rafmacalaba/datause-encoder-data huggingface dataset:
transaction costs are an important determinant of investment decisions of individual investors. Consistent with theoretical models of investor behavior, households rationally reduce the frequency with which they trade illiquid securities subject to high transaction costs. This finding is robust to controlling for household and stock characteristics.

Second, we show that there is cross-sectional variation in the relationship between holding periods and transaction costs across households. Particularly, we find that the relationship between transaction costs and holding periods is stronger among more sophisticated investors. Third, we show that paying attention to transaction costs has important implications for investment performance. Households whose holding periods are negatively related to transaction costs earn lower gross returns even before adjusting for transaction costs when compared to households whose holding periods are positively related to transaction costs, suggesting that those investors who pay closer attention to transaction costs have better trading ability.

Finally, to address endogeneity and selection concerns, we study how investors’ holding periods change around quasi-exogenous changes in transaction costs, and we extend our analyses to a sample of investors from Finland. We show that our main findings are robust to potential endogeneity and selection concerns.

# **References**

Acharya, V. V. and L. H. Pedersen, 2005, “Asset pricing with liquidity risk,” Journal of Financial Economics, 77, 375–410 Alexander, G., J. Jones and P. J. Nigro, 1998, “Mutual fund shareholders: characteristics, investor knowledge, and sources of information,” Financial Services Review 7, 301–316 36
Generally the windfalls lead to an appreciation of the real exchange rate by shifting production inputs ( _capital and labor - resource movement effect_<sup>12</sup> -) to the booming mineral sector and non-tradable sector (retail trade, service, and construction), thereby reducing the competitiveness of the non-booming sector mostly agriculture and manufacturing hence participating in their collapse. The shift into non-tradable sector accelerates domestic inflation<sup>13</sup> ( _the spending effect_ of the boom does increase the price of non-traded goods due to increased demand), which is responsible for the real exchange rate appreciation. These short-term macro-adjustment problems result in long-term effects on growth by reducing the country’s economic diversity and increasing its reliance on exports from its natural resource sector.

Another well-known issue of resource-rich economies is volatility, known as _Boom and Bust cycles,_ which can equally have negative impact on economic growth. This phenomenon plagues oil exporter<sup>14</sup> countries, given the importance of oil in the overall economy and government revenues. The economic impact includes unpredictable revenue streams because widely fluctuating export revenues lead to fluctuating levels in overall government revenues (Mikesell 1997). These frequent upward or downward adjustments of fiscal expenditures are costly because they simultaneously discourage private investment and destabilize the government’s budget, therefore impeding its ability to sustain investment and social provision and service deliveries (Katz et al. 2004). In addition, once expenditures become entrenched, it is harder for governments to make budget cuts; rather than reversing their spending patterns during busts, they often opt to borrow, and hence, risk incurring huge debt burden.

<!-- Start of picture text --> Chad Total Oil Revenues 2003 - 2009<br><!-- End of picture text --> <!-- Start of picture text --> 1400<br>1200<br>1000<br>800<br>Total Oil Revenues<br>600<br>400<br>200<br>0<br>2003 2004 2005 2006 2007 2008 2009<br>Years<br>Million US$<br><!-- End of picture text --> Sources: Chadian authorities and IMF projections.

12 The marginal productivity of labor in the booming industry will increase and attract labor away from the non-booming industry. This change in the sectoral composition of labor is called the resources movement effect of the boom (Corden, 1992). - Managing Oil Wealth: The Case of Azerbaijan. IMF 2004.

13 As the matter of fact, the non-tradable sector supply is inelastic in the short term so, when demand expands and resources move into the non-tradable sector, this causes prices to increase thereby induces real exchange rate appreciation.

14 Although market volatility is a problem for all exporters of primary commodities, the phenomena is acute for oil exporting countries given the importance of oil in the overall economy.

12
only properly reproduce the pre-treatment outcomes averaged over all twelve treatment forests, but also those for each of the treatment forests separately (see Figure C.2 in Appendix C). And this holds not only for the case of forest fires that are detected with a 30% confidence level, but also when using the 50% criterion; see Figure C.3 in Appendix C. Our results are thus robust to changes in forest fire detection confidence levels. And our results are also robust to excluding those non-treatment forests from the analysis that are contiguous to treatment forests; see Table C.2 in Appendix C. This is important because it allows us to rule out the possibility of treatment spillovers affecting the post-intervention outcomes in non-treatment forests, which would have resulted in a violation of the SUTVA assumption (Pearl, 2009).

Regarding the effectiveness of the intervention in reducing the number of forest fire occurrences, Figure 4 shows that the post-intervention differences between the treatment forests and their synthetic controls tend to be small in all months except for, possibly, November – and then only for the first three or four years. This is confirmed by the SCM _K_ treatment estimates _αt_ = _K_<sup><u>1</u></sup> � _i_ =1<sup>_αit_</sup> presented in Table 1. Columns (1) and (2) of � � this table present the estimated impact on fire occurrences with a minimum detection confidence of 30% and 50%, respectively. Note that the associated _p_ -values are presented in brackets, and that those with _p <_ 0 _._ 10 are printed in bold.

As already suggested by the graphical evidence presented in Figure 4, Table 1 documents that the FIP’s intervention only managed to significantly reduce forest fires in November, the post-harvest month. In the first year of the intervention, the number of days the average grid cell was on fire in that month decreased by 0.073 compared to the synthetic control; see Column (1). While this effect is sizeable (as it is equal to a 43% reduction in forest fire occurrences), treatment effects were too divergent between the twelve treatment forests for this reduction to be significant. In the subsequent years the treatment effect increased from 0.073 (in 2014) to 0.126 (in 2016), after which it started to decline. And while the effects were still considerable in 2018 (with the average grid cell 19
volatile than the economy of any single country, which leads to goods prices that are more stable worldwide than in any single autarkic economy. This opens up the possibility that greater openness may reduce the variance in individual incomes. Thus, overall, the relationship between openness and individual income risk is theoretically ambiguous, requiring empirical analysis for its resolution. In a recent paper, Krebs, Krishna and Maloney (2005), we have investigated this trade-income risk question empirically and have reported economically significant impacts of trade policy (changes) on the volatility of worker incomes.<sup>6</sup> The possibility that trade policy may affect income risk in labor markets raises additional questions. Is the effect uniform across workers? Or, for instance, are workers with higher human capital better able to insulate themselves against trade policy changes? Evaluating empirically the interaction between human capital, trade policy and income risk is important for a number of reasons. First, this helps us evaluate the merits of particular theoretical arguments that assume differential effects of openness on variables such as labor turnover for workers of different human capital levels. For instance, the well-known argument by Bhagwati and Dehejia (1994) regarding “kaleidoscopic comparative advantage” argues that the observed trend of increased inequality in wages between workers of high and low levels of education may be explained by the differences in the way that openness impacts labor turnover rates (a component of income risk) for these two groups. Second, from a public policy perspective, it is important to know how policy changes impact different segments of the population; public opposition to “globalization” is driven at least in part by concerns that openness affects most adversely the poorest workers. Finally, such an analysis will deepen our understanding of the trends in the supply of human capital in the labor market.

In this paper, we study _empirically_ the effects of trade policy on individual income risk for workers of different levels of human capital using the following approach. For each industry (sector), we use longitudinal data on individual earnings for different types of workers to estimate time-varying parameters of individual income risk using

> individual firms within that sector.

> 6 In this paper, we draw substantially on this earlier paper by us, specifically in presenting  the main issues and in describing the methodological approach  (Sections I-III).

2
stunting and adult survival rate used in the original HCI. The basic formulation of the Europe and Central Asia HCI (ECA-HCI) is as follows: where _B_ refers to the quality-adjusted basic education schooling level of the generation of children born today, with an associated productivity return captured by parameter _η_ and full basic education benchmark _B_<sup>_*_</sup> ; _C_ refers to the quality-adjusted higher education schooling level, with an associated productivity return captured by parameter _ω_ and full higher education benchmark _C_<sup>_*_</sup> ; _RF_ refers to the prevalence of adult health risk factors (namely the share of non-obese individuals in the adult population, the share of adult nonsmokers, and the share of adults who report no heavy drinking), with an associated productivity return captured by parameter _γRF_ . The benchmarks for these shares are set to 100 percent non-obese, nonsmokers, and non–heavy drinkers. _O_ refers to the value of the relevant health outcomes (adult survival rate and child stunting); γ _o_ refers to their productivity effects, estimated via their relationship with adult height, as in the original HCI.

# **_3. Education Component_**

The 2019 _World Development Report_ highlights the changing nature of work across the globe. In highincome countries, which include most of the countries in Europe and Central Asia, having a good basic education will not be enough for individuals to be productively included in the labor market in the next decades; higher education of good quality will be necessary for the next generations to be productive workers. The education component of the ECA-HCI therefore extends the original education component by adding a measure of quality-adjusted years of higher education (QAYH) to the measure of learningadjusted years of basic education. Like learning-adjusted years of basic education (LAYS), QAYH measures both quantity and quality.

The basic formulation of the education component of the ECA-HCI is the following: U m<sup>LLSS−LLLSS∗)+ωω(QQLQQ−QQLQQ∗)</sup> SSeem UU Where and _ω_ EEHHEE−H HH = ee<sup>ηη(L</sup> Where _η_ and _ω_ are the productivity returns of one additional year of quality basic and higher education respectively, and _LAYS*_ and _QAYH*_ are the benchmark number of years equivalent to full basic and higher education respectively.

As shown in equation 4, the education component of the ECA-HCI includes two subcomponents. The first measures the basic education schooling level expected for the generation of children born today. This 4
sample.<sup>12</sup> Second, there is no significant difference between response rates of treatment and control groups. Some of the final interviews could not be conducted face-to-face and we investigate whether the likelihood of face-to-face interview is correlated with treatment status, and reassuringly it is not (column 2). Nevertheless, we control for mode of interview in all subsequent analysis.

Finally, the take-up within both treatment and control groups was very high. Out of the sample of individuals assigned to either group, 96% reported watching their assigned soap during the study period, and there was no variation across treatment and control groups. Furthermore, Table 3 investigates whether there was substantial crossover or treatment contamination, with the control group also watching the treated show, Scandal. In either follow-up 1 or 2, we find that less than 12% of Muvhango viewers claim to have also watched Scandal in the last four weeks, and a third of these viewers watched several other shows too. The tabulations show that 2% of respondents watched Scandal and another show, about 1% watched two other shows, and 0.5% watched three or more other shows. A large proportion – 38% in follow-up 1 and 29% in follow-up 2 watched entirely other shows and did not watch Scandal. While these are reassuring findings, we note that such crossovers will only underestimate treatment effects in our subsequent analysis.

# **6. Results and Discussion**

This section presents the main analysis on financial knowledge and behavior. Since our research methodology uses random assignment, we compare the cross-sectional averages between the treatment and control groups in a regression framework, and conclude that any difference between the two groups is due to viewing the financial messages in Scandal. Additionally, we control for mode of interview and use robust standard errors.

Note that while the two intermediate CATI surveys provide insight into the immediate effects of our intervention, the outcomes measured in these surveys are not directly comparable with the outcomes in the final face-to-face survey since a richer set of questions were asked in the latter.

> 12 Note that the response rates on the first two telephonic surveys were relatively lower, 85% and 76%, respectively.

17
68 permanent settlers declined to 55,300 in 1985 and 46,800 in 1986 (SOPEMI, 1988). However, the average net emigration of 64,000 per year in 1980-1982 has been replaced by an increasingly large net immigration after 1982. Net immigration was estimated at 17,000 in 1983, 37,000 in 1984, and 59,000 in 1985. Apparently this trend continued in 1986.

Information from the international migration data bank at the United Nations shows a net outflow of 78,000 persons between 1980 and 1985. The quality and completeness of these estimates, however, is unknown. In 1987 alone, nearly 42,000 persons from the United Kingdom moved to Australia, Canada, and the United States. Under the circumstances, I would continue net emigration at a constant level of 150,000 per five-year period through the end of the century.

# Ireland

Net emigration from Ireland should be higher than the previous World Bank assumptions for all years. Illegal immigration to the United States is thought to have been quite substantial during the 1980s and legal immigration to the United States has also been increasing rapidly -- from 1,397 in FY1985 to 1,839 in FY1986 and further to 3,060 in FY1987 (Immigration and Naturalization Service, 1988). Moreover, recent changes in U.S. immigration law favor more immigration from Ireland at least for the 1988-1990 period. Emigration to other countries is also substantial and the prospects are for continuing emigration through the end of the century.

During the three-year period ending in mid-April of 1984, the Central Statistics Office of Ireland estimated that there was a net outflow of 18,000 persons (CSO, 1985). Based on this situation and other available information, the Central Statistics Office made population projections with two migration variants. The first variant assumed net emigration of 25,000 persons in 1981-1986 and 37,500 in 1986-1991. The higlh emigration variant assumed net emigration of 50,000 in 1981-1986 and 75,000 in 1986-1991. I would recommend net emigration rates of 35,000 for 1980-1985, and 50,000 for 1985-1990, 1990-1995, and 1995-2000 (evenly split between males and females for all years).

# Italy

International migration patterns in Italy are unusual in that Italians are leaving the country for other parts of Europe and the United States while foreigners are immigrating to Italy, both legally and illegally. SOPEMI (1988) notes that in 1986 there was a slowdown in migration both from and to other countries. At the same time, there was a tendency for Italians to settle permanently in other countries and a growing interest in immigration to Italy from foreign countries. The very large component of illegal migration continues to complicate the Italian migration picture. As of September 27, 1987, nearly 90,000 illegal immigrants had applied for regularization under a law put into force at the end of 1986, but it has been estimated that ten times that many illegal immigrants actually reside in Italy (SOPEMI, 1988).
**Table 8: Impact of PBF on community satisfaction**

||**Overall**||**p-**||
|---|---|---|---|---|
|**Variable**|**mean**|**PBF impact**|**value**|**N**|
|**Panel A: women with pregnancies in the p**|**receding tw**<br>|<br>**o years**<br>|||
|The staff is welcoming and respectful|0.988|0.005|0.592|3,033|
|The staff at the facility is competent|0.979|0.017*|0.070|2,955|
|The facility has the equipment needed to<br>provide high quality health services|0.867|0.036|0.412|2,583|
|The facility is in good physical state to<br>provide high quality health services|0.915|0.058**|0.042|2,669|
|The facility staff works closely with the<br>community on health matters|0.970|0.040**|0.017|2,859|
|<br>The staff listens to the opinions of the<br>community|0.977|0.023**|0.049|2,842|
|Changes in the past three years:<sup>c</sup>|||||
|Improved health facility infrastructure|0.949|0.027|0.325|2,168|
|Improved attitude of health workers|0.975|0.021**|0.033|2,442|
|Improved quality of health services|0.972|0.030*|0.069|2,433|
|Improved collaboration between community<br>|0.978|0.018*|0.067|2,390|
|and health facility|||||
|**Panel B: Adults 40 years and above**|||||
|The staff is welcoming and respectful|0.979|0.008|0.221|2,024|
|The staff at the facility is competent|0.972|0.036***|0.002|1,956|
|The facility has the equipment needed to<br>provide high quality health services|0.855|0.029|0.436|1,706|
|The facility is in good physical state to<br>provide high quality health services|0.903|0.010|0.737|1,740|
|The facility staff works closely with the<br>community on health matters|0.964|0.001|0.935|1,894|
|The staff listens to the opinions of the<br>community|0.971|0.016*|0.098|1,889|
|Changes in the past three years:<sup>c</sup>|||||
|<br>Improved health facility infrastructure|0.939|0.007|0.640|1,496|
|<br>Improved attitude of health workers|0.963|0.039**|0.015|1,656|
|<br>Improved quality of health services|0.963|0.009|0.406|<br>1,650|
|<br>Improved collaboration between community|0974|0007|0270|1632|
|and health facility<br>Note: data from household surveys. The PBF impact is estim<br>data were only collected during the follow-up. Standard error|.<br>ated using a pr<br>s are clustered|.<br>opensity score wei<br>at the district leve|.<br>ghting spe<br>l. All regre|,<br>cification as these<br>ssions include|

<mark>Note: data from household surveys. The PBF impact is estimated using a propensity score weighting specification as these data were only collected during the follow-up. Standard errors are clustered at the district level. All regressions include controls for age, gender, education level, employment status, household wealth quintile, province, number of health houses in the catchment area, an indicator for having any health houses in the catchment area, catchment population terciles, and an indicator for the catchment area’s RHC being more than 10km away from the district hospital.</mark> Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 30
were already high compared to the average. Those firms expanded at the expense of firms with low markups. This rise in markups can account for recent macroeconomic trends such as the secular decline in labor shares and the wage reduction of low-skilled workers. For a costminimizing firm, the labor share is inversely related to the markup. Greater market power also implies fewer firms, lower output, and reduced aggregate demand for labor, negatively affecting real wages and income inequality. Autor et al. (2020) also analyze the consequences of firm size on the labor market share by developing a framework for superstar firms characterized by a “winner takes most” feature. They provide evidence for the United States that industries that exhibited the largest increase in market concentration have also experienced larger declines in the labor market share. Cerra et al. (2021, Chapter 6) discuss the role market structure plays in shaping inclusive growth in more detail.

# **3.2.4.Unbalanced Growth**

For a variety of reasons, different sectors, industries, regions, and firms may grow at different rates. Many of the sources of growth, including technology and trade, could improve growth in some economic sectors more than in others. Uneven growth produces uneven returns. When some sectors boom but others lag, growth is not likely to raise incomes proportionately. Payments to factors may fall in some cases. As some industries emerge and others disappear in a process of “creative destruction” (Schumpeter 1942), some workers could be displaced or face stagnant wages. In addition, pecuniary externalities can cause an increase in market prices, such as housing rents, that may reduce real incomes of poor.<sup>10</sup> Economic development may entail unbalanced growth that affects inequality. For example, Kuznets (1955) postulated that inequality evolves as an inverted “U” shape function where inequality initially increases and eventually declines. In the initial stages of development, some workers migrate from rural agriculture to the fast-growing urban manufacturing sector. Workers in the manufacturing sector experience an increase in income, while the ones staying in the traditional sector remain with low wages, resulting in higher income inequality. As a larger share of workers shift to the manufacturing sector, inequality eventually declines at later stages of development.

# **_Sectoral composition_**

Empirical studies confirm that the sectoral composition of growth is important in determining poverty reduction. Loayza and Raddatz (2010) study a cross-section of 55 developing countries

> 10 Matlack and Vigdor (2008), using Census data for US cities, show that an increase in income at the top of the income distribution leads to an overall increase in housing rents that disproportionally affect the poor, exacerbating inequality.

18