rafmacalaba/datause-encoder-data huggingface dataset:
# 1 Introduction

Micro-foundations of growth have increasingly attracted attention of economists interested in comprehending the process of structural change and development (Hausmann and Rodrik 2003). Economists have realized that understanding how micro-heterogeneity affects macro responses is crucial in order to devise effective policies for promoting growth (Bartelsman and Doms 2000, Tybout 2000). The international trade literature has been at the forefront of this movement thanks to various groundbreaking contributions that allowed for incorporation of firm-level heterogeneity into trade models (Melitz 2003, Yeaple 2005, Bernard, Redding, and Schott 2007), innovative empirical studies evaluating firm-level responses to trade reforms (Pavcnik 2002, Trefler 2004, Schor 2004, Fernandes 2007, Harrison 2007), and pioneering work explaining the determinants of geographical expansion of exporters (Eaton, Kortum, and Kramarz 2004b, Eaton, Kortum, and Kramarz 2004a, Eaton, Eslava, Kugler, and Tybout 2007).

More recently the theoretical literature has moved even further by unwrapping firms and incorporating another layer of heterogeneity, namely heterogeneity at the product level (Bernard, Redding, and Schott 2006a, Eckel and Neary 2006, Nocke and Yeaple 2006). These models capture endogenous dynamics across firms (e.g. exporting activities undertaken by some firms versus the exclusive focus on the domestic market by others) as well as within firms (e.g. some products being exported but not others). The aggregate responses to policy changes emerge as a combination of these multiple dynamics.

Yet, due to data constraints, there is little evidence that can corroborate or contradict predictions coming from models of multi-product firms. Do firms indeed adjust the number of products exported in response to changes in trade policy? What about adjusting the volume of exports of each product? Which products are the most affected? Is there a positive or a negative relationship between the intensive margin (i.e., the volume of exports of each product) and the extensive margin (the number of products exported)?<sup>1</sup> Similarly, the dearth of stylised facts at the firm-product levels means that theorists have little guidance in terms of what the appropriate assumptions are. As a consequence, the models vary widely in how they formalize product attributes within a firm and the in-

> 1The notable exceptions are studies by (Bernard, Redding, and Schott 2006b) and (Bernard, Redding, and Schott 2006a). The former study is the closest to our paper but its focus is quite different, as it examines production patterns in general. The latter study is primarily a theoretical contribution but includes some empirical results on the link between the intensive and the extensive margins of exports. Both studies are based on the US data.

2
access or higher costs of external finance. These savings are kept in liquid accounts instead of being invested into productive assets or being distributed to shareholders.

Riddick and Whited (2009) develop a dynamic model of savings and find that firms save more when they have higher income uncertainty (because of higher income volatility) or more binding external finance constraints. The firm’s optimal savings policy will also depend on the firm’s expected future financing needs. In the environment of higher uncertainty it is more difficult for firms to estimate their financing needs, which may prompt them to hold higher cash balances and make more savings. Similarly, in an environment with high costs of external finance the firms can benefit from a large stock of internal cash which will allow them to make investments when the opportunities arise. In this model firms also hold more cash if their investment is lumpier. Some industries inherently have more lumpy investment than others (e.g., hotels).

It is plausible that firms producing the most cash flows will be the ones that can save the most. However, the allocation of savings between financial savings and physical savings (i.e. investment) will depend on the several factors. For example, if a firm is facing a positive productivity shock, its cash flow will raise. This increase signals that this firm has a good investment opportunity and need to expand the business. Therefore, the firm will make an investment into physical assets using the available cash flow. As a result the firms will have less cash to put away as financial savings in the year they make a large investment. Thus, savings in physical assets and financial assets will be negatively correlated contemporaneously. However, over time, increase in financial assets is likely to lead to increase in investment in physical assets in the future.

The trend in the US and other developed countries over the past two decades has been toward an increasing share of liquid assets. In other words, companies around the world have been making more savings recently than in the past.<sup>2</sup>

> 2 Bates, Thomas W., Kahle, Kathleen M. and Stulz, Rene M., Why Do U.S. Firms Hold so Much More Cash than They Used to? (April 2008). Fisher College of Business Working Paper No. 2007-03-006; Charles A. Dice Center Working Paper No. 2006-17. Available at SSRN: http://ssrn.com/abstract=927962

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Policy Research Working Paper 5589

# **Abstract**

This paper presents a detailed picture of how sustained growth in Egypt over 2005-2008 affected different groups both above and below the poverty line. This analysis, based on the Household Income, Expenditure and Consumption Panel Survey conducted by Egypt’s national statistical agency, compares the changes in the _static_ poverty profiles (based on growth incidence curves on a cross-section of data) with poverty _dynamic_ s (relying on panel data, growth incidence curves and transition matrices). The two approaches yield contrasting results: the longitudinal analysis reveals that growth benefited the poor while the cross-sectional analysis shows that the rich benefitted even more. The paper also shows the importance of going beyond averages to look at the trajectories of individual households. Panel data analysis shows that the welfare of the average poor household increased by almost 10 percent per year between 2005 and 2008, enough to move out of poverty. Conversely however, many initially non-poor households were exposed to poverty. As a matter of fact, only 45 percent of the population in Egypt remained consistently out of (near-) poverty throughout the period, while the remaining 55 percent of Egyptians experienced at least one (near-) poverty episode. This high mobility is not a statistical artefact: it reflects the actual process of growth. Taking high vulnerability into account is essential when designing policies to protect the poor and to ensure that growth is really inclusive.

This paper is a product of the Poverty Reduction and Economic Management Network, Middle East and North Africa Region. 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:// econ.worldbank.org. The author may be contacted at dmarotta@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 4: Properties of estimated wedges in Mexico

||||Correl<br>|ation of wedg<br>|e with<br>||
|---|---|---|---|---|---|---|
|Wedge|_σw_|Output|Consumption|Investment|Hours|Net Exports|
|Eciency|1_._58 (_._26)|0_._92 (_._03)|0_._86 (_._05)|0_._73 (_._14)|0_._37 (_._17)|_−_0_._71 (_._13)|
|Labor|2_._35 (_._41)|0_._76 (_._08)|0_._83 (_._05)|0_._69 (_._08)|0_._86 (_._02)|_−_0_._79 (_._08)|
|Investment|2_._58 (_._33)|_−_0_._25 (_._15)|_−_0_._51 (_._13)|0_._21 (_._09)|0_._18 (_._11)|0_._19 (_._11)|
|Risk Prem.|0_._15 (_._03)|0_._75 (_._07)|0_._86 (_._04)|0_._63 (_._11)|0_._16 (_._17)|_−_0_._82 (_._06)|
|Govt. Cons.|0_._26 (_._03)|0_._41 (_._05)|0_._39 (_._04)|0_._19 (_._09)|0_._20 (_._07)|_−_0_._33 (_._07)|
||||Correlat|ion matrix of|wedges||
|Wedge|_σw/σy_|Eciency|Labor|Investment|Risk Prem.|Govt. Cons.|
|Eciency|0_._74 (_._04)|1|0_._51 (_._16)|_−_0_._37 (_._16)|0_._86 (_._04)|0_._41 (_._05)|
|Labor|1_._11 (_._08)||1|_−_0_._24 (_._10)|0_._50 (_._14)|0_._27 (_._07)|
|Investment|1_._22 (_._19)|||1|_−_0_._51 (_._10)|_−_0_._35 (_._07)|
|Risk Prem.|0_._07 (_._01)||||1|0_._26 (_._07)|
|Govt. Cons.|0_._12 (_._02)|||||1|

This table shows the properties of the estimated wedges in Mexico for the period 1987:12007:4. The estimated wedges are detrended using the Hodrick Prescott lter with smoothing parameter of 1600. The column _σw_ reports the standard deviation of the wedges, while _σw/σy_ reports the standard deviation of the wedges relative to the standard deviation of output. GMM-based standard errors are reported in parentheses

# statistics from Table 1.

In measuring the contribution of, say, the labor wedge to aggregate uctuations, two counterfactual economies are especially illustrative. In one economy, only the labor wedge moves as in the data and the other wedges are xed at their average values in the sample. In the other economy, the labor wedge is xed at its average value and the rest of the wedges move as in the data. The rst experiment measures the direct contribution of the labor wedge: the closer are the counterfactual time series to the data, the more important is the direct contribution of the labor wedge to aggregate uctuations. The second experiment measures the contribution of the labor wedge when combined with other wedges: the farther away are the predicted time series from the actual data, the more important is the contribution of the 18
New Zealand). A reduction in visa processing time would be likely to facilitate Mode 4 trade, also by making the process more certain and predictable. In addition, longer visa durations for services suppliers would be a significant aspect of any TFiS agenda on MNP. Ideally, the duration of a visa should match the time needed by the service supplier to deliver the service.

## **Table 6 Attributes of business visa applications: summary statistics (2017)**

|**Attribute**|**Sample avg.**|**Min**|**Max**|
|---|---|---|---|
|Cost to obtain a business visa (USD)|84|7 (JPN)|297 (COL)|
|Duration of visa for crew (no. of months allowed)|5|0 (COL)|23 (IND)|
|No. of documents needed to obtain a business visa|9|2 (GBR)|16 (FRA)|
|Range of visa processing time (days)|13|1 (JPN)|32 (CAN)|

Source: Singhal (2019) based on OECD STRI; Finally, it is interesting to note that there is little, if any, statistical correlation between the various attributes of business visa applications. For instance, it would be expected that countries with more restrictive policies towards MNP to require more documents to apply for a business visa or to allow for smaller visa durations. Moreover, even the visa processing times and the duration of visa for CSSs, ISSs and ICTs are negatively correlated, which suggests that even if it may take longer for a country to issue a business visa, that may not result in longer stays in that country for service professionals.

Singh (2019) concludes that moving towards multiple-entry visas for service providers in all sectors, providing opportunities for e-visas/visas on arrival and/or exempting visas for temporary entry/transit of service providers should all be a part of a MNP TFiS agenda. One example is the increased use of enabling e-business visas.

# VI. Conclusion

The case for reducing or eliminating impediments to trade and investment in services, whether discriminatory or not, and for expanding competition in services markets is compelling and confirmed by the empirical literature.<sup>4</sup> Simply put, there is strong evidence that promoting more open services markets by eliminating discriminatory and non-discriminatory barriers, including needlessly burdensome or unnecessary impediments to trade, can contribute meaningfully to technological absorption and GVC upgrading. Doing so can also help promote inclusive growth as the service economy is an important source of formal sector female and youth employment.<sup>5</sup> And yet, even in instances where trade and investment barriers have been eliminated, service providers, including foreign ones, are often still subject to administrative and procedural requirements that they must comply with in order to operate in accordance with host country regulations. Foreign firms may also face home country regulatory compliance costs, for instance in respect of corporate social responsibility or responsible business conduct rules. Costs may include those linked to both

> 4 Hoekman and Mattoo, (2008); François and Hoekman, (2010).

> 5 OECD&WBG (2017).

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# **1. Introduction**

The purpose of this note is to illustrate how endogenous economic policy, in particular, environmental tax policy directed toward private domestic actors, combined with the existence of offset markets, can lead to excessive levels of greenhouse gas emissions (or prevent emissions from being reduced) in countries without binding commitments to reduce such emissions. Such increases in emissions work directly against the very purpose of offset markets, including the Clean Development Mechanism (CDM) under the Kyoto Protocol, by eliminating part of the environmental gains achieved through such markets.<sup>1</sup> The topic of this paper has been subject to some prior discussion, albeit largely in policy contexts. In particular, OECD (2009), on page 131, discusses various adverse side effects of the CDM, two of which are “perverse incentives to raise emissions”, and “reduced incentives for non-Annex I countries to take ambitious mitigation action”. Both arguments reflect the basic idea that the CDM (as well as other similar offset mechanisms) has questionable, possibly counteractive, incentive effects for countries hosting offset projects. The closest analytical connection to this paper is work on endogenous baselines of CDM projects, by Strand and Rosendahl (2009).<sup>2</sup> The difficulty (read: impossibility) of verifying “correct” baselines based on which offsets can be credited in CDM projects (this should in theory be emissions in a counterfactual situation where the CDM does not exist), here gives project hosts incentives to attempt to inflate these baselines, and thus also inflate the value of credits received when the CDM projects are implemented. For discussions of problems with defining, and enforcing, “correct” baselines, and the related methodologies applied to define project

> 1 It must however be stressed that an offset mechanism such as the CDM does not in itself reduce overall global carbon emissions, even when it operates with full efficiency. The reason is that offsets simply give room for increased emissions elsewhere, within a given overall cap. When the offsets promised by accredited CDM projects is not in fact forthcoming, the situation is worse: Global emissions then increase. See Rosendahl and Strand (2009) for further discussion.

> 2 For other related analytical work, see Fischer (2004), Akita et al (2007, 2008), and Hagem (2009).

2
# **_Holland 1561 and 1732_**

**Income distribution data** : The rental values of all dwellings (including the poor) were taxed. We know that dwelling rents were highly correlated with income (Williamson 1985; van den Berg and van Zanden, 1988: pp. 193-215), but we also know that the elasticity of rents to income was less than one (between 0.72 and 0.75 in 1852-1910 Britain: Williamson 1985, p. 225). Thus, income inequality should be understated by rental values. With that understood, the source of the Dutch data is van Zanden (1995). We have not yet been able to secure the underlying distribution data from the author.

**Population and area:** Population is interpolated between 1500 and 1600 (983,176), and between 1700 and 1820 (2,002.783), from Maddison (2001). We approximate the area of 21,680 km<sup>2</sup> to be modern Holland.

**Urbanization rate:** For 1561, the urbanization estimate (45%) is from van Bavel and van Zanden (2004). For 1732, the urbanization estimate (39%) is from de Vries (1985).

**Mean income in $PPP** : GDP per capita in 1990 international dollars interpolated between 1500 and 1600, and between 1700 and 1820, from Maddison (2001: p. 264).

# **REFERENCES**

de Vries, Jan (1985) “The Population and Economy of the Preindustrial Netherlands,” _Journal of Interdisciplinary History_ XV, 4 (Spring): 661-85 Maddison, Angus (2001), _The World Economy: A Millennial Perspectives_ , Paris: OECD Development Centre.

van Bavel, B. and J. L. van Zanden (2004), “The jump-start of the Holland economy

- during the late-medieval crisis, c.1350-c.1500,” _Economic History Review_ LVII 3: 503-32

- van den Berg, W. J. and J. L. van Zanden (1988), “Vier eeuwen welstandlijkheid in Alkmaar, ca 1530-1930,” _Tijdschrift voor Sociale Gwschiedenis_ 19: 193-2

- van Zanden, Jan Luiten (1995), “Tracing the beginning of the Kuznets curve: western Europe during the early modern period,” _Economic History Review_ XLVIII, 4: 643-64.

- Williamson, Jeffrey G. (1985), _Did British Capitalism Breed Inequality?_ Cambridge: Cambridge University Press.

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**Table 5. Second Stage Regressions: Square Root Trend, Salaried Workers Depend Variable Cyclical RTS**

||**_Panel A: Cu_**|**_rrent Exports_**|**_Panel B: L_**|**_agged Exports_**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Exports/Exports(-1))|0.165|0.136|0.231|0.235|
|Panel A / Panel B|(0.090)*|(0.102)|(0.113)***|(0.117)**|
|Hours *(Exports/Exports(-1))|-0.001|-0.002|-0.002|-0.002|
|Panel A / Panel B|(0.001)**|(0.001)**|(0.001)***|(0.001)***|
|Redundancy*(Exports/Exports(-1))|-0.001|-0.001|-0.001|-0.001|
|Panel A / Panel B|(0.001)**|(0.001)*|(0.001)**|*0.001)**|
|Wmin/Wmed*(Exports/Exports(-1))|0.124|0.138|0.099|0.099|
|Panel A / Panel B|(0.084)|(0.086)|(0.108)|(0.110)|
|Ext. Financ|-0.204|-0.198|-0.202|-0.159|
||(0.094)**|(0.101)*|(0.108)*|(0.111)|
|CPI|0.009||0.012||
||(0.004)**||(0.003)***||
|CPI(-1)||0.007||0.011|
|||(0.004)**||(0.003)***|
|Dom. Financ|||||
|Lending rate|-0.016|-0.015|-0.010|-0.008|
||(0.027)|(0.029)|(0.025)|(0.025)|
|Obs|151|151|151|151|
|R-square|0.29|0.28|0.31|0.30|

All regressions include country and year dummy variables and no intercept Levels of Sig: 1% ***, 5%**, 10%* 28
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE other African countries in recent years (figure 9). In response to power shortages, the government has leased 70 megawatts of emergency plant, which is estimated to cost around $0.14 per kilowatt-hour to run.

**Figure 9 Power outages lead to major economic losses**

Economic cost of power outages in selected countries <!-- Start of picture text --> % of GDP<br><!-- End of picture text -->

* Côte d‘Ivoire figure refers to 2009 based on a value of lost load of $2.50 per kilowatt-hour Source: Eberhard and others 2009.

CIE has improved operational performance in recent years and reduced the hidden costs of inefficiency (table 13). In particular, the collection ratio has improved from 66 percent in 2005 to 88 percent in 2009, reducing the losses associated with under-collection from $198 to $84 million per year, as the utility has again been able to collect revenues in the crisis-affected northern part of the country. System losses, which had increased significantly, have also improved recently. Notwithstanding these improvements, operational inefficiencies still cost the sector some $150 million a year.

Up until 2006, CIE was charging cost recovery prices for electricity with no apparent implicit subsidies to the sector. Since 2007, however, average operating costs nearly doubled from $0.08 to around $0.15 per kilowatt-hour. Although power tariffs were increased, the adjustments were modest relative to the cost escalation. As a result,the financial deficit in the sector is now around $200-300 million per year (or 0.5 to 0.8 percent of GDP) (figure 10). Like other countries that rely on thermal power, Côte d’Ivoire has suffered from the rise in oil prices that began in 2005.

**Table 13 Evolution of hidden costs associated with CIE**

|Load<br>System|Collection|Average total|Average effective|Total hidden|Total hidden|
|---|---|---|---|---|---|

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- 52 -

evidence on the direct and indirect distortions is consistent with the declining shares of agriculture and the general worsening positions of the economies of SSA over the period.

The second section of the paper developed a forward-looking real exchange rate model which allows the derivation of an equilibrium real exchange rate that is consistent with both home goods and current account equilibriums. In sections 3 and using data from the Sudan - an African country with sizable agriculture - this model is estimated and used to generate an index for the equilibrium rate for sustainable levels of the fundamentals, where sustainability is given by the permanent components of the data on fundamentals as obtained by time series decomposition. In section 4, the derived equilibrium real exchange rate index is used to derive indexes of direct and indirect taxation of agriculture in the Sudan following the procedure outlined in KSV (1988). The results corroborate evidence obtained from other studies (e.g KSV) in that direct taxation of agriculture tend to decline over the years perhaps as a partial requirement to enhance self-sufficiency in foodgrains for example. Direct taxation, however, has generally been dominated by indirect taxation which remains high and in the case of the Sudan it averaged more than five times the former form of taxation since 1975.

Finally in section 5, the two above indexes along with other growth fundamentals were used to test the implications of the endogenous growth model (ala Easterly (1990)) on sudanese agriculture. The estimation results show strong, negative and statistically significant effects on Sidanese agriculture for distortions as measured by the indexes of direct and indirect taxation. These results coupled with the fact that the distortions have increased quite substantially from relatively low levels, corroborate the predictions of the Easterly (1990) model which shows that the impact on growth is likely to be larger, the larger the increase in distortions and the smaller their initial levels. The results also as expected, provide strong support for the role of investment in the growth of agriculture, we could not, however, obtain significant influence for human capital, the other conventional growth fundamental. Lastly the slightly significant and negative effect due to permanent