rafmacalaba/data-use-mentions-extended huggingface dataset:
economy were then transmitted to the Brazil CGE model through tariffs and import prices changes, and shifts in the demand schedules for the Brazilian exports<sup>18</sup> .

An inspection of Table 7 and Table 8 can give an idea of the importance of these shocks combined with the importance of each commodity in Brazilian external trade. As can be seen, Brazilian exports are spread among many different commodities, with no specialized trend. Raw agricultural products have very small share in total exports, composed almost entirely of soybeans. Processed food and agricultural-based exports (including wood and furniture, rubber, paper, textiles and apparel), however, account for a significant 0.369 share of total exports in the base year, highlighting the importance of Agriculture in the Brazilian economy.

Imports as a share of each domestic production are concentrated in wheat, oil, machinery, electric materials and electronic equipment, and chemical products. In terms of total import shares, however, oil products (raw and refined), machinery, electric materials and electronic equipment, and chemical products are the most important products.

Table 7 also shows some relevant parameters and other production characteristics of the model. The Armington elasticities are borrowed from the GTAP database. The same is true for the export demand elasticities (not shown in the table), made equal to the GTAP region-generic elasticity of substitution among imports in the Armington structure.

The Agriculture sector is modeled as a multi-production sector, producing 11 commodities. Thus the capital/labor ratio (ratio of values) in Table 7 is the same for every agricultural product. The value of land is not included in the value of capital here. If land was included, the value of the capital/labor ratio in agriculture would rise to 0.99. The value

> 18 The shifts in the demand schedules for Brazilian exports were calculated using export price and quantity results from GTAP, together with export demand elasticities drawn from GTAP data.

17
# **Overall Impact of Taxes and Spending on Poverty and Inequality**

In Cambodia, the overall fiscal system reduces inequality. Bearing in mind the caveat that top income households are often underrepresented in household surveys—a common feature of most surveys, especially in developing countries—the fiscal system in Cambodia reduces inequality by 1 percentage point. Inequality, as measured by the Gini coefficient, falls between _market income_ and _final income_ (Figure 8). Before any fiscal interventions, the _market income_ Gini index is 32.4 percent. Once direct taxes and direct transfers are considered, the Gini index reduces slightly to 32.2 percent at _disposable income_ . Indirect taxes and subsidies have limited effect, and their consideration leaves the Gini index of _consumable income_ remain at 32.2 percent. “In-kind” transfers from health and education, on the other hand, have the largest effect on inequality with _final income_ Gini at 31.4 percent. This reflects the fact that in-kind transfers represent a significant share of pre-fiscal income and proportionally benefit lowerincome households more relative to those from the upper end of the income distribution.

Figure 8: Gini index before and after fiscal interventions in Cambodia <!-- Start of picture text --> 34<br>33<br>32.4<br>32.2 32.2<br>32 31.4<br>31<br>30<br>Market income Disposable Consumable Final income<br>income income<br>Gini index (%)<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

While Cambodia does reduce inequality through taxes and transfers, the degree of redistribution is small in international comparison. Figure 9 demonstrates that the redistributive effect of fiscal policy (including in-kind transfers) is low in Cambodia. Some lower-middle-income countries achieve inequality reduction of up to 9 percentage points from the pre-fiscal level. When in-kind transfers are excluded,
improvements in quality and efficiency, and reduced levels of inventory. Furthermore, the authors note that the main factor that impedes firms from independently adopting better management practices themselves is barriers to information. In a follow‐up paper, Bloom et al. (2018), the authors revisited these manufacturing plants in India and found that plants in the treatment group had dropped approximately half of the management practices that had been introduced as part of the experiment due to turnover of key personnel and managerial time constraints. However, nine years after the experiment was initiated, a gap remained between the management practices of treatment and control plants, and, furthermore, treatment plants continued to perform better. Finally, upon revisiting the plants, the authors found that the utilization of improved management practices had spread across plants within the same firm, but not, however, between firms.

In this paper, we focus on management practices of establishments in the LAC region. An earlier analysis of management practices in four countries in this region (namely, Argentina, Brazil, Chile and Mexico) by Lemos and Scur (2012) showed that firms in Latin America employ poor management practices when compared to firms in Europe and Asia. The authors note that one of the primary reasons for lagging managerial practices is the high prevalence of family owned and run firms in the region. Furthermore, the authors confirm that better management practices are linked to improved productivity, both at the firm and the national level. The positive impact of improved management practices on firm performance in the region is confirmed by Bruhn et al. (2018). Based on evidence from a randomized control trial of small and medium‐sized enterprises in Mexico, the authors show that firms that were given access to management consultancy services for one year had higher levels of total factor productivity and return on assets. Using administrative data, the authors also show that, five years after the treatment, those firms that had received consultancy services employed significantly more workers; showing that the impacts of improved management
decent work (full and productive employment; rights at work; social protection; promotion of social rights). We do not review these indicators here.

> 6 See ILO’s recent guidebook for a detailed overview (ILO, 2018c).

> 7 In practice, the definition of employment used by national statistical offices already varied before the 2013 revision of the definition of employment. An ILO paper, reviewing all LFS conducted between 2000 and 2010, shows that all countries include wage employment and self‐employment in market units within the concept of employment. Variation exists, however, with

4
PNG was likely even lower than other resource-intensive economies over this period.

# **Section 5. Comparing the Relationship between Economic Activity and Deprivation in Papua New Guinea to the Rest of the World**

Given the poor performance in PNG over the time period relative to all other comparison groups, we use the richness of the global data on well-being and economic activity

> 25The 2.63 percentage points translates into a large percentage improvement given the share deprived in the rest of the world is small on average.

> 26The countries were Estonia, France, Germany, Hungary, and Switzerland. For a list of High-Income Countries as categorized by the World Bank, see (accessed March 2024) ” _https_ : _//datahelpdesk.worldbank.org/knowledgebase/articles/_ 906519 _− world − bank − country − and −_ ” _lending − groups._

> 27Resource-intensive countries were identified by IMF (2012).

16
# **Internal rmance and Investment: Another look**

The positive relationship between internal finance/cash flows--the sum of retained earnings and depreciation--and capital expenditures/investment is one of the best documented empirical facts in economic research. However, the analytical basis for this positive relationship has been a matter of great dispute and a largely unresolved issue. Briefly speaking, the underlying cash flow theory of investment that leads to the positive relationship consists of two distinct approaches, viz. managerial and information-theoretic approaches to investment. This paper attempts to distinguish between these two approaches on the basis of observed firm characteristics, based on a panel of U.S. manufacturing firms for the 1972-1990 period taken from Standard and Poor's COMPUSTAT database. The results suggest that the observed reliance of firms on internal finance for capital expenditures is due to managerial rather than information-theoretic considerations. The paper is organized into two main sections. Section I considers the analytical issues and section II presents the empirical evidence and discusses the implications for developing countries.

I

# **Investment theories and the role of finance**

It is well-known that prior to the ascendancy of the Modigliani-Miller (1958) theorems and the neoclassical theory of investment, liquidity theory of investment had replaced the accelerator theory of investment as the leading explanation of investment decisions of firms.' ' The neoclassical theory of investment is due to Jorgenson and associates (1963, 1966, 1967, 1971), primarily based on the neoclassical theory of optimal capital accumulation. The liquidity theory is due to Meyer and Kuh (1957), Duesenberry (1958), Kuh (1963) and others. The accelerator theory, the oldest of the investment models, is due to Clark (1917), Chenery (1952), Koyck (1954), Eisner (1964) and others. The managerial nd information-theoretic approaches to investment can be considered as the versions of the liquidity theory and therefore fall under the rubric of cash flow theory of investment.

2
_i_ in month _m_ in year _y_ . The ease of mechanization index is the sum of a district’s Z score across measures of land concentration, combine presence and crop types. The mechanization index is calculated based on levels of each component variable in the district prior to 2006. I include district fixed effects, a year by month fixed effect, and weather controls. Each panel is a different quartile of the mechanization index with quartile 4 corresponding to the places mechanization is predicted to be easiest. N refers to the number of district _×_ months included in each regression. Districts reports districts in each sample. The average number of monthly fires (the outcome) in the pre-treatment period in each quartile are reported. The sample is a balanced, monthly panel of districts in India from 2003 to 2014. Heteroskedasticity robust standard errors clustered at the district level are in parentheses. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

40
in non-farm activity.

The paper is structured as follows: section 2 briefly reviews the development of non-farm activity in rural China; section 3 describes the income composition and characteristics of households in the provinces of Hebei and Liaoning, based on the LSMS data; section 4 discusses two analytical methods, namely, the decomposition of the Gini index and the simulation of household income; section 5 studies the determination of non-farm activity participation and the distribution of the household income with and without non-farm activity; and section 6 presents the conclusions.

> 3 See the results obtained by Adams (1994); Chinn (1979); de Janvry and Sadoulet (2001); Lachaud (1999); Stark et al. (1986).

> 4 See Annex 1 for more information on the location of the provinces of Hebei and Liaoning.

> 5 See Ravallion (2003); Ravallion and Chen (2004).

2
An important element of the analysis is the potential bias introduced by the non-response rate in ENCEL 2017. It should be noted that with a twenty-year follow-up period, significant loss to follow-up is expected.

The sample does not consider those who emigrated to other countries, specifically the group reported to reside in the United States. We note that on average, Mexican migrants in the US have higher incomes compared to those who remain in Mexico (Yaschine, 2015; González de la Rocha, 2008).

# _A. Variables_

The unit of analysis used is the youth who resided in rural households in the evaluation during 1997-2000 and who were also ENCEL 2017 respondents. Among the youth in ENCEL 2017, we are particularly interested in the cases in which the interviewee was also the main household provider when the participant was 14 years old. This allows us to assess intergenerational mobility (Velez-Grajales, Huerta-Wong and Campos-Vázquez, 2015; Torche and Wormald, 2004). Overall, data on 4,578 pairs (youth and provider) is available, although due to missing values some estimations have fewer observations. From this group of pairs (youth and provider), we identified the household of origin to include household baseline characteristics in the analysis for the period 1997-2000.

As part of ENCEL 2017, anthropometric measurements were completed; specifically height and weight were measured for the interviewed youth and their providers. Furthermore, fluid intelligence (Raven matrices) and memory tests were performed. The instruments developed enabled us to collect information on household providers, both from the direct interview as well as from youth’s responses.

Table A.1 of Appendix A lists the main variables of interest for the analysis.

Based on factors such as growth, development and schooling as seen in section IV, individuals 15 years or older when their households began receiving PROSPERA’s transfers were already at a moment in their growth, development and education that limited the ability of the conditional cash transfers to influence the formation of their human capital. With respect to educational capital,
Ponomareva 2000, Sinai and Meyer 2004, Abraham et al. 2010, and Du, Harrison, and Jefferson 2011). In contrast, some studies find that the gains from FDI are larger for exporting firms (e.g. Barrios and Strobl 2002, Schoors and van der Tol 2002, Lin at al. 2009, Jordaan 2011b).

# **2.3 Host Country Characteristics and Institutional Framework**

_Labor market regulations_ can influence the effect of FDI on domestic firms via various channels, including the amount and type of FDI being attracted in the first place, the domestic firm’s absorptive capabilities, and the frequency of labor turnover as a transmission channel of spillovers. Using firm-level data for 19 Western and Eastern European countries between 1998 and 2001 Javorcik and Spatareanu (2005), for instance, find that higher absolute and relative labor market flexibility compared with the foreign investor’s home country has a positive impact on the likelihood of foreign investment. Besides the amount of FDI, labor market regulations also affect domestic firm’s absorptive capacities. In a recent study, Hale and Long (2011) conclude that labor market regulations in general, and wage constraints in particular, affect the level of skills in a firm and, thus, their absorptive capacity. Labor market regulations directly affect the frequency of labor turnover and, thus, the transmission channel. Overly rigid labor markets can reduce the possibility for labor turnover and FDI spillovers. However, overly flexible labor markets may result in frequent labor turnover, lowering the possibility of domestic workers to acquire skills and knowledge in foreign firms.

A few studies stress the role of _financial markets_ in developing countries as a mediating factor for the absorption of spillovers (Alfaro, Chanda, Kalemli-Ozcan, and Sayek 2010; Havranek and Irsova 2011). Multinationals can have an ambivalent impact on access to finance for local firms. They might ease access to finance by bringing in scarce capital to developing countries, but if multinationals borrow from local financial institutions, they might increase
|
|Conscientiousness|does a thorough job|2|0.4286|
|Conscientiousness|tends to be lazy|2|0.703|
|Conscientiousness|does things efficiently|2|0.599|
|Extraversion|is talkative|3|0.5105|
|Extraversion|is outgoing, sociable|3|0.2589|
|Extraversion|is reserved|3|0.2196|
|Agreeableness|is sometimes rude to others|2|0.5878|
|Agreeableness|has a forgiving nature|2|0.4117|
|Agreeableness|is considerate and kind to almost everyone|2|0.3341|
|Emotional stability|worries a lot|5|0.69|
|Emotional stability|gets nervous easily|5|0.2792|
|Emotional stability|remains calm in tense situations|5|0.3463|

_Notes._ Using the data from Pakistan, this table provides the results from an exploratory factor analysis on self-reported grit and big-five items. All items are in a five-point format, which ranges from 1 ("Disagree strongly") to 5 ("Agree strongly"). Following Laajaj and Macours (2021), we applied an acquiescence bias correction (see Appendix D). We perform a principal factor estimation and retain five factors. We rotate the factor loadings so that each item mainly loads on one factor. For each item, we indicate the main factor the item loads on. We also provide the item’s uniqueness, which is the item’s percentage of variance that is not explained by the common factors.

43
2008); and leveraged social incentives among patients, for example, through social signaling (Karing 2019). Our paper is also related to a larger literature that examines various approaches to improving health outcomes in the developing world. A complete review of this literature can be found in Dupas (2011) and Dupas and Miguel (2017).

The rest of our paper is structured as follows. Section 2 describes the experimental design and details of the two interventions. Section 3 introduces our sampling procedure, the survey and Ebola case data, randomization and empirical strategy. Section 4 presents our medium-run findings and plausible mechanisms, longer run Ebola outcomes, and investigates alternative explanations. The final section concludes with a discussion of policy implications.

# **2. Healthcare in Sierra Leone**

## **2.1 Background**

According to the World Development Indicators, Sierra Leone had the second highest under-5 mortality rate in the world in 2011, with 149.8 deaths per 1,000 live births. In the same year, the country had the highest maternal mortality rate in the world. Figure A.2 displays Sierra Leone’s per capita health expenditure and under-5 mortality in 2010 relative to other countries that the World Bank classified as low income. Located in the upper-right quadrant, the country spent more and performed worse than countries at a comparable level of economic development.

In an effort to reduce infant and maternal mortality, the Government of Sierra Leone (GoSL) launched a free health care initiative in 2010, removing fees for pregnant women and children under 5 years old. The policy simultaneously increased pay for government healthcare workers; at the time 30–50 percent of staff did not receive a government wage and instead relied on charging illegal fees or inflated drug prices and accepting in-kind contributions from the communities they served. In addition to removing cost barriers and severe resource constraints, the GoSL saw a need to strengthen incentives for front-line healthcare workers. Absent incentives tied to service delivery, the government worried that
an individual level data base and LITS is household level. In FINDEX, 145 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : FINDEX, LITS Coverage of credit card ownership in Turkey is considerably higher at the household level than at the individual level (Figure 17). This difference is noteworthy since a similar change is not observed for the coverage rate in debit card ownership.

Compared to the benchmark countries, the effect of age on the d-index seems lower across the Turkish population (Figure 18) in 2011. At the individual level, the primary explanatory variables for dissimilarity in credit card usage are gender and age. However, at the household level, the dissimilarity in credit card usage across groups is driven more by the education of the household and its income level.

Between 2011 and 2014, Turkey succeeded in reducing the negative impact of gender disparity in credit card ownership as the contribution of gender fell form 44.4 percent to 31.7 percent. Age became the largest contributor to the d-index by 2014 as the percentage of 15-24 year olds that had a credit card fell from 32 to 12 in three years. This was a sharp reduction, however in comparison to benchmark country groups, 12 percent ownership at the youngest measured cohort was a more expected level for Turkey.

19
food insecurity due to drought in a given area. To optimally estimate this parameter, this variable should be based on field assessments to assess the number of people faced with drought-related food insecurity. However, in the absence of recently updated data, LEAP selects the maximum historical beneficiary number recorded in a given area.

- c. **_Lowest observed value of RWRSI:_** The Regional Water Requirements Satisfaction Index (RWRSI) is defined as the weighted average of woreda-level combined WRSI, where the weights are given by the population for each woreda. The lowest observed RWRSI reflects the lowest drought index based on historical data, reflecting the most severe drought on record in a given area. In cases where a drought occurs that is more severe than the most severe past drought, the model used is able to extrapolate the associated level of needs.

- d. **_Optimal RWRSI:_** The optimal RWRSI refers to the optimal rainfall conditions relative to a given region. Although a value of WRSI of 100% would reflect optimal crop growth conditions, the optimal RWRSI may be less than 100% depending on local conditions.

- e. **_Systemic failure level:_** This parameter estimates the complete failure of the crop seasons, reflecting the level of water stress beyond which community coping mechanisms are exhausted and beneficiaries require external assistance to meet food needs. The parameter is difficult to estimate, as it depends on farming practices and farmers’ skills. However, the failure level should be selected such that it is significantly lower than the lowest observed value of RWRSI (see c. above). One possibility for selecting the systemic failure level is to use a value of WRSI of 50, below which crops are assumed to fail, or to link the systemic failure level to the lowest observed value of RWRSI by scaling down the lowest observed value of RWRSI through a constant assumed to be the threshold beyond which crops fail.

> 43 Doorenbos and Kassam (1979).

11
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