rafmacalaba/data-use-mentions-extended huggingface dataset:
oriented reforms: our independent variable is the liberalization index _(LIBERALIZATION)_ developed by De Melo et al. (1996), which is weighted average of three indices of liberalization in internal markets (liberalization of domestic prices and abolition of state monopolies), foreign trade liberalization (elimination of export controls and taxes; reduced import tariffs and elimination of import quotas; current account convertibility) and private sector development (privatization of small and large scale enterprises; banking reforms), respectively. The index assumes values from 0 (no liberalization) to 1 (complete liberalization).<sup>8</sup> Note that our independent variable represent the policies themselves and not the outcomes of these policies (i.e., growth or increase in material welfare). We do not presuppose any link between freedom and welfare. Rather we examine the link between freedom and the adoption of policies that can, under favorable circumstances, lead to increases in welfare.

Figure 1 shows the relation, over the period 1992-1997, between this index of liberalization and an index of civil and political liberties _(FREEDOM),_ obtained from various Freedom House reports, which assumes values from 2 (no freedom) to 14 (complete freedom). The figure indicates a clear, positive relation between freedom and economic liberalization. In fact, the linear correlation coefficient is 0.86 and the Spearman rank correlation is 0.84. Does this mean that political freedom has significantly affected the economic liberalization process in the twenty-five countries in our sample? To address this question, we estimate a model that evaluates the role of freedom vis-a-vis other economic and political variables, during the transition.

Our regression model builds on earlier work by De Melo et al. (1996, 1997), but we look more in depth. at the influence of political variables on economic 9 liberalization9.

On the basis of the arguments developed in section II, the first variable we include in our regression model is the index _FREEDOM._ In addition
2015).

Moreover, IMTS2010 recommends listing the country of origin for imports, but the last known destination for exports (United Nations, International Merchandise Trade Statistics). This will structurally create transactions without a corresponding mirror in cases where exports pass through intermediate countries before reaching their final destination. In such cases, reported exports from the intermediate countries will not have a mirror as the partner that they list in their export transaction lists the original source country (and not the intermediate country) in their 5
number of control variables. This relationship is quite robust, persisting across different ranges of the moving window and different specifications of the model, and offers further support to the market failure hypothesis.<sup>11</sup> _<Insert Figure 2 Here>_

# **5. Conclusions and Policy Implications**

Recent research has highlighted the importance of productive diversification for developing countries: a process that may be hindered by spillovers in the discovery of new products for export. After extending this finding from domestic production data to export data, we use highly disaggregated export data to develop a measure of this insidethe-frontier innovation, and characterize its relationship to on-the-frontier innovation, productive diversification, and development. We then go on to use this discovery metric to test a highly stylized model of innovation and imitation.

According to the data and in the context of our model, imitation of first-movers may be leading to fewer cases of inside-the-frontier innovation than otherwise would be the case. Even after controlling for the quadratic relationship with GDP per capita, the amount of discoveries in past years, factor endowments, plus a plethora of additional explanatory variables, discovery has a larger response to increased profitability in countries with higher barriers to entry. While the Hausmann and Rodrik (2003a) attribute this imitation specifically to the learning of production costs which are not predictable _ex ante_ , our test makes no determination as to the importance of this particular channel. The spillover may not by from learning production costs, but instead from learning the characteristics of foreign demand (Vettas 2002), free-riding on investments to cultivate foreign demand (Bhagwati 1968, Mayer 1984), learning the redesigns needed to meet foreign safety standards (Granslandt and Markusen 2000), or some other unidentified channel.

Regardless of the particular channel, this finding suggests that there are indeed spillovers from demonstrating the viability of a new product for export. While this indicates that public support for experimentation in new sectors and activities may be warranted, it is important to note that it does not mean
<!-- Start of picture text --> Hybrid: Unit values<br>and CPI or price<br>survey, 2, 4%<br>Others (regional PLs, no<br>spatial price adjustment, |<br>unknown, etc.), 9, 19%<br>—Y<br>—_ Unit values,38% 18,<br>CPI, 6, 13% ~~<br>'<br>Price survey, 12,<br>26%<br><!-- End of picture text -->

<!-- Start of picture text --> Other (other index, no spatial<br>deflation, unknown), 8, 17% -<br>Paasche index,<br>12, 25%<br>Regional CPI, 2, _~<br>4%<br>~~ Laspeyres index,<br>| 7, 15%<br>Regional PLs, 12,<br>26% \ Fisher index, 6,<br>13%<br><!-- End of picture text -->
on developing countries (and Japan) and use countries that have sufficient data on revenues, capital and a measure of labor input (the minimum requirements for the analysis).<sup>7</sup> We use only firms in the manufacturing sector and, given our focus on within-industry moments, use only country-year-industry cells with at least 10 firms. The data cover the period 2002-2010. There are seven countries in our Orbis sample: Argentina (ARG), Brazil (BRA), Japan (JPN), Malaysia (MYS), Taiwan, China (TWN), Thailand (THA) and Turkey (TUR).

We supplement this sample with data on four additional countries. First, we use data on US publicly traded firms from Compustat. Second, we use data on Chinese manufacturing firms from the Annual Surveys of Industrial Production conducted by the China National Bureau of Statistics. Next, we use data in Colombia (at the establishment level) from the Colombian Annual Manufacturers Survey and lastly, data in Mexico (also at the establishment level) from the Mexican Annual Industrial Survey.<sup>8</sup> Throughout the paper, we report results first for the seven Orbis countries (in alphabetical order) and then for the four additional countries (again in alphabetical order).

Our use of multiple data sources has the advantage of yielding a wider set of countries on which we can perform our analysis – nine developing and two developed. A disadvantage is that direct cross-country comparisons become more difficult, given differences in coverage, sample sizes, the set of firms included, etc.<sup>9</sup> However, we will show that despite these difference, our study leads to a number of robust cross-country patterns with respect to the sources of observed misallocation.

**Investment Moments.** The key input into our estimation procedure is a set of carefully chosen moments from data on firm-level capital and revenues, both of which are directly available in our data. Since we
Chi et al. (2021) used several Demographic and Health Surveys (DHS) and a mix of publicly available and proprietary geospatial data to predict an asset index for 2.4 km grids across 135 countries. The authors trained the model on the asset index available in the DHS, using data for 56 countries. Proprietary predictors include internet connectivity information obtained from Meta. In validation exercises, R<sup>2</sup> varied greatly, depending on the context, level of geographic disaggregation, and comparison indicator. When validated using cross-validation across enumeration areas in the survey data, the R<sup>2</sup> of the estimates was 0.56, similar to the 0.6 value reported by Yeh et al. (2020) for Africa. Meanwhile, when validated against independent wealth measures, R<sup>2</sup> was 0.60 across rural Kenyan villages, 0.70 across Nigerian Local Government Areas, and 0.84 across Togolese Cantons. But when validated against the predicted probability of being poor, or predicted per capita consumption or income, R<sup>2</sup> values are much lower: 0.04 across Malawian villages, 0.17 in rural Kenya, and approximately 0.3 across Mexican municipalities (Gualavisi and Newhouse, 2022, Chi et al, 2021, Newhouse et al, 2022). This is due to key differences between wealth and predicted per capita consumption, including the fact that the latter is expressed in per capita terms.

Masaki et al. (2022) also consider the prediction of non-monetary poverty in Tanzania and Sri Lanka. Their study used census data from both countries to construct a non-monetary welfare index, and classified households whose index fell below a percentile threshold roughly equal to the prevailing national poverty rate as non-monetarily poor. The analysis combines survey-based estimates with publicly available geospatial indicators using an Empirical Best Predictor model following Molina and Rao (2010). Relative to direct survey estimates, the
category based on total population: 1 million or more, between 1 million and 250,000, between 250,000 and 50,000, and less than 50,000. Data source for the variable is ES.

The next set of robustness controls include several variables for the quality of the business environment. The rationale for this has already been discussed above. The controls include measures of the regulatory burden on the private firms, corruption in the country and the quality of enforcement of the rules and regulations. For the regulatory burden, we use two proxy measures. These are the country-level average of the firms’ senior management’s time that is spent in dealing with business regulations ( _Time Tax_ ) as reported by firms in the ES, and the Freedom from Business Regulations measure from Heritage Foundations Economic Freedom of the World ( _Freedom From Regulation_ ). For corruption, we use the Control of Corruption measure taken from the World Bank’s Worldwide Governance Indicators ( _Control of Corruption_ ). Similarly, for the quality of enforcement of rule and laws, we use the Rule of Law measure from the World Bank’s Worldwide Governance Indicators ( _Rule of Law_ ). Except for _Time Tax_ which is available only for the current year, all other business environment indicators are lagged by two years to allow for possible delays in their impact on employment growth.

Last, we control for market size. The assumption is that a larger market size offers greater opportunities for exploiting economies of scale and other scale related benefits. We proxy for market size by the (log of) total population in the country ( _Population_ ) in the year prior to the year covered by the ES.

# 2.5 _Endogeneity Checks_

To further raise our confidence against endogeneity concerns, we test for several predictions about how informal competition affects the formal sector. The tests can be classified into three groups.

17
03|0.00|.001|.000|.000|.000|.000|.000|
||(0.30)|(0.28)|(0.16)|(0.00)|||||||
|Observations|6,732|7,340|6,742|2,215|||||||

**Source:** Author’s analysis based on original survey data. **Notes:** The sample includes observations from the baseline survey. The sample is restricted to ultra-poor households in column 1, other poor households in column 2, middle class households in column 3 and upper class households in column 4. For variable definitions, see section 8. All monetary values are in Bangladeshi TAKAs. In 2007, 1USD=69BDT. Columns 5-10 report p-values for the equality of means across the four wealth classes (based on one-sided t-tests).
European household survey, with 1.8 million interviews conducted each quarter throughout the participating countries. The survey collects labor and demographic information for residents living in private households in the 28 member states of

> 2 There is a large literature on the effects of competition on innovation and technology adoption. Examples include Aguion et al. 2009; Ayyagari et al. 2012; Macher et al. 2015; Seim et al. 2011.

> 3 In a previous version of this paper we used, as a proxy for ICT intensity, the fraction of workers using a computer at work by sector taken from the Survey of Adult Skills (from the Programme for the International Assessment of Adult Competencies, PIAAC) for the United States. The results were very similar and are available from the authors upon request.

9
# 1. Introduction

Past decades have seen Malaysia experience a long period of rapid economic growth. GDP per capita grew from about MYR 7,000 in 1970 to MYR 43,000 in 2018 in constant Malaysian ringgit (WDI 2020). This is a more than sixfold increase. For the period studied in this paper, 2004–2016, cumulative real GDP growth was about 77 percent, while real per capita incomes grew by around 46 percent (WDI 2020). Interestingly, and somewhat unusually compared to many other countries experiencing rapid economic growth, survey-based income inequality as measured by the Gini coefficient fell from about 0.46 in 2004 to 0.41 in 2016. These two forces have combined to all but eliminate extreme poverty at the international poverty line of USD 1.90 at 2011 PPPs. Malaysia introduced a new national poverty line in 2020, which is roughly USD 10 per person per day at 2011 PPPs. Based on this new official poverty line, the poverty headcount fell from 7.6 percent in 2016 to 5.6 percent in 2019 (DOSM, 2020).

While this reduction in the headline poverty rate is well-documented, not much is known about poverty dynamics in Malaysia – i.e., statistics that capture the movements in and out of poverty of individuals or households over time. Studies of this kind have been published for other countries in the region, such as Indonesia and Vietnam (Imai et al. (2011), Justino et al. (2008), Purwono et al. (2021), Suryahadi et al. (2003)). On Malaysia, there have been studies based on retrospective data that investigate socio-economic intergenerational mobility (Khalid 2018, Hamid et al. 2019). However, this study is the first to provide information on poverty dynamics in Malaysia based on official micro-level survey data.

One particular feature of Malaysia’s development model concerns the preferential treatment of the Bumiputera ethnic groups, the population segment that was economically disadvantaged both before and after
take-up adjustment, the numbers of recipients of benefits would be inflated leading to an underestimation of poverty.

The remainder of this section compares RUSMOD aggregates with that from official statistics for 2017. For example, the number of employed people in the data appears to be underestimated by 18 percent compared with Rosstat (Table A-4), while the number of pensioners is overestimated by 19 percent and the number of the unemployment benefit recipients by almost 67 percent (Table A-4). This could be due to the sample bias typical for all longitudinal surveys such as RLMS-HSE. At the same time, the number of the unemployed by ILO definition is under-predicted by 9 percent.

**Table A-4. Number of employed and unemployed (in thousands), 2017**

||EUROMOD|External|Ratio|
|---|---|---|---|
|number of employed|59,282|72,324|0.82|
|number of unemployed(ILO)|3,867|4,264|0.91|

Source: Rosstat and RUSMOD for 2017 policy year and 2016 RLMS-HSE data Results for numbers of benefit recipients and taxpayers are presented in the tables below. Receipt of the _monthly cash payment_ and _categorical benefits for housing/utilities_ is underreported by about 50 percent (Table A-5). For the former, the reason might be that the payment is provided from the

> 38 This is, of course, a very rough treatment that disregards factors such as household saving behavior, life-cycle effects and other reasons why reported income may be lower than reported consumption. Nevertheless, the imputation needs to be done for income figures to be comparable with the official statistics. Furthermore, similar adjustments are done regularly for macro-statistics.

> 39 The means test for child allowances does not account for the household income. Only earnings or replacement incomes (pensions, maternity allowances, etc.) of parents have to be reported.

> 40 Given that information on assets reported in the survey
Two further points relating to the Bigsten et al. (2004) paper are worth stressing. The first is that the finding that firms seem to learn from exporting is not the typical finding in studies looking at this issue for other regions. One possible explanation is that the potential gains from exporting are large in Africa because of high trade restrictions in the past and a large technological gap to developed countries. In such economies exporting offers the maximum scope for the increased discipline of competition, and contact with foreign customers provides the maximum scope for learning opportunities. Arguably, if exporting induces efficiency in any environment, it should do so in these economies. The second point is that a productivity gain of 7-8 percent in an output production function corresponds to productivity gains in terms of value-added of 20-25 percent in the short run and up to 50 percent in the long run. These estimates are in line with the results reported by Van Biesebroeck (2005a). The quantitative effect of exporting, thus, is far from negligible.

# **Box 2: Macroeconomic Policy Reforms in Cameroon**

In 1994 Cameroon implemented major policy reforms which included tariff reduction and rationalization, increased domestic taxes as well as attempts to reduced corruption and evasion. Gauthier, Soloaga, and Tybout (2002) use data for Cameroon on prices and quantities of the firms’ major inputs and output to compute the effects of tariff changes and tax reforms. Before the reforms that tariff structure was highly diversified with rates ranging from 0 to 500 percent. The structure encouraged evasion and stimulated attempts to get special treatment by authorities. This case-by-case treatment was hardly beneficial for efficiency. With the reform more than half the firms lost their privileges after 1992/93. For firms operating under the normal regime, though, average customs rate fell from 66.8 percent in 1992/93 20.2 percent in 1994/95. This means that in the case of Cameroon was a leveling of the playing field, which would tend to improve resource allocation in the economy
monthly profits of around KM 1,000 (US$700). They are about 5 years old and 20‐30 percent of them are registered with the authorities.

Overall, the means of the baseline variables are very similar across the treatment and control groups. In particular, none of the stratification variables are significantly different in the sub‐ sample of business owners. There are only a few exceptions, most notably among the business variables. However, these differences are entirely due to chance. We can be sure of this since we performed the randomization ourselves. Following the suggestions in Bruhn and McKenzie (2009), we control for strata dummies and also for baseline outcome levels in our regression analysis.

# **V. Analysis**

# _V.I. Baseline Analysis of Financial and Business Knowledge_

Our baseline survey measures business and financial knowledge through eight questions that are listed in Appendix 2. We construct an overall business and financial literacy score by tallying the correct answers to these eight questions. The score thus runs from zero to eight. The average of this score is about 2.7, meaning that, on average, clients gave the correct answer to 2.7 out of 8 questions.

15
*<br> (0.24)||||
|**Standard**<br>**Controls**|√|√|√|√|√|
|**Sample**|Democracy|Democracy|Democracy|Democracy|Democracy|
|**Number of Observations**|28|28|28|28|28|
|**(Pseudo Log)-Likelihood**|-10.11|-10.29|-10.21|-66.15|-66.14|

Note: robust standard errors in parentheses. Standard Controls: Age of democracy, education attainment, latitude, (log) per capita income, and economic openness (long-term). Varieties of Capitalism Control is a dummy variable which takes the value of one for ‘Coordinated Capitalist Economies’* Significant at the 10% level, ** significant at the 5% level, ***significant at the 1% level. See Appendix A for details). _Source: The Author_ Aside from the EDD/NEDD ratio itself, the standard set of controls includes mechanisms that might capture the existence of alternative oversight mechanisms. The standard controls include the age of a democracy, controlling for the fact that democratic learning and institutionalization may affect the efficacy of elections. Furthermore, the controls include the Gastil Index whose civil liberties component controls for the existence of a free media and the ability of civil society to operate independently of government. This controls for some other critical contextual variables that may affect the efficacy of elections (Freedom House, 2011).

Apart from these broad contextual variables that may affect both EDD and NEDD, it is also necessary to control for NEDD-specific, non-electoral oversight mechanisms, as these may provide effective substitutes for electoral control. To do so, the paper uses the Index of Executive Oversight, an interval indicator (ranging from 0-9) constructed by Hamilton and Stapenhurst (2009) using data on the following factors to determine to
1983|527,054,370|126,573|4164|
|1984|555,515,747|129,273|4297|
|1985|599,129,793|131,978|4540|
|1986|644,002,821|134,653|4783|
|1987|666,708,887|137,268|4857|
|1988|666,304,312|139,819|4765|
|1989|687,391,828|142,307|4830|
|1990|651,627,236|144,091'|4522|
|1991|658,339,124|146,408|4497|
|1992|654,759,303|148,684|4404|
|1993|687,004,026|150,933|4552|
|1994|727,213,139|153,143|4749|
|1995|757,918,030|155,319|4880|
|1996|778,820,353|157,482|4945|

The GDP per capita growth rates plotted in Figure 1 are derived from this series. Annual inflation and unemployment rates also come from the relevant IBGE series.
# **2 Background**

## **2.1 Related Literature**

Our study is part of a growing literature that examines the drivers of people’s willingness to get a COVID-19 vaccine. This subsection provides a review of existing literature in two parts. Firstly, we discuss related research about attitudes towards vaccines in LICs and LMICs, and this is followed by an overview of studies related to intra-household dynamics and the acceptance of vaccines.

### **2.1.1 Attitudes towards vaccines in low- and middle-income countries**

Vaccination decisions are influenced by several factors, one of which is people’s beliefs and attitudes. The “increasing vaccination model” suggests that vaccine uptake is determined by three broad factors: individual cognitions, social processes, and practice issues (Brewer et al, 2017; MacDonald, 2015). Individual cognitions can range from individuals’ beliefs or attitudes towards vaccination, perceived efficacy, or safety concerns, to perceptions on types of vaccines. Factors of social processes can be interpersonal interactions with either friends or family about attitudes and perceptions of vaccination. Practice issues include behavior affecting vaccine accessibility, vaccination cost, and availability.

Studies prior to the COVID-19 pandemic suggest that vaccines in general are accepted by most people in LICs and LMICs. A 2018 Wellcome Global Monitor survey of 140,000 individuals in 140 countries regarding public attitudes towards health and science showed that over 90 percent of respondents in South Asia and East Africa described vaccines as safe. This is in contrast with results from Western Europe, where only 59 percent of the respondents believed vaccines to be safe. This is in line with another global study (Larson et al, 2016) on vaccine confidence conducted in 67 countries. The European region tended to be more negative when it comes to vaccine safety sentiment, with France being least confident (41 percent) in vaccine safety globally. The study also showed that LICs and LMICs, such as 6