rafmacalaba/datause-encoder-data huggingface dataset:
The results suggest that membership in government-supported programs contributes little to reducing starvation during monga, but does help to reduce hunger during the non-monga period. Perhaps this explains why safety-net support during monga was insufficient to substantially reduce seasonal hardship, but did help to alleviate it, albeit inadequately, during the non-monga period. Nonetheless, both sets of results clearly show that safety-net programs affect both transitory and chronic poverty, which is consistent with the findings of Devereux (2002) for poor African countries and unlike those of Ravallion, van de Walle, and Gautam (1995).<sup>7</sup>

# **6. Summary and Conclusion**

The basic objective of this paper has been to assess the role of social safety-net programs in mitigating seasonal deprivation (monga) using recent survey data of hard-core poor households in the greater Rangpur region of Bangladesh. We have also explored whether safety-net programs, which are overwhelmingly seasonal in nature, can avert the consequences of seasonal shocks or seasonality of income and employment. Although the extent of program coverage is limited by the availability of funds, we find that programs, where in place, are well-targeted. However, program inputs are not distributed in accordance with the perceived notion of area-specific poverty incidence. We also find that some programs, such as Old Age Allowance, are year-round, reaching the most vulnerable groups (e.g., the elderly). Interestingly, the distribution of program benefits is not random but is determined by a host of factors, including a household’s physical and human assets, plus the availability of alternative programs (e.g., microfinance in the vicinity of potential program beneficiaries).

Interestingly, the results of our impact assessments show that safety-net programs have a positive effect on mitigating monga. More importantly, the programs mitigate both seasonal and

> 7 We also tried to differentiate the potential effects of short-term versus long-term safety-net measures; however, the sample size for long-term measures was too small to run separate regressions and thus separate estimates.

24
2

# **Krueger/Schiff/Valdés Revisited: Agricultural Price and Trade Policy Reform in Developing Countries since 1960**

Two decades ago, a major World Bank study of distortions to agricultural incentives in 17 developing countries plus Portugal was published by Krueger, Schiff and Valdés (1988; 1991). That study covered roughly the period 1960-84, which for many developing countries was the first 25 years of independence from a colonial power. It found that policies in most of those developing countries were harming their farmers, either directly via such things as taxes on agricultural exports or indirectly via manufacturing protection or overvalued exchange rates.

Since the mid-1980s there has been a substantial amount of policy reform and opening up of many developing countries, but no systematic quantitative monitoring of those policy changes. To help fill this lacuna, a study by the World Bank has revisited this issue and provides indicators for a much larger sample of developing countries and for as many years as possible since 1955. The new study also covers European economies in transition from socialism and, for completeness, it extends estimates for high-income countries back three decades prior to the start of comparable estimates from 1986 by the Organization for Economic Cooperation and Development (OECD). In so doing it covers more than 90 percent of world agricultural output and employment, with the focus countries accounting for 96 percent of global GDP.

Some of the policy developments of the past half century have happened quite suddenly and been transformational. They include the end of colonization around 1960, the implementation of the Common Agricultural Policy in Europe from 1962, the floating of exchange rates and associated liberalization, deregulation, privatization and democratization in the mid-1980s in many countries, and the opening of China in 1979, Vietnam in 1986, and Eastern Europe following the fall of the Berlin Wall in 1989 and the demise of the Soviet Union in 1991. Less newsworthy and hence less noticed are the influences of policies that change only gradually in the course of economic development as comparative advantages evolve, but they too have made a substantial impact on the global economy.

The present paper is structured as follows. It begins with some background comments before summarizing the methodology used in the World Bank’s new study, pointing out its
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 2. Infrastructure deficits constrain firms’ productivity**

a. Degree to which infrastructure is perceived by firms as an obstacle to growth (%) <!-- Start of picture text --> b. Degree to which infrastructure is perceived by firms as an obstacle<br>to growth, by subsector (%)<br><!-- End of picture text --> <!-- Start of picture text --> Benin Mali<br>Senegal Senegal<br>Mali Mauritania<br>Mauritania Niger<br>Burkina Faso Burkina Faso<br>Niger Benin<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br>Infrastructure Others Electricity Customs clearance Transportation ICT Water<br>Source: Escribano and others 2010.<br><!-- End of picture text -->

# The state of Niger’s infrastructure

Niger’s demographic characteristics and distribution patterns make infrastructure development difficult. Niger is a large country with an area of approximately1’267,000 square kilometers (km2), but only one-third of the area in the southwest is arable. The country’s population of 15.3 million is concentrated in this arable strip (figure 3a). Niger’s high fertility rate (7 births per women) and rapid population growth (3.4 percent a year), among the highest in Sub-Saharan Africa, undermine the gains derived from its GDP growth and challenge infrastructure maintenance and expansion.

Niger has very limited water resources; the Niger River is the only source of permanent water for the country. Rain patterns are also highly volatile, and nearly 90 percent of the territory receives less than 350 millimeters (mm) of rain every year, which results in regular droughts (World Bank 2008a). This has a significant impact on the economy, since 45 percent of Niger’s GDP comes from the agricultural sector, which is predominantly rain fed.

Niger remains one of the poorest countries of the world, though it has ample natural resources. The country ranked last among 182 countries in the Human Development Index (HDI).<sup>2</sup> With an annual per capita income of $300—compared against the Sub-Saharan African average of $500—in 2008, 60 percent of the Nigerien population lived below the poverty line of $1 per day. Poverty incidence is highest in rural areas, where 85 percent of population lives. In the states of Tillaberi and Dosso, more than 70 percent of the population lives under the poverty line (figure 3b). The country’s precious mineral resources

> 2 The HDI provides a composite measure of three dimensions of human development: living a long and healthy life (measured by life expectancy), being educated (measured by adult literacy and gross enrollment in school), and having a decent standard of living, as measured by purchasing power parity (PPP) and income.

4
# <u>Table 8. Schooling, skills and earnings for women in Pakistan</u>

||**M**|**All**<br>**ean regressio**|**ns**|**Me**|**dian regressio**|**Working**<br>**ns**|**women**<br>**M**|**ean regressio**|**ns**|
|---|---|---|---|---|---|---|---|---|---|
||(1)<br>Monthly<br>earnings<br> (TC)|(2)<br>Monthly<br>earnings<br>(TC)|(3)<br>Monthly<br>earnings<br>(TC)|(4)<br>Monthly<br>earnings|(5)<br>Monthly<br>earnings|(6)<br>Monthly<br>earnings|(7)<br>Monthly<br>earnings<br>(TC)|(8)<br>Monthly<br>earnings<br>(TC)|(9)<br>Monthly<br>earnings<br>(TC)|
|Years of schooling|1.73***|1.91***|1.35***|5.92***|4.81***|5.25**|8.47***|8.51***|8.14*|
||(0.28)|(0.31)|(0.43)|(1.47)|(1.60)|(2.31)|(1.96)|(1.95)|(4.12)|
|Cognitive skills|||3.13*|||-3.22|||1.74|
||||(1.83)|||(9.75)|||(18.2)|
|SEM skills|||0.80|||-7.45|||3.51|
||||(0.91)|||(7.99)|||(7.99)|
|Constant|-11.9***|-13.1***|-8.22*|1.48|19.2|9.56|-28.6|-27.6|-23.2|
||(2.37)|(2.49)|(4.28)|(37.4)|(43.6)|(48.4)|(31.0)|(31.8)|(47.3)|
|Observations|2104|1925|1925|112|111|111|112|111|111|
|R-squared|0.060|0.060|0.070|0.22|0.22|0.22|0.26|0.26|0.25|
|Median/Mean dependent|7.22|7.84|7.84|62.50|57.69|57.69|107.33|107.43|107.43|
|Sample|All|Has skills|Has skills|All|Has skills|Has skills|All|Has skills|Has skills|
|||measures|measures||measures|measures||measures|measures|
|District FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

* p<.10  ** p<.05  *** p<.01

_Notes_ . This table reports estimates of the relationships between schooling, skills, and earnings for women in Pakistan. The dependent variable "Monthly earnings" in columns (4) to (6) is the raw monthly earnings while the dependent variable "Monthly earnings (TC)" in columns (1) to (3) and (7) to (8) is top coded at 100,000 PKR per month (961.5 USD). The cognitive skills index is the mean of the Urdu, Mathematics, and English scores computed using Item Response Theory with a two-parameter logistic (2PL) model. The items included in the model are those from the paper test and the computer adaptive test on tablet (excluding items that less than 50 respondents answered and those that less than 5% or more than 95% of respondents got it right). The socioemotional skills index is computed using principal factor analysis on the Big-Five items, Grit items, BART, and GoNoGo scores. The sample for column (1) is all women in the sample who are not currently enrolled, that is 2,104 women. For columns (2) and (3), the sample is only those who answered the direct version of the questionnaire, inperson (they have skills measures): 1,925 women. The sample for columns (4) and (7) is all women in the sample who are working, that is 112 women. For columns (5), (6), (8), and (9), the sample is only those who answered the direct version of the questionnaire, in-person: 111 women. All the regressions include age and district fixed effects. Robust standard errors are in parentheses. The R-squared shown is the pseudo R-squared for median regressions and the adjusted R-squared for mean regressions.

50
To get some intuition for these equations, suppose all growth rates are negative, except for the preCOVID growth rates. The income declines of rural households are then driven by three factors (with a similar intuition for urban households):

1. [The first half in the numerator of (3R)]: The more agricultural and service sector income declined, the greater will be the income decline for rural households that have been assigned an income loss.

2. [The second half of the numerator in (3R)]: The greater growth in agriculture and services expected before COVID-19, and the more rural households experiencing income increases, the larger drops rural households assigned to an income loss will have. The reason for this is that the total growth needs to match national accounts. When there are more households that experience an income increase and/or their incomes grow by more, the larger has to be the decline such that it adds up to the same overall growth.

3. [The denominator of (3R)]: If more rural households experience a decrease in incomes, the loss per rural household will be smaller. If many households experienced declines, their rate of decline needs to be smaller to add up to the same total decline.

# **(ii) Surveys reporting consumption changes**

For surveys that only report a consumption loss variable, we observe households with either a loss (-) in consumption or with no loss (*) in consumption. We follow a similar approach as outlined above to estimate growth rate for the households that experienced a loss in consumption. Following Equation (1R), we can write the growth in rural areas as follows: aarraa aarraa∗ aarraa∗ aarraa− aarraa− where represents the growth for those rural households that did not experience a loss in ggtt = ggtt sstt−1 + ggtt sstt−1 aarraa∗ consumption, represents growth for rural households that experience a consumption loss, tt gg and and aarraa− captures the share of total consumption held by the respective groups in the tt gg aarraa∗ aarraa− prior period. tt−1 tt−1 ss ss Equation (2R) can then be modified as: . cc,aarraa cc,a aa cc,ssa aarraa∗ aarraa∗ aarraa− aarraa− aarraa tt tt tt tt−1 tt tt−1 tt−1 As we did for households that experienced an income increase, we will assume that growth rate gg = gg + θθgg = (gg ss + gg ss ) × ss for households that experienced no loss in consumption is equivalent to expected growth rates forecasted before the COVID-19 pandemic. In particular, we assume p p + aarraa∗ cc,a aa p p , which allows us modify Equation (3R) to estimate the growth rate of rural tt tt,ppa pp gg = gg cc,ssa households with consumption loss as: tt,ppa pp θθgg <u>� �</u> − <u>� p</u> p p p p <u>�</u> = . cc,a aa cc,ssa aarraa cc,a aa cc,ssa aarraa∗ aarraa− ggtt + θθggtt /sstt−1 ggtt, pa pp + θggtt,ppa pp sstt−1 tt aarraa− gg tt−1 ss 42
after correcting for schooling of, and inheritance from parents (Binswanger and Singh 1 994).22 Finally, since forming interest groups involves costs, the evolution of the social and institutional environment should involve accumulation processes similar to those discussed above for physical capital. Theories of social capital formation explore characteristics of the social and institutional environment formally (Durlauf 1992). They demonstrate that initial differences in the quantity or quality of social capital, such as trust, affect the ability of group members to accumulate factors of production and can lead to stratification that is socially inefficient and very difficult to reverse (Benabou 1994). Conversely, destroying social capital and traditional institutions, by, for example, forcibly eliminating communal land tenure as occurred in much of Central America in the 1870s, may have long-term impacts far beyond eliminating the risk diversification and safety-net functions discussed earlier.

The importance of these factors in interest group formation has been confirmed by a number of studies (Bates 1983; Magee, Brock, and Young 1989 Swinnen and van der Zee 1994). Gardner (1987) has quantified and evaluated these factors for the United States in an econometric framework, in order to explore the interaction between collective action, material conditions, and the general characteristics of the economic environment. Spatial concentration of production of a specific commodity and lower variability of production (both of which reduce the cost of communication and organization) lead to greater protection. Higher output per producer, which would make it easier to form a pressure group with fewer members, does the same. A high rate of output growth, everything else constant, makes it more profitable to invest resources in productive rather than redistributive activities, and in less distortionary policies (observation **s** how that distortionary policies are less likely to be applied to fast-growing commodities). Regional shifts of production create divergent interests between "old" and "new" producers, and therefore reduce their ability to lobby for transfers. Finally, commodities that face import competition receive more protection

> 22 In most cases wage equations have been estimated using cross-sectional rather than panel data, and do not therefore correct for unobservable individual attributes. The South Indian panel data enabled accounting for both unobservable fixed household effects, as well as correction for parental endowment effects, and therefore represent stronger evidence for the persistence of discrimination.

27
The bottom half of Table 4 does compare between regions, by pooling the data **and estimating** inter-spacial efficiencies. In the fourth row, the efficiencies are calculated relative to a grand efficiency frontier, for the pooled sample of all three regions. There is no technical reason for not pooling the data for the three regions and measuring the efficiencies for the regions relative to a **meta-efficiency frontier** calculated from all the observations.'3 The results of this approach show that fewer farms lie on the combined frontier than on the separate regional frontiers. This is inevitable, since as the sample size is increased by pooling, a farm's efficiency can only decrease, as its comparison set is augmented **by new** observations (Nunamaker, 1985).

The efficient farms are thus a subset of the regionally efficient units, with only **three farms in** KaNgwane and one in Lebowa remaining on the frontier. The three KaNgwane farms that were on the regional frontier and are not on the pooled frontier, were traditional producers, using few modern inputs. These are dominated by the one Lebowa farm on the pooled frontier, which claims to **use no improved** seed and no chemical fertilizer<sup>14</sup> . None of the three efficient KaNgwane farms are current FSP members, because they have evolved beyond the need for the FSP and are all large, commercial **undertakings.** However, they are following the FSP recommendations, in that they reach the frontier by achieving high yields, using heavy applications of modern inputs.

The most striking feature of the pooled efficiency results is the scale efficiencies. **Whereas Venda** appeared to have the highest level of scale efficiency, this result depended on the fact that all the farms are small. Once the small farms in Venda are compared with the larger units in the other two **regions, none** are scale efficient and mean scale efficiency falls to 11.3%, giving a mean total efficiency level of only 6.9%. Thus, these results suggest that the small farms in Venda, which average only 1.15 ha., **are too** small to be viable.

The input-based DEA analysis measures efficiency relative to a best practice isoquant, as shown in Figure 1, but as Ali and Seiford (1993) explain, this problem should be viewed as the **first stage of a** two stage model. Farms may be efficient, in terms of being on the frontier, but one or more variables **may**

> **13** Differences in land quality can be a problem when estimating meta-functions, and should be accounted for. In this case, the tree sample areas are relatively homogeneous both within and between regions. Furthermore, for the production year under consideration, rainfall and climatic conditions were, to a large extent similar. This is the reason for analysing these three surveys.

> _14_ The reader does not need to believe that the figures for this particular farm are strictly accurate. It may, or may not, be possible to get 3,200 kgs. of maize per hectare without modem inputs in the most fertile parts of Lebowa; but if this farm were removed from the sample, there are several others very much like it, so the results are substantially unchanged.

22
Gregorio Impavido, Craig Thorburn, Mike Wadsworth

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- [7] **Bernheim, B.  Douglas** , 1991, How Strong Are Bequest Motives? Evidence Based on Estimates of the Demand for Annuities. _The Journal of Political Economy_ , 82: 29-62.

- [8] **Bernheim, B.  Douglas, Andrei Shleifer and Laurence H. Summers** , 1985, The Strategic Bequest Motive. _Journal of Political Economy_ , 93(6): 1045-76.

- [9] **Bernheim, B. Douglas** , 1987, Dissaving after Retirement: Testing the Pure Life Cycle Hypothesis. In Bodie, Zvi,  John B. Shoven and David A. Wise eds. _Issues in Pension Economics_ . (National Bureau of Economic Research Project Report series Chicago and London: University of Chicago Press).

- [10] **Bernheim, B. Douglas** , 1991, How Strong Are Bequest Motives? Evidence Based on Estimates of the Demand for Life Insurance and Annuities. _Journal of Political Economy_ , 99(5): 899-927.

- [11] **Brown, Jeffrey R.** , 2001, Private Pensions, Mortality Risk, and the Decision to Annuitize. _Journal of Public Economics_ , Vol.  82, No.  1.  (Oct., 2001), pp.  29-62.

- [12] **Brown, Jeffrey R., Olivia S.  Mitchell, and James M.  Poterba** , 2001, The Role of Real Annuities and Indexed Bonds in an Individual Accounts Retirement Program.  in Brown _et al_ ., 2001. _The Role of Annuity Markets in Financing Retirement_ .  (The MIT Press, Cambridge, MA.).  Also in Campbell, John Y.  and Martin Feldstein, eds.: _Risk Aspects of Investment-Based Social Security Reform_ . (The University of Chicago Press, Chicago, Ill.).

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