rafmacalaba/data-use-mentions-extended huggingface dataset:
machinery. While large firms usually obtain earmarked loans directly from the National Development Bank (BNDES), earmarked loans to medium and small firms are indirectly allocated through other financial institutions. By 2016, nearly 40% of the outstanding indirect earmarked loans to firms was originated by private banks. In their role as intermediators of earmarked loans, private banks screen and select loan applicants, price, disburse, and service the loans, and ultimately bear the credit risk.

In terms of pricing, earmarked loans are characterized by interest rate ceilings that vary across earmarked programs and are regulated by the government. Although the funding of earmarked loans comes from government sources at rates below the interbank market, private banks can add an interest rate spread to cope with credit losses as long as the final interest rate is below the interest rate ceiling. Due to these ceilings, interest rates of earmarked loans are not only substantially lower than those of free-market loans, but also below the monetary policy rate (i.e., the SELIC rate), and in some cases below inflation rates (see Appendix Figure A1).

# **2.2. Data**

Our analysis relies on two large data sources of corporate loan contracts in the Credit Registry maintained by the Central Bank of Brazil for the period between January 2005 and December 2016.

The first data set comprises all working capital loans above a 5,000 _reais_ threshold (approximately 2,200 USD) that were originated by all private banks during the period. For each loan, we observe the contracted interest rate, the loan amount, its maturity, collateral, and risk rating.<sup>11</sup> The data set also reports information about the borrower, including its industry (at the 2- digit CNAE level), location (at the 2-digit zip code level), number of workers, and the number of years with a relationship with the bank. There are two advantages of studying the universe of working capital loans. First, working capital
Simple OLS regressions using panel data by country and sector on trade and robots point to a positive relationship between robot intensity in own production and imports sourced from less developed countries. They also reveal an even stronger association with exports to these countries. The direction of causality is further supported by an instrumental variable strategy motivated by our theory, exploiting variation in exposure to robotization across countries and sectors. The positive impact of Northern robotization on imports from the South is mainly driven by exchanges of parts and components, which attests to the importance of explicitly modeling input-output linkages. Models that only consider final goods trade would fail to anticipate the sizable surge in imports of intermediates from the South that Northern robotization induces.

We then turned back to our theoretical model to study quantitatively the effects of robot price reductions on wages and welfare, both in the North and in the South. Increased robot adoption in the North initially depresses labor demand, and therefore leads to lower wages. Nevertheless, robotization induces welfare gains, as the income losses associated with lower labor income are more than offset by the gains associated with increased production, lower prices, and increased income from the rental rate of robots. Interestingly, the model points to an important nonlinearity in the relationship between wages and robot prices. As robot adoption proceeds in the North, production in the robotized sector continues to expand, thereby raising demand for the tasks in which robotization is technologically unfeasible. Eventually, the share of labor in the robotized sector starts to expand, thereby raising overall labor demand and leading to higher wages in the North.

Even if production in the South is not subject to robotization, Southern wages and welfare are impacted by robot-induced shifts in international specialization. While robotization makes Northern producers more competitive, it also raises demand for Southern inputs. Real wages and welfare are likely to increase in the South, reflecting also lower consumer prices. Welfare gains in the South are likely to be even more pronounced if robotization also becomes feasible there. Given that trade in intermediate and final goods
# **3. METHODOLOGY**

Tanzania’s response to the type of global maize production heterogeneity delineated in the five case-study years must be understood through mechanisms that can control for the substantial year-toyear changes in the global economy, and the frequent changes in Tanzanian maize trade policy that have occurred over the 1971-2001 period. In order to understand the poverty implications of the Tanzania’s responses, the mechanism must also account for the changes in prices and factor incomes that will occur across multiple markets, and other general equilibrium (GE) effects. For example, the Tanzanian maize sector employs about 30% of agricultural labor, or 13% of all workers in the economy. Shocks that affect the demand for labor in the maize sector thus have implications for all industries. Since we are interested in the sensitivity of poverty changes to climate volatility under the different maize trade regimes, we also need to track the impact on non-farm earnings as well as the consumption of goods and services. These variables will be affected directly by the shocks to the maize sector. However, given the importance of maize in the economy, they will also be affected indirectly through changes in other commodity prices and on the changes in wages, all determined in general equilibrium.

A computable GE simulation approach is thus necessary, and we employ a modified version of the GTAP simulation model. In addition to allowing us to estimate the changes in consumer prices and earnings stemming from changes to agricultural productivity due to climate effects, this approach also allows us to examine the additional sensitivity of Tanzania’s economic responses to the historical maize production heterogeneity under alternative trade regimes, without including the additional uncertainties of a forecasting approach.

# **3.1 MODEL DESCRIPTION**

We begin with the GTAP Database Version 6 (Dimaranan, 2006) and use this with a modified version of the standard GTAP model (Hertel, 1997). Maize is included in the GTAP database and model parameters as a component of the ‘Coarse Grains’ composite commodity group.
and social integration. On the other hand, Jaschke et al. (2020) rely on the questions on attitudes towards migrants available in the IAB-BAMF-SOEP-Survey of Refugees. They find that more negative attitudes towards migrants lead to a faster cultural convergence of refugees. In another study focusing on Germany, Albarosa and Elsner (2021) study the 2015 refugee inflow and its impact on social cohesion. The authors find no evidence that the inflow of refugees had an impact on social attitudes towards foreigners. However, they do find evidence of anti-migrant violence, and this effect lasted two years following on from the refugee inflow, and was stronger in counties with higher concentration of right-wing voters and lower employment rates.

# **3.2 Theoretical Contribution and Main Hypotheses**

The specific context of Switzerland where refugees are randomly allocated across cantons that are different in terms of local labor market conditions, natives’ attitudes towards refugees and size and nature of ethnic enclaves, allows us investigating empirically the role of these determinants on refugees’ labor market outcomes and economic integration.

Our paper makes the following contributions. First, we examine the role that attitudes towards refugees at the time of arrival in the host country have on their subsequent trajectory of economic integration. As previously mentioned, the few existing studies present limitations. Here we improve in two ways. We use an objective measure of attitudes derived from voting behavior (as opposed to selfreported measures of attitudes from surveys). Moreover, we measure attitudes by also including specific attitudes towards refugees instead of focusing only on the more general attitudes towards migrants as done in the existing studies. This is very important given that economic migrants and refugees are two distinct groups of foreign-born, and attitudes towards them may be significantly different as documented in recent surveys carried out in 18 countries (Pew Research Center 2019).<sup>17</sup> Second, we examine the relative importance of the three initial conditions, namely attitudes, unemployment and co-ethnic networks. This will allow us to determine the possible
# B. Classification of “GVC products” in product‐level trade data

The GVC product codes in disaggregated trade data<sup>6</sup> are compiled from lists generated by Athukorala (2010)<sup>7</sup> and Sturgeon and Memedov (2011). The first list by Athukorala contains the parts and components in the UN Broad Economic Classification (BEC) Registry and the product list of the WTO Information Technology Agreement. The author then used firm‐level surveys from Thailand and Malaysia to refine the list. The registry has been used a number of times to measure GVC trade, as in a recent paper by Wignaraga, Kruger and Tuazon (2013). The second list by Sturgeon and Memedov (2011) has broader sectoral coverage. Using the BEC registry, the authors combined capital and consumption goods into a single “final goods” category and then isolated “differentiated, customized, product‐specific” intermediates.

To get the broadest measure of GVC products, we combine the two lists of GVC codes.<sup>8</sup> In total, the Athukorala’s list provides 500 product codes and the Sturgeon and Memedov’s list provides 860 product codes. Duplicate codes were removed, resulting in 1,236 product codes. Table 2 shows the breakdown of the 1,236 GVC products by sector. Our list spans 12 key manufacturing product categories: chemicals, plastic/rubber, hides/skins (which includes leather goods), wood, textiles, clothing, footwear, stone/glass, metals, machinery/electronics, transport (which includes rail, road vehicles, aircraft and ships), and miscellaneous manufactures (which includes furniture). Two GVC sectors – wood and chemicals – comprise only one product. As shown in Annex Table A3, global trade in electronics, transport, clothing and footwear largely takes place mainly within the context of GVCs and seldom outside that context (93 percent of footwear and 88 percent of clothing exports are
90<sup>th</sup> percentiles of the high-income country distribution for <u>/ , and at the 50</u><sup>th</sup> <u>percentile for /</u> II<sup>GG</sup> YY II<sup>PP</sup> YY.

The efficiency of public investment, as reflected by the Infrastructure Efficiency Index (IEI) in the LTGMPC, is high in Malaysia. Malaysia’s IEI score of 0.877 puts it at about the 50<sup>th</sup> percentile among highincome countries. This indicates that infrastructure in Malaysia is well-constructed and of high quality, which is supported by the World Economic Forum’s survey on infrastructure quality for its Global Competitiveness Index – Malaysia ranked 21 of 137 countries in 2017/18. Given the high base, the potential growth impact from improvements in this measure of efficiency is limited, and so we assume quality is constant at its current rate in the baseline and all scenarios.

The IEI is best thought of as capturing public capital construction quality, which does not capture other less quantifiable, but important, aspects of public capital quality like poor project selection or an inflated cost of construction. We are, thus, unable to properly examine the implications of improvements on these elements in the model, especially, in the context of cross-country comparisons. However, it does not mean that these elements are not important for Malaysia. For instance, IMF (2019) notes that fiscal 10
# **Data Appendix**

The interest rates data used is from _International Financial Statistics._ Five interest rate categories are used: Official rates _(60)_ represent rates at which the central banks lend to financial institutions. Money market rates _(6013)_ - representing interbank lending - and treasury bill rates _(60C)_ are the two wholesale rates, while bank deposit _(60L)_ and lending _(60P)_ rates are described as retail rates, though the data collected does typically refer to rather large transactions. IFS also contains some long-term government bond interest rates which we have not examined in this paper.

Up to the mid-1970s interest rate data other than official rates was only available for a handful of countries. Country coverage of the iinterest rate series in _IFS_ improved rapidly in the late 1970s so that from 1980 on fairly comprehensive coverage exists for the wholesale interest rates of over 50 countries, and the official and retail rates for over 70 countries.

For the monthly time series analysis of wholesale rates, we confined our analysis to countries for which complete or nearly<sup>34</sup> com-plete data was available over the period 1980-97. Treasury bill rates _(60C)_ are available for some 40 countries, of which 27 developing countries.<sup>35</sup> As it happens, 5 of these countries are all tiny members of the East Caribbean Currency Board (ECCB), with a common interest rate and exchange rate policy. A further five have populations of under 0.5 million. Therefore most of our analysis concentrates on the remaining 17 larger developing countries, together with one representative for the ECCB. In addition we find a further 17 developing countries for which substantially complete monthly data on imoney market rates _(60B)_ is available, of which seven share a comnmon rate in the West African Monetary Union. Excluding all **but** one of the latter, this gives a total of 28 developing countries
changes following the onset of crisis actually work on average in the direction of lowering poverty (though this is not statistically significant), except in Latin America. Attempts to detect whether certain country institutional characteristics or crisis characteristics (using the Honohan, 2000, classification system) influence this effect were broadly unsuccessful. Interestingly, though, the KKZ indicator of corruption was associated with favorable distributional changes: possibly suggesting that in corrupt countries the crises were associated with a loss of previous privilege by beneficiaries of corruption. These regressions have large outliers, though (notably Kenya and the Kyrgyz Republic), and the size and significance of coefficients are sensitive to the inclusion or otherwise of these outliers (Regression Table 3).

This overview of the evidence from cross-country data confirms that the short-run pattern of inequality movements around banking crises differs from country to country. Outside of Latin America, these short-term changes are more likely than not to have been favorable to distribution.

The small number of specific crises for which appropriately-timed inequality data are available and the likely high noise-to-signal ratio in the inequality data militate against successful multivariate statistical analysis to control for independent causal factors influencing inequality movements around the time of the crisis.

# **5. Policy conclusions**

Available cross-country data on movements in inequality around the time of banking crises do not send very clear messages on the factors that influence whether crises will be associated with more or less inequality. This contrasts with recent evidence that financial deepening is a causal factor in reducing poverty (Honohan, 2004a and b, Beck DemirgűçKunt and Levine, 2004).

This is not the place to discuss general policy measures to reduce the frequency and scale of systemic banking crises and to improve containment and resolution of those that occur. Banking policies that improve the soundness of the banking system, thereby reducing the frequency of crises, are likely to be associated with less inequality and faster reduction in poverty in the long run.

Nevertheless, the discussion does suggest some general policy recommendations. For one thing, the way in which
A|BF GAP||G|BF GAP||S|SBF GAP||
||AGRI|MANF|SVC|AGRI|MANF|SVC|AGRI|MANF|SVC|
|Overall|0.57|0.76|0.74|0.61|0.79|0.79|0.53|0.79|0.56|
|Brazil (BR)|0.71|0.91|0.81|0.85|0.90|0.84|0.53|1.07|0.72|
|Vietnam (VT)|0.68|0.87|0.77|0.73|0.84|0.83|0.58|1.09|0.54|
|Senegal (SN)|0.33|0.51|0.65|0.24|0.63|0.69|0.48|0.22|0.42|
|Gap: BR - SN|0.38|0.40|0.16|0.61|0.27|0.15|0.05|0.85|0.30|
|Relative Gap|10%|10%|4%|15%|7%|4%|1%|21%|8%|

Note: AGRI, MANF, and SVC represent agriculture, manufacturing, and services, respectively. Overall is the average of Brazil, Vietnam, and Senegal. For the columns on SSBF, the sample is restricted to the firms having sector-specific technologies. Technology measures are weighted by the sampling weights.

74
br>ine. In Columns 4 and <br>lar social interaction wit<br>ue 1 if respondent repo<br>hold survey at follow-up<br>escribed in Cameron et <br>t level, respectively. Se|ation:_y ijk = α ijk + _<br>ividual reported to<br>_*Beneficiary ij + ε ij_ <br>ary of the NAIVS v<br>. The models prese<br>e that takes value 1<br>e value 1 if the resp<br>5, the dependent var<br>h DALDO, WEO, V<br>rts at follow-up ho<br> while data in Col<br> al. (2008) and rep<br>e Appendix Table 1|_β 1 Beneficiary ij + β_<br>be in the respondent's<br>, where_i_ indexes the<br>oucher program in 2<br>nted here represent <br> if the respondent re<br>ondent reports using<br>iables are dummies t<br>EO, VVC member,<br>usehold survey havin<br>umns 2-5 come from <br>orted below the coef<br>for definition of vari|_2 Lottery j + β 3 L_<br>network. In Colu<br>individual (house<br>010/11._Lottery j_ i<br>an intention to tre<br>ported giving agr<br> improved seeds a<br>hat takes value 1 <br>or Village or Ham<br>g paid to become<br> household survey<br>ficients. Clusterin<br>ables.|_ottery j *Beneficiary ij +_<br>mns 2-6 we report the e<br>hold) and_j_ indexes the<br>s a dummy that takes va<br>at analysis and are not <br>icultural inputs to a no<br>nd
arm 1 was significant at the 10 percent level. We also found large reductions in open defecation across treatment arms for this subgroup, which is in line with the findings from the DID analysis.

Hygiene Awareness and Hygienic Behaviors

# Adults

Based on the ANCOVA estimates, awareness of the importance of handwashing, both before eating and after defecation, improved significantly among adults across all treatment arms compared with the control group. All the coefficients were found to be significant at least at the 5 percent level. Awareness of the importance of handwashing after defecation increased by 4 to 5 percentage points, which is less than that shown by the DID estimates. Moreover, unlike the DID estimates, which found a statistically significant impact on the awareness of handwashing before eating only in arm 3, the ANCOVA results suggest an improvement of 5 and 7 percentage points across all treatment arms.

# **Table** **<u>6. Impact of Project Interventions: ANCOVA Unadjusted Model</u>**

**N No. of Clusters Adj. R^2 Treatment 1 Treatment 2 Treatment 3**

31
# **1. Introduction**

A wealth of evidence indicates that early childhood investments in human capital are critical for improving productivity and living standards. Poor families, however, often cannot afford to make these crucial investments in childhood health, nutrition and education. In part to monitor the extent to which poverty constrains human capital investment, the Sustainable Development Goals place particular emphasis on disaggregating poverty and other measures of welfare by age. This paper does so for children, in the context of the first Sustainable Development Goal, which aims to “By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.90 a day.” How many of the extreme poor population are children and what are their characteristics? Unfortunately global child extreme poverty estimates, unlike global counts, are only calculated and published sporadically. Batana et al (2013), estimate extreme child poverty rates using household survey data from 73 countries, collected between 1992 and 2005. That study estimates that 38.4 percent of children younger than 18 lived on less than $1.25 per day in 2000, as compared with 31.4 percent of people overall. Meanwhile, Olinto et al (2013) use data from 73 countries between 2000 and 2009, to which they apply the prevailing headcount poverty rates for 2010, measured using the 2005 Purchasing Power Parity (PPP) exchange rates. Their analysis finds that children under 18 comprise 47 percent of the poor, and that the poverty rate among children aged 12 and under in the developing world is 32 percent, compared to 19 percent for persons 13 years and older. Evans and Palacios (2015) apply a relative poverty line to a sample of 65 developing countries and find that on average children under 15 comprise 36 percent of the poor. Recent estimates by Watkins and Quattri (2016) were obtained by multiplying, for each country, the fraction of the population that are children times the number of poor, with an adjustment to account for
P a g e <u>|</u> **29**

|Illustrati|on of TDR Schemes at City Scale: Mumba|Numbers<br>i|Units|
|---|---|---|---|
|A|# of Slum Households in 2001|1,295,088|Households|
|B|Entitlement to a Flat per Govt<br>policy|270|Sqft|
|C|Total Floor Space Entitlement for<br>Those Living in Slums in 2001:<br>AXB|349,673,760|Sqft|
|D|Floor Space Allowed to be sold on<br>the market per space provided to<br>slum dwellers: 70-133%|102%|Average %<br>allowed|
|E|Total Floor Space Sold on Free<br>Market Needed to Provide Floor<br>Space to those Living in Slums:<br>DXC|356,667,235|Sqft|
|F|Per Cent of Households that can<br>Afford a New Formal Unit|25%||
|G|Total Number of Households in<br>Mumbai|2,515,589||
|H|# of Households that Can Afford<br>a New Formal Unit:FXG|628,897||
|I|Annual New Household<br>Formation|3.50%||
|J|Total Number of New Formal<br>Units Demandedperyear: IXH|22,011||
|K|Average New Formal Apartment<br>Size|700|Sqft|
||Annual Floor Space in New<br>Formal Apartments with Potential<br>to Provide for Affordable|||
|L|Apartments: JXK|15,407,983|SqFt|
|M|Annual Yearly Provision of<br>Affordable Apartment Units if<br>ALL Units use TDR L/B|