rafmacalaba/data-use-mentions-extended huggingface dataset:
policies) we see no evidence of treatment interaction by per capita consumption levels, education, or gender (Table A5).<sup>17</sup>

# **Heterogeneity by partisan alignment of central and local government**

A final dimension of heterogeneity relates to voters’ ability to correctly attribute credit for policies and programs in contexts of divided government and/or federalism. Following an earlier literature on retrospective voting (Powell and Whitten, 1993), several studies have focused on the partisanship of elected officials at central and regional or local levels as a potential modifier of citizen responsiveness to government actions. Divided control of government between different parties can affect citizen response, because citizens may not completely understand which party deserves credit for the program, either because of they have limited information levels about policy details in general, or because policy responsibility is genuinely shared by multiple actors in a political system. For example, in Pop-Eleches and Pop-Eleches (2012), voters changed their opinion of local government (which implemented the program) but not of the responsible national politicians or the central government (which started and funded the program).<sup>18</sup> The opposite pattern is present in Brazil, where Zucco (2013) finds that voters rewarded the president, but not legislative candi17We can also compare the effect of the 2008 Under 5 catch up ITN campaign to the 2010 universal coverage campaign. Like the 2010 campaign, the 2008 net distribution campaign also was contemporaneous with the a round of the National Panel Survey (the 2008-2009 round), meaning that the same RD identification strategy can be implemented, although the first stage treatment is notably smaller. The increase in respondents living in a household where someone used a free net the previous night increases by 13 percentage points rather than 44 percentage points in 2010.The increase is 22 percentage points when restricting analysis to households with children under 5, who were the target of the campaign. Interestingly, however, there is _no_ political benefit to politicians in the
of climate change impact in 184 countries. The Monitor provides an assessment of socioeconomic vulnerability, covering four impact areas, including (1) habitat change, (2) health impact, (3) industry stress, and (4) environmental disasters. Each of the four impact areas is composed of several sub‐ indicators. The Monitor assesses vulnerability in terms of the impact of climate change on each of these sub‐indicators, with the impact expressed as higher mortality (health), costs relative to GDP (for habitat change and industry stress), or both (for environmental disasters), in 2010 and 2030. More information can be found HERE.

**UN University and University of Bonn’s World Risk Index** . The World Risk Index is a tool used to assess the disaster risk of a country. The objective of this index is to measure the vulnerability to natural disasters in 171 countries, and is composed of four main indicators: (1) exposure to natural hazards; (2) susceptibility which depends on socioeconomic conditions, (3) coping capacity which is dependent upon preparedness, governance, and security, and (4) adaptive capacity relating to future natural events. The Index is calculated from 28 sub‐indicators from openly available data. More information can be found <u>HERE.</u>

**GermanWatch’s Global Climate Risk Index** . The Global Climate Risk Index, published annually, analyzes to what extent countries have been affected by the impacts of weather‐related loss events, including storms, floods, and heatwaves. The Index is populated using data from Munich Re’s NatCatSErVICE and the International Monetary Fund, among other sources. The following indicators were used in the Global Climate Risk Index: number of deaths, number of deaths (population‐adjusted), the sum of losses, and

28
to 2004. The R&D expenditure data were taken from Lederman and Saenz (2005) and updated with UNESCO data from its web site. The human capital related variables were taken from the World Bank’s World Development Indicators. We compute the stock of patents using the perpetual inventory method. The depreciation rate is assumed to be equal to 15%, which implies that the 90% of each patent is extinguished at the end of a 15-year period. Spillover variables for the stock of patents are computed considering different levels of aggregation over the world total stock or expenditure. Since the patent counts by country and year come from the USPTO, it is worthwhile to control for exports to the United States. This variable can be interpreted as a proxy for a country’s inventors to file patent applications with the USPTO. That is, the higher are the merchandise sales in the U.S. market, the stronger is the incentive to submit patent applications to the USPTO. As discussed in the results section 6.1, some coefficient estimates do appear to be sensitive to the inclusion of this variable. The data for exports to the U.S. come from the International Monetary Fund’s _Direction of Trade Statistics_ .

Table 2 shows the R&D effort across selected economies and regions. The most noticeable characteristic of the patterns of R&D is the significant difference between developed and develop13
green trade can spur the demand for skilled and semi-skilled workers in new green emerging industries, it can also raise concerns about gender inequality (McFarland, 2013; Rocha and Piermartini, 2023) or skills intensity and income inequality (OCDE, 2014), even depending on the types of green goods industries or sectors.

This paper examines the green goods trade landscape in the Philippines to understand the current level, evolution over time, and predominant green industries sectors. We estimate the ad valorem equivalent (AVE) of non-tariff measures (NTMs) on green goods to assess the cost of regulations on green goods imports and identify the most burdensome NTMs and candidates for regulatory climate-friendly trade policy reform. Finally, since reforms of NTMs will increase imports and exports of green goods, we assess the relationship between the imports and exports of green goods in the labor markets. This is done using trade data and labor force survey data at the industry level for the Philippines.

The Philippines makes for a compelling case for this analysis. As this country is vulnerable to climate change impacts like extreme weather events, agricultural disruptions, or rising sea levels, bolding the green trade can support the country’s transition towards a more sustainable area, enhance resilience, and alleviate environmental risks. First, as the Philippines heavily relies on fossil fuel imports, investing in green energy such as hydropower, wind, or solar can reduce the dependence on fossil fuel imports. Second, regarding international commitments, since the Philippines is a member of several agreements in addressing climate change issues like the ASEAN agreement, participating in green trade can help this country align with international obligations and enhance its commitment to addressing climate change. Finally, and more importantly, promoting green goods imports and exports can drive job creation, increase wages for people, and support economic growth (World Bank Group, 2022).

The paper finds that the Philippines’ share of green goods trade is relatively low, partly due to the high incidence of NTMs on imports of green goods. The paper
labor-market outcomes across age, education, and poverty ranks have been similarly observed for the youth population. The 2012 Mongolian Labor Survey shows, after controlling for a rich set of observable covariates, that young individuals with university education earned on average 50 to 60 percent more than individuals with secondary education, while individuals with a primary education earned between 18 and 23 percent less than people with secondary education. Likewise, individuals aged 25-29 reported 53 percent more earnings than individuals aged 15-19, while the NEET rates by poverty status ranged from 40 percent (poor) to 11 percent (non-poor) (Shatz et al. 2015). It would have been extremely difficult for a short-term program like ours to change these structural labor-market gaps.

Furthermore, because bias toward optimism has been reported to be particularly striking among the unemployed in other settings (e.g., Spinnewijn, 2015), treatment-effect heterogeneity by subjective beliefs regarding job prospects is also assessed. Measuring at baseline a subjective belief in finding a job within the successive six months, a median of eighty points is recorded in the sample (on a scale of 0 to 100), while around 70 percent of the sample self-reported being “very optimistic” about getting a job when answering a categorical-type similar baseline question. Compared to the actual number of employed individuals (46 percent) six months following the baseline, the sample of youth unemployed seems to show indeed over-optimistic beliefs regarding their job prospects.

Point estimates for the vocational treatment variable interacted with the dummy variable “very optimistic beliefs” is depicted in the bottom of Tables 5A and 5B.In the shortrun, positive differential effects emerge for individuals who are optimistic about their labor market prospects relative to individuals with lower expectations. These differential effects are statistically significant for employment and earnings. In the medium-term, positive 25
tion, are more likely to be married, and employed (see Table B.1). Additionally, Venezuelan children enrolled in school arrived some months earlier to Colombia relative to those not enrolled<sup>13</sup> . To gain deeper insights into the factors contributing to the low adoption rates of these services, we inquire on the primary reasons for non-utilization in the second wave of the survey.

# **V CHARACTERIZING GAPS BETWEEN FORCED MIGRANTS AND HOSTS**

In this section, we explore the human development gaps between Colombian children and adolescents and their Venezuelan counterparts. Our focus is to document and describe the gaps that exist between forcibly displaced minors and those from host communities. While our approach is descriptive, aiming to outline key patterns and facts, it is important to note that this study does not aim to estimate causal effects. However, given the limited availability of data and evidence surrounding the challenges encountered by forcibly displaced minors globally, this exploratory exercise offers valuable insights and contributes significantly to our understanding of the issues at hand.

We begin our analysis with a detailed examination of four critical dimensions of human development: (i) physical, (ii) cognitive, and (iii) socioemotional and mental health development of the study participants. In each case, we examine the distribution between groups and also characterize the development gaps between Venezuelan and Colombian children and adolescents using the following specification: where _yij_ are the outcomes of interest of each children or adolescent _i_ in household _j_ , Forced Migrant _i_ equals one if the minor is a Venezuelan forced migrant and zero otherwise, and _Xij_ stand for individual and household characteristics that were not affected

> 13Descriptive statistics of the characteristics of children enrolled and not enrolled to high school are shown in Table B.1. Similar results are found when analyzing access to health insurance and sisb ́en

32
and 1990s led to a significant improvement in firm performance, as profitability increased 24 percentage points and converged to levels similar to those of private firms.

> 13 Henderson and Wang (2005) analyzes how urbanization in a country is accommodated by increases in numbers versus population sizes of cities. Using a worldwide dataset on all metro areas over 100,000 population from 1960-2000, they show market potential, educational attainment, and the degree of democratization strongly affect growth in both city numbers and individual city sizes.

19
An important driver of this explosive growth is the recent price surge in ether. Ethereum is the dominant DeFi platform, although its share has been falling as rival platforms gain momentum.

While data gaps remain significant (e.g., IMF (2021)), it appears that crypto-assets activity is a global phenomenon. Some industry estimates claim that over 200 million people around the world own or use crypto-assets in 2021 (Figure 1). While its representativeness is not fully clear, a global Statista household survey conducted in 2020 found that there are at least 20 countries where over 10 percent of the respondents own or use crypto-assets (Table 1). According to industry analysis, global crypto-assets activity has grown by over 2,300 percent since Q3 2019 and over 881 percent to Q3 2021 and estimates suggest that the countries with relatively high activity are Emerging Market and Developing Economies (EMDEs) (Chainalysis (2021) and Table 1).

> 6 For example, the ownership status of all bitcoins on the Bitcoin ledger or the status of all smart contracts on Ethereum.

> 7 The benefits of decentralization come at a cost, typically by posing trade-offs with throughput capacity and/or security.

> 8 A “smart contract” is a piece of software that directly controls crypto-assets (also see Szabo (1997)). A smart contract runs on a distributed ledger technology network and can be created by anyone.

3
This version: 15 Feb 2024 2

# **1. Introduction**

In this paper we attempt to shed light on a Malaysian paradox that may have lessons for the rest of the world. Malaysia is widely celebrated for achieving high GDP growth with concurrent sharp reductions in income poverty and inequality (Stiglitz, 2007; Ravallion, 2020a, 2020b; Rongen et al., 2024). Yet, in a landmark election in 2018 the Barisan Nasional coalition, which had led the country for 60 years and steered it through its remarkable economic journey, was voted out of power, and Pakatan Harapan was elected instead. The 2018 election result was consistent with several public opinion polls showing a great deal of discontent with the state of governance. In the 2022 election Pakatan Harapan retained power as part of a “grand coalition.” In this paper we begin by documenting various dimensions of the Malaysian “miracle” with a diverse set of data. We then draw on findings from qualitative, openended discussions with 56 focus groups sampled from across the country to go below the surface of the quantitative data to unpack and analyze how Malaysian citizens perceive these changes, the challenges they face, and their sources of discontent. We find a broad consensus that while material living standards have improved, they have been accompanied by a large “imbalance” between income and expenses, a need to rely both on dual incomes and multiple jobs, growing indebtedness, increased stress, increased polarization across ethnic groups, and a sense of both ethnic and regional exclusion.

We argue that the Malaysian paradox may reveal something more general about the underside of economic growth. To interpret our findings we draw on the 2
# **5.1 What Do Firms Say the Consequences of Formalizing Are?**

The August 2010 survey asked the firms that formalized as a result of our intervention if they had benefited from being formal. The most common response, given by 36 percent of firms, was that they had yet to see any benefit from registering. The next most common response, coming from 20 percent of firms, was an answer related to improvements in the image of the business. This encompasses answers like ―it is good publicity‖, ―customers trust the business more‖, and ―social validity‖. Other common responses refer to feeling more secure and protected (12 percent), and to fact that the business registration could be used in the future to help obtain business loans (10 percent). Very few firms claimed to have obtained a loan, or to have received a government contract as a result of formalizing.

# **5.2 Econometric Estimation of the Consequences of Formalizing**

We use the follow-up data to estimate the impact of formalizing on firm outcomes, intermediate channels, and attitudes of firm owners. For outcome _Y_ and firm _i_ in randomization strata _s,_ we estimate: where are randomization strata fixed effects, is the baseline value of the dependent variable, are survey wave effects. Our main object of interest is in estimating , the causal impact of becoming formal (defined in terms of being registered with the DS) on the outcome of interest. The inclusion of the lagged dependent variable increases power, and helps control for any selective attrition based on the outcome of interest.<sup>9</sup> We pool together all rounds of follow-up data to increase power (McKenzie, 2011); Appendix Table 3 shows we cannot reject equality of impacts on profits across our three follow-up rounds (p>0.90). In addition, for the key outcomes of firm profits and firm sales, the March and December 2011 survey asked for each of the past
<!-- Start of picture text --> Landed households, maize<br>° | |<br>§e '| !3<br>F48 |\\ |\\<br>—_ 4<br>2<br>so |<br>Bo: a |<br>i}<br>I!<br>-2 0 2<br>Soil moisture anomaly<br>— Linear -——— Quadratic ——— Cubic<br><!-- End of picture text -->

<!-- Start of picture text --> Landed households, highland<br>= 1\ '\<br>c | '<br>a0ES |<br>FAc I! 1i}<br>8. | |<br>£<br>S 1 |'<br>Sw3g :1 :1<br>Sr | |<br>z<br>8. \<br>2° 1 '<br>-2 0 2<br>Soil moisture anomaly<br>——— Linear ———— Quadratic ——— Cubic<br><!-- End of picture text -->

<!-- Start of picture text --> Landed households, roots & lowland<br>re) |''\<br>|<br>c ' '<br>2 '<br>ee<br>Fa |<br>8 | \<br>bdry }<br>2 ° i |<br>o ! \<br>S |i} i|<br>= \ \<br>3°<br>& 1 '<br>re) | \\<br>7<br>-2 0 2<br>Soil moisture anomaly<br>——— Linear ———— Quadratic
>0.25<br>0<br>0.20<br>-0.1<br>0.15<br>-0.2<br>0.10<br>-0.3<br>0.05<br>-0.4<br>-0.5 0.00<br>Standarized 10-year Difference in 1/HDI<br>Argentina Chile Argentina Chile<br>Density<br>(percentage points)<br>Absolute Change in Unemployment Rate<br>-6.0 -5.5 -4.9 -4.4 -3.9 -3.4 -2.8 -2.3 -1.8 -1.2 -0.7 -0.2 0.3 0.9 1.4 1.9 2.5 3.0 3.5 4.0 4.6 5.1 5.6 6.1 6.7<br>Density<br>(percentage points)<br>Absolute Change in Monetary Poverty Rate<br>-18.7 -17.2 -15.6 -14.1 -12.5 -11.0 -9.5 -7.9 -6.4 -4.8 -3.3 -1.8 -0.2 1.3 2.9 4.4 5.9 7.5 9.0 10.6 12.1 13.6 15.2 16.7<br>Density<br>Absolute Change in 1/HDI<br>-10.0 -9.3 -8.5 -7.7 -7.0 -6.2 -5.5 -4.7 -3.9 -3.2 -2.4 -1.6 -0.9 -0.1 0.6 1.4 2.
, as well as training and job placement assistance through reemployment centers. Workers within five years of retirement age were encouraged to take early retirement with pension benefits. As women were much closer to retirement age than men, particularly blue collar workers facing retirement at age 50, a far higher share of women took early retirement than men.

After losing work during state sector restructuring, men, the young and the well-educated, generally faced a shorter duration out of work (Appleton et al, 2002; Giles et al, 2006a; Maurer-

> 10It should be remembered that the CULS sample used for these comparisons are representative samples from five large cities (Fuzhou, Shanghai, Shenyang, Wuhan and Xian), and may not be representative of work decisions in medium and small cities.

6
**Stylized Fact 15.** _The ranking of freight rates depends on their measure. In ad-valorem terms, the drybulk freight rate is up to 5 times_ greater _than the containerized (non-refrigerated) freight rate. However, the specific (per kilo) dry-bulk freight rate is up to 10 times_ lower _than the containerized one._

**Stylized Fact 16.** _The freight rate trends of the containerized and dry-bulk freight rates are not synchronized, implying that the cost and demand shocks are either asynchronous or have asymmetric effects on transport costs across commodity types._

**Stylized Fact 17.** _The aggregate per-kilo and per-TEU trends for a given commodity type are synchronized._

The last fact is important from a practical standpoint since per-kilo freight rate measures are available in many more datasets. These similarities in trends indicate that, in the absence of the per-TEU measures, the per-kilo trends are good proxies for the per-TEU trends.

Finally, some datasets provide the per-container charges. In some cases, these datasets contain the spot-market, but not the annual-contract charges (e.g., Drewry, as described by Wong, 2020). The US Bills of Lading data provide per-container charges, but only for a limited sample of the shipments.<sup>23</sup> These data limitations impede utilizing the per-container freight rates in the context of international trade.

# **4 Containerized Maritime Shipping: Freight Rate Determinants**

Containerized maritime shipping is a multi-billion industry with complex and sophisticated technology. While an exact functional form of transportation cost is difficult to derive, it includes both fixed and variable cost components. The variable cost depends on the market structure, network organization, distance and geography, volume of trade and directional imbalances, good- and variety-specific imbalance: e.g., empty reefer boxes are needed for the Chilean exports, and therefore the empty returns to Chile are very cheap.

> 23These data have been used by Holmes and Singer (2018) and Terner (2021)
all service variables. In particular, access to cell phones (as opposed to telephones), and the overall quality of infrastructure are significant, while the other service variables, such as access to water, electricity and sewerage, and to telephone lines show less significance. See Section 6 below for a more detailed treatment of some of the city characteristics and related results. Bribery in utility seems to also be important, showing significant results for all service variables in Table 4.

Definition of city as a space is sensitive to the definitions of city boundaries, whether core city or broader agglomeration. We have corrected for thisby using city as an outcome for the analysis. Such a construct: (a) allows us to use “city as a place” as an input to the analysis and hence is less sensitive to the boundary definitions; and (b) the UN database 17 In particular, for each independent variable, for calculating the magnitude of the coefficient rating (left hand side), we computed the product of the average coefficient magnitude across all specifications times twice the standard deviation of the variable. Then we assigned magnitude values according to the following criteria: for positive coefficients, we applied a “++++” for any value above 1.2; “+++” for any value between 0.7 and 1.2; “++” for any value between 0.4 and 0.7; “+” for any value between 0.1 and 0.4; and finally a “0”’for any value below 0.1. Similarly, minus signs were applied to negative coefficients. For the rating on the significance of the coefficient in each cell (right hand side), we averaged the various significance levels across the 21 specifications, where “***” indicates significance at 1%, “**” indicates significance at 5%, “*” indicates significance at 10% and ‘ indicates significance at 15%.

24