rafmacalaba/datause-encoder-data huggingface dataset:
interview. Thus, the HIES data can be arranged to show seasonality in income and consumption and its consequences in household welfare such as poverty.<sup>10</sup> According to the distinct seasonal cycle of agricultural production, we group both consumption and income information into 4 seasons in terms of the harvest period of 3 main crops in Bangladesh—Aus, Aman, and Boro. The 4 seasons are the Boro (MarchMay), Aus (June-August), pre-harvest Aman, which is the _monga_ period (SeptemberNovember), and Aman (December-February).<sup>11</sup> Figure 1 represents food consumption of rural households based on the HIES of 2000 and 2005. In both years, the overall food consumption per capita is much lower in Rangpur than the rest of the country, although the level of food consumption has increased in greater Rangpur in 2005 compared to the level of 2000. However, the seasonality in food consumption is much more pronounced in Rangpur than in other regions. Food consumption falls sharply during the pre-harvest period (i.e., _monga_ period). This clearly indicates that seasonality in agriculture must result in severe shortfalls in food and, perhaps, starvation without recourse to other means to cope with the shortfall.<sup>12</sup>

> 10 The HIES survey of 2000 included a total sample of 7,440 households of which 5,040 households were drawn from rural areas.  In 2005, the rural sample was 6,040 out of a total sample of 10,080 households  This paper deals with the rural samples of both surveys.

> 11 Note that Bangladesh has only 3 seasons based on crop cycle—Aus, Aman, and Boro.  We create another season as pre-Aman or monga period o define whether household response behavior differs in pre-Aman compared to other seasons.

> 12 Seasonal pattern in food consumption may reflect seasonal pattern in prices as well.  In particular, if prices of food also fall (due to lower demand, foe example), it is not clear whether lower food consumption reflects seasonality in consumption of food quantity or food prices.  For this, we examine patterns of food calorie intake (not shown here), which follows a similar seasonal pattern as food consumption.

7
Escalating nominal annuities also start with lower initial payments than fixed nominal annuities and are exposed to a selection bias like real life annuities. The same UK data (see Annex A) show that for a 3 percent escalation rate, initial annuity payments are lower by 28 percent for single life annuities and by 33 percent for joint life annuities (the latter reflects the longer life expectancy of the covered annuitants).

Annuities denominated in a reserve currency (either the US dollar or the euro) also provide some protection against inflation and are often recommended when there is a limited supply of domestic inflation-linked bonds.<sup>9</sup> However, reserve currency annuities provide protection against runaway domestic inflation and domestic currency depreciation but not against global inflation. In addition, persistent deviations from purchasing power parity imply that, for prolonged periods, reserve currency annuities do not provide adequate protection even against domestic inflation. This is corrected when large devaluations take place.<sup>10</sup>

# **Fixed annuities and annuitization risk**

Fixed annuities involve exposure to annuitization risk. This is the risk that at the time of retirement, financial markets are depressed, lowering the value of accumulated balances, especially those invested in equities and real estate, while long-term interest rates may be low, implying a high cost of fixed annuities. Annuitization risk is greater when retiring workers purchase fixed nominal or real annuities and the accumulated assets are heavily invested in instruments other than long-term fixed income securities. It does not arise with variable annuities.

Various measures can be taken to address annuitization risk. Retiring workers may be allowed, or even encouraged, to purchase fixed nominal or real annuities in installments, with purchases spread over a number of years before and after retirement. They may also be encouraged to switch their investments into bonds as they near retirement. Use of lifecycle funds, which increase their allocations into long-term bonds as workers approach retirement, significantly mitigates their exposure to annuitization risk. Retiring workers should be discouraged from investing in short-term debt instruments because these maximize their exposure to annuitization risk.

> 9 Reserve currency annuities may be offered in two forms: those where the benefits are actually paid in the reserve currency; and those where the benefits are indexed to the value of the reserve currency but are actually paid in local currency. The former clearly provide greater protection against domestic currency depreciation but they still do not protect against global inflation.

> 10 Reserve currency annuities require access to reserve currency assets to enable their providers to hedge their risks.

17
**Table 10: Earnings and Selection into the Wage Employment and Self Employment: 1997, 2000, and 2004**

||Log an|nual sales in|year|Log annu|al wages in y|ear|Structural<br>ction into s|(simple) pr<br>elf-employ|obit of sele-<br>ment inyear|
|---|---|---|---|---|---|---|---|---|---|
||1997<br> (1)|2000<br> (2)|2004<br> (3)|1997<br> (4)|2000<br> (5)|2004<br> (6)|1997<br> (7)|2000<br> (8)|2004<br> (9)|
|Female|0.748|0.061|-0.129|-0.354|-0.294|-0.184|-4.009|-0.425|-0.009|
||(2.37)*|(0.12)|(0.22)|(5.33)**|(3.66)**|(1.87)|(12.11)**|(2.91)**|(0.05)|
|Married|||||||-0.038<br>(0.17)|-0.074<br>(0.49)|-0.146<br>(0.83)|
|Log age|0.939|-7.286|-20.522|9.818|3.017|9.216|33.008|13.515|76.826|
||(0.12)|(0.68)|(1.29)|(4.73)**|(1.32)|(3.27)*|(7.33)**|(3.64)**|(12.44)**|
|Log age squared|-0.023|1.235|2.781|-1.291|-0.326|-1.171|-4.689|-1.993|-10.063|
||(0.02)|(0.87)|(1.30)|(4.41)**|(1.02)|(2.96)*|(7.28)**|(3.89)**|(12.15)**|
|**Education (reference=n**|**o formal sch**|**ooling):**||||||||
|Grades 1 to 10|0.398|0.281|1.412|0.187|-0.001|0.319|-0.818|-0.338|-2.853|
||(1.36)|(0.68)|(1.93)|(1.78)|(0.01)|(2.28)*|(2.89)**|(2.22)*|(10.07)**|
|Preparatory school|1.722|1.560|1.871|0.890|0.575|0.508|-3.078|-1.200|-3.440|
||(3.71)**|(2.46)*|(2.21)*|(7.97)**|(4.48)**|(3.14)*|(8.65)**|(5.90)**|(10.01)**|
|Some tertiary education|1.652|2.613|2.906|1.254|1.186|0.897|-1.633|-1.800|-5.526|
||(2.40)*|(3.28)**|(2.58)*|(10.52)**|(8.70)**|(4.15)*|(3.50)**|(7.48)**|(10.71)**|
|Self-employment||||||||||
|earnings permium|||||||3.601<br>(19.44)**|1.271<br>(14.64)**|2.734<br><br>(13.38)**|
|Selectivity term 1||||-0.390|-0.569|0.060||||
|||||(3.17)**|(4.34)**|(0.46)||||
|Selectivity term 2|-0.111|-0.692|-0.630|||||||
||(0.37)|(1.66)|(1.33)|||||||
|Constant|5.092|18.032|47.048|-10.699|1.703|-9.869|-60.109|-21.993|-147.524|
||(0.36)|(0.90)|(1.60)|(2.93)**|(0.42)|(1.99)*|(7.72)**|(3.28)**|(12.71)**|
|city dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|ethnicity dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Father's education|No|No|No|No|No|No|No|No|No|
|Mother's education|No|No|No|No|No|No|No|No|No|
|Father's occupation|No|No|No|No|No|No|No|No|No|
|Observations|177|99|115|628|452|212|1748|750|507|
|R-squared|0.14|0.29|0.19|0.36|0.37|0.30||||
|Log likelihood|||||||-8|0<br>-25|9 -183|
|Pseudo R-squared|||||||0.9|3<br>0.4|5 0.47|

Absolute value of z statistics in parentheses

* significant at 5%; ** significant at 1%

38
Table 5: Descriptive statistics results for dyad-level variables in the analysis

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|_CDMI_|2771|0.065|0.247|0|1|
|_Number of Projects_|2771|0.657|8.04|0|321|
|_Total CO2 Abatement_|2771|866.50|14820.18|0|672990.5|
|_AnnualCO2 Abatement_|2771|112.80|1977.78|0|89793.5|
|_Total Volume of Investment_|2771|1.96E+07|3.35E+08|0|1.53E+10|
|_Annual Volume of Investment_|2771|2574199|4.45E+07|0|2.03E+09|
|_Trade_1|2771|0.00037|0.00101|0|0.02397|
|_Trade_2|2771|0.00090|0.00294|0|0.05724|
|_hst_Averag GDP_|2771|42233.17|79493.96|864.5967|407822.8|
|_inv_Averag GDP_|2771|289208.43|559055|5154.85|2914536|
|_hst_Average Annual Energy Use_|2771|33.03|95.20|0.885|790.7972|
|_inv_Average Annual Energy Use_|2771|82.97|129.83|1.69|640.80|
|_hst_Ease of Doing Business_|2771|98.90|42.00|1|178|
|_inv_Ease of Doing Business_|2771|37.46|33.26|2|139|
|_hst_Governance_|2771|-2.39|4.45|-12.45|11.34|
|_inv_Governance_|2771|7.91|4.74|-4.11|13.48|
|_hst_Renewable Energy_|2771|5516.84|12786.63|0|62220|
|_inv_Renewable Energy_|2771|113.96|353.60|1|1937|
|_hst_Impact Vulnerability_|2771|46.44|16.88|1|80.72|
|_inv_Impact Vulnerability_|2771|27.51|12.53|1|50.38|

Note: See Appendix 2 for units of measurement. Interaction terms are not shown.

29
While the first camp emphasizes incentives, often based on rather little evidence, the second camp has essentially ignored such concerns, and with no stronger empirical basis. With the availability of new micro data sets there has been a huge expansion in studies of tax and benefit incidence. It has become routine in such studies to ignore incentive effects, and this is true in countries at all levels of development.<sup>5</sup> Consider what is clearly the most common method of assessing benefit incidence, by which one studies how mean transfer receipts (or tax payments) vary by class intervals of households ranked by their “net income,” defined as observed total income minus transfers received and/or taxes paid. Various “targeting measures” are then calculated (as reviewed in Ravallion, 2009b). This method (or some variation on it) is what Bourguignon and Pereira Da Silva (2003, p.9) term the “accounting method.” The method has the attraction of simplicity, in that the calculations are straightforward. However, net income (so calculated) need not accord well with income in the absence of intervention given behavioral responses. The potential for bias in assessments of benefit incidence is plain.

Reviews of past literature using the accounting method have warned that incentive effects are being ignored (see, for example, van de Walle, 1998). However, it has been argued that the method provides a “satisfactory short-cut for the study of a policy’s distributional impact” (to quote just one example, from Sahn and Younger, 2003, p.29). The main defense is what can be dubbed the fixed-income assumption. This says that people have little ability to influence their income and so incentive effects are minimal. In supporting this view, applications in developed countries have pointed to the relative inflexibility of working hours to justify non-behavioral incidence analysis; see, for example, Kakwani (1986, p.117) in the context of Australia. However, it is unclear that this is plausible even in rich countries, especially given that part-time work has become more common, and the assumption is hardly plausible in developing countries with large informal sectors. While the best examples in the literature are explicit about their assumptions, non-behavioral benefit incidence analyses continue in applied work, and often uncritically. It would be hard to exaggerate the policy influence that these empirical studies have had across the world.<sup>6</sup>

> 5  The examples, of which there are many, include Kakwani (1986), Atkinson and Sutherland (1989), Sahn and Younger (2003), Bourguignon et al. (2003), Goni et al. (2011), and Ben-Shalom et al. (2012). Reviews of studies of benefit incidence in developing countries can be found in van de Walle (1998) and Demery (2003).

> 6  Coady et al. (2004a,b) and Grosh et al. (2008) survey existing programs in developing countries. Virtually all of the work covered by these meta studies has ignored incentive effects.

3
aggregation affects certain types of consumption more than others and can lead to reranking of households on overall consumption (Joliffe, 2001).

For financial service use questions, the most disaggregated level would be to ask respondents about each and every service available, from ATM cards to health insurance, to informal saving associations to formal bank loans. This is the approach taken in the FINMARK surveys. Such an approach should prevent accidental omission of service use. It does, however, increase the burden of the interview which can lead to lower data quality. It also may preclude other surveys from addressing financial service use as there simply is not ‘space’ in the interview for so many questions.

The opposite approach, more similar to that taken by LSMS surveys, is to ask about financial service use at an aggregate level with a focus more on relationships with types of financial service providers than the specific products used. Clearly this approach is simpler and less expensive to implement. The concern is whether, as with consumption, certain types of items might be omitted. This may be of particular concern in terms of financial services given the heterogeneity of knowledge of financial instruments in the population. If there is more room for misunderstanding among respondents, it may lead to greater under-reporting.

The experiment carried out in Ghana explicitly tests the effect on reported financial service use of changing the respondent and changing the set of questions asked. In order to develop and implement the experiment, we worked with the national statistical office of Ghana (Ghana Statistical Service (GSS)). The GSS carries out a series of national household surveys and other statistical activities. The most comprehensive of household surveys, the Ghana Living Standards Survey (GLSS), is an LSMS survey 11
What drives the links between urbanization and increased factor reallocation? An implication of the Melitz‐Ottaviano model is that high transport costs can reduce the scale of market selection via firm entry and exit. This is partly because, as pointed out by Syverson (2004a, 2004b), transport costs affect the ease with which consumers substitute the output of one producer for that of another. If high transport costs lower product substitutability, less productive firms can survive, even in long‐run equilibrium. An important policy question following from this is how much of a constraint urban transport costs pose to African firms. Are high transport costs (or other factors that increase barriers to substitution between varieties of a product) reducing the pace of factor reallocation in the region?

We investigate these questions using a 10‐year panel of manufacturing firms from Ethiopia.<sup>1</sup> This panel contains annual production data for the period 2000 to 2010 and covers all manufacturing firms employing 10 or more employees.<sup>2</sup> It is arguably the most comprehensive longitudinal dataset on manufacturing firms in Sub‐Saharan Africa at this moment. Industries are classified in the dataset at the 4‐digit ISIC level and each producer is geo‐referenced at the town level. Importantly, the Ethiopian data include producers’ physical outputs, �� , along with their respective prices, �� . This allows us to distinguish between revenue‐based measures of total factor productivity (TFPR) and those based on physical outputs (TFPQ). As is standard in the literature, we define TFPR as the value of revenue ������ per input unit ���� and TFPQ as the number of physical units produced per unit of output ���/���. TFPQ measures the technical efficiency of a plant.

As discussed by Foster, Haltiwanger, and Foster (2008), it is important to distinguish between a plants’ TRPR and TFPQ when estimating the effects of plant turnover on an industry’s productivity. If all firms are price takers, simultaneous entry and exit processes leads to a selection outcome in which the industry’s least productive firms (in the sense of having have lower TFPQ) exit the market in (long‐run) equilibrium. However, it is also possible to have an alternative scenario in which a plant’s revenue productivity (TFPR) is a better predictor of its

> 1 This panel is compiled from ten years of the Ethiopian Survey of Large and Medium Scale Manufacturing Industries which, despite its name, is a census of all manufacturing firms with 10+ employees.

> 2 Data from 2005 are dropped because a survey was conducted during that year rather than a census.

4
(Indonesia, Malaysia, Vietnam, and Thailand), this paper shows that the rise of price of agricultural goods relative to price of manufactured goods slows the rate of structural change. According to these authors, in order to avoid unexpected consequences on structural change of policies that indirectly reduce tax on agricultural goods, there is a need for a significant increase of agricultural productivity in the studied countries.

In addition to regional perspectives, recent empirical work on structural change also entails country studies. For instance, McMillan _et al_ . (2016) edit a book that provides an overview and seven country studies related to structural change. The overview lays out a unifying framework for thinking about economic growth as a combination of two challenges. The “structural change challenge” is focused on moving resources from traditional low-productivity activities into modern, more productive industries. The “fundamentals challenge” faced by policy makers in the developing world is about how best to develop broad capabilities such as human capital and infrastructure. Of the seven countries studied (Botswana, Brazil, Ghana, India, Nigeria, Vietnam, and Zambia), only Vietnam seems to be in the midst of rapid structural change based on an expansion in modern manufacturing. By contrast, India’s growth has been driven primarily by increases in within-sector productivity. In Ghana, Nigeria, and Zambia, structural change has helped boost growth in labor productivity largely as a result of an expansion in the services sector. Botswana and Brazil experienced rapid structural change decades ago, but recent growth in these middle-income countries has come from investments in fundamentals. The book and its findings demonstrate that with all things considered, rapid structural changes are now more difficult to achieve and investments in fundamentals will be key to sustaining growth in low-income countries.

Diao _et al_ . (2018) find that labor productivity in Tanzania grew at roughly 4 percent per annum between 2002 and 2012, a growth rate more rapid than any other time in recent history. Roughly 80 percent of this productivity growth is accounted for by structural change as employment shares in agriculture declined while employment shares in services and manufacturing rose. Although employment in the formal sector has increased, the bulk of employment growth is accounted for by firms in the informal sector; these informal firms contributed more than one percentage point to economy-wide labor productivity growth.

7
|Grain Distribution and MarketingProject|Alan L. Piazza|
|---|---|
|Grain Distribution and MarketingProject|Alan L. Piazza|
|Grain Distribution and MarketingProject|Alan L. Piazza|
|Grain Distribution and MarketingProject|Alan L. Piazza|
|Grain Distribution and MarketingProject|Alan L. Piazza|
|Grain Distribution and MarketingProject|Alan L. Piazza|
|Grain Distribution and MarketingProject|Alan L. Piazza|
|Grain Distribution and MarketingProject|Alan L. Piazza|
|Grain Distribution and MarketingProject|Alan L. Piazza|
|ShipWaste Disposal Project|Toshiro Tsutsumi|
|ShipWaste Disposal Project|Toshiro Tsutsumi|
|ShipWaste Disposal Project|Toshiro Tsutsumi|
|ShipWaste Disposal Project|Toshiro Tsutsumi|
|ShipWaste Disposal Project|Toshiro Tsutsumi|
|ShipWaste Disposal Project|Toshiro Tsutsumi|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|ENVIRONMENTAL TECHNICAL ASSISTANCE PROJECT|L. Richard Meyers|
|Telecommunications Project(01)|Albert Fuchslocher Cruzat|
|Telecommunications Project(01)|Albert Fuchslocher Cruzat|
|Telecommunications Project(01)|Albert Fuchslocher Cruzat|
|Telecommunications Project(01)|Albert Fuchslocher Cruzat|
|Telecommunications Project(01)|Albert Fuchslocher Cruzat|
|Telecommunications Project(01)|Albert Fuchslocher Cruzat|
|Telecommunications Project(01)|Albert Fuchslocher Cruzat|
|Comprehensive Maternal and Child Health Project|Enis Baris|
|Comprehensive Maternal and Child Health Project|Enis Baris|
|Comprehensive Maternal and Child Health Project|Enis Baris|
|Comprehensive Maternal and Child Health Project|Enis Baris|
|Comprehensive Maternal and Child Health Project|Enis Baris|
|Comprehensive Maternal and Child Health Project|Enis Baris|
|Comprehensive Maternal and Child Health Project|Enis Baris|
|Comprehensive Maternal and Child Health Project|Enis Baris|
|Vocational Education Reform Project|DingyongHou|
|Vocational Education Reform Project|DingyongHou|
|Vocational Education Reform Project|DingyongHou|
Babecký, J., Du Caju, P., Kosma, T., Lawless, M., Messina, J. and Rõõm, T. (2010), ”Downward Nominal and Real Wage Rigidity: Survey Evidence from European Firms”, _Scandinavian Journal of Economics_ , vol. 112(4), pp. 643–910.

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Bertola, G., Dabusinskas, A., Hoeberichts, M., Izquierdo, M., Kwapil, C., Montornès, J. and Radowski, D. (2012), “Price, Wage and Employment Response to Shocks: Evidence from the WDN Survey”, _Labour Economics_ , Vol. 19-N° 5, October 2012.

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- Campbell, C.M. and Kamlani, K.S. (1997), “The Reasons for Wage Rigidity: Evidence from a Survey of Firms” _Quarterly Journal of Economics_ , Vol.112, No.3, pp. 759789.

- Card, David and S. de la Rica, (2006). “Firm-level contracting and the structure of wages in Spain,” _Industrial and Labor Relations Review_ , vol. 59(4), pages 573-592.

- Dickens, W. T., Goette, L., Groshen, E. L., Holden, S., Messina, J., Schweitzer, M. E., Turunen, J. and Ward, M. (2008), “Downward Real and Nominal Rigidity: Micro Evidence from the International Wage Flexibility Project”, Wage Dynamics Network, mimeo.

- Dickens, W. T., Goette, L., Groshen, E. L., Holden, S., Messina, J., Schweitzer, M. E., Turunen, J. and Ward, M. E. (2007), ”How Wages Change: Micro Evidence from the International Wage Flexibility Project”, _Journal of Economic Perspectives_ , vol. 21, pp. 195–214.

32