rafmacalaba/datause-encoder-data huggingface dataset:
information on financial sector development for the pre-1995 period is largely missing. Lack of micro data is also part of the issue. Without labor and capital data by sector over time, it would be difficult to examine the contribution of each factor across different sectors.

Nevertheless, our analysis suggests a set of policies that could help SubSaharan Africa continue its high growth performance and use this momentum for economic takeoff. Reducing fertility and improving human capital is a first step toward demographic dividend. Policies to promote informed decision-making on fertility, recognizing the tradeoff between quantity and quality or utilizing contraceptive methods, for instance, can contribute to favorable demographic structure. For capital accumulation to continue, policies to attract private capital should be considered. Maintaining openness to international trade, fostering an investment climate and political environment conducive to international business and investment, and building skilled labor should be prioritized. In addition, structural transformation from traditional agriculture to modern sectors should be accelerated. Policies that diversify income sources by promoting non-farm business for agricultural workers, transitioning workers to higher productivity sectors, and improving productivity in each sector are critical.

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Exchanges. However, there is some evidence that from 2005 onwards, continued capitalization growth could be the result of some overvaluation.<sup>3</sup>

# _3.1.2 Market concentration: In line with similar countries and has been improving consistently_

In 2007, market capitalization of the ten largest companies on the New York Stock Exchange was 19.3 of total capitalization. However, typically, in less developed markets this percentage is much higher. Exhibit 3.2 shows that the number in Egypt is roughly 50 percent, which is somewhat better than countries such as Argentina, Colombia, and Hungary and seems relatively stable over time. Next, we look in more detail at stocks covered in Bloomberg. Comparing the 2008 cumulative capitalization distribution of the 100 largest companies in Egypt with other markets<sup>4</sup> , Egypt does well: 30-40 companies capture 80 percent of market capitalization. In contrast, in Manama, less than ten companies capture 80 percent of market capitalization. This is true for 10-20 companies in Abu Dhabi, Casablanca, Muscat, and Qatar. In addition, Egypt’s distribution has been improving consistently. According to Bloomberg data, in 2005, the ten largest companies comprised 70 percent of capitalization which―similar to the WFE number of 45 percent―dropped to 50 percent in 2008.

# **Exhibit 3.2 – Concentration of market capitalization**

_Capitalization of top ten largest companies as % of total capitalization, 2004-07 (upper graph) Capitalization of top 100 largest firms as % of total capitalization for selected countries, 2008 (left lower graph) Capitalization of top 100 largest firms as % of total capitalization in Egypt, 2005-08 (right lower graph)_ <!-- Start of picture text --> Egypt Argentina Chile Colombia Hungary Israel Jordan Mexico Peru Phillipines Turkey<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Cumulative fraction of<br>total market capitalization, % Cumulative fraction of<br>Total market capitalization, %<br>100% Egypt 100%<br>90% 90%<br>Abu Dabhi<br>80% 80%<br>70% Amman 70% 2008<br>60% Casablanca 60% 2007<br>50% Kuwait 50% 2006<br>40% Manama 40% 2005<br>30% Muscat 30%<br>20% Qatar 20%<br>10% Firms, 10%<br>0% 1 11 21 31 41 51 61 71 81 91 101 from large to small 0% 1 11 21 31 41 51 61 71 81 91 101 Firms, from large to small<br>2004 2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007 2004 2005 2006 2007<br><!-- End of picture text --> _Source: World Federation of Exchanges, Bloomberg; World Bank staff analysis Note for lower graphs: 2008 information is up to and including June 20_<sup>_th_</sup> _._ 3 Billmeier and Massa (2007). “Go long or short in pyramids? News from the Egyptian stock market.” IMF working paper 179. 4 Exhibit A1 in Annex 1 shows the MSCI investible indices for Egypt and the seven countries in the benchmark group. Exhibit A2 shows the number of firms simultaneously covered by Bloomberg.

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economies when they lost guaranteed markets and subsidies.<sup>2</sup> However, the failure to adopt market reforms and encourage competition has choked off growth during the recovery and led to lower productivity levels.<sup>3</sup> The other Central Asian countries, the Kyrgyz Republic and Tajikistan, appear to have an investment climate more like Uzbekistan’s than Poland’s, while Moldova’s investment climate appears to be somewhere in the middle. Managers in the Moldovan sample report higher levels of competition and better infrastructure than those in the Central Asian countries, but worse performance on these measures than in Poland.

The transition economies inherited infrastructure of poorer quality than that in OECD countries, though of much better quality than that in most developing countries. All the transition economies had a well-educated workforce, with very high literacy. In the 2002 Business Environment and Enterprise Productivity Survey managers in most of the 27 transition economies included reported that the biggest constraints to private sector operations centered on governance issues, the regulatory regime and the uncertain policy environment.

For managers in the smaller sample of transition economies used in this paper, macroeconomic uncertainty was the biggest concern, reflecting the transition experience

> 2 This information comes from interviews with Uzbek officials conducted as part of the World Bank’s recent investment climate assessment in Uzbekistan.

> 3 Uzbekistan suffered a smaller decline in output than any other transition economy. Uzbek output at its lowest point, in 1995, was still 85 percent of the level in 1991. Several factors may have mitigated the decline in output. The country had a smaller industrial base to begin with, its main exports were commodities (such as gold and cotton) that were easy to reorient for export to the world market, and oil and energy production increased enough during this period to make Uzbekistan self-sufficient in these resources.

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that the large lag for MENA is due more to access to existing surveys than to the frequency of those surveys, while for SSA it is due more to infrequent production of adequate surveys.

The second indicator is the percentage of the population covered by household surveys. Table 3 gives the coverage rate by region and for each reference year; a country is defined as being covered if there was a survey (in our data base) within two years of the reference date (a five-year window). Note that our method only strictly requires one survey per country, though we have almost six surveys per country on average. Naturally, the more surveys we have for a given country the more confident we are about the estimates.

The weak coverage for EECA, MENA and SSA in the 1980s is evident in Table 3. Our estimates for these regions in the 1980s are heavily dependent on the extrapolations from NAS data. We will discuss the likely biases.

Note that there is a “hole” in coverage for South Asia in 1999. This reflects a problem in India’s National Sample Survey (NSS) for 1999/2000.<sup>30</sup> We dropped that NSS survey round given that we now have a new survey for 2004/05 that we consider to be reasonably comparable to the round of 1993/94. We use only the 5-yearly rounds of the NSS, which have larger samples and more detailed and more comparable consumption modules (aside from the 1999/00 round). Unfortunately, this leaves a 10-year gap in our survey coverage for India; the estimates for India over the intervening period use our interpolation method. Including all available survey rounds for India adds to the variability in the series but does not change the trend.<sup>31</sup> Given the lags in survey data, our estimates do not include the impacts of the recent rise in food and fuel prices and the global financial crisis (GFC). _Ex ante_ projections of the welfare impacts of the rise in food prices for a set of nine low-income countries by Ivanic and Martin (2008) predict that, on balance, the rise in food prices over 2005-07 will have been povertyincreasing. Elsewhere we have argued that the same is likely of the impact of the GFC on the incidence of poverty in the developing world as a whole in 2009; in Chen and Ravallion (2009) we estimate that the GFC added about 1% point to the headcount index for $1.25 a day in 2009.

**_Heterogeneity in surveys._** As in past work, we have tried to eliminate obvious

comparability problems, either by re-estimating the consumption/income aggregates or the more

> 30 Further discussion and references can be found in Datt and Ravallion (2002).

> 31 If one uses the 1999/2000 survey for India one obtains a sharp fall in that year, and a subsequent rise in poverty incidence to 2005.  However, this is clearly spurious, being driven by the fact that the 1999/2000 survey over-estimates level of consumption relative to other survey rounds.

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|Policy Variable||m|Aver|age|Pro-|Intensity|Pro-Cap|
|---|---|---|---|---|---|---|---|
||Cap|0|0|(0.00)|0|(0.00)|0<br>(0.00)|
|Emissions|LIT|1|0.11|(0.11)|0.06|(0.06)|0.18 (0.18)|
||GIT(1)|.5|0.05|(0.05)|0.03|(0.03)|0.09 (0.09)|
||Cap|0|0.21|(0.21)|0.42|(0.42)|0.10 (0.10)|
|Abatement|LIT|1|0.32|(0.32)|0.38|(0.38)|0.53 (0.52)|
|Efort|GIT(2)|_m_<sup>_∗_</sup><br>R|0.19|(0.19)|0.38|(0.38)|0.10 (0.10)|
||GIT(3)|.22|0.19|(0.19)|0.41|(0.41)|0.14 (0.14)|
||Cap|0|0.73|(0.76)|0.92|(1.03)|0.68 (0.68)|
|Marginal|LIT|1|0.81|(0.89)|0.87|(0.97)|1.03 (1.19)|
|Costs|GIT(2)|_m_<sup>_∗_</sup><br>R|0.72|(0.75)|0.87|(0.97)|0.68 (0.68)|
||GIT(3)|.22|0.72|(0.75)|0.90|(1.01)|0.69 (0.72)|
||Cap|0|0.84|(0.95)|1.23|(1.65)|0.72 (0.79)|
|Relative|LIT|1|1.11|(1.55)|1.16|(1.54)|1.64 (3.58)|
|Costs|GIT(2)|_m_<sup>_∗_</sup><br>RC|0.83|(0.95)|1.16|(1.54)|0.72 (0.75)|
||GIT(3)|.10|0.83|(0.95)|1.21|(1.62)|0.76 (0.89)|

Table 9: Normalized standard deviation for each policy variable and instrument, for each of the three representative cases, as derived from the analytical formulae, and computed by using a fully general numerical model (values in parenthesis). (1) _m_ = 0 _._ 5 for all cases. (2) GIT calibrated using optimal values for _m_ for each representative case. (3) GIT calibrated using only one value for _m_ , which corresponds to the optimal value for the ‘average’ case.

shows the actual value, as computed numerically with a bivariate normal distribution for _E_ and _Y_ , fully taking into account the possibility of ‘compliance by chance’.

Table 9 confirms that our assumptions lead to acceptable results for a reduction target of _−_ 25% w.r.t. baseline emissions/intensity, at least for the three representative cases that we have selected. In fact, there are generally only modest deviations between the analytically approximated and rigorous numerical values, except for total relative costs, where analytical formulae systematically underestimate real uncertainty, sometimes by a wide margin.

Since the main purpose of the paper is to examine dominance conditions, we also test the validity of the formulae for _ρ_ minR and _ρ_ minRC, the threshold values of _ρ_ for marginal abatement costs and total relative costs, respectively. Table 10 shows that the approximations made at the beginning of the paper do not lead to significant errors on the values of these parameters: the errors made on _ρ_ minR and _ρ_ minRC remain small compared with the range of uncertainty on the actual value of _ρ_ .

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# **Table 6**

# **Maximum Temperature (MT)**

|Independent|Brazil<br>|India|
|---|---|---|
|Variables|||
|January MT|-3.4|119.|
||(0.35)|(2.09)|
|April MT|-24.5|-267.|
||(1.75)|(4.62)|
|July MT|5.8|-128.|
||(0.59)|(2.17)|
|October MT|14.8|187.|
||(1.86)|(3.80)|
|R<sup>2</sup>|.397|.724|

The maximum temperature variables have been added to the regression in column 6 in Table 4 for Brazil and column 6 in Table 5 for India. The dependent variable is net revenue per hectare. Only the added variables are shown here.

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- _Trade data_ at the transaction level from customs, which is available from ASYCUDA raw files. The data can be easily anonymized by deleting firm names and keeping only TINs (tax identification numbers) and will provide information on firm-level outcomes;

- _Program data_ including enrollment status, dropouts, and rejects;

- _Firm characteristics data_ from an industrial survey (typically balance-sheet information including employment, turnover, age, as well as ownership, number of establishments, etc.); with the survey’s key for the classification of firms being compatible with that of customs data for reconciliation, precluding the use of “dummy” firm identifiers.

Clearly, these data requirements are heavy and raise confidentiality issues; whether the data will actually be made available to the evaluation team by government authorities depends on interest (buy-in) for the IE’s results, donor involvement, and quality of the dialogue.<sup>15</sup>

- _b. The Cost of Implementation_

In practice, efforts to generalize the use of IE in trade interventions face two types of constraints: incentives and resources.

In terms of incentives, an IE risks slowing down project roll-out and diverting managerial attention for results that are unlikely to be available within a manager’s tenure horizon; and if they did, they might do more harm than good. Moreover, in order to be incentive-compatible, IE should be used only to generate new knowledge and should be fully decoupled from the evaluation of project managers. However, it is not clear that an organization could make such a claim credible, as it would obviously suffer from a time-inconsistency problem.

> 15 The World Bank has recently launched the « Exporter Dynamics » project which aims at collecting precisely this type of data (at least the customs data) from Customs administrations around the world. However, sharing the data with researchers has proved a difficult and laborious process because of the confidentiality issues involved.

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less likely to use pure bilateralism. Notably, we find that regional variation in the use of governance structures is more important than cross-country variation. This is somewhat of a puzzle given that institutional rules relevant to transactions are set at the national level in all the countries that we analyze. It suggests that the practicalities of institutional implementation are at least as important as the quality of formal rules.

Nevertheless, generating the conclusions reached in Section VI has not been the prime objective of this paper. They are provided as examples to show the validity of the methodology we have developed and the potential in the datasets that we generate. Our methodology allows readers to go further than we have done, to consider testing other hypotheses by linking their own data to the data we have posted.<sup>34</sup> Moreover, given the information we have provided, readers could add different countries, or cities, or sectors to those we have studied here. If readers implemented the questions that we lay out in Section II in a survey of any size, even one firm, then they could use our posted tools to characterize the governance structure of the firms in their survey. Readers could produce results that are comparable with ours—thereby facilitating diagnosis of a country's strengths and weaknesses—without repeating the laborious steps described above.

> <sup>34</sup> The data is posted at www.enterprisesurveys.org/portal. Users need an account (available free of charge) to gain access to the data.  After signing in, go to the “Combined Data” tab.  There look for the “Landscape of Transactions” section in the listing of datasets.  Then in that section download the zip file at "2018 Enterprise Survey Data".

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and Clemens, 2013; Bun and Windmeijer, 2010). System GMM estimators, in addition, might also suffer from the lack of valid exclusion restrictions.<sup>6</sup> We contribute to the literature by proposing a plausibly valid instrument for foreign aid. The IDA income threshold is exogenously determined and applicable to all recipient countries, and hence uncorrelated with their current characteristics and historical backgrounds. Controlling for a continuous measure of per capita income, crossing the IDA threshold from below is found to significantly reduce aid levels.

# **3. Data and Sample**

The data used in this study are primarily from two sources. Income, investment, economic growth, and other country characteristics are from the World Bank’s World Development Indicators (WDI).<sup>7</sup> Aid data are obtained from the Development Cooperation Directorate (DAC) of the OECD.<sup>8</sup> Following much of the previous literature, aid is measured by total net Official Development Assistance (ODA) disbursements as a share of GNI, in current US dollars.

We identify 35 countries that crossed the IDA income threshold from below between 1987 and 2010.<sup>9</sup> Table 1 shows the names and years of crossing for these countries.

6 A few other recent studies exploit donor-recipient connections interacted with over-time variations in total donor contributions. For example, Werker et al. (2009) use the political connections between OPEC countries and other Islamic countries and changes in oil prices to identify how aid money is spent. They find a small and marginally significant effect of aid on growth. Nunn and Qian (2013) find that humanitarian aid extends civil war by using variation over time in crop harvests to instrument for the amount of humanitarian aid that a country receives.

> 7 http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=2, accessed and extracted in August, 2012. <mark>The WDI dataset is usually updated 4 times a year. It not only adds the most up-to-date data, but also revises historical data, some of them from many years back. Most of the revisions are minor.</mark>

> 8 Data are from DAC Table 2a, available at http://stats.oecd.org/Index.aspx?DatasetCode=TABLE2A#, accessed in August, 2012.

> 9 Sao Tome and Principe crossed the threshold in 2009. It has only 2 periods of data in the sample and is thus automatically dropped from the analysis and hence also from the sample.

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which measure the "cost" of additional household members in terms of fractions of adults (cf. Deaton and Muellbauer, 1980, Ch. 8). In addition, money-metric measures of utility also need to be adjusted for differences in prices. This can be done by dividing the value of household consumption by a price index.

These theoretical considerations imply that household surveys must collect the necessary data for creating a comprehensive measure of household consumption, including data needed to adjust for differences in household size and in prices faced by households. LSMS surveys begin with direct consumption data, which include all explicit expenditures in the last 12 months as well as the value of food produced and consumed by the household. In addition to this, there is also a consumption component from the ownership of housing and durable goods (e.g. cars, televisions, bicycles, cameras), which are not consumed when they are built (housing) or purchased (durables) but are used over a long period of time. Household welfare derived from the ownership of such goods can be based on estimated yearly rental values of those goods. For housing, the best approach is to estimate hedonic rent equations (i.e. to predict the rental value of housing based on the characteristics of the dwelling) for those households which are renters. From these estimates imputed rents for owner-occupied housing can be calculated. For other durable goods, the rental value can be estimated based on depreciation in the real value of those goods over time. LSMS surveys collect data both on dwelling characteristics and on the ownership of durable goods which are sufficient to calculate the appropriate rental values both. They also collect data on household composition and local market prices so that adjustments can be made for their variation across households.
**Figure 3** The equilibrium real interest rate in advanced economies and EMDEs

<!-- Start of picture text --> Percent<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 2019<br>Advanced economies EMDEs<br><!-- End of picture text -->

# **V. Co-movement of equilibrium interest rates across countries**

# _a. Synchronization_

Next, we analyze co-movements between equilibrium interest rates. To do so, we rely on a standard synchronization measure from the GDP growth literature (Morgan _et al_ . 2004, Giannone _et al._ 2008, Kalemli-Ozcan _et al._ 2013, Cesa-Bianchi _et al._ 2019, among others). We define Sij,t as the absolute bilateral differential in _r*_ changes: where ∆ri,t∗ and ∆rj,t∗ are the first differences of the equilibrium real interest rates in countries _i_ and _j_ at time _t_ . According to its definition, Sij,t increases with the degree of synchronization, with negative values close to zero between countries that are more synchronized. Given our random walk assumption for the equilibrium interest rate dynamics, this is equivalent to considering the synchronization between the permanent shocks to _r*_ in two countries.

Although this measure is preferable to the Pearson correlation coefficient, since it is invariant to the volatility of the underlying shocks (Forbes and Rigobon 2002, Corsetti _et al._ 2005), it still has some ambivalent properties. Even if equilibrium rates in two countries move in the same direction, so that co-movement is high, Sij,t can still fall if the magnitude of those responses differs across the countries. In other words, Sij,t conflates co-movement and dispersion.<sup>24</sup> Against this background, Cesa-Bianchi _et al._ (2019) propose an extension to control for the existence of common shocks with country-specific effects. They suggest two additional measures based on the straightforward assumption that the variable of interest is impacted by a vector of common shocks Ϝt with heterogeneous country loadings bi:

> 24 See Cesa-Bianchi _et al._ (2019) for a more detailed discussion.

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