rafmacalaba/datause-encoder-data huggingface dataset:
firms’ incentives to bribe the judicial system. Therefore, as both consumers’ connectedness decisions and firms’ bribing decisions depend on the relative efficiency of the two mechanisms, formal and informal mechanisms interact via consumers and firms’ investment decisions.

The model has both “partial” and “general” equilibrium implications. At the partial equilibrium level, results only partly confirm the common belief that formal and informal institutions substitute each other. More precisely, we find that _consumers invest more in building informal enforcement networks when legal enforcement works poorly_ ; on the other hand, however, _well-performing informal enforcement networks improve legal efficiency, as firms have less incentives to bribe court officials_ . This last result is consistent with the analyses of Putnam _et al._ (1993) and Knack and Keefer (1997), who find a positive relationship between social capital (interpreted, however, as _trust_ ), institutional quality, and economic performance. At the general equilibrium level, we study how the equilibrium institutional mix is affected by uncertainty in the production process, measured by the frequency of bad productivity shocks. Shocks can be of different types, such as physical (e.g. electricity shortages, transportation difficulties, unskilled labor), socio-political (e.g. crime and corruption), or stemming from policy uncertainty.<sup>4</sup> _Ceteris paribus_ , when the frequency of productivity shocks increases, firms bribe more, and consumers connect more with one another. Adding up all the partial effects, we obtain that - up to a certain threshold - increased uncertainty leads to lower legal efficiency, and to increased reliance on informal enforcement mechanisms. Therefore, in accordance with common wisdom, _in equilibrium_ corruption in the legal system is associated with a more widespread use of informal enforcement, via increased uncertainty in the production process.

To conclude, we present empirical evidence of the link between uncertainty, corruption, and consumers’ incentives to rely on informal enforcement mechanisms. We use firm-level data from the _Investment Climate Assessments_ of the World Bank, which survey more than 28,000 firms in 58 countries, and provide information on firms’ characteristics, firms’ productivity, and the investment climate. In a context of intra-industry trade, firms are both producers and consumers: we use therefore _membership to business association_ as a proxy for the use of informal enforcement networks. In accordance with the theory, we find that firms that suffer more from corruption, and firms that face a more uncertain production climate, are more likely to be members of business associations. Interestingly, the relationship continues to hold when we look at firms in Sub-Saharan Africa, Asia, Latin America, and East-

> 4A growing body of literature documents how policy uncertainty is a serious concern for businesses in developing countries. See, for instance, Hallward-Driemeier and Stewart (2004), World Bank (2005), and The Economist Intelligence Unit (2005).

3
decomposition of financial openness into equity‐related and debt‐related financial measures (Lane and Milesi‐Ferretti, 2003): where _FDIA_ and _FDIL_ are stocks of foreign direct investment assets and liabilities, _PEQA_ and _PEQL_ are the stocks of portfolio equity assets and liabilities, _PDBA_ and _PDBL_ are holdings of portfolio debt assets and liabilities, _OIA_ and _OIL_ are stocks of other investment assets and liabilities, and _RA_ represents reserve assets. In short, _Eq‐FO_ and _Db‐FO_ are indicators of the level of equity‐ and debt‐related cross holdings. Analogously to the definition of overall financial openness, we also define these ratios for only liability holdings, _Eq‐FO(L)_ and _Db‐FO(L)_ , and asset holdings, _Eq‐FO(A)_ and _Db‐FO(A)_ .

Our variable of interest, _trade openness,_ affects growth through various channels. It allows production specialization through the exploitation of comparative advantages, enabling technological diffusion and expanding potential markets for the country’s goods, among other things. Trade openness is measured as the ratio of real exports and imports to real GDP (all these magnitudes are expressed in local currency at constant prices) and the data is collected from the World Bank’s World Development Indicators. We also use an alternative measure of openness that adjusts the volume of trade over GDP for the size (area and population) of the country and for whether the country is landlocked or an oil exporter.<sup>11</sup> Loayza, Fajnzylber and Calderon (2005) argue that this structure‐adjusted volume of trade maybe preferable than the unadjusted ratio given that the econometric analysis is based on cross‐ country comparisons. Unadjusted measures of trade volume may unfairly attribute to trade policy what is merely the result of structural country characteristics —e.g. small countries are more dependent on foreign trade than larger countries, oil exporters may have large trade volumes and also impose high import tariffs, and landlocked countries tend to trade less than other countries due to higher transport and trading costs.

Finally, we will describe two sources of data for which we lack extensive time series but we have a good cross‐country coverage: research and development, and economic regulations. We argue that

> 11 A similar adjustment is presented in Pritchett (1996).

9
old and new EU economies. In many countries, households with dependents are more likely to own with rather than without mortgage, and in most countries house owners are more likely to be outright owners. Full-time employees are more likely to own with a mortgage, as are urban dwellers in many countries. In most old EU countries, more educated households are more likely to be outright owners, while few new EU countries show any significant relationship between education and tenure status. Finally, and as before, few of the interaction terms between age and income, on the one hand, and education dummies, on the other hand, enter significantly.

In summary, the results of the nested logit regressions confirm the conclusions from the multinomial regressions that tenure status is less sensitive to income and to age in the new EU countries. Overall, the explanatory variables have less power in the new EU countries explaining crosshousehold variation in tenure status. Neither for most of the old nor for any of the new EU countries do we find much evidence that prospective, rather than current, income determines access to mortgage finance. Comparing the results across surveys in different years, we do not find many significant differences (Appendix Tables A5 and A6). The lower ability of our variables in explaining crosshousehold variation in tenure status in the new EU countries can be explained both by historic and policy legacies (inherited housing, rental subsidy programs etc.) as well as financial systems that are still in the process of developing.

# **5. Can mortgages turn into a financial burden?**

The main benefit of a mortgage loan, as of loans for consumer durables such as cars, is that the consumer is not forced to save the whole amount for the good or house upfront, but can smooth payments over time. In the case of mortgages, the underlying asset – the house – typically appreciates over time, both in real and nominal terms, at least in the medium to long-term. However, while a 22
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Cavendish, W. 2000. Empirical regularities in the poverty-environment relationship of rural households: evidence from Zimbabwe. _World Development_ (U.K.); 28, No. 11:1979-2003, November 2000.

Cooke, P. A. 1998a. Intra-household labor allocation responses to environmental good scarcity: a case study from the hills of Nepal. _Economic Development and Cultural Change_ vol 46, no. 4: 807-830.

Cooke, P. A. 1998b. The effect of environmental good scarcity on own-farm labor allocation: the case of agricultural households in rural Nepal. _Environment and Development Economics_ , No. 3: 443-469.

25
**Relationship between Energy Intensity and Economic Growth: New Evidence from a Multi‐Country Multi‐Sector Data Set**<sup>**1**</sup>

**Uwe Deichmann**<sup>**a**</sup> **, Anna Reuter**<sup>**b**</sup> **, Sebastian Vollmer**<sup>**b**</sup> **and Fan Zhang**<sup>**a**</sup>

aWorld Bank, USA bUniversity of Göttingen, Germany JEL: Q43, Q48, O47

> 1 Financial support from the World Bank’s Knowledge for Change Program is gratefully acknowledged. 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.
interesting contribution of this naïve analysis may simply be the visual confirmation that the poorest country group shows signs of catching up. The trend in efficiency is indeed increasing significantly over the 12-year period covered by the sample.

Although the panel data and models for health proved to be too unreliable to deserve much coverage here, we relied on the latest data offered by WHO (2005) to generate a comparison across income groups. We used the general theoretical production function as with the panel data, but focused on different inputs and outputs. We considered two outputs, life expectancy at births for males and for females, expressed in years. For inputs, we relied on one of the following variables: per capita government expenditure on health or total (i.e. government + private) expenditure on health. The data on output were for 2004; on inputs, for 2003. All monetary variables are expressed in U.S. dollars and PPP. The results are not significantly affected by the choice of the input variable, as seen in figure 5.

Once more the average efficiency level estimated for the sector is not that helpful, either because of its specificity to the model or the gaps in relevant information, but the relative position of the various income groups is interesting and has proven quite robust to the various models we tried (as it was with the models relying on panel data). The only problem is that we cannot track the evolution of efficiency. As in energy or education, the results are expected. The highest-income countries have the highest efficiency levels, and the lowestincome countries the worst. Somewhat surprisingly, income level is not a perfect predictor of efficiency as defined here, since the upper-middle-income countries did somewhat worse than those in the lower-middle-income group in 2004. The important gap, however, is that between the lowest income groups and the other groups.

**Figure 5 Efficiency of health expenditure in 2004**

<!-- Start of picture text --> Mean efficiency for per capita government Mean efficiency: of total per capita expenditure on<br>expenditure on health health<br>Efficiency mean 0.96<br>Efficiency Mean<br>1.00 0.94<br>0.92<br>0.95 0.9<br>0.90 0.88<br>0.86<br>0.85<br>0.84<br>0.80 0.82<br>High income Low income Lower middle Upper middle High income Low income Lower middle Upper middle<br>income income income income<br>Country Group Country group<br><!-- End of picture text --> Overall, this section tells us simply that in the poorest countries there is more scope for performance improvements in all sectors than in higher-income countries. This is not really news. Moreover, because the specific performance levels are driven by the choice of models and outputs, they should not be taken at face value. The only thing that comes out strongly enough is that the differences across income groups are significant and that in education and in energy at least, the lower-income countries are not catching up very quickly, at least for the outcome measures with which we worked.

18
case of the under-five mortality rate, from -0.4167 (Dominican Republic) to -0.0487 (Niger) in the case of malnutrition, and from -0.2530 (Peru) to -0.0048 (Central African Republic) in the case of the TFR.

The concern here is not so much with inequalities per se (important as these are) but rather with the extent to which measured inequality varies according to the weight attached to the poor in the computation of the inequality index. As expected, raising the value of v above 2 results in more prorich inequality. Thus, for example, for malnutrition the average value of _C(8)_ is -0.3375 while the average value of _C(2)_ is only -0.1475. Interestingly, the impact of raising v varies across countries. For example, raising the value of v from 2 to 8 causes the extended concentration index for TFR in Chad to fall from -0.0157 to -0.0777-a fourfold change. By contrast in Cameroon, the change is far smaller-from -0.0627 to -0.0843. This reflects the fact that in Chad, the TFR amongst the poorest group differs quite dramatically from the rest of the sample while in Cameroon the poorest group actually has a lower TFR than the second poorest group.

Another country whose extended concentration index is highly sensitive to the choice of _v_ is Brazil. In the case of the TFR, for example, raising the value of v from 2 to 8 causes the extended concentration index to fall from -0.1197 to -0.6593. This is a smaller percentage change than the change in the case of Chad, but the absolute change is much larger. This reflects the fact that the TFR amongst the poorest quintile in Brazil is much higher than that amongst the other four quintiles. The heavy concentration of high fertility in the poorest group in Brazil is reflected in that county's dramatic change of rank in the TFR inequality "league table" as v is raised above 2. For v=2, Brazil is ranked 34 out of 43. When v reaches 8, Brazil is almost bottom (number 42). Namibia, by contrast, where the poorest group has a somewhat _lower_ TFR than the second poorest group, sees its rank position improve from 25 to 17. While these are just examples, they serve to illustrate the point that both measured inequality and the rankings of countries by inequality can be quite sensitive to the decision of whether to depart from the implicit weighting scheme of the standard concentration index and of so by how much.

> malnutrition to under 15 percent by 2015 (International Monetary Fund and others 2000).

11
14 occur for each regional country group as a result of the removal of nontariff measures (see World Bank, 1992 for published details concerning these estinates). Total exports from developing countries are assumed to equal pre-Uruguay Round trade plus the expanded trade that should result from the NTM liberalization. The NTM coverage ratios are then recomputed using the procedures oudined in foomote 13, but with this new larger trade base.'<sup>4</sup> Figure 1 provides a graphical illustration of the projected magnitude of change in pre and post-Uruguay Round NTM coverage ratios for all non-oil exports of developing countries.

Both Table 3 and Figure 1 indicate that the Uruguay Round dramatically changed the share of developing countries' exports that faced OECD nontariff restrictions. Overall, the developing countries' NTM coverage ratios decline by approximately three-quarters, but for South Asia the ratio falls from 37 percent to about one-tent its pre-Uruguay Round level. The main reason for this 33 percentage point decline is the high share of textile and clothing products in South Asia's exports which are subject to MFA restrictions. A similar decline (from 31 to 1 percent) for middle income Europe is attributable both to textiles and clothing and temperate zone agricultural products.

Figure 1 indicates that Eastern Europe and Sub-Saharan Africa will be the regions least affected by the Uruguay Round achievements on nontariff measures. The Eastern European results are largely "The projections incorporate two distinct elements i.e., trade creation (TCi,) and trade diversion TIDi). The former represents the substitution of imports for domestic production as trade barriers are lowered. SMART estimates this term using,

# (2) TCii = [fru*e%*dwj - [(1 + t,*( - _(efi/e)],_

where M, is the value of iports of product i from couny j, e is the price eLasticity of import demand, tj is the tariff on the product, and e,, is the supply elasticity. The projections were run using an assumed infinitely elastic supply term. Trade diversion was estmated from,

# (3) TDjj = TCbi/[ii,

where the term in parentheses is the share of imports from non-prefece receiving countries in domestic consumption of the product.
- de Walque, Damien. 2005. “Selective Mortality During the Khmer Rouge Period in Cambodia.” _Population and Development Review_ 31 (2): 351-368.

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- de Walque, Damien. 2007. “How Does the Impact of an HIV/AIDS Information Campaign Vary with Educational Attainment? Evidence from Rural Uganda.” _Journal of Development Economics_ , 84(2): 686-714.

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- Gakidou, Emmanuela, and Gary King. 2006. “Death by Survey: Estimating Adult Mortality Without Selection Bias from Sibling Survival Data.” _Demography_ 43:569-585.

- Gwatkin Davison, Shea Rutstein, Kiersten Johnson, Eldaw Suliman, Adam Wagstaff, and Agbessi Amouzou. 2007. _Socioeconomic Differences in Health, Nutrition, and Population Within Developing  Countries. An Overview._ Country Reports on HNP and Poverty. Health Nutrition and Population, The World Bank, Washington DC.

- Gwatkin Davidson R., Adam Wagstaff, and Abdo S. Yazbeck. 2005. _Reaching the Poor with Health, Nutrition and Population Services_ . The World Bank, Washington DC.

- Halvorsen, Robert and Raymond Palmquist. 1980. “The Interpretation of Dummy Variables in Semilogarithmic Equations.” _The American Economic Review_ . 70(3): 474-475.

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- Preston, Samuel H. 1975. “The Changing Relation Between Mortality and Level of Economic Development.” Population Studies. July, 29:2, pp. 231-48.

- Preston, Samuel H. 1980. “Causes and Consequences of Mortality Declines in Less Developed Countries during the 20th Century” in Population and Economic Change in Developing Countries. R. A. Easterlin, ed. Chicago: University of Chicago Press.

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**Table 1:  The sample of concessions used**

||**Total by Country**|**Total by company**||**Total B**|**y Sector**||
|---|---|---|---|---|---|---|
||||**Energy**|**Water**|**Port**|**Rail**|
|Low income|9|18|12|2|2|2|
|Lower middle income|11|38|16|8|11|3|
|Upper middle income|11|64|20|14|19|11|
|**Total**|**31**|**120**|**48**|**24**|**32**|**16**|
|Sub-Saharan Africa & Mena|9|11|7|3|1|0|
|South Asia|3|11|7|0|2|2|
|East Asia & Pacific|4|31|12|5|11|3|
|Europe and Central Asia|6|12|7|0|3|2|
|Latin America and Caribbean|9|55|15|16|15|9|
|**Total**|**31**|**120**|**48**|**24**|**32**|**16**|

The actual sample we collected was somewhat larger in the hope to have enough coverage of every region and sector but we also imposed a number of restrictions to maintain a minimum level of quality in the sample. To be included in our sample, a company had to have a minimum of at least 5 years of operations (in order to have a time series of data of adequate duration for the analysis). Moreover, only audited financial statements and official company information releases were used.

We have four major potential problems with our data sample which need to be taken into account when analyzing the results. First, because companies must obey the accounting standards of the countries where they operate, they may follow different accounting rules when preparing their financial statements. Although accounting standards in all the countries considered are based on international accounting standards (IAS), discrepancies across countries may generate differences in earnings. No attempt was made to adjust financial statements for possible differences in accounting standards.

Second, no matter where they operate, companies generally do not publish certain data that would have been helpful for the analysis. This includes, for example, information on the fair value of some assets, depreciation and amortization rules, and detailed classification of costs. It also applies to the market value of assets and liabilities, so the analysis is based on their book value.

Third, some analysts argue that regulations sometimes create incentives for investors to present their accounts in a way that shows the lowest possible return or profitability. This can happen, for example, when regulated tariffs are set to ensure a minimum return to concessionaires—encouraging them to minimize their historical returns in order to maximize future tariff increases. Since different countries and different sectors follow different regulatory regimes, this may be additional source of distortion. We did not take this one into account either.<sup>3</sup> Fourth, the financial results of infrastructure concessions are usually sensitive to their life cycles. It is not uncommon to incur losses in the early years, when processes are being adjusted and heavy investments are often made. By contrast, profitability usually increases in

> 3 See for instance Alexander et al (2001) for illustrations of the relevance of the regulatory regime for the assessment of the cost of capital

3