rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3 Results**

We first provide descriptive evidence on the unconditional average association between firm innovation and external finance. Figure 1, Panel A shows the mean percentage of fixed assets financed through internal funds for non-innovative firms, innovative firms, firms adopting innovations, and firms undertaking invention. Countries are grouped according to their income level using the 2019 World Bank classification. Overall, a large percentage of enterprises in EMDEs (i.e., countries other than high-income countries) finance working capital through internal funds, though there is a noticeable difference across income levels. The difference between innovative and non-innovative firms is wider in upper-middleincome countries, and it flips for high-income countries. In general, firms pursuing invention finance working capital with sources other than internal funds, especially in low income and lower-middle-income countries.

Firm innovation is higher for firms that collect funds through external sources (Figure 1, Panel B). Firms at the 25<sup>th</sup> percentile of the distribution of the percentage of fixed assets financed through internal funds have an innovation score of around 0.41, while firms at the 75<sup>th</sup> percentile of the distribution have an innovation score of around 0.30. Likewise, there is a strong association between firms pursuing adoption and firm financing of fixed assets, as the interquartile difference is negative and statistical significant at the 1 percent level. The magnitude of the interquartile difference is greater for firms undertaking invention, with a 0.15 interquartile difference between firms that finance working capital through internal funds.

# **Figure 1: Relationship between firm innovation and funding**

# **_Panel A: Mean percent of fixed assets financed through internal funds, manufacturing_**

<!-- Start of picture text --> Non-innovative firms Innovative firms Adoption Invention<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>65<br>
|5051|
||(1.000)|(0.044)<sup>_∗∗∗_</sup>|(0.057)<sup>_∗∗∗_</sup>|(0.050)||
|||[0.026]<sup>_∗∗_</sup>|[0.010]<sup>_∗∗∗_</sup>|[0.404]||
|(3) Dwelling materials index|0.000|0.053|0.020|0.087|5052|
||(1.000)|(0.053)|(0.060)|(0.058)||
|||[0.875]|[1.000]|[0.404]||
|(4) Total consumption expenditure|0.000|0.019|_−_0.027|0.066|5053|
||(1.000)|(0.045)|(0.050)|(0.055)||
|||[1.000]|[1.000]|[0.404]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A21
Caribbean|<br>Income|
|South Sudan|Sub-Saharan Africa|Consumption|
|Tajikistan|Europe & Central Asia|<br>Income|
|<br>Tunisia|<br>Middle East & North Africa|Income|
|Uganda|Sub-Saharan Africa|Consumption|
|Uzbekistan|Europe & Central Asia|Income|
|Zambia|Sub-Saharan Africa|Income|
|Zimbabwe|Sub-Saharan Africa|Consumption|

_Note_ : _Question type_ reports either income (preferred) or consumption variable from the phone survey used to calculate the change probabilities reported in Table B.2. The income change question asks whether the household experience a loss, gain, or no change in income since the beginning of the pandemic. The consumption loss question reports whether a household experienced a loss in consumption.

44
second period of our sample could be due to the fact that _markets are learning,_ i.e. acquiring experience and gathering information on the developing countries, so that learning spillovers are less relevant; but it could also signal the absence of any relevant shock to generate such spillover effects, while in fact the market is still vulnerable to country-specific shocks. On this issue further research is needed. 37

**_35_** An interesting possibility for future research is to look at data on portfolio holdings to see whether there is a convergence in the allocation of funds during periods of high volatility.

**36** "It was the middle of October 1991 that the market received the sudden shock that the price rises of the previous 18 months were not immune to gravity. [...] massive selling of Brazilian paper one Friday morning, inspired by JPMorgan in NY, while a massive coup for the bank, left a number of financial institution reeling. Within three weeks the debt was trading at 22% of face value, and managed to bring down prices across the board. 'What upset the market is that some traders would not come to the phone, and if they did, the prices were wholly unrealistic', says Russell Hanlon, vice president at Chartered WestLB in NY." by Rupert Wright, Trade Finance September 1992 (page 64). But in 1995, after the Mexican crisis: "'One thing is that this market demonstrated its resiliency and liquidity. The worst scenario would have been an illiquid market, but people who wanted to sell could', said Michael Chamberlin, executive director of the Emerging Markets Traders Association." by Paul Kilby, Latin Finance N. 70 (page 34).

**37** There is a recent growing literature on exchange rate correlations that analyses to what extent such correlations are explained by a set of fundamentals (for example B. Eichengreen, A.K. Rose and C. Wyplosz (1996), J.
# **Appendix I: Data, Sources and Methodology**

# **Data**

In this paper, we investigate the relationship between the distribution of education, economic reform and economic growth by a panel data set of 12 countries from 1970 to 1994. The data set consists of variables on GDP per capita measured by purchasing power parity (PPP) and by constant dollars, physical capital stock, labor, level of education of the labor force, and the distribution of education in the labor force, terms of trade, a dummy variable on economic reforms, and a few time dummies.<sup>5</sup> Most data was extracted from the World Bank's main database, except for a few.

Output level is measured by GDP per capita in constant US dollar at 1987 prices. Growth rates are calculated using the log-difference method. In order to allow cross country comparison of "unexplained residual" or total factor productivity, we also converted GDP into one measured by Purchasing Power Parity (PPP) or international dollars, by using a single year (1995) PPP conversion ratios. Table 3 shows the average growth rates by decades and for reform versus non-reform periods. See also the charts at the end of the paper.

For physical capital stock, we used the variable estimated by Nehru and Dhareshwar (1993) using the perpetual inventory method. Gross domestic investment as a ratio of GDP was used as a proxy measure in some regressions. Labor input was taken from the labor force information from the World Bank main database. For human capital stock, we used the variable---average years of schooling for the labor force, as estimated by Nehru, Swanson and Dubey 1994.

The distribution of education was characterized by coefficient of variability of education for population aged over 15, and several other dispersion measures. The calculation took several steps. First, we obtain data on educational attainment at various levels for the population over the age 15 from Barro and Lee 1997. Second,
Figure 13. Decomposing the EACR Differences Across Countries (2011) <!-- Start of picture text --> Individual (FINDEX 2011) Household (LITS)<br>2<br>0.8<br>1<br>0.4<br>0.0 0<br>‐0.4 -1<br>‐0.8 -2<br>‐1.2 -3<br>‐1.6 -4<br>Senegal Argentina Algeria Mali Azerbaijan Bulgaria Tajikistan Moldova<br>Composition Equalization Scale<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base and LITS is household level. In FINDEX 2011, 141 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : FINDEX, LITS Turkey’s dissimilarity index in savings falls substantially when measuring at the household-level despite a similar coverage rate. As a result, Turkey has a significantly lower dissimilarity index than the benchmark countries (Figure 28). Part of this effect can be explained by some homogeneity in households. Even with this reduction, the composition of the d-index remains fairly stable as age of the household head and income of the household are the principal sources of dissimilarity. (Figure 29) The decomposition of the difference in the EACR using the LITS survey shows that households save at a more equitable level in Turkey compared to the benchmark countries (Figure 13); the equalization effects explains why the EACR is lower in the comparator countries of Azerbaijan, Bulgaria, Tajikistan, and Moldova.

# **iii. DEBIT AND CREDIT CARD USE**

Debit and credit card usage in Turkey is notably higher than the BRICS economies and the rest of ECA countries in 2011 (Figure 14). Over 42 percent of the lowest
and height, parental education, and some indicator variables on access to water sources (Haughton and Haughton, 1997). In West Africa, residence in a dry zone was found to be associated with wasting, but not with stunting once other variables are introduced as controls (Curtis and Hossain, 1998). Frongillo et al. (1997) make a cross-country comparison and find that higher energy availability, female literacy and gross domestic product were the most important factors associated with lower prevalence of stunting.

Monteiro et al. (1997) investigate the patterns of intra-familiar distributions of undernutrition in Brazil. They analyze data for four income strata separately and find that undernutrition is significantly associated among household members for the 25 percent poorest families. Khorshed Alam Mozumder et al. (2000) look into the effects of the length of the interval between children on malnutrition in two districts in Bangladesh. They conclude that the results indicate the potential importance of longer birth intervals in reducing child malnutrition. Zeini and Casterline (2002) explore the importance of different levels of geographic clustering in Egypt, including regional level, governorate-level, local level, household level and individual level, using the 2000 Egypt Demographic and Health Survey. They find that spatial clustering does seem to be important in that country. They also find that, even after controlling for socioeconomic factors, significant household-level clustering remained and, interestingly, that individual clustering ( _i.e._ across indicators) was also important. Furthermore, they find that underweight was correlated with stunting and/or wasting in some, but not all, governorates of Egypt.

# **3 Methodology**

**Overview** We describe in this section the methodology used to estimate the indicators of malnutrition at the level of small geographic areas. The methodology developed here is similar to the small area estimation procedure developed by ELL (2000; 2002; 2003a) for

6
evolution of the Egyptian labor market over the past years, the literature tends to address these questions from a cross-sectional perspective, thus falling short on tracking individual trajectories between survey rounds. Tansel and Ozdemir (2019) is the only paper thus far using transition matrices to study the labor market in Egypt, which provides us with a methodological guide, as well as an understanding of transitions from 2006 to 2012. This paper updates their work with more recent data from 2018 and provides further insights with more disaggregated analyses by education and age groups. Second, our paper provides a cross-country and cross-regional perspective on such transitions by comparing Mexico’s labor market transitions with Egypt’s. Such cross-country or cross-regional perspectives remain wanting in the existing literature, while they are of utmost importance to better understand the functioning of the Egyptian labor market and to assess its relative rigidity with respect to a relevant comparator country.

Our paper relies on the two most recent rounds of the Egypt Labor Market Panel Survey (ELMPS), conducted in 2012 and 2018, and the 2005 round of its Mexican counterpart, _Encuesta Nacional de Ocupación y Empleo_ (ENOE). For an overview of the Egyptian labor market, we use cross-sectional data to summarize individuals’ employment statuses and employment profiles across sectors in both years. To track individual transitions between the two rounds in Egypt, we take advantage of the panel structure of the ELMPS, as well as the detailed information on labor market status, employment, and individual characteristics. We also rely on the panel structure of ENOE to simulate labor market transitions in Mexico over the same time span. Relying on the Mexican data, we observe individual transitions over at most one year in Mexico. To compute transitions commensurable with those in Egypt, we simulate 6-yearly transitions in Mexico with Monte Carlo simulations of repeated discrete-time Markov chains. Next, this paper provides regression analysis of the potential determinants of labor market transitions in Egypt, with a focus on transitions to and from the dominant absorbing labor market states in Egypt for both women
(0.019)|(0.019)|(0.027)|
|female|0.44|0.414|0.475|‐0.061***|
||(0.004)|(0.004)|(0.007)|(0.008)|
|home support index|0.526|0.52|0.538|‐0.018|
||(0.006)|(0.009)|(0.008)|(0.012)|
|_2 year exposure sub‐sample_|||||
|average TEGRA score (SD)|‐0.034|‐0.064|0.021|‐0.085**|
||(0.017)|(0.018)|(0.032)|(0.038)|
|female|0.423|0.392|0.478|‐0.086***|
||(0.006)|(0.008)|(0.01)|(0.012)|
|home support index|0.537|0.536|0.539|‐0.003|
||(0.007)|(0.009)|(0.009)|(0.013)|

Standard errors included in parentheses. For differences, statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively.

# _Effect of the CLRW intervention_

The effect of the CLRW intervention on the percent of teachers fully implementing the teaching practices promoted by the intervention is presented in Table 8. The effects for each category of teaching practice are presented in Annex Table 6. Statistically significant, positive effects were 27
should be relied on to compensate for supply shortfalls (World Bank 2006).<sup>4</sup> The food crisis has led many researchers and experts to question the dominant approach (Timmer 2012; Galtier 2009; Abbott 2012a; HLPE 2011; Oxfam 2011). The dominant approach has attracted criticism because safety nets have proved complex to use in times of crisis, market-based risk management instruments have not yet been successfully developed, and the countries that were relying on the world market for their imports were the ones that suffered the most during the crisis. Indeed the countries that weathered the food crisis best were those countries with very interventionist policies related to both trade and storage, such as China and India. Despite international recommendations, stabilization policies are widespread in most developing and emerging countries. For example, Demeke, Pangrazio, and Maetz (2009), based on information obtained from 81 countries, show that 68 of them used trade policy measures during the 2007/08 food crisis, and 35 released public stocks at subsidized prices.

The present paper attempts to make sense of this divide between policy advice and practice. Drawing on the theoretical literature and on accounts of policy responses to price volatility, this paper tries to answer the following questions: What are the justifications for domestic stabilization policies? Following the food crisis, is the policy framework put forward by international organizations still relevant, or should countries rely more on price-stabilization policies? And if so, what type of price stabilization?

The liberal paradigm is facing reasonable criticism. Why should food importing countries trust a world market that is susceptible to sudden spikes and can even disappear if major exporters close their borders? In our view, the weakness of the dominant approach to a large extent is related to the fact that it requires countries to trust each other and to adopt the same cooperative policies. Indeed, the impact of domestic policies on stability of world prices is negative in the case of countercyclical trade measures and potentially positive in the case of storage policies.
the expected sign sorted by our conjectures as to which inputs are of most and which of least _direct_ importance to teachers1 ' 5. In the most recent of these compendia, Fuller and Clarke (1994) summarized in Table 3, there is a markedly higher confirmation ratio for the inputs, like facilities and instrumental materials, which are _less_ likely to appear directly in teacher's utility (two other reviews, Harbison and Hanushek, 1992 and Velez, Schiefelbein and Valenzuela, 1993 report similar results and are included as appendix 1).

In Table 3 "teacher's salary" has a confirmation rate of 36 percent for primary and 18 percent for secondary schools, the confirmation rates of teacher-pupil ratio are 35 and 9 percent respectively. In contrast, the presence of a school library has a confirmation rate of 89 percent and the availability of textbooks a confirmation rate of 73 percent for primary and 54 percent for secondary schools. Interestingly, a review of 43 studies (appendix table A.2) found that only 4 of those found a significant and positive relationship between "teacher satisfaction" and student achievement.

14 We include only inputs reported in the surveys of the literature that were subject to 10 or more studies.

" This ranking is problematic as the interpretation of a coefficient depends critically the other variables included in the regression. For instance, some regressions include "instructional time," if teacher salaries are included in the same regression then more time for the same salary would be a real wage reduction which teachers should oppose so that the marginal product should be very high. If teacher salaries are not included then its sign is ambiguous. Similarly, the coefficients on "teacher training" or "teacher education" all depend on what else is included in the regression.

20
Unfortunately, in the ICA surveys there are no data on liquid assets, and therefore it is not possible to construct a good proxy for financial savings.<sup>5</sup> However, we consider several proxies that are related to cash flows, since cash flows are related to savings as represented in the Flow of Funds Chart in Figure 2. Specifically, the ICA data allows for a calculation of operating income to sales. This is the closest measure for cash flows available in ICA data because there is no data to calculate net income or retained earnings and the total assets data are unreliable. Specifically, we construct Gross Operating income, which is defined as sales revenues minus the costs.<sup>6</sup> To make the comparison of firms of different sizes meaningful, GOI is scaled by firm sales, which in effect becomes a proxy for gross operating margin (GOM).

While there is no data on total retained earnings, there are questions in the survey about what proportion of investment and working capital is financed with retained earnings. These questions are used to gauge to what extent the firm has to rely on its own earnings to finance their investment and working capital.

To summarize, for ICA data we use five measures related to savings:

- Indicator for whether the firm makes any investment in a given year (called “any investment”);

- Investment to capital ratio, IK;

- Operating income to sales (a proxy for cash flows);

- Proportion of retained earnings used to finance working capital;

- Proportion of retained earnings used to finance investment.

> 5 The ICA data contain very few balance sheet indicators. Specifically, it contains data on total assets, total gross and net value of fixed assets (i.e. property, plant, equipment, land and buildings), and data on inventories and accounts receivables. Theoretically, it is possible to use  these items to estimate the value of liquid assets as total assets minus the sum of fixed
# Figure 9: ECA: Financing needs and constraints

# A. Bond issuance and yield spreads

<!-- Start of picture text --> US$, millions Bond issuance Basis points<br>2,000 Bond spread (RHS) 1,600<br>1,500 1,200<br>1,000 800<br>500 400<br>0 0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text -->

# C. GDP per capita relative to EU-27

<!-- Start of picture text --> Percent of EU-27 2021 (2019 for TUR)<br>75 2012<br>50<br>25<br>0<br>ECA CE WBK TUR<br><!-- End of picture text -->

# B. Efficiency gaps in public infrastructure investment

<!-- Start of picture text --> Percent<br>Minimum-maximum range<br>75<br>Median<br>50<br>25<br>0<br>ECA Euro area<br><!-- End of picture text --> D. Cumulative absorption rates, 2014-20 EU <!-- Start of picture text --> spending program<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>100 Bulgaria<br>Croatia<br>75 Poland<br>Romania<br>EU<br>50<br>Best absorber<br>25<br>0<br>2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> <!-- Start of picture text --> E. Planned EU investments in transport F. State-owned enterprise activity and<br>and green projects in the
bargaining, these marginal rents would accrue mainly to workers and mine owners from non-marginalized racial groups, who had stronger bargaining power to begin with and resided outside the former homelands. The evidence we provide reveals that initial exposure to resource rents—as measured by the initial employment share in the mining industry—is a robust positive driver of local income growth for black communities in the former homelands, not of local per capita income growth in general. This points to a key role of the interaction between coercive institutions and the distribution of rents, rather than just variation in the volume of resource windfalls.

We provide further evidence on the specific mechanisms emphasized by the model by examining complementary individual-level data from the 10% sample of the 1996 population census. Consistent with the existence of large resource rents in the mining industry, we document a large wage premium associated with employment in this sector. In line with the expected impacts of coercive institutions, we document a large income gap against 3