rafmacalaba/datause-encoder-data huggingface dataset:
declines only slowly with increasing compliance costs under harmonization, but for FR this effect is much more rapid. This difference reflects the fact that from R’s point of view, harmonization leads to the imposition of a more burdensome standard in F’; however, from F’’s point of view, the standard prevailing in R does not change.

Although the impact of harmonization on H’s export cutoff is positive vis-a-vis both foreign markets, the effect varies substantially by destination. The effective rise in the export threshold to R remains approximately constant over the full range of compliance costs considered here. This again reflects the fact that R’s standard does not change due to harmonization. On the other hand, F’’s standard does change, and this results in an export cutoff increase that declines sharply in the costs of compliance under harmonization: with equally burdensome standards in F’ and R, harmonization lifts H’s export threshold by just under 13%, but when R’s compliance costs are 33% higher than F’s, the rise is limited to 4.6%.

3 Data and Stylized Facts The remainder of the paper tests the hypothesis that international harmonization is associated with an increase in partner country export variety in a way consistent with the mechanics set out above. This section presents two new data sources that play an important role in the empirical work: the World Bank’s EU Standards Database, and measures of export variety covering up to 200 countries based on highly-detailed, 8-digit mirror (import) data from Eurostat.

3.1 The EU Standards Database Measuring the extent of standardization in EU product markets is not an easy business.? Each member state sets both voluntary and mandatory standards on a national level, while centralized EU bodies also have the power to set standards with transnational application. Swann et al. (1996) and Moenius (2004) examine the trade impacts of voluntary national standards, while Chen and <u>~ For a general review of these mechanisms,</u> see EC (2000).

13
# **1. Introduction**

The adoption of energy efficiency measures has been touted as a major policy option to curtail energy demand in response to increasing price volatility. Its importance has been further lauded to reduce greenhouse gas (GHG) emissions. The International Energy Agency (IEA) estimates that energy efficiency measures account for the highest potential of the total GHG mitigation required to limit global temperature rise by 2050 to 2°C above pre-industrial levels (IEA, 2012). Many studies that develop marginal abatement cost curves for GHG mitigation show energy efficiency measures entail negative costs (i.e., value of energy savings exceeds investment costs even if GHG mitigation benefits are not accounted for) and therefore these options are interpreted as ‘low hanging fruits’ for climate mitigation (McKinsey & Company, 2009; ESMAP, 2012; ADB, 1998).

In practice, however, the scale of implementation of such seemingly win-win options is small relative to their apparent economic potential. The rationale for this disparity is that implementation of these options is constrained by financial, institutional, and information barriers (Jaffe and Stavins 1994; Howarth and Sanstad 1995; Sorell et al. 2004; Mundaca et al. 2013). Moreover, the economics of energy efficiency measures is normally evaluated using engineering benefit-cost approaches (e.g., Goldstein et al. 1990; Blumstein and Stoft 1995; Brown et al. 1998; McKinsey & Company 2009; Gillingham and Sweeney 2012) and such an analysis usually omits variables such as opportunity costs (Allcott and Greenstone, 2014). If the costs imposed by barriers are accounted for, energy efficiency measures would be expensive, and firms lose interest to adopt (Anderson and Newell, 2004). The gap between cost efficiency of energy efficiency measures and their implementation is also coined as the “energy efficiency gap” (Blumstein et al., 1980; DeCanio, 1993; Jaffe and Stavins 1994; Sanstad and Howarth, 1994; Schleich, 2009; Sorrell et al., 2004).

A number of studies have attempted, through empirical analysis, to understand the energy efficiency barriers in different countries, economic sectors and energy end-uses (see e.g., Rohdin and Thollander, 2006; Sardianou, 2008; Schleich, 2009). Using semi-structured interviews of the largest 8 non-energy intensive manufacturing firms in Oskarshamn municipality in Sweden that had participated in government sponsored energy audits around 2000, Rohdin and Thollander 2
(e.g. Frankel, Stein, and Wei, 1997, Eichengreen and Erwin, 1998, and Soloaga and Winters, 2001) ...nd the distance coe¢ cient increasing over time for various samples of countries and time periods, others (e.g. Coe et al., 2002, Brun et al., 2005) reach the opposite conclusion. Our strategy does not rely on a particular direction of change in the coe¢ cient: all we require are changes over time, and, preferably, di¤erentially across sectors. Note also that both sector-level and across-time studies of gravity coe¢ cients have been primarily about the coe¢ cient on distance. Our approach exploits sector and time variation in all of our estimated coe¢ cients.

Finally, we use our estimates to generate predicted exports as a share of GDP in each sector, as outlined in Section 3. Using that, we construct the predicted external ...nance need of exports. Figure 7 plots it against the actual EFNX for the period 1995-99, along with a 45-degree line. We can see that while there is a strong positive relationship between the two, it is not at all one-toone. In particular, our procedure clearly underestimates the external ...nance need of exports for countries in which it is unusually high, and overestimates it for countries where it is low. This is comforting for us, as it indicates that our approach is not so mechanical that is reproduces the actual values perfectly. Appendix Figure A1 plots the actual and predicted values of EFNX for our entire sample of countries over time. When it comes to time variation in the actual and predicted values of EFNX, the picture is broadly similar: the predicted value most of the time follows a similar trend as the actual EFNX, though it is usually ‡atter.

# 5.2 Financial Development Results

## 5.2.1 Cross-sectional Speci...cations

We start with the cross-sectional OLS regression. We estimate equation (12) using the averages of the left-hand side and all of the controls for the entire time period, 1970-99.<sup>11</sup> The results are presented in Table 2, with White robust standard errors in parentheses. Column 1 reports the bivariate relationship between ...nancial development and simple trade openness. While trade openness is signi...cant at 10% level, the relationship is not close, with the R-squared of 0.05. When instead we use EFNXc, as is done in Column 2, the R-squared is 0.28, and the variable of interest is signi...cant at the 1% level, with a t-statistic of 4.1. Column 3 includes both the trade openness and the external ...nance need of exports. The coe¢ cient on EFNX is virtually unchanged. Columns 4 and 5 attempt to control for other determinants of ...nancial development. We ...rst include the legal origin dummies from La Porta et al. (1998), and then per capita income. The latter is meant to capture a country’s overall level of development. While in both of these speci...cations the coe¢ cient on EFNXc is about one third smaller, it nonetheless remains signi...cant at the 1% level. Finally,

> 11 Note that since we have an unbalanced panel, our procedure results in averaging over di¤erent numbers of years for di¤erent countries.

14
WHO Regional Office for Africa. 2008. "HIV and AIDS Epidemiological Surveillance Report for the WHO African Region 2007 Update." In.

Zaidi, J., E. Grapsa, F. Tanser, M. L. Newell, and T. Barnighausen. 2013. 'Dramatic increase in HIV prevalence after scale-up of antiretroviral treatment', _AIDS_ , 27: 2301-5.

- Zimbabwe National Monitoring and Evaluation Taskforce. 2008. "United Nationals General Assembly (UNGASS) Report on HIV and AIDS " In.

Zimbabwe National Statistics Agency (ZIMSTAT), and ICF International. 2012. "Zimbabwe Demographic and Health Survey 2010-11." In.

80
incidence curve of Figure 5. Aggregate poverty declines marginally due to a relative increase in farm wages as a consequence of out-migration from the agricultural sector.

**Figure 4 Sectoral Reallocation: Urbanization with Few Creation of Formal Jobs**

<!-- Start of picture text --> 37% 40% 36% Informal<br>63% 60% 64% Formal<br>2003 BaU 2014 BaU 2020<br>Source: Authors' calculations<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> The operation phase is characterized by a larger average real income gain, of about 243 Balboas, and a noticeable increase in inequality. About ten thousand individuals (or 0.3 percent of the population) escape moderate poverty, with 10 percent of it explained by labor reallocation and the rest is accounted for by the increase in wages of unskilled workers in urban areas. However, the poverty gap increases, meaning that poorer individuals are negatively affected and their incomes fall further away from the poverty line.

The increase in inequality during the Canal operation is underpinned by a contraction in real incomes of the poorest parts of the society. On the one hand, households in the left tail of the income distribution (mainly rural and indigenous communities) are mostly detached from the dynamic formal sectors in urban areas. For these households, labor remuneration accounts for as little as 30 percent of total income; the remainder is made up of remittances, government transfers, imputed rents, pensions and other transfers, none of which directly benefit from increased output of the Canal or its related activities. On the other hand, with rising goods prices – as shown by the last two rows of Table 3 – the cost of a consumption basket for these households rises.<sup>24</sup> Consequently, as illustrated in Figure 5 **,** households in the left tail of the distribution (percentile 17 and under) suffer a real income loss of 1.3 percent relative to the BaU scenario.<sup>25</sup>

> 24 This result should be taken with caution since we are assuming that consumption baskets are fixed although prices are changing. Moreover, the household-specific price index that we are using only allows for differences in the shares of food to non-food expenditures between households. One would expect that the basket of food consumed by the poor differ substantially from the basket of food consumed by the nonpoor.

25 Real incomes of the families under extreme poverty would increase 1 percent under the assumption that the real value of the exogenous components of income remain constant. In other words, if the government 19
while Burkina Faso has the highest. We also find that higher fractions of children in Northern Ghana would be considered resilient to the shocks, while Burkina Faso and Mali have the lowest. Mali, Northern Nigeria and Senegal have large fractions of non-resilient children (those who are neither chronically poor nor resilient).

Is this subdivision by groups meaningful? Are those households we defined as resilient really better equipped to cope with a shock? One simple way we found to test this was to compare, for a number of key characteristics, the mean differences between groups. Households belonging to the resilient group should show, on average, better characteristics than those in the non-resilient group while those in the chronically poor group should be worse than both groups. For simplicity, we report the comparison between the resilient and non-resilient groups<sup>3</sup> .

In Tables 7 and 8, we report the group means and t-statistics of their differences, for consumption and in Table 9 for malnutrition. In almost all countries, the resilient group tends to have a smaller household size, fewer dependents, higher levels of education and a higher percentage of quality items. Most of these differences are highly significant as testified by the high values of the t-statistics.

A number of findings are noteworthy. First, our methodology to measure resilience produces rather meaningful results. Provided that the underlying welfare model is accurate, we are able to subdivide households into three different groups (chronically poor, non-resilient and resilient) and the characteristics between these groups are significantly different: resilient households are more educated, own more assets and tend to have less dependents. Second, the results seem to reproduce expected differences between countries. Among the countries analyzed, Niger and Burkina Faso perform worse, when we use consumption. Northern Nigeria performs better than Burkina but worse than Senegal. Finally, and this is probably the most promising part of our work, this analysis can be easily replicated in other countries. Our methodology to test resilience does not require ad hoc data sets, but simply one or more cross section survey rounds and some easily accessible climatic data.

# **7 Conclusions**

The recent experience of the 2010 fuel and food crisis in Sub-Saharan Africa has shown that a single large shock is enough to strain the resilience of plenty of households at once. When such shocks occur in succession and are covariant, that is, affecting an entire region, the consequences are often particularly dreadful for poor households. To worsen conditions, traditional responses that are effective against small-scale shocks are gradually losing momentum, whereas collective responses such as public safety nets are either not available or underfunded and poorly targeted.

> 3 Results for group means test between chronic and resilient are available upon request. Differences are almost always significant and non–resilient show better characteristics.

17
|Albania|**Commodity importers**<br>Jordan|Syrian Arab Republic|
|---|---|---|
|Antigua and Barbuda|Kiribati|Taiwan,China|
|Austria|Korea,Rep.|Thailand|
|Bangladesh|Latvia|The Bahamas|
|Barbados|Lebanon|Tonga|
|Belarus|Lesotho|Tunisia|
|Belgium|Lithuania|Türkiye|
|Bhutan|Luxembourg|Tuvalu|
|Bosnia and Herzegovina|Malaysia|United Kingdom|
|Bulgaria|Maldives|United States|
|Cambodia|Malta|Vanuatu|
|China|Marshall Islands|Viet Nam|
|Croatia|Mauritius||
|Cyprus|Mexico||
|Czechia|Micronesia||
|Denmark|Moldova||
|Djibouti|Morocco||
|Dominica|Nepal||
|Dominican|Netherlands||
|Egypt,Arab Rep.|North Macedonia||
|El Salvador|Pakistan||
|Eritrea|Palau||
|Estonia|Panama||
|Eswatini|Philippines||
|Finland|Poland||
|France|Portugal||
|Georgia|Romania||
|Germany|Samoa||
|Greece|Serbia||
|Grenada|Singapore||
|HongKongSAR,China|Slovak Republic||
|Hungary|Slovenia||
|India|Spain||
|Ireland|Sri Lanka||
|Israel|St. Kitts and Nevis||
|Italy|St. Vincent and the Grenadines||
|Jamaica|Sweden||
|Japan|Switzerland||

_Source:_ World Bank.

44
su¢ ce to capture common shocks to the countries in the sample.

Using the parameter estimates summarized in Table 1, the innovations to permanent income can be readily constructed employing (17). We construct two sets of innovations, corresponding to the ...rst and third model speci...cations in the table; we leave aside the second speci...cation because of the unstable root mentioned earlier. The two sets of innovations constructed in this way are not very di¤erent from each other: the correlation between them equals 0.92.

# 4.2 Estimation of the risk sharing model

Table 2 summarizes the NLS estimates of the risk sharing parameters obtained using the two sets of permanent income innovations constructed in the previous step. The results are very consistent across the two speci...cations. They can be summarized in three main facts. First, 46 of the 50 individual-country estimates lie between zero and one, as predicted by theory. When using the AR1-based innovations, the four remaining point estimates are negative; in contrast, when using the system-based innovations, three are negative and one exceeds 1. However, in no case can we reject the null hypothesis that these parameters lie between zero and one, whether individually or jointly. Moreover, the vast majority of the individual estimates in the admissible region (41 out of 46) are statistically signi...cant at least at the 10 percent level; they are overwhelmingly signi...cant jointly.

Second, the country-speci...c risk sharing parameters are centered around 0.40-0.42. This is roughly similar to the result obtained by Crucini (1999) using the restricted homogenous model of partial risk sharing; he reports an average partial risk sharing parameter between 0.37 and 0.60 for the group of G7 countries over the period 1970-1987 (his estimates vary depending on the assumed income process). In turn, Asdrubali and Kim (2008) obtain higher coe¢ cients of partial risk sharing, namely 0.77 for a group of 15 European Union countries and 0.82 for all OECD countries, both over the period 1960-2004.

- And third, the data overwhelmingly reject the null hypothesis that the risk sharing coe¢

18
period and finds that only mergers of banks that are similar in activity and geographical location create value. Similarity in activity is defined on the basis of co-movements of stock returns of the two merging banks.

Laeven and Levine (2007) instead estimate a relationship between the q-value of a banking firm and an income diversity variable that measures closeness of the non-interest income share to 0.5. In fact, by this measure firms with equal net interest and non-interest incomes are completely diversified. Using data for 43 countries over the 1998-2002 period, the authors find that banks with highly diversified income streams tend to have low q-values – relative to banks that produce the same income combination in separate, specialized firms. While the authors can not identify a single causal factor, they interpret their results as evidence of significant agency problems. Our results can be seen as consistent and complementary since we find diversification benefits accrue at relatively low levels of fee-income share, potentially providing an alternative explanation of why greater diversification may lead to a discount. In addition, the q-value measures the market value of a firm’s assets relative to their replacement cost and as such summarizes market valuation of the banking firm’s risky income stream. Our paper instead directly measures the impact of a bank’s fee income share on its risk and return.<sup>7</sup> Our paper is also related to Baele, De Jonghe and Vander Vennet (2007), who examine how a bank’s share of non-interest income affects bank risk, as reflected in bank stock returns, for a sample of European banks over the period 1989-2004. Systematic risk, measured by the market beta, is found to increase with a bank’s non-interest income share. Idiosyncratic risk, in turn, is found to be

> 7 Our paper thus can be seen to provide information on how a bank’s risk and return are affected as some traditional bank intermediation (through loans and deposits) is replaced by other bank-assisted financial intermediation, rather than on the implications of re-organizing an existing pool of traditional and non-traditional bank activities in more specialized or less specialized institutions.

9
of oil per day in 2012. With this speed of production, Kenya’s reserves would be depleted only in 52 days. In practice, however, the production in Kenya will be spread over a broader time frame, which reflects the time required to develop the fields and optimize the costs of production. Hence, based on the current exploration results, oil production will not be substantial over decade’s period and is unlikely to provide a global market niche for Kenya to specialize.

# **5.1 Fiscal revenue projections**

Despite being small in global standards, oil and gas production is expected to have a non-negligible impact, especially on fiscal revenues. Kenya’s possible recoverable reserves could reach about 1.4 billion barrels of oil and 1.7 billion barrels oil equivalent of natural gas (PWC 2015). The most recent estimates show that oil production will start in 2022, and reach a plateau of about 77 million barrels a year soon after that (figure 5.2). Starting in 2032, production will decrease gradually, reflecting the maturing of existing fields. In comparison, the production of natural gas is estimated to start in 2025 and peak at 95 million barrels of oil equivalent per year in 2033. Calculating the fiscal revenues associated with these production profiles requires a detailed approach with information on cost profiles and the production agreements between the Government of Kenya and the producing companies. In the absence of such information, rough estimates, using World Bank oil price projections and general industry rules of thumb, show that Kenya’s fiscal revenues from oil production are projected to peak at about US$8.9 billion in 2033. This is roughly equivalent to 16 percent of Kenya’s 2013 gross domestic product (GDP).

In light of these fiscal revenue projections, we next investigate the implications of alternative fiscal rule scenarios characterized in the previous section.

# **5.2 Calibration**

The full model, which is reported in the Appendix, is calibrated to Kenya at an annual frequency. Table 1 summarizes the baseline calibration, which is explained as follows.<sup>10</sup>

- _National accounting._ To reflect Kenya’s recent experience, the shares of exports and imports are set at 19 and 35 percent of GDP, respectively; government consumption

> 10This section should be read in conjunction with the Appendix A.

16