part of importers and corrupt customs officials.

Using product-level data on German exports to 10 Eastern European countries we demonstrate empirical support for this hypothesis. We show that the trade gap, defined as the positive discrepancy between the value of exports reported by Germany and the value of imports from Germany reported by an Eastern European importer, is positively correlated with the level of tariff in 8 out of 10 countries, thus generalizing the result of Fisman and Wei (2004) found for China. Further, we demonstrate that the responsiveness of the trade gap to the tariff level is greater for differentiated products than for homogeneous goods. A one-percentage-point increase in the tariff rate is associated with a 0.6% increase in trade gap in the case of homogeneous products and a 2.1% increase in the case of differentiated products. Finally, our results indicate that the greater tariff evasion observed for differentiated products tends to take place through misrepresentation of the import price.

While our study does not explicitly focus on the effects of customs reform, its findings suggest that limiting discretion of customs officials, introducing systems allowing for verification of import documents or price comparisons with similar products and introducing effective audits of customs officials are likely to lower tariff evasion. Our results also provide evidence in favor of having a uniform tariff structure which would dampen the incentives and the ability to misclassify imported products.

# **References**

Anderson, James E. and J. Peter Neary. 2006. “Welfare versus Market Access: The Implications of Tariff Structure for Tariff Reform,” _Journal of International Economics,_ forthcoming.

Aris, Ben, 2000. “Russia: Come clean,” _Business Russia,_ Economist Intelligence Unit, October.

Baumgartner, Edward, 2001. “Russia: More harm than goods,” _Business Eastern Europe,_ Economist Intelligence Unit, June 4.

Besedes, Tibor and Prusa, Thomas J., 2006. "Product Differentiation and Duration of US Import Trade,” _Journal of International Economics_ , forthcoming.

Bhagwati, Jagdish, 1964. “On the Under-Invoicing of Imports,” _Bulletin of the Oxford University Institute of Statistics,_ Vol. 2: 389-397.

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Figure 4.5 <!-- Start of picture text --> FDI In ASEAN by Country of Origin<br>100%<br>* _ 80@ro- 1|11 t l_ j | j 4 1--- |L<br>80%<br>0 60% 8<br>40 2% 8<br>20%<br>.,<br>40 ____<br>0%<br>IL<br>-20% 4*<br>1995 1996 1997 1998 1999 2000<br>El Japan *USA DEU 3ANIEs *ASEAN Australasia *Others<br>FDI In ASEAN by Country of Origin - Excluding Indonesia<br>100% -<br>90% /1<br>80% i L<br>1 70% G<br>60% 30% ' 201G ~ ~ j . I -o 1- l l---l* __<br>20% J<br>10% LLJ; !<br>0% - _ _ _ __<br>1995 1996 1997 1998 1999 2000<br>*Japan EUSA DEU DANIEs gASEAN MAustratasia mOthers<br><!-- End of picture text -->

Source: Compiled from<sup>Statistics of Foreign Direct Investmentin ASEAN:Enhanced Data Set,2001 Edition, Tables</sup> 3.1.2. - 3.1.7., pp<sup>24</sup> -<sup>35</sup> .

Figure 4.5 reveals which<sup>countries/regionsare the primary investorsin ASEAN</sup> and how these relationships<sup>have changed over the last half decade.The bottom chart in</sup> this figure, excluding<sup>Indonesia,is presentedbecauseof the distortion effect of</sup> Indonesia's recent disinvestment<sup>on ASEANas a whole.</sup> The investor with the<sup>largestshareof FDI in ASEANis the EU whichaccounts</sup> for 20 to 30 percent of<sup>investment between1998 and 2000, compared to only 17-18</sup> percent in 1995.38 This considerable<sup>rise inshare may have been helped by theeffortsof</sup> 19