rafmacalaba/datause-encoder-data huggingface dataset:
physical capital is Granger-caused by the levels and changes in economic freedom, in the case of levels this relation is bilateral. The authors also find bilateral Granger-causality relationship between the level of investment in human capital and economic freedom.

# _Aggregated index and its components_

Several authors have made the case against aggregation (see e.g. Heckelman and Stroup, 2000 or Lundström, 2003). Apart from the problems with the adoption of various weighting schemes discussed above, one can argue that some elements of economic freedom might not be positively and robustly related to economic growth. Ayal and Karras (1998) find that out of 13 components of economic freedom, six are statistically significant and related to economic growth according to the predictions of economic theory (money growth, government enterprises, negative interest rate, black exchange rate, trade size and foreign capital). Inflation variability impact is not robust to different model specifications.

Carlsson and Lundström (2002) analyze seven categories from the 2000 edition of the Economic Freedom of the World index. They found a positive and robust relationship between two categories of economic freedom i.e. freedom to use alternative currencies, legal structure and security of private ownership and economic growth. The impact of the freedom to trade with foreigners is counterintuitive as authors find a robust negative relationship with growth. The authors find the hump-shaped relationship between economic growth and the size of government consumption and transfers. Increased freedom in terms of lower level of government consumption and transfers decreases the growth rate for the index values lower than 8.46; at higher values the impact is reversed.

Heckelman and Stroup (2000) analyze 14 elements of economic freedom and find that only four of them have a positive and significant effect on growth in bivariate regressions, while only two remain significant in multivariate regressions. These elements of economic freedom with a robust impact on economic growth are the difference between the official exchange rate and the black market rate with a maximum for countries with no black market for their currencies (positive impact) and top marginal tax rate (negative rate).

Dawson (2003) finds that the level of two components of economic freedom namely use of markets and property rights Granger-cause growth, while size of government is found to be caused by growth. When changes in the components of economic freedom are related to growth, the author finds that none of them are found to cause growth. The growth seems to Granger-cause improvements in economic freedom related to size of government and international finance.

37
**Table 7:** Effects of AIA on total investment and cloud: robustness to continuous measure

|Regressions|(1)|(2)|
|---|---|---|
|Dependent variable|Total investment|Cloud|
|AIA available allowance|4.260***|-2.663***|
|(continuous measure)|(1.043)|(0.678)|
|Observations|23,711|2,200|

Note: Time Period: Investment regressions use annual data for the years 2007 to 2013. Cloud regressions use data for 2007 and 2013. The AIA allowance is calculated taking the average investment values for firms in 2005/06 and calculating how far this lagged investment average is above or below the AIA threshold in any given year. This is expressed in millions of pounds. All regressions are estimated using the approach of Gardner (2022). All regressions include year and firm fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age, not reported for brevity. Robust standard errors in parenthesis are clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively.

of investment. This removes around 110 observations from column 1 and a little under 220 from column 2. We also test whether the results for cloud adoption are driven by the use of firms that make very little investment in capital, including IT capital. In columns 3 and 4 we also exclude the smallest 5 and 10 percent of firms. The results for cloud adoption are robust to this sample restriction, with the effect of the AIA being negative throughout. Importantly the coefficient estimate remains broadly stable across these regressions.

In a second robustness test, we exclude firms that were eligible for other policies during our sample period which may have influenced investment and cloud adoption behaviors. Until 2008 and again for the year 2010, a First Year Allowance (FYA) policy existed in the UK which provided tax allowances to small firms. Firms with sales up to £22.8 million were eligible to receive a tax rebate on capital investments through accelerated depreciation, as considered by Maffini et al. (2019). In order to examine the robustness of the effects of AIA on firm investment decisions, we exclude firms in our sample that ever-had sales of less than £22.8 million in any year during our sample period. This is a conservative approach and 29
52

**Annex 2: Provincial Fiscal Responsibility Laws in Canada: Fiscal Targets**

|**_Provinces_**|**_Key Fiscal Targets_**|
|---|---|
|British Colombia|<br>Main budget estimates must not contain a forecast of deficit|
|Alberta|<br>Deficits and opening debt are not allowed.<br><br>Net assets of Sustainability Fund may not be reduced to less than zero.<br><br>Net assets of Capital Account is may not be reduced to less than zero<br><br>Contingency allowance => 1% of revenue p.a. for fiscal policy purposes|
|Quebec|<br>No budgetary deficit. For an overrun of less than $1 billion, an equivalent<br>surplus must be achieved in the next fiscal year<br><br>If surplus is achieved in a fiscal year, overruns can occur in subsequent<br>fiscal years up to the amount of that surplus.<br><br>With overruns more than $1 bn, it should be offset with a maximum of 5<br>years.|
|Ontario|<br>Maintain a prudent ratio of provincial debt to gross domestic product<br><br>Plan a balanced budget except extraordinary circumstances.<br><br>If a deficit is planned, the Executive Council should also develop a<br>recovery plan for achievinga balanced budgetwithin specifiedperiod|
|New Brunswick|<br>Balanced budget: the total amount of the expenses should not exceed the<br>total amount of revenue for that fiscal year<br><br>Reduction of debt: Ratio of net debt to GSDP at the end of each year<br>should be less than at the end of theprevious fiscalperiod|
|Nova Scotia|<br>No budget deficit (from FY2002/03 onward)<br><br>When deficit occurs,it should be recovered bythe end of next fiscalyear|
|Saskatchewan|<br>Balanced budget or budget with surplus with 4-year financial plan<br><br>Actual balance of revenue and expenses or surplus of revenues over<br>expenses each year<br><br>If a deficit results for a fiscal year, an offsetting surplus must be achieved<br>the followingfiscalyear.|
|Manitoba|<br>Presented budget must project a positive balance as at the end of that<br>year.<br><br>The balance as at the end of a fiscal year is determined as the average of<br>the net results for the fiscal years within the four-year period ending at<br>that time.|

Sources: 1) Various Fiscal Responsibility Laws from LexisNexis, www.lexisnexis.com and CanLII, <u>www.canlii.org. 2) Kennedy and Robbins (2003).</u>
4

**Figure 1:  Chilean Firm Breakdown by Size, Sales, Exports and Employment**

|**Firms**|**Compa**|**nies**|**Sales**||**Export**||**Employment**||
|---|---|---|---|---|---|---|---|---|
||**Number**|**%**|**US$ million**|**%**|**US$ million**|**%**|**‘000 of people**|**%**|
|Micro|535,537|82%|7,041|3%|14|0%|2,124|50%|
|Small|96,842|15%|19,920|9%|176|1%|845|20%|
|Medium|13,597|2%|18,632|7%|527|3%|539|13%|
|Large/ Mega large|6,469|1%|164,393|7%|17,477|9%|713|17%|
|**Total **|**652,445**|**100%**|**209,985**|**100%**|**18,194 **|**100%**|**4,221**|**100%**|

Source: Government of Chile Note: Data are for 2001, except exports (2000) Definitions: Mega large firms are defined as those with annual sales net of VAT above UF600,000 (US$17,172,000), large firms have sales between UF100,000 (US$2,862,000) and UF600,000, medium firms have sales between UF25,000 (US$715,500) and UF100,000, small firms have sales between UF2,400 (US$68,688) and UF25,000 and micro firms have sales below UF2,400.

# **_Overall Corporate Financial Performance_**

An analysis of the financial statements of some 500 firms that file annually with the Chilean Superintendence of Firms and Insurance (the SVS)<sup>**1**</sup> was carried out. These statements include detailed financial accounts and notes in a standard format (the Uniformly Codified Statistical Form or FECU)<sup>**2**</sup> . Only private limited firms (Sociedades Anónimas SA), listed and non-listed, are required to file their accounts with the SVS (public firms and closed companies are not required to do so, while insurance companies have distinct requirements). Beyond this legal requirement, filing is also useful to get access to external funding, in particular to capital market financing. Therefore, filing firms are concentrated among Chile’s largest firms and among those which have issued bonds or listed equity (such as infrastructure concession companies). They include only a minority of Chile’s large universe of smaller firms, probably the top ones in terms of openness and access to external financing. While the sample can be considered representative of Chile’s large, and mega large universe of firms, the same is not true for medium, small and even more so micro firms. For these, the sample is likely to include the most open firms.

> 1  Superintendencia de Valores y Seguros

> 2 Ficha Estadística Codificada Uniforme
<!-- Start of picture text --> (a) On budget expenditure, % of GDP, 2010-2018 (b) Utilities expenditure, % of GDP, 2010-2018<br>2.00% S> 2.00% ;7<br>9 1.50% a 9 1.50% S=<br>s a s mA<br>©cc1.00% a © 1.00% ;<br>:2 E a<br>0.50% ao = 050% sa<br>aa = ° e<br>0.00% @i=©e—eeas—_@-e e 0.00% “@pe—®@a ee oe . e<br>0.00% 0.50% 1.00% 1.50% 2.00% 0.00% 0.50% 1.00% 1.50% 2.00%<br>Investments Investments<br>1.0%<br>=<br>2<br>a=]<br>c<br>7& 0.6% SEE<br>3<br>wo e<br>c=° Solomon Islands south Africa Bhutan<br>& 0.4% e e<br>wv<br>£2<br>oO<br>= 0.2%<br>Kosovo<br>a e<br>Ethiopia Brazil @ Kenya<br>0.0% gentind Pee<br>0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 40% 45% 5.0%<br>Capital expenditure<br><!-- End of picture text -->
Lucas, R. E. B. 2005. “International Migration: Lessons from Recent Data.” Presentation in the Migration Seminar Series. World Bank. Washington, D.C. (March 8).

Martorell, R. 1999. “The Nature of Child Malnutrition and its Long-Term Implications.” _Food and Nutrition Bulletin_ 20: 288-92.

Mattoo, A., I. C. Neagu and C. Ozden. 2005. “Brain Waste? Educated Immigrants in the US Labor Market.” Mimeo. Research Program on International Migration and Development. International Trade Unit. DEC Research Group. World Bank. (March).

McKenzie, D. and H. Rapoport. 2005. “Migration Incentives, Migration Networks and the Dynamics of Education Inequality in Rural Mexico.” Paper presented at the IADB “Economic Integration, Remittances, and Development” Conference.

McKenzie, D. 2005. “Beyond Remittances: The Effects of Migration on Mexican Households.” Chapter 5 in this volume.

Mountford, A. 1997. “Can a brain drain be good for growth in the source economy?” _Journal of Development Economics_ 53 (2): 287-303.

Ozden, C. 2005. “Skilled Migrants in the US: Brain Drain or Brain Waste?” Chapter 8 in this volume.

Patinkin, D. 1968. “A ‘Nationalist’ Model.” In Adams, W. (ed.) _The Brain Drain_ . New York: Macmillan.

Romer, P. 1986. “Increasing Returns and Long Run Growth.” _Journal of Political Economy_ 94: 1002-37 (October).

SOPEMI. 2003. _Trends in International Migration_ . OECD. Paris.

Schiff, M. and A. Valdés. 1990. “Nutrition: Alternative Definitions and Policy Implications.” _Economic Development and Cultural Change_ (January).

____ and ____. 1990. “Poverty, Food Intake and Malnutrition: Implications for Food Security in Developing Countries.” _American Journal of Agricultural Economics_ (December).

Schultz, T. W. 1961. “Investment in Human Capital.” AEA Presidential Address. _American Economic Review_ 51 (1): 1-17.

Solimano, A. 2001. “International Migration and the Global Economic Order: An Overview.” Mimeo. World Bank. Washington, DC.

Stalker, P. 1994. _The work of strangers. A survey of international labour migration_ . International Labour Office: Geneva.

Stark, O. “Rethinking the Brain Drain.” 2004. _World Development_ 32 (1): 15-22.

____, A. Casarico, C. Devillanova and S. Uebelmesser. 2004. “The New Economics of the Brain Gain: Mapping the Gains.” Paper presented at the ‘Conference on Advanced Perspectives on Migration and Mobility.’ University of Bonn (Sept. 30-Oct.1).

34
ln Xij = ln a0 + a1ln Yi + a2ln Yj + a3ln Ni + a4ln Nj + a5ln Distij + a6Adjacent + a7English + a8French + a9Spanish + a10Arabic + a11Chinese + a12German + a13Portuguese + a14Russian + a15Asean + a16APEC + a17FTAA + a18Nafta + a19LAIA + a20AUNZ + a21Comesa + a22Mercosur + a23EU + a24SADC + a25AEO + a26MRA + a27SW + log eij where Xij is the dollar value of exports from the country i to country j; Yi is the nominal value of country i's Gross Domestic Product (GDP); Yj is the nominal value of country j's GDP; Ni is the population of country i; Nj is the population of country j; Distij is the distance between the commercial centers of country i and country j; Adjacent is a dummy variable equal to 1 if both countries are adjacent (share borders); English, French, Spanish, Arabic, Chinese, German, Portuguese, Russian are dummy variables equal to 1 if both countries speak that language, and zero otherwise; Asean, APEC, FTAA, Nafta, LAIA, AUNZ, Comesa, Mercosul, EU, and SADC are dummy variables equal to 1 if both countries belongs to that bloc, and zero otherwise; AEO and SW are dummy variables equal to 1 if the importing country has implemented a Authorized Economic Operator or a Single Window program, respectively, and zero otherwise; MRA is a dummy variable equal to 1 if both countries have implemented a mutual recognition arrangement between themselves. The function of the last three variables is to capture the impact of trade facilitation measures on international trade flows, thus acting as a proxy for trade facilitation.

We used panel data,<sup>5</sup> including all observations of non-zero trade between countries, and we estimated four different equations:<sup>6</sup> a Pooled Cross Section (PCS) model; a Fixed Effects (FE) model; a Random Effects (RE) model; and a Poisson maximum likelihood (ML) estimator. Modelling using panel data often is better than estimating cross-section models, as it helps to account for different econometric

> and Zimbabwe. We used the same sample of countries as in Wilson, Mann and Otsuki (2005), except that we excluded Bangladesh, Trinidad and Tobago and Taiwan, China, from our sample, since there were no trade data for these three countries in the years 2011 and 2012.

> 5 The source of the trade data is UNCTAD (2014). The GDP and the population data for the countries in the sample was obtained from the World Bank (2014). The distance, adjacency and language information were extracted from the World Atlas MPC CD-ROM. The information on regional blocs comes from WTO (2014). The information on the Authorized Economic Operator (AEO) programs and on the Mutual Recognition Arrangements (MRA) comes from WCO (2014b). Finally, the information on the Single Window (SW) programs comes from WCO (2011).

> 6 The four equations were estimated using Stata.

9
reason for apparently high aggregate risk. However, note that regional decomposition of risk does not show significant variation in the magnitude of risk across regions.

**Table** **<u>6.</u>** Decomposition of risk into <u>aggregate and idiosyncratic risk components</u>

||**Risk**|**Aggregate risk**|**Idiosyncratic risk**|**Unexplained risk**|
|---|---|---|---|---|
|Vojvodina|0.0632|0.0271|0.0141|0.0220|
|West Serbia|0.061|0.0281|0.0104|0.0226|
|Central Serbia|0.0554|0.0253|0.0103|0.0198|
|East Serbia|0.0751|0.0341|0.0126|0.0284|
|Southeast Serbia|0.0677|0.031|0.0121|0.0247|
|**Total**|**0.0634**|**0.0285**|**0.0121**|**0.0229**|

Source: Author’s calculations based on Serbia SLS 2002 and 2003.

# **Conclusions**

This paper dealt with rural vulnerability in Serbia with the notion that the well-being of poor households depends not only on households’ current consumption or expenditures, but also on risk and uncertainty about their future welfare state. Accordingly, rural vulnerability in rural Serbia are estimated and decomposed into poverty and risk. While we find that poverty is the major contributor of rural vulnerability in Serbia, risk also contributed to rural households’ perceived vulnerability. The fact that poverty accounts for such a high share of the vulnerability suggests that the characteristics of those who are observed to be poor are strikingly similar to the characteristics of those who are estimated to be vulnerable, whether they are currently poor or not.

Risk contributes a non-trivial 30 percent of household vulnerability in rural Serbia. Households and regions with greater share of their livelihood sources depending on agricultural activities are more at risk of vulnerability and poverty than those with significantly higher share coming from non-agricultural sources. Larger share of agricultural income is directly associated with higher aggregate risk, underscoring agricultural sector’s exposure to covariate shocks such as drought. Preoccupation with risk and uncertainty may negatively affect growth and deter profitable investments from occurring (e.g., Collier and Patillo, 1997). A clear evidence of risk in rural Serbia suggests that the effectiveness of government programs aimed at poverty reduction should not only be assessed under the assumption of normal circumstances, but also with due regard for the possibility of risk and uncertainty. In order to address vulnerability, poverty reduction programs need to be augmented with policies dealing with risk and fluctuations in welfare.

High level of human capital such as educational level of household heads significantly decreases household vulnerability and poverty. Households with a member having higher than secondary face significantly lower vulnerability than those with lower educational attainment. Household demographic composition also matters: households with bigger and more elderly members are more vulnerable and are more likely to be in poverty. Aging population and reduced pool of active workers and the opportunity to generate income, compounded with low educational 12
# Cost-benefit analysis

Across the nine countries, the annual healthcare costs per capita (across the entire population) was US$2.75, or US$2.04 per capita for the low-income countries.<sup>16</sup> This compares with a total economic cost of US$11.5 per capita for all 9 countries, or US$7 per capita for the low-income countries. The costs of providing basic WASH, HCWM and environmental cleaning in low-income countries is estimated at US$0.81 per capita per year (US$0.30 for capital cost and US$0.51 for recurrent cost when universal coverage is achieved).<sup>17</sup> Assuming 50% reduction in HAIs through the provision of basic WASH, HCWM and environmental cleaning the resulting benefit-cost ratios are 1.22 for financial health care savings alone (1.38 with middle income countries included), and 4.2 for all economic costs (5.8 with middle income countries included)(see Table 4).

**Table 4. Estimation of benefit-cost ratios for WASH and HCWM interventions**

|**Countries**<br>**included**|**Cost-of-i**<br>**capita**|**llness per**|**Averted costs (50%**<br>**reduction)**|**Intervention**<br>**costs**|**Benefit-c**|**ost ratio**|
|---|---|---|---|---|---|---|
||**Health**<br>**care**|**Total**|**Health**<br>**care**<br>**Total**|**Health care**|**Health**<br>**care**|**Total**|
|Low-income|2.04|7.0|1.02<br>3.5|US$0.83|1.22|4.2|
|All 9 countries|2.75|11.5|1.38<br>5.8|US$1.00|1.38|5.8|

Source: author estimates. Intervention costs from Chaitkin et al (2022).

16
Policy Research Working Paper 8394

# **Abstract**

Corporations often have strong incentives to exert influence on the tax code and obtain additional tax benefits through lobbying. For the U.S. financial crisis of 2007–09, this paper shows that lobbying activity intensified, driven by large firms in sectors that depend more on external finance. Using a heterogeneous agent model with financial frictions and endogenous lobbying, the paper studies the aggregate consequences of this rise in lobbying activity. When calibrated to U.S. micro data, the model generates an increase in lobbying that matches the magnitude and the cross-sector and within-sector variation observed in the data. The analysis finds that lobbying for capital tax benefits, together with financial frictions, accounts for 80 percent of the decline in output and almost all the drop in total factor productivity observed during the crisis for the non-financial corporate sector. Relative to an economy without lobbying, this mechanism increases the dispersion in the marginal product of capital and amplifies the credit shock, leading to a one-third larger decline in output. The paper also studies the long run effects of lobbying. Restricting lobbying implies welfare gains of 0.3 percent after considering the transitional dynamics to the new steady state.

This paper is a product of the Macroeconomics, Trade and Investment 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://econ.worldbank.org. The author may be contacted at gzaourak@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