rafmacalaba/datause-encoder-data huggingface dataset:
## **6.1.2 Experimental Treatments**

The self-affirmation exercise used in this experiment is adapted from the psychology literature (Steele 1988, Cohen et al. 2009, Hall et al. 2013). The exercise involves asking the respondent to reflect on a recent experience or achievement that made them feel proud. We show that this treatment delivers a boost to one’s self-esteem, as measured using standard tests such as the Rosenberg (1965) scale. Following this literature, customers assigned to the _self-image treatment_ group were asked to complete the following task before receiving an upgrade offer: _At [name of bank], we think it’s important to understand our customers really well. So before making you a new offer relating to your [name of credit card], we would like to ask you a quick question. Can you please describe a specific incident in your life, something you did or achieved, that made you feel successful or proud of yourself? It could be from any aspect of your life, whether family related, education, or professional._ Customers assigned to the _self-image control_ group completed a placebo exercise, which asked participants to describe their media preferences and did not contain any statements or questions that might affect the respondent’s self-image: _At [name of bank], we think it’s important to understand our customers really well. So before making you a new offer relating to your [name of credit card], we would like to ask you a quick question. Can you please tell me which are your favorite TV channels and why? This would be a great help to us in understanding our clients media preferences._ After completing one of these tasks, all customers received either an offer to upgrade to the platinum card, or an offer to upgrade to the platinum benefits package as an add-on to their current credit card. These offers were made using the same protocol and experimental scripts as in the first experiment, described in Section 3.2.2.

# **6.2 Self-Esteem Intervention:** **_mTurk Experiment_**

## **6.2.1 Set-up and Experimental Protocol**

The above experiment has a number of limitations. First, the bank permitted only a small sample of customers for this final experiment, thus limiting statistical power. Second, a phone call from ones bank is likely not the ideal format for the self-affirmation treatment, and some customers understandably prefer to skip the question (although they are still included in the results). Thus, we designed an additional experiment using the online crowdsourcing platform _mTurk_ . This allows us to test for the substitutability of self and social image motivations in an alternative setting and population, where we can implement a parallel experimental design, but have tighter experimental control, and more statistical power (due to larger sample sizes and likely a stronger first stage).

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# _Transitions out of Poverty_

While the aggregate poverty headcount is unchanged, few may be considered “chronic poor.” More than half the poor in 2001 moved out of poverty in 2004. The key drivers of such outflows are described below.

Workers who moved out of poverty experienced very large increases in wages. While aggregate wages rose sharply over the last four years, outstripping the growth of GDP per capita, the wages of those who moved out of poverty grew even faster (Figure 8). The increase in wages among those who moved out of poverty is close to double the increase in average wages and close to four times the rate of increase in the informal <!-- Start of picture text --> Figure 8. Index of Real Wages<br>(2001=100)<br>190<br>170<br>Wages of those who<br>moved out of poverty<br>150<br>Average wage<br>130<br>110 Informal sector<br>90<br>2001 2002 2003 2004<br>Source: LSMS and staff estimates.<br><!-- End of picture text --> sector. This is consistent with the previous analysis of wage increases by wage deciles, where low-paid workers experienced relatively sharper increases in wages.

Movement out of poverty is related to the labor market performance, especially among male workers. Many workers who moved out of poverty between 2001 and 2004 also moved from out of the labor force or from unemployment into employment. A significant fraction also moved from informal employment into formal employment, underscoring the role of wages and productivity in lifting people out of poverty. These labor market developments are especially pronounced among males. For example, of men who were unemployed and in poverty in 2001, over half transitioned into employment and out of poverty in 2004. Among those who were out of the labor force in 2001, over 42 percent moved into employment in 2004. And of those who were employed informally in 2001, over 57 percent subsequently found jobs in the formal sector. Finally, many of those already employed in the formal sector held on to their formal sector jobs.

In contrast, only 36 percent of men who were in poverty throughout the period (the “chronic poor”) moved from unemployment into employment. Of the 36 percent, over two-thirds found employment in the informal sector, where wages have been stagnant. Of those who were employed in the formal sector in 2001, a significant share (36 percent) moved into informal sector employment.

Among female workers, the relationship between exits out of poverty and labor market performance is less clear. In general, labor market performance matters more for males than for females, who have much lower rates of labor force participation. Of those who were unemployed in 2001, many remained unemployed or left the labor force completely by 2004.

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results suggest that there is actually something quite worthwhile in the current draft agreements on agricultural market access.

**Figure 5. Percentage cuts in applied agricultural tariffs**

## **(a)** _in average tariffs_ **_levied_**

<!-- Start of picture text --> 60<br>(b) in average tariffs faced<br>Formula<br>50 60<br>Flex Formula<br>40 50<br>Flex<br>30 40<br>20 30<br>10 20<br>0 10<br>Developed Developing<br>0<br>Developed Developing<br><!-- End of picture text --> Source: Laborde, Martin and van der Mensbrugghe (2011)

# **Average Protection versus Price Insulation**

The literature on determination of tariffs offers two major explanations for tariff protection. The first is the political-economy approach exemplified by Grossman and Helpman (2004), under which protection is the result of politicians’ preference for political contributions over general improvements in economic welfare. The second is the terms-of-trade approach associated with Broda, Limao and Weinstein (2008). These models share of a common feature of explaining protection patterns that involve domestic prices set at a constant markup over international prices—a formulation consistent with an _ad valorem_ tariff on imports—unless parameters such 26
**Figure 1: Country coverage by rating groups**

<!-- Start of picture text --> A-rated issuers (AAA to A-); 17 countries<br>B-rated issuers (BBB+ to BB-); 20 countries<br>No data<br>IBRD 45611 |<br>MARCH 2021<br><!-- End of picture text -->

emissions. Instead, we are interested in the positive effects that renewable resources have on the economy. That is, instead of asking if countries are punished for deforestation through higher yields, we ask if countries are being rewarded for afforestation with lower borrowing costs.

The remainder of this paper is structured as follows. Section 2 introduces the reader to the wealth data, the rationale behind it and how it differs from other natural resource data, along with other data sets. Section 3 conveys the gist of this study graphically and discusses the confounding factors and the ingrained income bias. Section 4 revisits the problem in an econometrically more rigorous manner. Section 5 discusses limitations of the study and Section 6 concludes.

# **2 Data**

Our sample comprises _푁_ = 37 countries, with 20 A-rated countries that have an average long-term debt rating between AAA and A- and 17 B-rated countries with ratings between BBB+ and BB(see Table 1). We cover _푇_ = 120 months between January 2009 and December 2018. The countries were selected based on the availability of sovereign bond data, i.e. countries with established domestic capital markets. This limits our analysis to high and middle income countries.

## **2.1 Wealth data**

Hamilton et al. (2006) argue that short-term economic variables alone are insufficient to measure a country’s sustainable, long-term growth potential. Wealth accounting is necessary to have a complete picture.5 The World Bank (Hamilton et al., 2006; Lange et al., 2021; Lange et al., 2018; Lange et al., 2011) has compiled data on the wealth of 141 nations, divided into three major categories: human capital (discounted lifetime earnings), produced capital (e.g. machinery, buildings, urban land) and natural capital (e.g. forests, cropland, protected areas, subsoil assets).

> 5Lange et al. (2018) draw the illustrative parallel between a country’s GDP growth and its wealth, on the one side, and a company’s income statement and balance sheet, on the other side.

5
**Table 6. Correlations with steady-state employment ratios and country characteristics (continued)**

|<br>Correlate|Number of<br>observations|I<br>Total|II<br>Women|III<br>Men|IV<br>Non-<br>agriculture|V<br>Women in non-<br>agriculture|
|---|---|---|---|---|---|---|
|Average establishment size (Bento and Restuccia 2021)|||||||
|Non-agriculture|46|0.1993***|0.0505|0.3119***|0.2968***|0.1601***|
|Manufacturing|60|0.0157|-0.0024|0.0278|0.0493**|0.0293**|
|Services|55|0.2019***|0.0293|0.3292***|0.3287***|0.1629***|
|Product market regulation index (EFW)|||||||
|Overall index|103|0.0779|-0.0929|0.2105|0.3089***|0.1666**|
|Bureaucracy cost|103|0.0827|0.0412|0.1003|0.2491***|0.2071***|
|Impartial public|103|-0.0181|-0.0256|-0.0198|0.1436***|0.1348***|
|Labor market regulation index (EFW)|||||||
|Overall index|103|-0.0474|-0.0400|-0.0662|0.1265*|0.1086|
|Labor regulations and minimum wage|103|0.0781|0.0192|0.1199**|0.1658***|0.1136***|
|Firms identifying major constraint in (percent of firms,_En_|_terprise Survey_)||||||
|Tax rate|95|-0.0134***|-0.0132**|-0.0127***|-0.0069|-0.0065|
|Tax administration|95|-0.0134**|-0.0146**|-0.0106**|-0.0102*|-0.0099|
|Access to land|95|-0.0037|0.0004|-0.0060|-0.0270***|-0.0233***|
|Political instability|95|-0.0108**|-0.0155***|-0.0053|-0.0072**|-0.0107***|
|Corruption|95|-0.0094*|-0.0152***|-0.0021|-0.0056|-0.0083*|
|Access to finance|95|-0.0032|0.0036|-0.0086*|-0.0200***|-0.0150***|
|Source of finance for firm working capital (percent of wo<br>|rking capital,_Ente_<br>|_rprise Survey_)<br>|||||
|Internal fund|95|0.0054|0.0075|0.0032|-0.0179***|-0.0184**|
|<br>Banks|95|-0.0020|-0.0085|0.0025|0.0416***|0.0364***|

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models of an economy suggested by De Janvry and Sadoulet (2002), we may conclude that Bangladesh combines some features of Asian and Latin American archetype countries. They postulated that in Asia indirect agricultural employment effects are likely to be large owing to a large pool of landless wage workers in rural areas, and in Latin America the dominant effect of productivity improvements is through multiplier effects, both of which seem to be true in the case of Bangladesh.

# **6. Conclusion**

Increasing the purchasing power of rural dwellers is central for poverty reduction in Bangladesh because the majority of the poor live in rural areas. Despite the structural transformation of the Bangladeshi economy and the declining role of agriculture, the latter is still the most direct vehicle to reduce poverty. Over half of Bangladesh’s working population still depends on agriculture for livelihood and income and the sector’s contribution to overall growth is still important. Because rice constitutes over half of real agriculture GDP and it is Bangladesh’s major staple food, any policy change that affects the rice economy is bound to have important poverty impacts.

Using a combination of ex-post and ex-ante approaches and Bangladesh’s 1996 and 2000 household surveys, we analyze the distributional impacts of major trade reforms directed on rice markets undertaken in the early 1990s. The liberalization of trade in irrigation equipment and fertilizer markets has indeed led to a significant increase in rice productivity and a sharp decline in producer and consumer rice prices. We examine the effect on net consumers, net sellers of rice, non-farmers and farmers. We find that rural households who are net consumers of rice have experienced an increase in income. Income growth between 1995-96 and 2000 has tended to be pro-poor for this group. Net sellers of rice have experienced a decline in the income level, while income of the poor households in the poor/non-poor model has increased. A possible explanation for this are greater employment opportunities in the rural non-farm sector and higher agricultural wages, which members of poor households are more likely to be affected by.

Our findings suggest that for some categories of households income changes can be attributed to the changes in productivity and rice prices. Since net buyers in rural areas tend to be poorer than net sellers, productivity improvements induced by trade 24
**Table 2 – Tobacco Related Deaths, by Gender and Age-Group, Russian Federation, 2016**

||||**Women**|||||**Men**|||
|---|---|---|---|---|---|---|---|---|---|---|
||**25-34 **|**35-54 **|**55-74**|**75+ **|**Total **|**25-34 **|**35-54**|**55-74**|**75+**|**Total**|
|**Total Tobacco-related deaths**|**397**|**6,549 **|**19,779 **|**8,076 **|**34,801 **|**1,814 **|**36,357 **|**126,200 **|**44,080 **|**208,451**|
|Bladder cancer|0|8|43|32|**83**|1|195|1,588|792|**2,576**|
|Cervical cancer|3|115|111|23|**252**|-|-|-|-|**-**|
|Chronic obstructive pulmonary disease|2|180|1,239|1,338|**2,760**|18|1,168|9,072|6,987|**17,245**|
|Colon and rectum cancer|1|55|234|103|**392**|2|259|1,433|444|**2,138**|
|Esophageal cancer|0|31|143|82|**256**|2|696|3,091|592|**4,381**|
|Ischemic heart disease|192|3,164|9,777|3,533|**16,665**|1,081|18,878|52,413|17,682|**90,055**|
|Larynx cancer|0|36|112|45|**194**|2|645|2,747|534|**3,927**|
|Leukemia|0|7|17|5|**29**|5|230|737|177|**1,149**|
|Lip and oral cavity cancer|1|52|138|54|**245**|4|781|2,252|360|**3,397**|
|Liver cancer due to other causes|0|2|7|3|**12**|1|83|339|88|**510**|
|Nasopharynx cancer|0|10|18|4|**32**|1|91|227|28|**347**|
|Stomach cancer|1|66|215|92|**374**|5|855|3,690|900|**5,450**|
|Tracheal, bronchus, and lung cancer|3|460|1,970|801|**3,234**|21|4,783|26,023|6,958|**37,785**|
|Tuberculosis|37|141|43|3|**223**|231|1,301|775|44|**2,350**|
|Stroke|156|2,224|5,712|1,958|**10,049**|441|6,391|21,814|8,495|**37,141**|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

**Table 3 – Tobacco-Related Events, by Gender, 2016**

||**All**|**Women**|**Men**|
|---|---|---|---|
|Bladder cancer|17,898|4,051|13,847|
|Cervical cancer|15,718|15,718|0|
|Chronic obstructive pulmonary disea|334,223|83,351|250,873|
|Colon and rectum cancer|73,256|39,421|33,835|
|Esophageal cancer|7,943|1,510|6,433|
|Ischemic heart disease|1,200,833|651,505|549,328|
|Larynx cancer|9,221|748|8,473|
|Leukemia|10,843|5,276|5,567|
|Lip and oral cavity cancer|11,517|3,944|7,573|
|Liver cancer due to other causes|1,845|475|1,370|
|Nasopharynx cancer|845|171|674|
|Stomach cancer|40,978|18,991|21,987|
|Tracheal, bronchus, and lung cancer|53,673|10,624|43,049|
|Tuberculosis|82,910|24,279|58,631|
|Stroke|673,818|404,503|269,315|
|**Total**|**2,535,522**|**1,264,569**|**1,270,953**|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

_Note_ : Incidence is defined as the number of new cases of a given disease during a given period in a specified population. It is also used for the rate at which new events occur in a defined population. It is differentiated from prevalence, which refers to all cases, new or old, in the population at a given time.

7
the crisis but believe its impact to be transitory may expect to move up the ladder in the next four years as part of regaining the lost status due to the crisis. It is similarly possible that those who were strongly affected by the crisis in a way that is not easy to recover from over the short term may have modest expectations of upward mobility in the future. To account for such confounding effects, the model controls for the respondent’s assessment of the degree to which she has been affected by the financial crisis.

It is also important to account for the individual’s degree of risk aversion, since those who are less risk averse may be more likely to engage in risky projects and expect to move up the ladder based on their belief of their success. Risk averse individuals, on the other hand, may be more likely to prefer stability, which in turn may lead to expectations of a stable position on the society’s welfare ladder in the future. To account for the individual’s degree of risk aversion, I include dummies based on a hypothetical drought scenario. In the LiTS respondents are asked to respond to the following hypothetical: “I will now ask you another hypothetical question. Imagine that you are a farmer. If all goes well, you expect to sell your harvest for [insert country specific amount] in a few more months. However, there is a risk: If there is – a drought the harvest will be lost this has happened to your neighbors in half of the recent years. You consider installing an irrigation system which would protect your crop in case of a drought, but it costs [insert country specific amount] and you would need to sell your car to buy it. Which of the following is more likely to be your decision? (a) I would take the risk and hope there is no drought; (b) I would sell my car and buy an irrigation system.” I distinguish in the empirical analysis between these two groups, where group (a) captures those who are not averse to idiosyncratic shocks, and group (b) captures those who are risk averse.

The literature on inequality of opportunity generally distinguishes between inequality due to unequal opportunities, and inequality due to effort (see Ferreira et al. 2008, 2010 and references above). However, as noted by Lefranc et al. (2009), luck is also an important factor, but generally gets bundled together with effort. In the LiTS survey it is possible to account for the influence of luck by conditioning on the 14
2. Our forecast for annual inflation in the G-5 countries in the 1990s (3.2 percent) turned out to be considerably higher than the actual value of 1.5 percent. A contributing factor was the near-deflation in Japan, where the GDP deflator grew at an annual rate of 0.1 percent in the 1990s, down from 1.5 percent in the 1980s. Japan actually entered a deflationary phase in the second half of the 1990s. In all G-5 countries, particularly the United States, inflation in the 1990s was lower than in the previous decade. (This feature of the 1990s was covered in some detail above in the discussion of the Great Moderation.) None of the agencies whose forecasts are shown in table 6 picked up the impending change in the inflation regime in the high-income countries.

3. The World Bank used the U.S. dollar–denominated average unit value of manufactured exports of the G-5 countries (the MUV index) as a component of the terms of trade between developed and developing countries. It is a measure of how expensive developing country imports are; a low value for the growth rate of the index represents favorable condition for growth. This variable was key to the Bank’s country economic and sector work and formed part of the Bank’s planning assumptions. Over the 1990s, the MUV index declined. Before the 1990s, it occasionally fell over the course of a year, when the U.S. dollar was unusually strong relative to other major currencies; never before in the postwar period did it decline over a decade. The relative strength of the U.S. dollar in the 1990s is only part of the explanation for the decline in the index, however. The principal cause is likely to be the general reduction in the export price of manufactures from the industrial countries as a result of increased diversification into manufactures by many developing countries, led by East Asian countries, particularly China.<sup>11</sup> This development is an indication of a major structural change in the global manufacturing sector. Thus, the assumption that developing countries export commodities to and import manufactures from the high-income OECD countries is 11 For a general discussion of recent trends in the terms of trade of developing countries, see Athukorala (1993), Ram (2004), and Erten (2010). For discussion of the China effect, see Kaplinsky (2006).

48
2 In addition to commodities, the popularity of investing in emerging capital markets has been increasing in recent years. Today one in every four dollars invested in foreign equity markets goes to emerging stock markets. According to _Risk_ Magazine (1993), in 1992 alone, $14 billion flowed into foreign emerging market equities, taking total foreign holdings to about $86 billion. Key vehicles are the proliferating country funds, regional funds and global emerging market funds. According to the same article, the high risks involved in foreign equity markets have led to the growing popularity of global emerging market funds, which seek to reduce risk through diversification (note that the correlation between emerging markets is mostly quite low).

The objective of this paper is to conr.ruct an international portfolio that includes commodity assets as well as assets from developed and emerging markets to evaluate the riskreturn characteristics of such a portfolio. The returns from commodity assets is measured by the recently developed Goldman Sachs Commodity Index (GSCI) which incorporates the notion that commodity asset returns must include an opportunity cost of money component. The composition of the optimal international portfolio is examined and an efficient frontier for the international assets is derived.

The outline of the paper is as follows. Section 1 describes the salient features of the Goldman Sachs Commodity Index and the return-risk characteristics of commodity assets. Section 2 analyses the return-risk structure of the international portfolio while Section 3 derives the optimal composition of the international portfolio and the efficient frontier of assets. Section 4 remarks on the policy implications of the study.
Exporter Dynamics, Firm Size and Growth, and Partial Year Effects The consequences of partial year effects may extend to work on more aggregated data such as that assembled in the Exporter Dynamics Database at the World Bank and described by Cebeci, Fernandes, Freund, and Pierola (2012). Stylized Fact 1 in that paper states that more developed and larger countries have larger average exporter size and exhibit significantly lower exporter entry and exit rates. However, the findings reported here suggest that large shares of entrants and exits will by themselves be likely to induce downward bias in firm size as measured in unadjusted annual export flows. As a result, the cross-country correlation between average exporter size and GDP per capita may be driven in part by partial year effects. The importance of entry and exit in the exporting sector and the reliance on export value data for analysis means that such comparisons based on even aggregate data must be viewed with caution.

23