rafmacalaba/datause-encoder-data huggingface dataset:
parameters<sup>βˆ</sup> τ , _k_ , termed “unconditional quantile partial effect”, can be interpreted as the effect of a small change in the distribution of _X k_ on the quintile _q_ τ – when the distribution of other covariates remains unchanged – or as linear approximation of the effect of large changes of _X k_ on _q_ τ (e.g., Firpo et al., 2018). In the first set of regressions by country, therefore, we estimate the impact of FLFP on the income deciles. This is to verify whether the returns on female labor force participation differ across deciles and indeed contribute to explain the pro-equality or proinequality impact of FLFP. In this study, particular focus is paid on the distribution of (log) household disposable income,<sup>11</sup> which corresponds to the sample of household members (aged 14 and over) forming part of the economically active (employed or unemployed) population.

The RIF-OLS regression method, however, can easily be extended to draw conclusions on the impact of a set of covariates on a variety of distributional functionals – analytical expressions for (re-centered) influence functions have, in fact, been derived for many distributional statistics other than sample quantiles (see Essama-Nssah and Lambert (2012) and Rios-Avila (2019) for a comprehensive list of formulas). In addition to the sample deciles _q_ τ , τ = 0.1,0.2,  ,0.9 , to assess the contribution of FLFP on the level of inequality among households we also use the percentile ratios P90/P10 and P80/P20.<sup>12</sup> The RIF for ratios of quantiles is given by (Choe and Van Kerm, 2018; Rios-Avila, 2019):

> 11 For the countries and years selected, income is expressed in the units of national currencies that were in use at the time of the data collection. To directly compare real amounts across countries and over time, we apply the 2017 PPP deflators available via the LIS remote execution system to convert LIS monetary values into 2017 US$ PPPs. Furthermore, to maintain consistency with the set of methodological practices adopted by the LIS staff to construct - - the “Inequality and Poverty Key Figures” (https://www.lisdatacenter.org/data <u>access/key figures/), the focus is placed</u> on what is called “equivalized income”, i.e. the household disposable income divided by the square root of household size to adjust for differences in household demographics. We also exclude all households where disposable income is missing or exactly equal to zero and use person-level adjusted weights (the product of household weights and the number of household members) when generating estimates for the total population. Finally, before proceeding to an estimation of RIF-regression models, data have been bottom-coded at 1 percent of equivalized mean income and topcoded at 10 times the median of non-equivalized income.

> 12 The most popular Gini coefficient to measure income inequality has not been used as the income micro-data are previously transformed to the log-scale in the empirical application.

11
# **_6.5 Robustness checks_**

We also test whether these findings are robust to the consideration of shorter periods to try to minimize the effect of omitted changes in ownership along the sample period. We also explore to what extent these results are robust when restricting the sample only to fully owned BOSs. For this purpose, we conduct two exercises. First, we estimate the model proposed in (1) for shorter time windows of 5-years and 3-years. The results of the first exercise are presented in the Annex for the different specifications, but for brevity are summarized in Table 18 for the 5-year period (2016-2020) and Table 19 (3-year period, 2018-2020).

Findings show the effects are robust when analyzing shorter timeframes of 5 and 3 years. The magnitude, direction, and statistical significance of the effects remain confirming a negative relationship between state ownership and labor productivity, profit margins, short-term liquidity, and returns of investments. When reducing the timeframe, the results still indicate that BOSs are on average 32-38% less productive, obtain between 6.3 and 7.8 percentage points lower profit margins, have 0.34-0.35 fewer dollars in assets to cover each dollar in short-term debts, and provide lower returns of the order of 4.9-5.6 dollars less for each dollar invested in assets. As before, BOSs also appear to rely more on external financing vis-à-vis equity for covering long-term debt.

_Table 18. Robustness check considering 5-year period (2016-2020)_

||**(1)**<br>**Labor productivity**|**(2)**<br>**Profit margin**|**(3)**<br>**Current ratio**|**(4)**<br>**Solvency ratio**|**(5)**<br>**ROA **|**(6)**<br>**ROE **|
|---|---|---|---|---|---|---|
|BOS|-0.476***|-7.486***|-0.347***|4.710***|-5.667***|-17.05***|
||(0.034)|(0.517)|(0.109)|(0.468)|(0.147)|(0.52)|
|Labor|0.385***|-0.652***|-0.605***|-2.175***|-0.535***|-1.034***|
||(0.012)|(0.078)|(0.022)|(0.112)|(0.0378)|(0.087)|
|Age|-0.0133***|0.0363***|0.0558***|0.731***|-0.0636***|-0.549***|
||(0.001)|(0.010)|(0.003)|(0.011)|(0.0039)|(0.019)|
|Listing status|-0.552***|0.669|0.142|5.895***|-2.746***|-8.024***|
||(0.041)|(0.427)|(0.096)|(0.516)|(0.293)|(0.888)|
|Constant|5.622***|7.652***|4.701***|37.76***|9.362***|31.07***|
||(0.15)|(0.191)|(0.072)|(0.383)|(0.151)|(0.33)|
|Observations|2,892,917|2,848,595|1,475,667|2,865,387|2,870,563|2,649,938|
|R-squared|0.694|0.115|0.072|0.146|0.075|0.047|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Sector-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Clustered errors|Sector 2d|Sector 2d|Sector 2d|Sector 2d|Sector 2d|Sector 2d|

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

29
because by definition, this consumption takes place outside the home, and therefore out of sight of the informant.<sup>15</sup>

# **3.2 Descriptive statistics on FAFH in Peru**

To describe the context of this work Figure 1 presents recent trends on the incidence of FAFH and its participation in the household food budget. The figures report statistics for all components of FAFH, i.e. food consumed away by adults, food consumed away by children, and take-out; and also for the subcomponent of food consumed away by adults.<sup>16</sup> The panel on the left shows that FAFH is fairly widespread in Peru, and despite its high incidence FAFH has continued to grow over the last few years. In 2006, 84 percent of the households report having at least on household member eating away from home. The figure grew to 89 percent by 2013, so that almost 9 out of 10 households eat at least a meal or snack out. If we only focus on adult food consumption outside the home, the trend shows a similar pattern, only shifted about 5 percentage points downwards. By 2013, 84 percent of the household have at least one adult household member eating out.

The panel to the right shows that, not only the incidence but the contribution of FAFH on the overall household budget is substantial. The share of FAFH on overall food expenditures grew from 23 to 27 percent over the last 7 years, a magnitude that represents a 21 percent increase. This is largely explained by adult consumption away from home, consumption that represents almost a quarter of total household food consumption.

# **3.3 Poverty methodology in Peru**

As discussed above, the main source of information is the ENAHO survey. Peru went through an important methodological change in their poverty methodology in 2010, when among other things 15 In a small-scale study in an urban slum in India Sujatha et al. (1997) interview husbands and wives about the men’s dietary intake, and find that women are not aware of the foods consumed by their spouses outside of their home. Similarly, Gewa et al. (2007) find that mothers of rural school-aged Kenyan children missed 77 percent and 41 percent of the energy intake originated in FAFH in the food shortage and harvest seasons respectively (where FAFH contributes to 13 percent and 19 percent of daily energy intake in each season).

16 We only include meals produced by commercial sources. While the questionnaire asks for consumption provided by social programs, the valuation of such consumption is controversial, and therefore we abstract from that component. Information on food consumed at other households is also reported, but in order to avoid double count of meals INEI does not include that component of consumption in the computation of total household consumption.

15
# **III. Empirics**

This section reviews the available empirical evidence on the link between infrastructure and development outcomes. Following the structure of the theoretical section, it does so by looking first at macro-level contributions, then turning to analyses that explicitly integrate a spatial dimension. Finally, it also provides an overview of micro-econometric studies touching upon the issue of infrastructure. The objective is to assess the suitability of the type of data used, the strengths and weaknesses of each technique, and the specific econometric issues that they raise, keeping always in mind the ultimate objective of deriving practical policy implications.

## **1. General Literature Review**

In addition to reviewing these contributions, an overall assessment of the literature is intended based on the analysis of 140 specifications from 64 papers between 1989 and 2007, looking in details at the type of data used, the level of aggregation, the technique, the nature of the sample, etc. Of the 64 contribution reviewed, 43 (67%) were published in peer-reviewed outlets, while 21 (33%) were not.<sup>28</sup> Of course, this is not an exhaustive coverage of the relevant literature, which is probably at least ten times as large, and it cannot be claimed to be a random selection either. Before going on, we summarize a few general lessons from this analysis.

**<u>Table 1</u>**

|**Technique**||||||
|---|---|---|---|---|---|
|Prodfn|Cross-country|Cost<br>function|Growth<br>Accounting|Household<br>survey data|Others*|
|69|29|13|4|7|18|
|49.3%|20.7%|9.3%|2.9%|5.0%|12.9%|
|**Dependent**<br>**variable**||||||
|Output|Growth|Productivity|Others**|||
|67|24|18|31|||
|47.9%|17.1%|12.9%|22.1%|||
|**Independent**<br>**variable**||||||
|Public capital<br>expenditures|Physical<br>indicators|||||
|65|75|||||
|46.4%|53.6%|||||

* Firm-level regressions, assets prices...

** Poverty, inequality, investment, asset prices...

In terms of techniques used, Table 1 shows that macro-econometric techniques have largely dominated the field. Among these, specifications based on the estimation of some version of a production function represent half of our sample, followed in

> 28 Journals include the American Economic Review, the Journal of Monetary Economics, the Review of Economics and Statistics, the Journal of Development Economics, the Annals of Regional Science among others.

18
32 Appendix Table 1: EU and EFTA members represented in the Agricultural Distortions database (a) European Union (EU) members represented<sup>a</sup>

|**Year**|**Countries**|
|---|---|
|1956|France, Germany, Italy, Netherlands,|
|1973|plus UK, Ireland, Denmark|
|1986|plus Portugal, Spain|
|1995|plus Austria, Sweden, Finland|

(a) European Free Trade Association (EFTA) members represented

**<u>Year Countries</u>** 1960 Austria, Denmark, Norway, Portugal, Sweden, Switzerland, UK 1970 Austria, Denmark, Norway, Portugal, Sweden, Switzerland, UK, Finland, Iceland 1973 Austria, Norway, Portugal, Sweden, Switzerland, Finland and Iceland 1986 Austria, Norway, Sweden, Switzerland, Finland and Iceland 1995 <u>Norway, Switzerland and Iceland</u>

a Several of Europe’s transition economies joined the EU in 2004 and 2007. These countries are not included in the EU aggregates provided in this paper, but instead are included as part of the Eastern Europe and Central Asia (ECA) aggregation. Also not included at Cyprus and Malta, which joined the EU in 2004.

Source: Authors’ aggregations.
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**Table 1. Banking Institutions in Zambia in 2006**

||**Total A**|<br> **ssets**|**Loans and ad**|<br>**vances**|**Depo**|<br>**sits**|**Bra**|<br>**nches**|
|---|---|---|---|---|---|---|---|---|
|**Commercial Banks**|Assets|% oftotal|Loans and advan|% oftotal|Deposits|% oftotald|branches|% oftotal|
|**Foreign banks**|||||||||
|1.BarclaysBank|1,587,948|20%|750,489|32%|988,120|18%|17|11%|
|2. Standard Chartered|1,214,629|16%|344,322|15%|967,324|18%|15|10%|
|3. StanbicBank|1,122,275|14%|414,368|18%|735,346|13%|9|6%|
|4.FinanceBank ZambiaLtd|604,535|8%|174,556|8%|384,176|7%|38|24%|
|5. Citibank Zambia Ltd|560,095|7%|80,710|3%|338,161|6%|2|1%|
|7.Indo-Zambia Bank|453,116|6%|71,532|3%|314,287|6%|9|6%|
|6.BankofChina|112,117|1%|109|0%|93,123|2%|1|1%|
|_Subtotal_|_5,654,715_|_73%_|_1,836,086_|_79%_|_3,820,537_|_69%_|_91_|_58%_|
|**Domestic banks**|||||||||
|8.African Banking Corporation|124,314|2%|68,539|3%|31,240|1%|1|1%|
|9. CavmontMerchantBank|72,510|1%|6,381|0%|58,836|1%|11|7%|
|10.First Alliance Bank|110,884|1%|21,074|1%|58,343|1%|4|3%|
|11.IntermarketBanking Corporation|87,108|1%|21,292|1%|70,170|1%|2|1%|
|12.Investrust Bank|197,333|3%|80,109|3%|162,912|3%|5|3%|
|13.ZambiaNationalCommercial Ban|1,537,857|20%|278,552|12%|1,297,752|24%|42|27%|
|_Subtotal_|_2,130,006_|_27%_|_475,947_|_21%_|_1,679,253_|_31%_|_65_|_42%_|
|**Total**|7,784,721|100%|2,312,033|100%|5,499,790|100%|156|100%|

Source: Bank of Zambia Among the group of seven foreign banks, three large banks – Barclays, Standard Chartered and Stanbic Bank -- play a dominant role in Zambia’s financial system. They hold 50% of assets in the banking system, 55% of the total loan portfolio, 49% of deposits, and 27% of all branches.<sup>4</sup> Nowadays the banking system is not only dominated by foreign-owned banks, but also shows strong signs of soundness and profitability. According to data from the Bank of Zambia, at the end of 2005 all the 13 commercial banks operating in Zambia reported a capital adequacy ratio above the minimum level of 8% on risk-weighted assets and a moderate level of non-performing loans, 8.9% of total lending portfolio. In addition, in 2005, banks recorded high profits that allowed them to achieve a 7.4% annual return on assets.<sup>5</sup> 4 Of the remaining seven banks, the Zambia National Commercial Bank – a state-owned bank -- is the second largest institution operating in Zambia, with 20% of the banking system assets. Five other banks owned by local investors -- African Banking Corporation, Cavmont Merchant Bank, First Alliance Bank, Intermarket Banking Corporation, and Investrust Bank— hold together 8% of the banking system assets. Finally, one bank, the Indo Zambia Bank is a joint venture between the Governments of Zambia and India and holds the remaining 6% of the banking system assets.

5 In addition to commercial banks, the financial sector comprises non-bank financial institutions (comprising the three building societies, some micro finance institutions, the National Savings and Credit Bank (NSCB), the DBZ, 37 Bureau de changes and leasing companies), insurance companies, pension funds and the capital markets.

9
for emerging market bonds was reduced due to a stronger US dollar and higher returns on US bonds —where the 10-year bond yield surpassed the 3 percent threshold during the month of May. Concern about the escalation of trade tensions between the United States and China resulted in outflows from emerging market funds and lower Eurobond prices —including for Sub-Saharan African countries. Specifically, the performance of Eurobonds has been dismal in the countries with the largest perceived macroeconomic vulnerabilities —that is, Argentina, Turkey, and Zambia.

_Borrowing costs in international bond markets have been gradually increasing._

**Figure 15** shows the evolution of the cost of borrowing for international sovereign bonds across different groups of emerging markets and developing economies (EMDEs). Until 2015, Africa’s emerging bond index spread (Africa EMBI) was roughly aligned with JP Morgan’s global emerging market bond index spread (Global EMBI) over the US Treasury. In contrast, sovereign issuers in Sub-Saharan Africa have repeatedly issued above the Global EMBI spread since 2015— and, notably, above emerging markets in Asia and Europe.

**Figure 15. Bond spreads of Emerging Markets and Developing Countries** _(in basis points)_

<!-- Start of picture text --> EMBI Global EMBI Africa EMBI Asia<br>800<br>EMBI Europe EMBI Latin America EMBI Middle East<br>600<br>400<br>200<br>0<br>Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17 Jan-18<br><!-- End of picture text --> _Source: World Bank, Bloomberg. Published in: World Bank (2018) Africa’s Pulse, Volume 18. Washington, DC: The World Bank, October_ In the first half of 2018, the gap between the global and African spreads narrowed as countries in the region issued international bonds at favorable conditions. However, unfavorable market sentiment to emerging market and developing countries bonds —triggered by the macroeconomic imbalances in Argentina and Turkey— may have widened again the gap between the African and global bond spreads.

17
|AFCC2/RI-Central African Backbone - APL3 - Republic of Congo|Jerome Bezzina|
|---|---|
|AFCC2/RI-Central African Backbone - APL3 - Republic of Congo|Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|West Africa Regional Communications Infrastructure Project - APL|-1Jerome Bezzina|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Kabeli-A Hydro Electric Project|Pravin Karki|
|Yemen Private Sector Growth and Social Protection DPG|Wilfried Engelke|
|Yemen Private Sector Growth and Social Protection DPG|Wilfried Engelke|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
||vif|1.6|1.6|1.5|76.7|2.5|1.5|114.0|108.1|2.5|76.7|2.6|131.0|114.0|2.6|108.1|131.0|are<br>ificant at|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Gini WIDER|z-stat|(12.42)**|(11.82)**|-1.66|(4.74)**|(11.27)**|-1.66|(6.12)**|(3.57)**|(10.90)**|(4.83)**|(2.63)**|(3.23)**|(6.24)**|(2.63)**|(3.57)**|(3.23)**|All equations<br>at 5%; ** sign|
|ple 2**|coeff.|0.011|0.011|0.011|-0.030|0.013|0.011|-0.047|-0.030|0.013|-0.030|0.013|-0.047|-0.047|0.013|-0.030|-0.047|otherwise.<br>ignificant|
|Sam|vif|1.6|1.6|1.6|10.6|2.0|1.6|14.0|9.8|2.0|10.6|2.0|12.9|14.0|2.0|9.8|12.9|d with `0'<br>ction. * s|
|Gini WVS|z-stat|-1.73|-1.64|-0.24|(10.57)**|(7.11)**|-0.24|(10.09)**|(5.69)**|(6.69)**|(9.81)**|-1.65|(6.04)**|(9.07)**|-1.65|(5.69)**|(6.04)**|included an<br>.=Life satisfa|
||coeff.|0.002|0.002|0.002|-0.030|0.009|0.002|-0.034|-0.030|0.009|-0.030|0.009|-0.034|-0.034|0.009|-0.030|-0.034|d with `1' if<br>. Dep. Var<br>ar points.|
||vif|1.5|1.5|1.4|60.0|1.9|1.4|67.8|76.6|1.9|60.0|1.9|79.6|67.8|1.9|76.6|79.6|e indicate<br>Individuals<br>country/ye|
|Gini WIDER|z-stat|(26.17)**|(24.66)**|(3.40)**|(9.69)**|(15.31)**|(3.40)**|(11.00)**|(2.84)**|(14.19)**|(9.71)**|(2.83)**|(2.81)**|(11.14)**|(2.83)**|(2.84)**|(2.81)**|he options ar<br>ble 1. Units=<br>rvations, 56|
|ple 1*|coeff.|0.016|0.016|0.016|-0.037|0.011|0.016|-0.045|-0.037|0.011|-0.037|0.011|-0.045|-0.045|0.011|-0.037|-0.045|options. T<br>used in Ta<br>6,630 obse|
|Sam|vif|1.7|1.7|1.7|7.3|2.1|1.7|12.5|7.7|2.1|7.3|2.2|17.4|12.5|2.2|7.7|17.4|and set of<br>d controls<br>mple 2: 6|
|Gini WVS|z-stat|-0.49|-0.47|-0.09|(20.97)**|(10.80)**|-0.09|(13.19)**|(4.38)**|(10.37)**|(20.62)**|(2.67)**|(5.91)**|(12.60)**|(2.67)**|(4.38)**|(5.91)**|nt equation<br>variables an<br>ints; (**) Sa|
||coeff.|0.000|0.000|0.000|-0.035|0.010|0.000|-0.029|-0.035|0.010|-0.035|0.010|-0.029|-0.029|0.010|-0.035|-0.029|th a differe<br>set of key<br>try/year po|
|**effects**|**Year**|0|0|0|0|1|0|1|0|1|0|1|1|1|1|0|1|timated wi<br>de the full<br>s, 77 coun|
|**Fixed**|**Country**|0|0|0|1|0|0|1|1|0|1|0|1|1|0|1|1|able is es<br>and inclu<br>servation|
|**rd error**|**Cluster **|0|0|1|0|0|1|0|1|0|0|1|1|0|1|1|1|nt in the t<br>red logit<br>5,612 ob|
|**Standa**|**Robust**|0|1|0|0|0|1|0|0|1|1|0|0|1|1|1|1|i coefficie<br>d with orde<br>ample 1: 9|
||**Eq.**|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|Each Gin<br>estimate<br>1%. (*) S|

23