rafmacalaba/data-use-mentions-extended huggingface dataset:
a system with lower financial efficiency. As a result, rationing phenomena and cuts in investment funds could be limited.

This prediction, for which no empirical evidence is referred to in the next section, will be tested in a subsequent study.

# _5._ Evidence from the literature

Although specific investigation will have be to be carried out to provide empirical corroboration of the theoretical predictions of the model above,'<sup>2</sup> recent studies seem to bear interesting evidence in their support, thereby encouraging further research along the lines of this study. This section briefly reviews such evidence.

First of all, it is interesting to learn from Demirguc-Kunt's and Levine's (1999) recent cross-sectional analysis of a 60 country data set that the financial systems of countries with less developed financial infrastructure (i.e., less investor-prone law system, poor protection of shareholder and creditor rights, weak contract enforcement, high levels of corruption, poor accounting standards, restrictive banking regulation) tend to be bank-dominated. In particular, the analysis study finds that countries with lower levels of corruption tend to have more market-based financial systems. It also finds that, as financial infrastructure develops, financial systems tend to become more market-based and less bank-dominated.

As regards the relationship between financial development and the quality of capital accumulation, Carlin and Mayer (1998), using data from 27 industries in 20 OECD countries over the 1970-1995 period, find that equity financed industries tend to grow faster, carry out more R&D, and employ higher skilled workers in countries with relatively better accounting standards. Interestingly, under better accounting standards, equity finance industries also tend to undertake less fixed capital accumulation and to invest more in intangibles. This latter result can be explained by the lesser need - made possible by the improved financial infrastructure - for enterprises to accumulate reputational capital in the form of non-salvageable (productive and/or nonproductive) capital assets necessary to signal the enterprises' commitment to honest and prudent behavior."<sup
our results: corporate taxes and cyclicality. Djankov et al. (2008) present data on effective corporate tax rates across 85 countries and find that effective corporate tax rates have an adverse impact on entrepreneurial activity leading to less demand for VC. Groh et al. (2010) also include taxation as a component of their PE attractiveness index. Similarly, in un-tabulated analysis, we also test if taxation plays a role in how much buyout capital is invested across countries but do not find any statistically significant relationship regardless of the corporate tax data we use.<sup>24</sup> Private equity managers have to deliver returns to their investors and hence might be looking for opportunities to buy companies at a discount when economies are going through downturns. To test this, we create an across-country and time recession dummy based on jumps in the unemployment rate and include it as a control variable in our country-level models. We do not find a significant association between this recession dummy and buyout activity, suggesting that there is no evidence of PE bottom-fishing during times of dislocation.

Another interesting question is if the response of buyout investments to institutional factors differs across industries as the level of institutional involvement may vary across industries. To explore this, we repeat our industry-level specification on subsamples created based on various industry groupings. One such difference we find evidence for is that the effect of regulatory environment on buyout activity seems to be more pronounced in non-traded versus traded industries, and in services versus goods industries. This might reflect that private equity managers have to deal with institutions more when making investments into portfolio companies from these industries and thus respond more to regulatory factors. However, it should be noted that these

> 24 We use other data on corporate taxes from different sources like the OECD and the World Bank’s Doing Business data. Our results remain unchanged if we include corporate tax rates as an additional control in our models. The reason we do not include it in the main analysis is that the tax data are either only available
21

**Figure 11:  State-owned banks are still holding on especially in India and MENA**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>India MENA EAP ECA LAC Developed<br>1985 1995 2003<br>private commercial banks<br>Percentage of bank assets at<br><!-- End of picture text --> Source: World Development Report 2005

# _Energy_

41. In energy, political issues surrounding private and in particular foreign investment are paramount in sectors such as oil and gas and thus many such companies remain either fully or majority state-owned. Some inroads are being made: in Mexico, for example, the need to inject funds into heavily indebted Pemex, the state-owned oil company accounting for 36 percent of government revenues, has recently led policy-makers to consider a “new model” for the company, including the removal of an explicit prohibition on private investment and possible share offering of shares on the domestic stock market.

42. In short, the above analysis suggests that despite a long track record of privatization over more than a decade, government ownership still appears to be prevalent in many regions and sectors.

43. The costs of not reforming state enterprises are high: the productivity of state enterprises is typically two to three times lower than private firms, and in some cases significantly lower; they continue to remain a fiscal burden; lack of investment capital has led to poor service delivery in critical sectors that impact the economy as a whole. A high percentage of subSaharan African and South Asian firms identify poor infrastructure services in telecommunications and electricity as major or very severe obstacles to private sector development and growth (World Bank Investment Climate Database). Thus the longer governments wait to address these issues, the greater the economic and financial burden and the greater the barriers to achieving economic growth.

# **IV.   EMERGING ISSUES**

44.
04 *** *** 0.0<br>-0.5<br>0.00 -1.0<br>Competitive Partially Natural Competitive Partially Natural<br>Contestable Monopoly Contestable Monopoly<br>Odds ratio<br>Logarithmic form<br>10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more<br>Odds ratio<br>Logarithmic form<br>10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more 10-100% 10-24.9% 25-49.9% 50% or more<br><!-- End of picture text --> Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.

Note: The sample includes all firms. Estimates in graph (a) are odds ratios indicating for the probability of receiving a subsidy based on a logit model with a dummy variable taking the value one if the firm reported a positive subsidy amount. The estimates in (b) are in logarithmic terms with the dependent variable being the log of the subsidy amount the firm reported as received. SOE variables are dummy variables taking the value one if national or subnational governments have ownership stake of 10-100% in the firm. The reference group is private-owned enterprises (POEs), defined as firms where national or subnational governments own 0-9.
data, we first preprocessed the entire Kenya Law corpus of judgements by removing punctuations (but retaining hyphenated words). To avoid case sensitivity, we transformed all our words to lower case. We then retained only the most common 50,000 words in all judicial opinions. To obtain judge-specific gender slant measures, we took the set of majority opinions authored by each judge as a separate corpus and trained separate GloVe embeddings on each judge’s corpus. To ensure convergence, we trained vectors for 20 iterations with a learning rate of 0.05.

Since each judge might not have a sufficiently large number of tokens, we follow the approach suggested by Antoniak and Mimno (2018) and train embedding models on 25 bootstrap samples of each judge corpus. Specifically, we consider each sentence written by a judge as a document and then create a corpus by sampling with replacement from all sentences. The number of sentences contained in the bootstrapped sample is the same as the total number of sentences in the original judge corpus. We then calculate our slant measure for all bootstrap samples and assign to each judge the median value of the measure across the samples. Given that embeddings trained on small corpora tend to be sensitive to the inclusion of specific documents, the bootstrap procedure produces more stable results. In addition, bootstrapping ensures stability with respect to the initialization of the word vectors–a potential concern given that GloVe presents a non-convex objective function (Spirling and Rodriguez 2019). The two variables resulting from this process are _Median slant, career vs. family_ and _Median slant, good vs. bad_ . For both measures, positive values indicate greater slant against women.

To validate that the embeddings capture meaningful information about gender, after following the bootstrapping procedure, we compute the cosine similarity between the gender dimension and each of the vectors representing the five most common male and female names for each judge and bootstrap sample. We then regress a dummy for whether the name is male on the median cosine similarity between the vector representing the name and
**Figure 7: Actual and simulated annual poverty gap changes, ca. 1995-2008**

<!-- Start of picture text --> GMB<br>MDV<br>TJK<br>MDV<br>MWI GMBPAK MDA KAZPOL RUS<br>MLIMLICAFCAFBDIBDIMWIMOZMOZNERNER TZA SWZSWZMDGNPLNPLNGANGAUGAUGAVNMVNMGNBGNBBGDBGDINDTJKETHETHZMBPAK IND LAOLAOSENSENIDNIDN CHNCHNGHAGHAKHMKHM MRT MNGMNGCMRCMRCIVCIVLKALKAPHLPHLAZEAZEMDAEGYEGY ROM ARMARMNICNIC HNDJAM MARMARNAMNAMJORJORALBALBTUNTUNZAFZAFKGZKGZVENVENGTMGTMMKDMKDMEXMEXSLVSLVECUECUPER PER TURTURUKRUKR BOL DOMDOMMYSMYSCRICRIPANPANBGRBGRPRYPRYRUSBRABRACOLCOL ARGARGCHLCHL<br>MDG<br>ZMB KEN<br>KEN<br>1 5 10 15<br>Average daily income per capita, 1995<br>Actual Equal initial conditions<br>15<br>10<br>5<br>0<br>Annual percentage change in poverty gap<br>-5<br><!-- End of picture text -->

# **Do initial conditions matter for the Millennium Development Goals?**

The previous analysis comprised a quite heterogeneous group of countries, from low to middle income ones. The equalization of initial average income across all countries can thus imply, for the poorest and richest countries, major shifts of where the poverty thresholds crosses the income distribution.

It is then natural to ask whether the drastic reversals of trends observed in the previous figures would hold for more homogenous groups of countries. Such a question is essential in gauging whether the MDG targets may have been excessively unfavorable towards the poorest countries.

Towards this purpose, we repeat the simulations for those countries in our database that in 1990 (the benchmark year of the MDGs) had rates of extreme poverty above 10 percent (that is, 10 percent or more of the population in 1990 living on less than 1.25 dollars a day). To capture the largest time span, we run the simulations for the period 1990-2008, but since the timeframe varies significantly across countries at times, we report average annual changes.

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most households. In contrast, large fluctuations in the nominal exchange continue to be commonplace around the world and have meaningful economic consequences for many households (Gouvea, 2020; Cravino and Levchenko, 2017). Despite the economic importance of exchange rates, there is little evidence on how households form exchange rate expectations and how such expectations may affect consumption decisions.

This study also relates to a literature on household finance and the impacts of consumer expectations on personal financial decisions. Giglio et al. (2020a) and Giglio et al. (2020b) use survey data to test how macroeconomic beliefs affect the decisions of retail investors. They show that beliefs are reflected in asset allocations and change in response to discrete macroeconomic events, such as a stock market crash. Aaronson, Agarwal, and French (2012), Agarwal, Liu, and Souleles (2007), and Agarwal and Qian (2014) use credit card data to test consumer responses to minimum wage increases, tax changes, and unanticipated income shocks and find effects inconsistent with models of fully rational expectations.<sup>4</sup> We make both an empirical and methodological contribution to this literature. On the empirical side, we merge information on consumer beliefs, as elicited via survey, with rich administrative data that covers the universe of credit card transactions. On the methodological side, we generate exogenous variation in beliefs

> 4. Related evidence shows that behavioral factors, such as social norms and conspicuous consumption, affect spending and debt repayment decisions (e.g. Bursztyn et al., 2018; Bursztyn et al., 2019)

5
9 10 11<br>Ln GDPpc Ln GDPpc<br>12 17<br>10 16<br>8 15<br>Ln Number of Exporters 6 14<br>Ln Mean Exports per Exporter<br>4 13<br>1 3<br>2.5<br>.8<br>2<br>.6 1.5<br>Share of Top 5% Exporters<br>Ln Number of Products per Exporter<br>1<br>.4<br><!-- End of picture text --> 37 GDP per capita, GDP, and total exports in current USD are taken from the World Development Indicators (WDI) of the World Bank.

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Our conclusion is that labor market policies in Mexico are still limited and underfunded. Even though the country has implemented a variety of active labor market programs, the funding and coverage of these programs are insufficient for dealing with either the current crisis or future ones. Total unemployment withdrawals from pension funds represented 0.14 percent of nominal GDP in 2009. This contrasts with allocations of 0.5 to 2 percent of GDP in European Union (EU) and other Organisation for Economic Co-operation and Development (OECD) countries for passive labor market policies (see figure 4.1). The budgets of temporary employment programs, training, and intermediation services represent less than 0.3 percent of the Mexican GDP whereas these active labor market policies account for between 0.5 to 1 percentage point of the GDP in EU and OECD countries. In order to have better mechanisms for dealing with the aftermath of the current crisis and, more important, with future crisis, Mexico needs to enhance its labor market policies both in terms of funding and design. As mentioned before regarding the expansion of PET, Mexico seems ready to consider a technical analysis for an enhanced unemployment protection mechanism, as well as further expansions of its labor policies.

**Figure 4.1: Unemployment Withdrawals from Pension Accounts and Unemployment Rate**

<!-- Start of picture text --> Unemployment withdrawals from pension accounts<br>and unemployment rate<br>180,000 7<br>160,000<br>6<br>140,000<br>5<br>120,000<br>4<br>100,000<br>80,000 3<br>60,000<br>2<br>40,000<br>1<br>20,000<br>0 0<br>number of withdrawals<br>unemployment rate (in percentage)<br>Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 Jan-10 Mar-10
as the Demographic and Health Surveys (DHS) or the Multiple Indicator Cluster Survey (MICS), for historic or budgetary reasons collect data for some key variables such as child care and environment and health without much useful information collected on food security. These surveys are characterized by a strong path dependence, in the sense that the original surveys collected information about specific components of one or two of the three pillars mentioned above with the additional questions added over time constrained by the “straight jacket” of maintaining compatibility with the earlier surveys. In contrast, other specialized surveys ended up collecting information on different dimensions of food security but no information at all child nutrition and anthropometric measures, or on child care or health and environment.<sup>3</sup> With these considerations in mind, this report contributes to the existing literature in three ways. First, it provides one of the first comprehensive investigations of the data availability and data constraints associated with a more systematic application of the UNICEF conceptual framework emphasizing the interrelationship among access to adequate food security, environment and health, and child care practices in the prevalence of malnutrition rates among children in developing countries. Using detailed demographic and health survey data are used from Bangladesh, Bolivia,

> 2 Notable exceptions are Chong et al. (2003) and Alderman et al (2003).

> 3 Interestingly the empirical literature on the determinants of child health does not appear to be affected much by the scarcity of relevant data on the determinants of child malnutrition. The practice prevalent in the majority of studies is to focus the analysis on the contribution of a few key variables on child nutritional outcomes. Examples, of such variables include the economic status of the household, measured by income, consumption expenditures, or household assets (Behrman and Deolalikar, 1987), and the level of education of the mother of the child (Barrera, 1990; Behrman and Wolfe, 1984; Skoufias, 1999; Webb and Block, 2004). In many studies village‐level (or
18 percent (average gross capital formation over the period 1990-2010; IMF WEO); For the base year 2010 in the simulations a debt/GDP ratio of 41 percent (Banco Central do Brazil); the contribution rates to the health system is residually estimated based on 1.46 percent for health benefits (based on health spending/GDP over the period 1984-2009; Tafner, 2010); the contribution rate to the social PAYG pension system is calibrated to 31 percent (11 percent from workers and 20 percent from employers; Queiroz and Figoli, 2010).

The average replacement rate, weighted by the share of the population receiving pension benefits at various rates, for pensions is estimated to be 45 percent. The replacement rate to public servants is assumed to be 95 percent since older public servants participated in a plan of virtually 100 percent replacement rate, while younger generations receive slightly less; the replacement rate for nonpublic servants is estimated residually based on pension payment data for the two groups; the weighted average is constructed based on the share of the population is each group; the effective weighted average is found to equal 45 percent; sources: DATAPREV, SUB, Plano Tabular da DIIE, and Ministerio da Fazenda. Productivity growth is assumed to follow output growth and equals 168 percent (growth over the period 1988-2008: World Bank GDP); discount rate equals 0.9.

# _<u>Simulations</u>_

The model simulations start in 1990 so the past 20 years of demographic transition is taken into account in projecting the economic implications from the year 2010 to 2050. The model is simulated in Matlab using Dynare software (Jorgensen, 2006). The results should be interpreted as the 18
allows the _benchmarking_ of those indicators between countries; two, it describes social sustainability conditions or _profiles_ within a given country; three, it expands the _analytics_ around social sustainability; and four, it _monitors_ country, regional, or global social sustainability trends over time.

Figure 1 details the four steps used to construct the global database. First, we _mapped available social sustainability indicators_ . We conducted a thorough review of the indicators used by different authors as well as available international and national data sources and evaluated whether the existing data can be feasibly disaggregated. Table 1 below reports the chosen social indicators and the available disaggregation of each indicator. This mapping exercise identifies 11 data sources with fitting indicators related to social sustainability. These sources include harmonized living standards and income and expenditure household surveys from the World Bank's Global Monitoring Database (GMD); regional databases such as Afrobarometer, Arab Barometer, Asian Barometer, and Latinobarómetro; and global databases such as the <mark>Armed Conflict Location & Event Data Project (ACLED), V</mark> arieties of Democracy (V-DEM), World Development Indicators, Worldwide Governance Indicators, World Justice Project, and World Values Survey. Using those sources, we identified 71 variables that maximize the number of countries included in the database, as detailed below. The database contains 230 countries and 5
cannot fully rule out these alternate explanations. We therefore present results from a field experiment in the Indian state of Andhra Pradesh designed to specifically determine the pattern of crowd-out and we show that the results obtained are virtually identical across these different contexts. The randomized school grants address the second and third caveats above, while the first limitation is addressed by collecting spending data from a large sample of households at multiple points in time (both when the grants were a surprise, and later when they were expected), which allows us to test differential household responses to anticipated and unanticipated funds across a large representative sample of schools.

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lationships were mother/father, uncle/aunt, brother/sister, grandparents, cousin, guardian, spouse, friend and other.

The average age of youth in our sample is 23 years (25th percentile: 20 years; 75th percentile: 26 years), and half of the sample are women. In terms of contacts, about half of the youth listed a parent as their contact; another 9% provided a spouse as a contact. Women are 18.5% less likely than men to choose women as contacts. 15.6% of women choosing their husband as a contact, compared to only 1.7% of men choosing their wife (Table 1). These statistics are balanced across treatment groups (Table 2).

To complement the quantitative data, we conducted a qualitative survey to better understand the quantitative findings, including 12 individual interviews and 4 focus group discussions among youth and their contacts in August-September 2023. In the focus group discussions, men and women were interviewed separately to better understand the gender differences in the observed results.

We use the following specification for our main estimates: Where _Enrolledi_ is a binary variable indicating whether individual _i_ has completed the enrollment process. _T_ 1 _i_ is a binary variable indicating whether the individual was in the first treatment group, where an SMS was sent to youth only. _T_ 2 _i_ is a binary variable indicating whether the individual was in the second treatment group, where an SMS was sent to youth and their contact. _Womani_ is a binary variable taking the value of one when the individual is a woman. _Xi_<sup>_′_isavectorofcontrolvariables,includingageandgeographiclocation.We</sup> also run a heterogeneity test with the same specification, comparing two sub-groups: youth with male contacts versus youth with female contacts.

4