rafmacalaba/data-use-mentions-extended huggingface dataset:
These quantitative results hint at the mechanism of our measured impacts. Specifically, the behavior change in our study tracks well with the storyline that featured the main soap character, who herself depicted poor financial behavior before transforming her habits. This character was part of the show prior to the two-month financial storyline and remained part of the show afterwards. The non-result on knowledge of the NDMA could at least partly be explained by the fact that the NDMA messages were delivered not by the main character, but by a different character who was external to the main soap cast and who appeared in 2-3 episodes as an NDMA counselor, and then disappeared. Hence, the audience did not get the opportunity to establish or maintain a connection or following with this character.

In order to further assess the merits of this emotional connections channel, we conducted three qualitative focus group discussions with study participants three months after the final quantitative survey. In these meetings, the focus group leaders were unable to elicit recall of NDMA from the participants and only a few women seemed to remember, and they associated their memory more with the fact that the NDMA character was female rather than her representation of the NDMA. In contrast, all participants readily recalled the main soap character and aspects of the financial storyline delivered by her. Upon further inquiry, they attributed better recall to the fact that the main character was a mainstay in the soap opera, and was popular and likeable.

Overall, our findings suggest an important role for entertainment media as an accessible and important tool for policymakers to deliver carefully designed educational messages that resonate with the audience and can potentially influence financial knowledge and behavior. Further, our findings suggest that emotional connections and familiarity with media personalities certainly play a role in motivating knowledge and behavior change among viewers, and that harnessing such potential can be an important channel for achieving development impact.

This paper is organized as follows. Section 2 provides a brief literature review on financial literacy and mass media. Section 3 focuses on financial education in South Africa and explains
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Box 2. Understanding the differences between JMP and government data**

The AICD uses the Joint Monitoring Program (JMP) coverage statistics as the main source of access data on water supply and sanitation, and proceeds under a standardized methodology to allow cross-country comparisons. These data might differ from those reported by governments. Whereas the JMP data are based on household surveys and therefore reported by users of the services, the government data are based on utility reports. This implies that there is a time lag between output data (provider) and outcome data (users). Other underlying factors explaining potential differences are the definition of what technologies constitute improved access to water supply and sanitation, and the JMP’s use of several household surveys vis-à-vis the use of a single data point by several governments. Therefore, the conclusion on progress toward the MDGs might differ according to the data source used.

_<u>Source:</u>_ Adapted from AMCOW (2010).

**Table 11. Benchmarking water and sanitation indicators**

||Unit|Low-income<br>countries||**Mozambique**||Middle-<br>income<br>countries|
|---|---|---|---|---|---|---|
|||**Mid-2000s**|**1997**|**2003**|**2008**|**Mid-2000s**|
|Access to piped water|% pop|10.5|7.0|8.0|8.7|52.1|
|Access to standposts|% pop|16.2|19.0|20.6|16.7|18.9|
|Access to wells/boreholes|% pop|38.3|47.0|54.7|59.0|6
age, sex, height and weight from the NFHS on a set of common variables available in the NSS Schedule 10 employment survey. Because age and sex are always observed, this procedure generates additional variation in TEE conditional on age and sex that captures average differences in height and weight associated with socioeconomic status, maternal and childhood nutrition. Appendix Table S.4 reports the variables and coefficients for these regressions, which include individual and household age, education, demographics, school attendance, land holdings, and two-digit primary occupation code (using NCO1968 classification and available concordances for the later NFHS rounds). Separate equations are estimated by sector and sex, pooling the available years of data (which includes 1998 for women and 2015 for both men and women) and including year fixed effects (the omitted category is 2005). In principal one could also use separate predictive equations for height and weight, and then apply the (Henry 2005) equation, but results are similar either way.

Note that these equations can only be estimated for the adult age ranges in the NFHS eligible for height and weight measurement (15-49 for women and 15-54 for men). For individuals outside of this range, the following procedure is adopted:

1. Assume that their age is equal to 15 or 49 (54 for men) and use the predictive equation for REE

2. Scale this REE by the ratio of average REE for that individual’s age and sex relative to the average REE for their “assumed age” used in the predictive equation.

REE is scaled using the age and gender specific mean height and weight reported by the National Nutritional Monitoring Bureau for rural areas of 16 Indian states between 2000-2002 (reported in (Indian Council of Medical Research 2009)), together with the (Henry 2005) equations. Note that (Indian Council of Medical Research 2009) uses the 95th percentile of this same distribution to generate their recommended daily caloric intake, highlighting an important difference between prescriptive estimates of recommended daily caloric intake and the descriptive estimates
other things, due to changing conditions that initially supported the adoption of the rules, potentially weakening commitment over time.

Our sample includes 108 countries, also including countries that never adopted fiscal rules. With fiscal rules data available up to 2021, we study their adoption until 2012. This allows us to analyze the evolution of the primary balance over period of ten years. The

> 9 Chrysanthakopoulos and Tagkalakis (2024) present another recent study on the dynamic effects of fiscal rules, but their analysis uses a methodology that restricts the type of effects that can be measured (see Section 4). Surprisingly, their findings show that fiscal rules lead to lower primary balances in the medium term, and they associate this counterintuitive result to possible lower interest payments associated with the increased credibility of governments.

9
*2. The In-Tech Program**

Poland’s National Center for Research and Development (NCBiR) is a government entity whose programs account for around 40 percent of the national research and development budget. One of the programs it offers is Innotech, which is designed to support research entities and businesses in

> 4 Previous studies typically rely on balance sheet data and firm surveys conducted by the country’s statistical agency. They obtain information on publications or patents by matching researcher names or firm names to databases of publications and patent applications.

5
Reliable measures of x, the capital share in total income, are hard to come by. Bosworth and Collins (1996) after reviewing the existing literature find that a plausible range for capital share is 0.3 to 0.4. In their computations of TFP they use a fixed value of 0.35 for developing countries, including the East Asian countries. While this is valid only for a limited set of production functions, they report that existing studies find little evidence of major changes in factor shares over time. Sarel (1997) who analyzed TFP growth in East Asian countries found no major change in the value of ax for Malaysia which hovered around 0.32 over most of the 197896 period. We adopt here a one-third share of capital, as some others have done (for instance Klenow and Rodriguez-Claire 1997). However, we also estimated TFP using the end-range values of 0.3 and 0.4 and find that it did not have any significant impact on the analysis.

A series of capital stock was constructed using the perpetual inventory method, as is common in the literature. The initial capital stock was obtained from Nehru and Dhareshwar (1993)3. Investment data are taken from World Bank's World Development Indicators cd-rom. We were unable to obtain disaggregated data on capital stocks in order to conduct quality adjustments. The capital stock was computed with depreciation rates ranging from 0.05 to 0.12. The impact on TFP estimates was however not appreciable.

We obtained data on employment of the labor force over the 1970-97 period from official Malaysian publications and World Bank country reports. Labor force was adjusted for what has been found to be it's most important quality adjustment, namely, educational attainment<sup>4</sup> . A series on the educational attainment of the labor force was constructed<sup>5</sup> by employing the methodology described in Nehru, Swanson, and Dubey (NSD, 1995).

Figure 1 plots the trend of
. For the Philippines 2011. For Vietnam South Africa and it is 2008. The end-of-period year information is based on cumulative number of greenfield FDI projects over the last period interval for which the data is available: 2009-2014 for Brazil and Colombia, 2010-2014 for Ethiopia, 2009-2012 for Mexico, 2009-2012 for the Philippines, and 2005-2008 for Africa and Vietnam.

# **2.2 Household Surveys**

We use household surveys from the World Bank’s International Income Distribution Data Set (I2D2) which contains harmonized household and labor surveys. While surveys for Brazil, Colombia, Ethiopia, Mexico, the Philippines, South Africa and Vietnam are available for our baseline year, 2004, the subsequent years available vary by country. The additional years for Brazil and Colombia are 2008 and 2014. The additional years available for Ethiopia are 2009 and 2014. For Mexico they are 2008 and 2012. The additional years available for the Philippines are 2008 and 2011. For South Africa and Vietnam the only additional year we can use is 2008.

The I2D2 data contain harmonized information on wages, gender, sector of activity, location, and education. We adjust the data sets before conducting the analysis in the following way. First, we reduce the sample to individuals between 15 and 59 to focus on 6
pronounced focus on informality in Bolivia when analyzing the quality of employment has made less visible other dimensions of a good job such as working conditions, stability and the linkage between wages and the job.<sup>3</sup> This paper proposes a Job Quality Index (JQI) that encapsulates key elements that define a good job. It builds on the literature on measuring the quality of jobs, and also incorporates country-specific considerations related to Bolivia’s regulatory and institutional labor market context. We employ a Principal Component Analysis (PCA) to facilitate the aggregation of various labor dimensions into a composite value, with each component representing a distinct job quality aspect. Additionally, the usage of the PCA allows to endogenously select the job dimensions’ weights and avoid any arbitrary assignment. These dimensions are derived from the Bolivian Household Survey, which offers extensive information on workers’ labor conditions, including contract types, pension enrollment, monthly wages and other relevant factors. The analysis utilizes data from 2007 to 2021 to assess the evolution of these labor conditions over time, examining the economic boom in Bolivia during the past decade as well as the pandemic crisis.

We find that job quality mostly remained constant during the analysis period, hovering around 0.46 (in an index between 0 and 1, where values closer to 1 indicate higher quality). At the same time, the index highlights high variation in the quality of jobs across groups. For example, men have jobs with a quality 28% better than those of women, representing a gap that has been persistent over time. We also find that workers in micro and small firms have jobs with a quality 0.17 points below that of self-employed workers (representing a quality 42% lower).

Overall, these results suggest that Bolivia faces challenges in increasing job quality as evidenced by a relatively steady measure even during an economic boom. In addition, it shows the disparities that exist and persist among different groups. Namely, women, workers in micro and small firms
Distortions Database. Because of the possibility that governments set agricultural protection to exploit terms of trade effects, we also control for country size using the log of population, _logpop_ . We proxy for political institutions by adding the Polity2 index of democracy taken from the Polity IV database (Marshall and Jaeggers 2007).<sup>7</sup> In addition to these standard control variables, we use a series of covariates to check the robustness of our findings. They include two different indicators of (aggregated) openness: the ratio of trade to GDP from the Penn World Tables and the Sachs-Warner index of openness as defined by Wacziarg and Welch (2008).<sup>8</sup> We use government consumption to GDP from the Penn World Tables as a proxy for government size. Finally, because economic crises may trigger policy reforms, we add two lags of a crisis variable measured with a dummy equal to one for every year that the real GDP per capita growth rate is negative (zero otherwise). Table 1 presents summary statistics for the variables described above.

7 The Polity2 index assigns a value ranging from −10 (autocracy) to +10 (democracy) to each country and year, with higher values associated with stronger democracies.

8 Wacziarg and Welch (2008) updated the Sachs and Warner index and exploited its time dimension. The index is equal to one when a country is considered open and zero otherwise. Thus, it captures reforms in the overall trade policy.

16
# **Online-Only Online Appendix to “Coping or Hoping? Livelihood Diversification and Household Welfare in the COVID-19 Pandemic”**

# **A Additional Data Considerations**

## **A.1 Diversification Indices**

Following our pre-analysis plan, in addition to the indices specified and discussed in the main text, we also present findings from four additional indices. These are summarized in Table A1.

Each of the indices, here and in the paper, has advantages and limitations. The fractional indices consider engagement in income-generating activities. These measures include binary responses and the dichotomous nature of these variables allows for comparison in income-generating activities over time with the inclusion of the post-outbreak HFPS rounds. However, these fractional indices do not consider the amount of income earned from each source. As such, these indices are a less nuanced representation of household income diversity than the Herfindahl-Hirschman Index (HHI) measures. For example, suppose Household A was engaged in casual employment for one week in 2019. During that week, the household earned five percent of their total annual income and the remaining 95 percent was generated through farm work. Suppose their neighbor, Household B, was also engaged in casual labor and farm work but generated equal incomes from these two sources (a 50 percent split). In our data, Households A and B would receive the same fraction score, even though Household A is much more dependent on a single income source than Household B and as a result Household A would have a higher HHI than Household B. There is a trade-off between using all of the data, pre- and post-outbreak, and just the pre-COVID data. The former gives us more observations over time but less detail about income. The later provides more detail about income but is only a snap shot in time.

Similarly, there is a trade-off between the simplicity of the fractional indices and the HHI. The fractional indices lack detail because they encode that detail in simple “yes” or “no” answers. Conversely, the
|
|Skewness of<br>household error|||||-0.002|||
|Kurtosis of<br>household error|||||3.00|||
|Wilks-Shapiro P-<br>value of household<br>error|||||0.483|||

Notes: Results reflect averages across 8000 estimates, representing 100 simulations of 80 simulated areas. Diagnostic results reflect averages over 100 simulations. H-T indicates Horvitz-Thompson approximation of variance.

7. Use the modified EMDI package to generate point and MSE estimates for each district based on the census data of 1.17 million households containing all model prediction variables and household size for these regions. The model estimates derived from the sample are not weighted, and the dependent variable is normalized welfare calculated in step 4. Census households are weighted by household size when estimating district level headcount poverty rates. As above, we use 100 Monte Carlo simulations to generate point estimates of headcount poverty and 100 bootstrap replications to estimate the mean squared error.

After repeating this seven-step process 250 times, we can calculate average evaluation statistics of the type reported in Tables 7 and 8 across the 24 districts and 250 simulations.<sup>41</sup> Since the Fay-Herriot

> 41 Poverty rates for each district are calculated based on a poverty line set at the 46.5th percentile of the national distribution of non-monetary welfare.

38
of the agricultural land in Sub-Saharan Africa is estimated to have had at least 10% tree cover during 2008-2010 (Zomer et al., 2014). Trees and agricultural activities therefore often co-exist not only in larger landscape contexts but also in single landowner holdings.

The available research on trees on farms has so far largely focused on case studies within particular countries (e.g. Dewees, 1995b; Godoy, 1992; Pouliot and Treue, 2013). Region-wide aggregated approaches have also shed light on the prevalence of on-farm trees (Zomer et al., 2014), but because they are based on remotely sensed data such studies have not directly accounted for household perspectives and practices. Cross-national (Wunder et al., 2014) and global (Agrawal et al., 2013) syntheses of forest and broader environmental income also exist, but systematic comparative information on the prevalence and economic contribution of trees on farms remains missing. This is especially problematic given intensifying competition for land in Africa (Peters, 2013) and the challenge of simultaneously advancing human development and environmental protection goals.

2
highlighted not only the critical role of digital connectivity for the continuity of public service provision and economic activity, but also the digital divide across income groups and geographic regions. Although access to broadband internet has expanded over the past decade in ECA, with almost all households having access by 2018, a large share of the population still lacks basic digital skills and does not use digital technologies. In 2021, fewer than half of Central and Eastern Europeans had basic digital skills. This has limited the use of the internet for e-commerce and interaction with public authorities to levels much lower than in the rest of Europe.<sup>15</sup> Moreover, it has been much easier for highly skilled and high-wage workers to work remotely compared to low-skilled workers. Thus, low-skilled workers experienced a significantly larger drop in employment,

> 13 Labor market slack is measured by Eurostat and is defined as unemployed, inactive, unavailable, and underemployed people as a share of the labor force and potential additional labor force (that is, those inactive and unavailable).

> 14 As measured by Eurostat’s NUTS 2 and NUTS 3 regions, which comprise Bulgaria, Hungary, Montenegro, Poland, Serbia, Romania, and Türkiye.

> 15 In 2021, ECA countries ranked among the lowest in the EU in the European Commission’s Digital Economy and Society Index. Low rankings reflect weakness in digital connectivity (for example, in Bulgaria, where only 59 percent of households subscribe to broadband services, well below the EU average of 77 percent), online public service delivery (Bulgaria, Romania), and digital skills (for example, in Bulgaria, Poland, and Romania; only 29 percent of Bulgarians aged 16 to 74 years have basic digital skills compared to the EU average of 56 percent).
prices of various commodities. Serra et al. [61] used nonlinear time-series models to assess the price relationship within the U.S. ethanol industry. They used daily data on ethanol corn and crude oil prices and identified equilibrium relationships between these prices. They found that when corn prices are high relative to fuel prices, ethanol prices are mostly affected by the price of corn. When the price of corn is low relative to the price of fuel, then ethanol prices are likely to follow the price of fuel. Similarly, Balcombe and Rapsomanikis [9] established a consistent long-term equilibrium between ethanol prices and the price of sugarcane and oil using data from Brazil.

In summary, the literature suggests that one has to contend with several factors in order to explain the causes for the food crisis. On the demand side, another major factor is rapid economic growth in emerging economies, which increased demand for meat, a highly grain-intensive product. On the supply side, bad weather in key grain-producing regions (especially wheat-growing regions such as Australia), stagnation of productivity growth (due to underinvestment in agricultural research and technology and infrastructure such as irrigation), and increase in production costs (due to high energy prices) have resulted in slow or negative growth in production. Prices spiraled even further as a result of policies such as export bans on grains and import tariffs on non-grain biofuels (especially the U.S. import tariffs on cane ethanol from Brazil) and on account of speculative activity in reaction to such policies. Lastly, the depreciation of the U.S. dollar relative to major world currencies has also been a contributing factor to commodity price increases. Historically, when the dollar is weak, commodity prices tend to be higher and, when the dollar is strong, commodity prices tend to be lower. However, with different countries adopting different policies toward biofuels and trade, assessing the country-level impacts of these factors require case-by-case analysis.

With several such factors at play, identifying the contribution of any one factor such
<!-- Start of picture text --> Abstract<br>This paper investigates whether the Pesticides Iniative Program has sig-<br>nificantly affected the export performance of Senegal’s horticulture industry.<br>We apply two main microeconometric techniques, difference-in-differences<br>and matching difference-in-differences, to identify the effect of the Pesti-<br>cides Initiative Program on exports of fresh fruits and vegetables. We use a<br>unique firm-level dataset containing data on sales, employment, and exports<br>by product and destination markets, as well as firm enrolment year, over<br>2000-2008. The results suggest that while the program had no significant<br>effect on exports pooled over all products and destinations, it had a positive<br>effect when considering fresh fruits and vegetables exports to the European<br>Union.<br><!-- End of picture text --> <!-- Start of picture text --> 2<br><!-- End of picture text -->