rafmacalaba/datause-encoder-data huggingface dataset:
# **III. Data and Methodology**

## **3.1 Data**

## **_3.1.1 GVC data_**

Intermediate inputs increasingly cross-national borders multiple times as production processes become more fragmented across different countries. Because of the growing importance of doublecounting, traditional trade statistics have become increasingly less reliable as a measure of the value contributed by each country. Value-added trade statistics help to identify and net out the “double-counting” effect of GVCs on gross trade figures. This is done by mapping out how value addition moves between countries along the Global Value Chain before final consumption of endproducts. The GVC data used in this study were obtained from the UNCTAD-Eora GVC database. This database provides value-added trade statistics for 187 countries (including 45 Sub-Saharan African countries) from 1990 to 2015 covering between 25 and 500 industries depending on the country under consideration.

To calculate both domestic and foreign value-added in trade, the Eora data set provides a global multi-region input-output (MRIO) table. A simplified MRIO table is presented in _Figure 1_ to illustrate how value-added is calculated in the manufacturing industry<sup>3</sup> for trade between two countries. Consider a situation where country produces a manufactured good x (say, wood products) which can be used domestically to satisfy final demand or to serve as an intermediate input in the production of another good in the same industry. Alternatively, the same good x can be exported to country B where it can be used as a final product or as an intermediate input in the production of another good in the same industry. The first row of _Figure 1_ summarizes how gross output X produced in country A (X�) can be shared between country A and country B as an intermediate input or final product. Conversely, the same good can be imported from country B and used in country A as an intermediate input or final product. This is summarized in the second row of _Figure 1_ . As a result, total exports of country A to B can be obtained by adding the intermediate and final output produced in country A and used in country B (the yellow cells in _Figure 1_ ). For country B, total exports can be calculated by adding the intermediate and final output produced in country B and used in country A (the orange cells in _Figure 1_ ). Since the

> 3 In the appendix, we classify a list of sectors into three different industries based on the definitions used by the World Trade Organization. Thus, the sectors included in manufacturing follow the WTO classification.

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|||Below media|n||Above media|n|
|---|---|---|---|---|---|---|
|Wife'ssavings(dummy)|Total|Institutions|Athome|Total|Institutions|Athome|
||(1)|(2)|<br>(3)|(4)|(5)|<br>(6)|
|Panel A: equation (3), incl.|time, t and t|*, union FEt|||||
|Estimated risk ofpolygamy,|FF,<br>1.868**|2.013**|0.229|0.821|1.035|0.454|
||(0.917)|(0.907)|(0.426)|(1.025)|(0.996)|(0.728)|
|Number of observations|760|760|760|788|788|788|
|Numberofunions|380|380|380)|394|394|304|
|<br>Within R2|0.039|0.037|0.063|0.0049|0.0037|0.015|
|Panel B: equation (4), incl.|time-varying|controls X,, t|and t?, FE|F|||
|Estimated risk ofpolygamy.|F,<br>1.736"|2.069%|0.133|0.921|1.190|0.277|
||(0.982)|(0.945)|(0.475)|(1.041)|(1.017)|(0.749)|
|Number of observations|726|726|726|756|756|756|
|Number of unions|363|363|363|378|378|378|
|Within R2|0.048|0.045|0.10|0.041|0.042|0.052|
|Panel C: equation (5), incl.|1st-step cont|rols x time X1|x t, Xz, t|and t*, u|nion FE*||
|Estimated risk ofpolygamy,|F,<br>1.933+|2.076*|-0.536|1.402|1.301|0.370|
||(1.243)|(1.215)|(0.618)|(1.458)|(1.366)|(0.940)|
|Number of observations|726|726|726|756|756|756|
|Number of unions|363|363|363|378|378|378|
|Within R2|0.15|0.12|0.18|0.12|0.12|0.12|
|Unconditionalmean(wave1|)<br>0.34|0.33|0.33|0.43|0.41|0.41|

Panel union fixed effect model estimates. Standard errors in parentheses are bootstrapped (300 replications); + p<0.12 * p<0.10, ** p<0.05, *** p=<0.01.

Dependent variables: col. (1) and (4): a dummy equal to one if wife has savings, col. (2) and (5) a dummy equal to 1 if wife has savings entrusted to informal or formal institutions, col. (3) and (6) a dummy equal to 1 if wife has savings held at home.

Sample: Col. (1) to (3): monogamous unions in the two waves, below median household per capita expenditures. Col. (4) to (6), monogamous unions in the two waves, above median household per capita expenditures Fi: predicted failure probability estimated by a Cox survival model stratified by the polygamy status of the husband's father (see Table 1).

All regressions include union fixed-effects t Controls not shown: time (in months) between the two survey waves and its square Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents

* Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents, all step-1 controls (see Table 1) interacted with time

Data source: PSF Survey, waves 1 and 2? (2006-2007 and 2010-2012)
- (d) Temporary or permanent changes in relative prices of (i) consumption goods, (ii) wage rates, (iii) production goods (iv) asset prices, that arise through knock-on effects on the rest of the economy

- (e) Involuntary unemployment if the crisis leads to a generalized economic downturn.

Some relative price shifts and some changes in fiscal policy resulting from the crisis can also benefit some households. For instance, a program of debtor relief may be adopted which conveys considerable benefits to debtor households.

# _Impact on depositors_

Some indications of the distributional implications of the first channel can be calculated from surveys of financial assets holdings. This has been done for four Latin American countries by Halac and Schmukler (2004). For example, in their data the top decile of income earners own 36 percent of the total _number_ of household deposit accounts and the bottom half of the income distribution own only one of every six accounts. Unfortunately, we do not know from this evidence what is the distribution across income deciles of deposits by _value_ .<sup>5</sup> We do know that only about 24 percent of the adult population has a bank account in Mexico City. Even lower percentages are reported elsewhere.<sup>6</sup> It might, therefore seem that loss of deposits is likely to reduce inequality. If so, this might be taken as evidence that bailing-out depositors is a _regressive_ policy. However, in order to see just how regressive , the skewed distribution of bank deposit holdings needs to be compared with the skewed distribution of household incomes. After all, the top decile of income earners in the Mexico receives about 42 percent of household income whereas the bottom quintile receives little more than 3 percent of household income. Thus the departure from proportionality represents only a small fraction of the total sums disbursed in the bail-out.

Ironically, a policy which begins by financing liquidity problems of the banking system (and foreign exchange outflows) arising from depositor withdrawals but is then followed by suspension and not paying the remaining depositors (or paying them only in depreciated currency) after the bank fails, or the currency collapses, is likely to be much more regressive. As is comprehensively documented for the cases of Argentina, Ecuador and Uruguay by Halac and Schmukler, it is the largest depositors who withdraw first (even while smaller depositors are still adding to their deposit holdings). Likely the large and well-informed depositors who withdraw early are mostly corporates, but wealthy individuals will also be in this category.<sup>7</sup> Their relative gains are unlikely to show up in surveys of income and expenditure.

> 5 This information is available for some advanced economies.  Interestingly, bank deposits form a much lower proportion of financial asset holdings for higher income deciles in advanced economies, reducing the dispersion of deposit holdings.

> 6 At one extreme, a recent Tanzanian survey found that 94 percent of households had no bank deposits. 7  In Uruguay, most of the withdrawals were by non-residents, presumably from Argentina.

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start picking up on the country 3-4 years _prior_ to the release of data. Visually, there is no change in the slope around the time of release. Publications keep increasing till around 9 years after data release and then drop-off (although the drop-off is based on data for fewer countries). Between the very low numbers to the peak, the difference is approximately 5-6 publications a year on the country.

We also conducted a formal event study analysis (see appendix for details). The quantitative results from this analysis are consistent with the hypothesis suggested by Figure 4. While it is certainly possible to find small effects (and different specifications could also yield significance) of data release on publications, the formal analysis confirms a strong pre-release trend without a substantive increase in publications after release. Although the lag structure relating publications to data release is not clear and it may be that more sophisticated modeling yields different results, at first glance more data, do not lead to a sustained increase in research on the country.

# _3.2._ **Stylized Fact 2: Likelihood of Publication in the Top-5 Journals**

Our second stylized fact looks at whether research on the US is more likely to be published in a high-quality journal relative to research on other countries. We specifically look at the geographic determinants of publication in one of the top-5 journals in economics, namely _Econometrica_ , the _American Economic Review_ , the _Quarterly Journal of Economics_ , the _Journal of Political Economy_ , and the _Review of Economic Studies_ .

The basic fact that the top-5 journals are more likely to be accepting of empirical papers written about the US is shown in Table 3. Column 2 shows that of the 3083 papers published in the top-5 journals between 1985 and 2004, a staggering 2,383 focused on the US. Since around 50 percent of overall research volume is on the US, this represents a substantial premium over what would be expected from an equal probability of selection. Column 2 shows just how large the differences are. Across all countries, roughly 1 out of every 25 papers is accepted into the top-5 journals. The difference between the US and the rest of the world is enormous—6.5 percent of all papers published on the US are in the top-5 journals relative to 1.8 percent of papers from other countries. Of particular interest is that once we move outside the US, there is no difference in the likelihood of publication across the different regions—papers from the UK and other OECD countries (some of 12
In addition to these factors, Kenya’s general improvement in various categories also positively impacts the market for private equity and venture capital. For example, the government has simplified the process of starting a business, which benefits startups by cutting costs and saving time as they seek to get up and running in Kenya. Moreover, improvement in insolvency laws benefits businesses at all ends of the spectrum by allowing them to reorganize and continue operating rather than face liquidation if they face business challenges.<sup>25</sup>

**Table 12: Overview of Kenya’s Doing Business Rankings**

||**2013**|**2014**|**2015**|**2016**|**2017**|
|---|---|---|---|---|---|
|**Starting a business**|126|134|143|150|116|
|**Getting credit**|12|13|116|29|32|
|**Protecting minority investors**|100|98|122|112|87|
|**Paying taxes**|164|166|102|122|125|
|**Trading across borders**|148|156|153|107|105|
|**Enforcing contracts**|149|151|137|85|87|
|**Resolving insolvency**|100|123|134|140|92|

# _Source: World Bank_

# **G. Capital Markets Overview**

As noted, private equity managers struggle to generate public market exits in Kenya and East Africa. This challenge persists even though Kenya’s capital markets are the largest in East and Central Africa with a market capitalization of approximately US$20 billion<sup>26</sup> , which makes the Nairobi Securities Exchange (NSE) the fifth largest stock market by value in Africa (after South Africa, the Arab Republic of Egypt, Nigeria, and Morocco).<sup>27</sup> Despite its size, however, the NSE ranks sixth with respect to IPOs, with just two companies – neither private equity-backed - raising less than US$50 million from 2013-2016. Moreover, companies raised far less capital in Kenya than in the other two regional hubs for private equity in SSA, South Africa and Nigeria.

**Table 13: IPOs in Africa, 2013-2016**

||**2**|**013**|**20**|**14**|**2**|**015**|**2**|**016**|**T**|**otal**|
|---|---|---|---|---|---|---|---|---|---|---|
||# IPOs|Raised<br>(US$MM)|# IPOs|Raised<br>(US$MM)|# IPOs|Raised<br>(US$MM)|# IPOs|Raised<br>(US$MM)|# IPOs|Raised<br>(US$MM)|
|South Africa|4|261|9|742|12|658|7|823|25|1,661|
|Egypt|0|0|1|109|4|752|4|214|5|861|
|Nigeria|1|190|1|538|1|23|0|0|3|751|
|Tunisia|12|191|6|125|2|43|1|21|20|359|
|Morocco|1|122|1|127|1|74|1|195|3|323|
|**Kenya**|**0**|**0**|**1**|**7**|**1**|**35**|**0**|**0**|**2**|**42**|
|Rwanda|0|0|0|0|1|39|0|0|1|39|
|Tanzania|0|0|0|0|1|9|1|5|1|9|

_Source: 2016 Africa Capital Markets Watch, PWC_

> 25 World Bank (2017). Doing Business 2017: Kenya.

> 26 Nairobi Securities Exchange (2016). Annual Report & Financial Statements 2016.

> 27 Ibid.

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**Table 6: Convergence test: Agro-climatic zones**<sup>**1**</sup>

|**Period**|**Rice**<br>(1)|**Wheat**<br>(2)|**Maize**<br>(3)|
|---|---|---|---|
|||**Panel A: Southern districts**<br>||
|2005-2017|2.698|0.090|-0.348**|
||(0.168)|(0.146)|(0.164)|
|2005-2011|1.077|0.047|0.046|
||(0.158)|(0.154)|(0.149)|
|2012-2017|0.300|-1.554***|-1.674***|
||(0.117)|(0.025)|(0.029)|
|||**Panel B: Northwestern districts**||
|2005-2017|5.465|-1.366***|-0.653**|
||(0.808)|(0.140)|(0.328)|
|2005-2011|-1.763|0.015|-0.127|
||(2.160)|(0.084)|(0.201)|
|2012-2017|-1.447*|**<br>-1.739***|-2.510***|
||(0.064)|(0.047)|(0.052)|
|||**Panel C: Northeastern districts**||
|2005-2017|2.387|-0.036|-1.122***|
||(0.125)|(0.082)|(0.136)|
|2005-2011|1.818|0.733|-1.234***|
||(0.023)|(0.095)|(0.100)|
|2012-2017|-0.057|-1.351***|-1.927***|
||(0.055)|(0.015)|(0.103)|

1Standard errors in parentheses. *Rejecting convergence at 10 percent, **Rejecting convergence at 5 percent, ***Rejecting convergence at 1 percent.

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# **Annex: Notes on Data**

We decided not to include data on maternal and child health (MCH) indicators from the World Health Survey (WHS): the rates are typically very different from those obtained from the Demographic and Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS) surveys, which are the gold standard for MCH indicators, even after we made adjustments to the three surveys to ensure consistency in recall period, the number of children covered by the question, etc. We also decided not to include data on financial protection from both the WHS and the MultiCountry Survey Study (MCSS) surveys: rates of catastrophic and impoverishing spending were significantly different from those obtained from other surveys, likely due to the fact that data on household consumption were collected through just a handful of questions, with a focus on expenditure rather than the broader concept of consumption.<sup>24</sup> We also ended up excluding data from the Luxembourg Income Study (LIS) for China and India: the data for both countries yielded estimates of financial protection that were very different from those obtained using other surveys, and our suspicion is that the data for these two countries have been subject to a substantial amount of trimming, reducing the incidence of large out-of-pocket payments; by contrast, the LIS data for Mexico and South Africa were in line with estimates from other datasets.<sup>25</sup> Finally, where we had multiple sources over time in a country, we analyzed their consistency with one another, and where inconsistencies emerged we selected one or two sources that looked the most reliable.

> 24 The consensus among researchers who have looked at the issue (cf. Lu et al. 2009; Heijink et al. 2011; Raban et al. 2013) seems to be that total consumption is likely to be underestimated in the WHS especially among the poor. How the WHS fares vis-à-vis out-of-pocket spending seems to depend on which WHS questions are used to construct the out-of-pocket spending variable. Lu et al. (2009) find that use of the single-item question (Q0804) leads to a _smaller_ estimate of out-of-pocket spending than the multi-item question (Q0807- Q0814). They also find that using the inpatient question with a 4-week recall period (Q0807) leads to a _larger_ estimate of out-of-pocket spending than the inpatient question using the 11-month recall period (Q0816).

> 25 We did not include LIS-based financial protection estimates for Mexico in 1984.

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# **3.1 Macroeconomic Performance**

As a background for our analysis, we first compare the countries along different measures of aggregate economic indicators. The left panel of Figure 1 summarizes the performance of the sample countries by showing the evolution of real per capital GDP relative to that of the United States from 1980 to 2015. Ethiopia is the poorest country in the group. In 2015, its income per capita is only 1 percent that of the United States. The corresponding number for Cote d’Ivoire is 2.8, Ghana is 3.3 and 2.2 for Kenya.

Although these countries are all developing, there are clearly some differences in terms of their economic structure and performance. Looking at the share of sectors to GDP (right panel of Figure 1), while manufacturing is a more important sector of activity in Cˆote d’Ivoire and Kenya with a share in GDP of more than 10 percent, it accounts a small share of GDP in Ethiopia and Ghana. Ethiopia’s manufacturing sector contributes relatively little (about 4.1 percent) to the overall economy, which is far below the SSA average. The percentage in Ghana is about 5.1 percent, but still roughly half of Kenya’s and Cˆote d’Ivoire’s.

<!-- Start of picture text --> GDP per capita relative to the US Manufacturing Value Added<br>1980 1985 1990 1995 2000 2005 2010 2015 1980 1985 1990 1995 2000 2005 2010 2015<br>year year<br>Cote d’Ivoire Ethiopia Cote d’Ivoire Ethiopia<br>Ghana Kenya Ghana Kenya<br>SSA SSA<br>.08 20<br>.06 15<br>.04 10<br>.02 5<br>0 0<br><!-- End of picture text --> Figure 1: GDP per capita and Manufacturing Value Added (percentage of GDP) One concern that may arise from looking at the right panel of the figure is that our analysis is focusing on a sector that contributes little to the total value added, specially in Ethiopia and Ghana. In principle, no matter how large the productivity gains we find associated with a potential reversal of misallocation distortions, these are going to be down-weighed by the small share of manufacturing in value added. Even though this is a fair concern, there are at least two reasons why understanding barriers to productivity growth in manufacturing is essential for the development prospects of the region. Firstly, the aggregate implications of manufacturing activity go beyond its contribution to valued added because of linkages in the input-output network with other sectors. Jones (2011) finds evidence of large input-output multipliers resulting from an increase in a given sector’s aggregate productivity through linkages in production. Even though market frictions presumably reduce the degree of interconnectedness in SSA, there is still a multiplier effect at stake. Secondly, increasing productivity in manufacturing, by raising income levels, can help accelerate the typical process of structural transformation accompanying development in which resources are shifted away from agriculture. Quantifying the effects of manufacturing productivity gains should be a subject of future research.

6
**Figure 14. Institutional quality**

_Various measures of institutional efficiency in the ECA region are below the advanced-economy average. The western part of the region performs better than the eastern part on every measure. Governance and stability indicators in the eastern part of the region are often worse than the EMDE average_ .

<!-- Start of picture text --> A. Government and policy efficiency B. Governance and stability<br>Index Range Average Index Range Average<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>Government Regulatory Quality Control of Corruption Political Stability and<br>Effectiveness Safety<br>Advanced ECA West ECA East EMDE Advanced ECA West ECA East EMDE Advanced ECA West ECA East EMDE Advanced ECA West ECA East EMDE<br><!-- End of picture text --> Source: World Bank.

A.B. The blue bars mark the range. ECA is Europe and Central Asia. The eastern part of the region comprises Eastern Europe (Belarus, Moldova, and Ukraine), South Caucasus (Armenia, Azerbaijan and Georgia), Central Asia (Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan) and Russia. The western part of the region includes Central Europe (Bulgaria, Croatia, Hungary, Poland and Romania) and the Western Balkans (Albania, Bosnia and Herzegovina, Kosovo, FYR Macedonia, Montenegro, and Serbia), and Turkey. Scores range from 0 (not efficient) to 5 (efficient). Data for 2015 or latest available.

# **IV.1. How has investment growth evolved in the LAC region?**

The LAC region accounted for 7 percent of global investment during 2010-15, less than LAC’s 8 percent share of global output. This investment underperformance reflects low investment-to-GDP ratios in LAC, averaging around 22 percent during 2010-15, significantly below the EMDE average of 32 percent. Current private investment-to-GDP ratios have fallen below levels prior to the global financial crisis (IMF 2015b).

Regional investment has contracted since 2014 amid deep recessions in several of the region’s largest economies (Argentina, Brazil, República Bolivariana de Venezuela) and growth slowdowns in the rest of the region (Figure 15). In 2015, investment growth was below its longterm average in two-thirds of LAC economies and negative in one-third of them (Brazil, Chile, Ecuador, Jamaica, and Peru). Preliminary data point to a further investment decline in the first half of 2016.

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