rafmacalaba/datause-encoder-data huggingface dataset:
also give an "adjusted" estimate in which a control variable was added for the first difference of the log of the ratio of the consumer price index to the national income deflator (i.e., the difference in the rate of inflation implied by the two deflators).<sup>44</sup> This was included to allow for possible bias in estimating the growth elasticity due to the difference in the deflator used for the national accounts data and that used for the poverty lines.

For the 1957-2012 period as a whole, the national poverty measures responded significantly to growth in all three measures. The IV estimates to address survey measurement errors (also likely to be negatively correlated between the dependent variable and the explanatory variable) are slightly lower than the corresponding OLS estimates. The (absolute) elasticities are higher if one uses the NSS estimate of mean consumption, rather than the national accounts estimate. The elasticities are lowest for per capita income. There are a few possible reasons. Inter-temporal consumption smoothing may make poverty (in terms of consumption) less responsive in the short-term to income growth than to consumption growth. Imperfect matching of the time periods between the NSS and the NAS could also be playing a role in attenuating the elasticities using NAS growth rates. A further reason for lower (absolute) elasticities with respect to NAS consumption or income has to do with the increasing divergence between NSS and NAS growth rates of mean consumption; significantly faster growth in NAS means relative to NSS means is reflected in the lower elasticities with respect to the former.

When we split the period into two at 1991, we find appreciably higher (absolute) elasticities of poverty indices with respect to the survey mean in the post-1991 period; the difference in the estimated elasticities over the two periods is statistically significant.<sup>45</sup> The pattern of higher post-1991 elasticities is similar for all poverty measures, for higher and lower poverty lines and for the OLS as well as the IV estimates (Table 3).

By comparison, we find that NAS-based growth of both income and consumption per capita indicate significantly higher (absolute) elasticities in the post-1991 period for the headcount index, but the difference between the two periods is not statistically significant for SPG, and for PG it is significant only for the per capita income growth elasticity. It is

> 44 The consumer price index is a consumption share-weighted average of the rural and urban price indices. 45 See Table 3.  These results are based on regressions of log poverty measures on log survey mean interacted with dummy variables for pre- and post-1991 periods, and a dummy variable for MRP surveys. The regressions also incorporate a kink at NSS round 47 (July-December 1991) such that there is no discontinuity in the predicted values of log poverty measures between the pre- and post-1991 periods.

16
- 45 -

whole manufacturing sector. Collecting the required data and doing the necessary computations is a time consuming task, but one that is well worth the effort, given the important policy implications of the results.

Table 13 presents a summary of the results obtained in this paper on the effectiveness of tax incentives. The results are quite mixed and vary by industry. For example, in Pakistan investment tax credit had a highly stimulative impact on investment in chemical and pharmaceutical industries but little impact on the textile industry. R&D expensing also proved to be a cost-effective measure for the same industries. Turkish tax incentives measures (both an investment allowance and R&D expensing), on the other hand, resulted in higher revenue losses as compared to their investment impacts.

Table 13 A SUMMARY VIEW ON THE EFFECTIVENESS **QF** INVESTMENT INCENTIVE8

|Tax Instrument<br>Incremental|benefit-cost ratio|
|---|---|
|Investment Tax Credit||
|Pakistani Textile Industry<br>Pakistani Chemical & Pharmaceutical<br>Industries|0.017<br>2.6|
|Investment Allowance||
|Turkish Chemicals & Petroleum Derivatives Industries|0.02|
|R&D Expensing||
|Pakistani Chemical & Pharmaceutical Industries|1.75|
|Turkish Chemical and Petroleum Derivatives Industrie|s<br>0.008|

Source: Model-basec. results
11 wheTe min is the legally mandated minimum wage which is lagged one year in most specifications,<sup>17</sup> Pi represents the price paid to wage labor of varying types, pk is a measure of the cost of capital (available in the Mexico data only), Pm is a raw materials price, Pe is an energy price (available in the Colombia data only), and ppi is an output price deflator for the ISIC at the 4-digit level.<sup>18</sup> Generalizing equation (3) allows estimation of the following system of equations for skilled and unskilled labor: (6') Eus = aI + cL2 min/ppi + a3 Pus/ppi + a 4 Ps/ppi + aL5Pk/ppi + cX6Pm/PPi + a7 Pe/PPi + El (6") Es = Y1 + Y2 min/ppi + Y3Pus/PPi + y4 Ps/ppi + Y5Pk/PPi + Y6Pm/PPi + Y7Pe/PPi **+2** All variables estimated in logarithms. Less restrictive versions of the model that ignore the implicit homogeneity assumption implied by the output price deflation are presented as well in the results that follow.

Table 7 presents estimates of equations (6') and (6") for Mexico. Ordinary-least squares estimates in columns (1) and (7) of the table for unskilled and skilled workers respectively yield large estimated gross elasticities of employment with respect to the minimum wage, but wrong-signed ownfactor price elasticities for both skilled and unskilled employment. The estimates of cross-price elasticities from the unskilled regression are correctly signed, although the skilled estimates fail to provide evidence in favor of capital-skill complementarity. Industry, occupation, region, and year effects are highly significant in both OLS equations.

Columns (2)-(6) and (8)-(1 1) of the table present various estimates of the model controlling for within-firm variation in the response of employmen **t** o changes in factor prices. The fixed effects estimates abstract from differences in scale, technology, and various other factors that are likely to influence the speed and magnitude of factor share response in so large a cross-section of firms. The fixedeffects estimates for the basic model are presented in columns (2)-(4) of the table for unskilled workers, 17In both Mexico and Columbia, minimum wages are frequently revised throughout the year, and annual minimum wages may represent an average of multiple year revisions. In addition, even where minimum wages are not revised throughou **t** he year, they may typically be announced (as was the case in Columbia) in mid-year.

1<sup>8</sup> Factor price data are derived using quantity and value data on the purchase of the relevant factor.
period as a convenient choice of units). And more substantively, this evidence highlights major governance challenges worldwide, where improvements in governance in some countries are largely offset by declines in others, with the result that world averages show little change.

# **4.  Conclusions**

In this paper we have reported on the latest update of the Worldwide Governance Indicators for 2008. The WGI are available biannually since 1996, and annually for 2002-2008. We have also continued our practice of reporting the individual indicators underlying the aggregate WGI. It is our hope that timely annual reporting, as well as providing access to individual indicators, are making the aggregate indicators more useful to users in academic and policymaking circles.

We nevertheless emphasize to all users the limitations of these measures of governance, which are shared by virtually all efforts to measure governance across countries and over time. The aggregate indicators we construct are useful for broad cross-country and over-time comparisons of governance, but all such comparisons should take appropriate account of the margins of error associated with the governance estimates. These margins of error are not unique to our perceptions-based measures but are present -- if not explicitly acknowledged -- in any effort to measure governance. They naturally reflect the inherent difficulty in measuring something as complicated and multifaceted as governance. However, we have shown the feasibility of using the aggregate indicators to make comparisons of governance across countries and over time, subject to appropriate consideration of margins of error. In this paper we also gave a brief account of the evolution of the WGI over the past decade or so, including how the margins of error have declined over time, mostly due to the increasing availability of individual sources. Thus, while margins of error remain non-trivial, it is worth noting that for the current 2008 data we see that 64 percent of all cross-country comparisons result in highly-significant differences (at 90 percent confidence levels), and that about onethird of countries have experienced substantial changes in at least one dimension of governance between 1998 and 2008.

24
Table A2.2: Which businesses close te the shore were more likely to suffer damage? Marginal effects from probit regression for firms within 750 metres of the shore Dependent variable: Suffered business damage

|Within|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|250 metres<br>Within 500 metres<br>Firmisregistered|O34<br>{0.067)<br>0.222"<br>{0.073)<br>0.0813<br>|0347"<br>{0.067)<br>0.196"<br>{0.074)<br>0.0648<br>|0.3958"<br>(0.067)<br>0.201%<br>(0.075)<br>0.0531<br>|0.333""<br>(0.070)<br>0.170"<br>(0.077)<br>0.0287<br>|
|<br>Femaleowner|{0.062)<br>0.0831<br>{0.056)|{0.064)<br>0.00437<br>{0.063)|(0.065)<br>0.0165<br>(0.064)|(0.066)<br>0.0211<br>(0.066)|
|Business is 3 years orless in age|-0.1439"|-0.141TM|0.141"|-0.163"|
||{0.068)|{0.069)|(0.069)|(0.073)|
|Ageofentrepreneur|0.000327|0.00136|0.00182|0.00158|
||(0.0029)|(0.0029)|(0.0029)|(0.0030)|
|Dummyformarried|0.121<br>{0.078)|0.147"<br>{0.079)|0.130<br>(0.080)|0.138*<br>(0.082)|
|Yearsofeducation ofentrepreneur|0.0268"|0.0221"|0.0218"|0.0194|
|Manufacturing|{0.011)<br>-0,0207<br>|{0.011)<br>0.0241<br>|(0.011)<br>0.0454<br>|(0.012)<br>0.0350<br>|
||(0.059)|(0.061)|(0.063)|(0.070)|
|Log(pre-tsunamlprofits)||0.109"*|0.0664|0.0676|
|||{0.032)|(0.043)|(0.044)|
|Log (pre-tsunaml sales)|||0.0562<br>(0.039)|0.0581<br>(0.040)|
|Digltspan recall||||0.0174<br>(0.026)|
|Househasa dirtfloor||||0.0448<br>(0.077)|
|House has brickwalls||||0.0706<br>(0.061)|
|Business operated outofhome||||-0.0288<br>(0.072)|
|Observations|331|331|331|316|
|StandarderrorsInparentheses|||||

* significant at 5%; ** significant at 1%
**Table 1: Global Trade Flows, 1980 and 1992 (US** $ **billion and percentage)**

||1980|1992<br>Average annual change|
|---|---|---|
|Total trade in services|358|931<br>8.3|
|OECD (shares in parentheses)|283 (79%)|765 (82%)<br>8.6|
|Rest of world|75 (21%)|166 (18%)<br>6.8|
|Services as share of goods and services|17.0|22.0<br>2.2|
|OECD|18.8|22.7<br>1.6|
|Rest of world|12.7|19.2<br>3.5|

Source: World Bank. Data pertain only to countries reporting to the IMF.

Many services are not tradable, in the sense that cross-border, 'long-distance' exchange or temporary physical movement of provider/consumer does not suffice for an exchange to be feasible (Bhagwati, 1984). Producers of such services can contest foreign markets only through establishing a long-term physical presence in these markets, i.e., engage in foreign direct investment (FDI). Not surprisingly, FDI in services accounts for a large share of the total stock of inward FDI in most host countries. As of the early 1990s, some 50 percent of the global _stock_ of FDI was in services activities. The share of annual flows to many countries has been over 60 percent in recent years.<sup>2</sup> The relative importance of _trade_ in services (as registered in the balance of payments of a country) as opposed to _sales_ of services by affiliates is not known. Conventional wisdom holds that FDI is likely to be the dominant 'mode of supply' for many services, but that this dominance is being eroded by technological developments. U.S. data, the only country that currently collects detailed data on both its trade in services and sales of services by affiliates of U.S. parent companies, suggest that trade and sales are of roughly the same importance.<sup>3</sup> Not too much can be inferred from this, however, as both trade and sales via FDI will in part reflect the barriers to the various modes of supply that are imposed by partner countries. Quantitative measures of these barriers currently do not exist (see below).

Available data on trade in services are very weak compared to those on merchandise (see Box 1). Only a limited number of industrialized countries collect and report statistics on trade in services at a relatively disaggregated level (e.g., ten categories or more). Most non-OECD countries only report data on trade in "commercial services" broken down into "transport" (largely freight and passenger transport by sea and air), "travel" (expenditures by nonresidents - mostly tourists - while staying in a

**2** See UNCTAD and World Bank (1994) for data on FDI in services.

> 3 In 1987 foreign sales by U.S. majority owned affiliates were **15** percent smaller than service exports; in 1992 they had grown to be 20 percent larger (World Bank, 1995).

2
# **Figure 2.3 Policy uncertainty and investment in EMDEs**

_Elevated policy uncertainty in Europe set back investment in ECA. Policy uncertainty has been a significant cause of the investment slump in Brazil since 2013._

## **A. ECA investment response to 10 percent increase in EU policy uncertainty**

<!-- Start of picture text --> Percentage points<br>0.5<br>-0.5<br>-1.5<br>-2.5<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text -->

## **C. Contribution of EU policy uncertainty to ECA investment growth**

<!-- Start of picture text --> Percent<br>15 EPU Others Actual<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

**B. Investment response to 10 percent increase in policy uncertainty in Brazil**

<!-- Start of picture text --> Percentage points<br>0.0<br>-1.0<br>-2.0<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text -->

**D. Contribution of domestic policy uncertainty to Brazil’s investment growth**

<!-- Start of picture text --> Percent<br>20 EPU Others Actual<br>10<br>0<br>-10<br>-20<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: Bloomberg, Haver Analytics, World Bank estimates.

A.C. Vector autoregressions are used for estimation on a sample of aggregate EMDE variables for 1998Q1‐2016Q2. The model includes the EPU for the Euro Area, emerging market stock price (Euro Area) index, emerging market bond index, aggregate real output and investment growth in 6 ECA countries, with G7 real GDP growth, U.S. 10‐year bond yields, and MSCI World Index as exogenous regressors and estimated with two lags.

B.D. Country‐specific autoregressions are estimated for Brazil for 1998Q1‐2016Q2. The model includes the domestic EPU, domestic stock market index, domestic short‐term interest rates, and real output and investment growth, with G7 real GDP growth and VIX as exogenous controls and estimated with two lags.

A.B. Show cumulative responses of investment to a 10 percent policy uncertainty shock in the Euro Area and Brazil. Solid lines indicate median responses. Dotted lines indicate the 16‐84 percent confidence intervals. Figures C. and D. indicate historical decomposition to investment growths in ECA and Brazil, respectively. Estimates for 2016 are based on the first half in 2016 (annualized).

48
# **3. Conclusions**

1. All international and global inequality concepts show a higher degree of inequality with the new 2005 PPP data than with the extrapolation based on the previous round of the ICP. This is very significant since the new data include China (first time ever) and India (first time since 1985).

2. After 20 years of mean-income (GDP per capita) divergence, GDPs per capita of the countries of the world have begun a process of convergence since 2001. This is due to the pick-up of growth in Africa, post-Communist countries and Latin America. It is unclear how the global crisis will affect this process.

3. Population-weighted (Concept 2) inequality, which has gone down during the last 20 years of the 20<sup>th</sup> century thanks to the high growth rates of China, shows, after year 2000, a decline even when China is excluded. This is due to the high growth rates of India. Uncertainly as to the effects of the crisis remains there too although to the extent that the “locomotives” of Concept 2 decline, China and India continue to expand at a higher (per capita) growth rate than the rest of the world, Concept 2 may go on with its downward trend.<sup>16</sup> 4. Global inequality between individuals amounts to more than 70 rather than 65 Gini points as when calculated with the “old” PPPs. This is a significant upward revision. The trend, over the period 1988-2002, however, is absent as the global Gini bounces between 68.4 and almost 71 Gini points. Its further evolution will, of course, depend on growth rates of China, India and the US (the “triangle” which explains about 10 Gini points of global inequality<sup>17</sup> ), but also on how the next stage of the global crisis affects African, post-Communist and Latin American countries in particular.

> 16 Up to a point, quite remote now (and made remoter after the 2005 ICP results) when China’s growth— due to its having become a relatively rich country—turn to be globally disequalizing. 17 See Milanovic (2005, p. 89).

14
# **1. Introduction**

The recent hostage crisis at the Tigantourine gas facility in Amenas, Algeria, serves as a grim reminder that investing in politically unstable countries is risky. The four-day occupation of the gas complex, jointly owned by the Algerian state oil company Sonatrach, the British firm BP, and the Norwegian firm Statoil, resulted in the deaths of 39 expatriates, including four BP employees. In spite of initial trepidation over involvement in petroleum projects in Algeria in response to this unsettling event, it was only a matter of days before BP’s Press Office issued a communique stating that “ _BP remains committed to Algeria where it has high-quality assets_ ” (BP, 2013), a statement later reinforced by U.K. ambassador to Algeria Martyn Roper, who went on record asserting that “ _BP will stay in Algeria. It is very engaged and will pursue these projects_ ” (Boston Globe, 2013) _._ While this example contradicts the popular view that political uncertainty hampers investments,<sup>1</sup> BP is certainly not the only multinational that remains engaged in countries in turmoil. Recently, the Emirati Dana Gas and the Italian Eni SpA announced major investments in Egypt’s oil and natural gas sector, while the Kazakh oil company KazMunaiGaz invested in Libya (fDi Markets, 2013). Likewise, empirical studies of the effect of political instability on foreign direct investment have yielded distinctly divergent results. Some have documented a very strong negative relationship between foreign direct investment (FDI) and political turmoil<sup>2</sup> (Busse and Hefeker, 2007; Daude and Stein, 2007; Alfaro et al., 2008). However, others have found no significant effects and concluded that political unrest and institutional quality are not important determinants of investment flows (Noorbakhsh et al., 2001; Blonigen and Piger, 2013). In specific instances, certain foreign companies have gained from instability. Incumbent diamond mining companies operating in Angola benefitted from its civil war; they were able to attain

> 1 According to the World Bank’s most recent Investment Climate Surveys in the MENA region (World Bank, 2013), political instability is the most important deterrent to investing.

> 2 See Schneider and Frey (1985) for a discussion of the relative importance of political and economic variables in FDI models.

2
using its assets. Low ROA indicates low productivity and thus potential vulnerability to shocks and financial difficulties. Jordan reports healthy average profitability of around 6%, which compares well with Israel and Turkey, but below that reported in Morocco and Egypt.

Leverage is one of the key measures of corporate vulnerability. High leverage suggests higher risk of insolvency in response to shocks that reduce the value of assets relative to liabilities. Two main measures of leverage are total debt to assets and total liabilities to assets.<sup>2</sup> In Jordan these ratios are relatively low. In fact, these ratios are the lowest of all the comparator countries, only 24% for total debt and about 30% for total liabilities, scaled by assets. While these ratios do not suggest high vulnerability in Jordan, they might point to difficulty with obtaining debt financing, which affects even the largest corporations that are represented in the Worldscope.

While total leverage shows the overall indebtedness of the company, the maturity structure of the debt is also an important factor in determining corporate vulnerability. Shortterm debt makes companies more vulnerable to hikes in interest rates and credit crunches that may reduce creditor’s willingness to roll over short-term debt. In Jordan firms have very high proportion of short-term leverage. The ratio of current liabilities to total liabilities is above 80%, which is the largest of all comparator countries, closely followed by Morocco. The ratio of shortterm debt to total debt is also quite high – at 68%, which is just below Morocco at 70% and higher than in other comparator countries.

While the total levels of debt and leverage do not indicate high vulnerability in Jordan, high proportions of short-term debt and short-term leverage might be a source of concern from stability perspective (as they make companies more vulnerable to shocks) and from developmental perspective (as inability to raise long-term financing might slow down the potential growth).

> 2 To increase comparability with Stock exchange data discussed in the next section, the total debt is estimated as total liabilities minus accounts payable, which is trade credit.

4