rafmacalaba/datause-encoder-data huggingface dataset:
# Hybrid Survey to Improve the Reliability of Poverty Statistics in a Cost-Effective Manner

Faizuddin Ahmed, World Bank Cheku Dorji, National Statistics Bureau, Bhutan Shinya Takamatsu, World Bank Nobuo Yoshida, World Bank JEL: I32.

Keywards: Survey to survey imputation, subsample, poverty measurement

## **Acknowledgments**

The authors thank Peter Lanjouw for his thorough review of the previous draft and useful suggestions, which improved the quality of analyses in this paper; Kinnon Scott for her support in designing the pilot survey to test price data collection and the effect of switching from diary- to recall-based approaches in collecting consumption data; and the Bangladesh Bureau of Statistics for their help in designing the sampling of the pilot survey and for providing details of survey logistics to help estimate survey implementation costs. Finally, the authors are grateful for their discussions with Roy van der Weide and Kathleen Beegle, and insightful comments from Salman Zaidi.
Increase in river salinity will inevitably generate significant changes in the ecosystems in the coastal districts. Even in the best case future scenario, optimum river area for Catla, Rui, and Mrigal is likely to decrease in Khulna, Bagerhat, and Satkhira Districts by 49, 20, and 15 percent, respectively. Wild habitats ideal for Golda prawn are expected to decrease by 88 percent in Khulna, 35 percent in Bagerhat, 25 percent in Gopalganj, and 14 percent in Satkhira Districts. In the worst case future scenario, expected reductions in the prime habitats for Catla, Rui, and Mrigal will be 100 percent in Khulna, 81 percent in Bagerhat, 43 percent in Bhola, and 34 percent in Satkhira; and that for Golda prawn will be 99 percent in Gopalganj, 97 percent in Khulna, 57 percent in Bagerhat, 26 percent in Satkhira, 23 percent in Patuakhali, and 14 percent in Barguna. Significant changes in the freshwater small fish species in the wild are also probable. Changes in the availability of freshwater fish will likely affect the composition of capture fishery. However, an increase in brackish water will enhance opportunities for brackish water aquaculture, for example, farming of _Tilapia Nilotica_ and Bagda.

The incidence of poverty is very high in Bagerhat, Barguna, Bhola, Khulna, Patuakhali, and Satkhira Districts. A poverty mapping exercise conducted by the Bureau of Statistics in Bangladesh, the World Bank, and the World Food Programme in 2009 identified many _upzilas_ in Bagerhat, Barguna, Barisal, Bhola, Khulna, Patuakhali, and Satkhira Districts with high poverty headcount ratios<sup>44</sup> (BBS et al. 2009). Hence, it is important to assess the probable impacts of increase in river salinity on the wellbeing of the poor. Data from rural areas in Bangladesh suggest that low-value, wild, small freshwater fish are the most common fish consumed in rural areas and the most important source of dietary protein (Belton et al. 2011; Thilsted 2010, 2012).<sup>45</sup> Small fish are generally sold in rural markets and can be purchased in affordable quantities by the rural poor and shared more equitably among household members, including women and children (Roos et al. 2007). Hence, probable decline in the biodiversity of freshwater, low-value, wild fish species with increased river salinity may have significant implications for the nutrition of the rural poor. It is also likely that intrusion of salinity into freshwater will adversely impact the livelihoods and incomes of fishing communities that currently rely on freshwater capture fisheries. However, expansion of brackish water aquaculture and the shrimp sector may offer numerous lower-value livelihood opportunities for

> 44  The proportion of the population with food expenditure at the level of the food poverty line using the Cost of Basic Needs by the Government of Bangladesh.

> 45 The nutritional contribution of small fish species is generally high. And, as many small fish species are consumed whole, they provide a significant percentage of recommended intakes of calcium, vitamin A, iron, and some minerals (Thilsted 2010, 2012).

30
imply a rent transfer to the exporter, since the beneficiary’s exports will face no (or a reduced) tariff, but will sell at the non-preferential tariff-inclusive price in the US market. The value of the tariff preference would then be composed of: (i) the rent earned on the level of exports prior to the preference; (ii) the rent earned on the additional exports sold as a result of the preferences.

Following Alexandraki and Lankes (2004), we make the following simplifying assumptions: A.1. Products are perfect substitutes irrespective of their country of origin.

A.2. The exporting country is a price-taker in world markets.

A.3. All rents from preferential access accrue to the exporter.

A.4. A change in the US trade policy regime will not lead to a change in world prices.

We then add the additional assumption that in programs which have apparel preferences, all apparel is potentially eligible for those preferences. Under these assumptions, the duty savings from preference programs can be approximated by the difference between the total duties that would have been collected on existing levels of US imports from a beneficiary country in the absence of any program, and the actual duties collected given the program(s). The value of preferences is then the beneficiary country’s duty savings as a share of its dutiable exports to the US. Assuming that preferences are fully utilized, their value can be calculated using equation (1): where: _i_ is US HTS 8-digit product, _j_ is exporting country _p_ indicates preference program _Customs Value_ is the value of dutiable exports to the US Note that calculating equation (1) will yield an upper bound estimate on value for at least two reasons: imports from beneficiaries would be smaller than existing levels in the absence of preferences; the MFN 17
implication of the Dutch disease and resource-curse hypotheses to the outcome of industrialization in resource rich countries.

Manufacturing has been touted as a key driver for economic development, especially for developing countries (Kuznets 1953, Matsuyama 1992). Jones & Olken (2008) examines the allocation of resources underlying economic growth, documenting that movements into and out of the labor force in manufacturing tend to coincide with accelerations and decelerations in the growth rate. In examining the constraints for sustained growth in Africa based on a comparison to benchmark countries (e.g., Indonesia and Thailand), Johnson, Ostry & Subramanian (2007) finds that, in almost all cases, these benchmark countries have escaped poverty through manufacturing exports, and posits that the manufacturing sector may be crucial for sustained growth in Africa. A small but growing literature documents the manufacturing experience of developed and developing countries with particular emphasis on the evolution of manufacturing output and employment shares. Dasgupta & Singh (2007) examines data for a set of 14 developing countries for the period 1986-2000, and finds evidence of deindustrialization, that is, manufacturing tends to monotonically increase with per capita income until it peaks and begins to decline.<sup>1</sup> They also note that such deindustrialization appears to occur at lower levels of income than developed countries. Similar findings have emerged for Indian states, where Amirapu & Subramanian (2015) document premature deindustrialization and reports that by 2010, manufacturing employment shares peaked at about 45 percent of the corresponding 1988 turning point value. To our knowledge, the only study examining deindustrialization in SSA is Rodrik (2016), which uses a larger set of countries (albeit only 11 African countries are included) and comes to a similar premature deindustrialization for Sub-Saharan Africa and Latin America.

Thus, there is a dearth of literature focused on sub-Saharan Africa’s manufacturing experience, and those that do have utilized a limited number of SSA countries due to data inadmissibility. This paper attempts to fill this void by assembling data on 41 sub-Saharan African countries from multiple sources including the Groningen Growth and Development Center 10-sector Database, the Maddison Project Database, the World Development Indicators, and the International Labor Organization’s ILOSTAT database. Our data spans 1960-2016, which is the longest panel data on output and employment shares on Sub-Saharan African countries to our knowledge. If sub-Saharan African countries are experiencing prematurely deindustrialization, this has potentially negative ramifications for the region’s economic development.

> 1See also Dasgupta & Singh (2005).

2
<!-- Start of picture text --> Power is not an obstacle<br>Power as minor obstacle<br>Power as moderate obstacle<br>Power as major obstacle<br>No obstacle<br>Power as severe obstacle Minor obstacle<br>Moderate obstacle<br>Major obstacle<br>Power as biggest obstacle Severe obstacle<br>Biggest obstacle<br>-.2 -.1 0 .1<br><!-- End of picture text --> Figure 6: The effect of power constraints on the firm’s revenue-based TFP Notes: Figure shows the coefplots with the smoothed confidence intervals (1, 3, 5, ..., 99) of the average treatment effects on the treated obtained by weighted least squares regressions (Entropy Balancing) from the Table (6). The dependent variable is the logarithm of the firm-level revenuebased total factor productivity for all regressions. In each regression, the treatment variable represents a specific degree of power constraints for firms. Firm-level controls variables are: Firm size, Firm’s longevity, Own website, Foreign private participation. Country-level controls variables are: GDP growth (annual %), Inflation (annual %), log(Bank concentration in %), log(FD index), log(GDP per capita), log(Net ODA in % of GNI), log(Regulatory Quality). Each estimate includes Year FE, Country FE, Region FE, Income group FE and a Constant. Standard errors are clustered at country level.

outages on firms’ productivity. This implies that most firms manage to deal with outages when other sources of constraints in the power sector are absent (voltage problems, high cost of service, connection difficulties, etc.). In column (2), the effect becomes negative when we consider the presence of minor constraints. The negative effect becomes progressively stronger when we consider the presence of moderate and major obstacles, until it becomes significant when we consider the presence of severe obstacles for firms. This negative effect is reinforced and becomes very significant when power becomes the greatest obstacle for firms.

# **5.3 Robustness tests**

The World Bank study suggests clustering by industry when each industry has at least 500 observations.<sup>15</sup> Otherwise, the appropriate clustering is by economies or countries as we have done so far. However, in order to test the sensitivity of our results to this, we have repeated our regressions by clustering by industry (see Table 8) even though we have fewer than 500 observations for some industries in our sample. Although we have some small variations in our standard errors, the results remain almost the same (in quality and magnitude) as our initial results with the clustering by country.

We also wanted to test the robustness of our results using the ordinary least squares (OLS) estimator in Table (9) to reassure that our results are not highly influenced or biased by our choice of Entropy Balancing estimator. Obviously, we have some small differences in magnitudes for some of the coefficients due to the downward bias of the OLS estimates in presence of endogeneity (Neuenkirch and Neumeier, 2016), but

> 15World Bank Group, Enterprise Analysis Unit. 2017. “firm-level Productivity Estimates”.

20
<!-- Start of picture text --> Cameroon<br>40<br>|, “Se<br>=<br>20 3 O_o<br>10<br>0<br>1982 1987 1992 1997 2002 2007<br>eee Agriculture, value added (% of GDP)<br>—-— Industry,value added (% of GDP)<br>Scale: Units<br><!-- End of picture text -->
problem of selective attrition. Although there is 23 percent attrition over 30 years, the attrition is unrelated to midwife exposure.

Finally, the identification comes from variation in allotment of midwives across communities and birth cohorts. Other programs initiated by the government at the same time can confound the results. In 1993, the Indonesian government initiated the _Inpres Desa Tertinggal_ (IDT) program. Besides the goals of strengthening the democratic institution and supporting the decentralization policy, the program aided families below the poverty line and human resource development (Shah et al., 1994). Since the timing of IDT overlaps with the timing of the midwife program, it is important to evaluate whether the exposure estimates of the midwife are robust to the inclusion of IDT exposure. Fortunately, the second wave of the IFLS has information on the availability and start time for IDT in a community. A dummy exposure measure based on the timing of IDT and birth year of children is used: it takes a value of 1 if a child was exposed to program at an age younger than four and 0 otherwise. Appendix Table S7.12 reports the results. The results indicate that midwife exposure estimates for different indices remain comparable to those without the inclusion of IDT.

# **6.3 Multiple Inference Adjustment**

Recall that this paper has presented results on four indices. It is imperative to calculate statistical significance after adjusting for multiple comparisons. Appendix Table S7.13, presents both naive p-values as well as p-values after controlling for family wise error rate (FWER).<sup>42</sup> The table shows that the _in utero_ estimates of health index and economic index for males remain statistically significant (less than 0.10) even after controlling for FWER.

There are 14 adult outcomes in this paper. Given these large number of individual outcomes, one may observe statistical significance for these outcomes by chance. To address this concern, q-values, following Anderson (2008), are calculated. Appendix Table S7.14 reports the q-values for treatment estimates for males and females separately. The results indicate that none of the _in_

> 42The FWER is calculated using the free step-down resampling method (Westfall and Young, 1993).

21
characteristics included economic, technological, administrative, and organizational features, and were closely connected to the broader sociopolitical environment.

An early draft of Hirschman’s proposal was criticized by those at the Bank who thought it was oriented “too much towards ‘side’ effects and too little towards the economic analysis of the Bank-financed projects.”<sup>16</sup> In a revised version, Hirschman identified three areas of focus for questions and analysis of project effectiveness. The first was the decision process in the recipient country. Who took the initiative for the project? What political and interest groups were for or against it? Did any of these groups shift their initial position about the advisability of the project? A second focus area was the decision process within the Bank. Here, too, Hirschman underscored the importance of comparing the Bank’s initial positions with ex-post considerations. Thus, one question would be whether the original arguments for or against the loan, including qualitative arguments, would stand up even after the project’s implementation; or how wise the Bank’s choice would seem in the light of the consequences of implementing the project. Hirschman was particularly interested in understanding whether the Bank, in its decision-making processes, could reach correct conclusions despite analytical flaws; more generally, as will be discussed in section 6 below, his goal was to shape a method by which project planners could navigate the intrinsic uncertainties of a project’s performance and predict probable lines of behavior. A third focus area was the project in retrospect. Relevant questions included a review of the effects of the construction phase (from a description of unexpected events, to the direct and indirect capacity-building effect on local engineering, managerial, and planning capabilities) and the project’s ultimate impact on the society and economy of the recipient country.<sup>17</sup> Of course, Hirschman’s curiosity dwelt on the linkage effects which could originate from the construction or the operation of the projects. The concept of “backward” and “forward linkage” had been introduced by Hirschman in 1958, and was at the center of his view of growth as a fundamentally unbalanced process. According to Hirschman, certain economic activities might create the conditions for establishing new activities for which there were previously no incentives. Thus, through these linkage effects, these new activities would in fact function as substitutes for entrepreneurial skills and as more efficient and pragmatic undertakings than central planning efforts, thereby fostering economic

> 15 Albert O. Hirschman, “A study of completed investment projects which have received financial support from the World Bank”, June 1963, World Bank Hirschman Folders Vol. 1.

> 16 Dragoslav Avramovic to Mr. Geoffrey M. Wilson, June 25, 1963, World Bank Hirschman Folders Vol. 1.

10
47 commercial farms (centred on the Irrigation districts) generated rapid agricultural growth; these coexisted with a subsistence sector of low growth and Increasing poverty, consistng of small farms in the ejido and private sectors (contred on the rain-fed agricultural regions).

103. Agrkiutural credit subsidies, public Investment (notably In irrgation and roads) and gre.n revokution technologies were prkwarily channeled toward larger enterprbse (Hewitt de Alckntara,1 978). In the 1940s and 1950s, the ejldo sector received respectively only 21 percent and 22 percent of total agricultural credit (MueSler,1970:258-257); yet Its share In total arable land was 44 percent In 1950 (Yates,1981a:154). In the same decades the total area of ejido land that received fertilizer was only half that In the private farm sector (Mueller,1970:258). However, smail prtvate farms were not much better off than ejidos.

104. To conclude, up until 1970 at least, private farms over five hectares remained more capitalized than the ejidos (both In terms of working capital and fixed capital stock) (Hewitt de Alcant&ra,1978). The large private farms and (to a lesser extent, the collective ejidos) contributed a disproportionately large share of farm production and agricultural output growth up to 1970 (Eckstein et al,1978:38).

(a) The census record. These trends would suggest that any study comparing the performance of ejidos and private farms would surely point to the superior productivity of the latter. However, the published works referring to the pre-1970 perlod reach varying conclusions. Comparisons between ejldos and private farms that are based on the census data have focussed on differences In (a) crop yields, (b) labour productivity, (c) the relationship between purchased capital Inputs and output value and (d) the relationship between an aggregate measure of land,
# **Table 6. Women’s brain drain: most and least affected countries**

|Highest net high-skilled emi|gration rate|s|||||Lowest net high-skilled e|migration rate|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||2000|||1990||||2000|||1990||
||||non-|||non-||||non-|||non-|
|**_Country_**|Net<br>(%)|Gross<br>(%)|OECD<br>(%)|Net<br>(%)|Gross<br>(%)|OECD<br>(%)|**_Country_**|Net<br>(%)|Gross<br>(%)|OECD<br>(%)|Net<br>(%)|Gross<br>(%)|OECD<br>(%)|
|Jamaica|87.7|88.6|0.7|87.5|88.5|1.0|United Arab Emirates|-202.9|6.6|69.4|-123.4|2.4|69.1|
|Haiti|83.3|83.5|3.4|78.8|79.0|5.8|Israel|-93.5|16.5|14.2|-20.5|11.2|12.2|
|Sierra Leone|73.7|75.9|9.6|72.1|74.4|13.5|Kuwait|-77.7|32.9|56.6|-73.3|26.8|64.0|
|Liberia|68.7|75.8|8.8|64.3|72.5|11.5|Australia|-59.9|5.8|7.6|-66.7|5.2|6.7|
|Afghanistan|54.3|54.8|34.0|45.8|47.1|51.8|Canada|-23.0|6.4|2.3|-18.8|6.8|3.0|
|Laos|51.4|51.7|0.5|42.9|45.4|2.1|Oman|-21.7|18.1|97.2|-18.3|15.9|97.9|
|Cameroon|51.1|61.3|17.3|30.0|35.1|26.9|Switzerland|-19.1|14.4|6.3|-10.6|11.2|8.6|
|Congo, Rep. of the|49.3|51.2|18.9|27.4|39.4|26.2|Singapore|-15.2|14.3|16.3|6.1|14.7|13.5|
|Kenya|48.6|55.1|8.5|59.7|61.3|10.2|Saudi Arabia|-12.7|1.8|56.7|-18.4|2.2|57.2|
|Somalia|45.5|46.4|19.3|34.4|36.2|25.3|United States|-11.2|0.6|14.3|-10.8|0.8|14.5|
|Uganda|44.3|50.7|8.0|57.7|59.2|13.2|Netherlands|-8.8|13.2|5.4|-6.7|14.1|4.9|
|Lebanon|42.1|60.4|14.2|57.4|70.7|14.6|Burkina Faso|-8.7|22.0|76.2|11.4|20.7|84.9|
|Eritrea|41.8|42.4|28.3|46.8|48.4|35.9|Cote d'Ivoire|-7.4|11.2|45.5|-21.8|4.8|19.6|
|Bosnia and Herzegovina|41.6|45.0|48.2|29.7|34.3|24.2|Libya|-6.5|9.2|18.3|-9.5|12.8|12.5|
|Mongolia|37.2|42.5|81.2|22.3|25.9|9.8|Sweden|-6.2|5.5|2.4|-3.5|4.8|3.5|
|Macedonia|36.5|39.0|18.1|30.9|34.9|5.2|New Zealand|-5.4|30.1|1.1|-14.4|24.8|3.1|
|Congo, Dem. Rep. of the|35.6|46.3|12.4|45.8|55.4|18.9|Norway|-3.0|7.1|2.4|0.6|8.2|1.7|
|Rwanda|35.1|45.2|31.1|-68.8|70.7|38.0|Spain|-2.6|4.2|13.0|-1.5|3.3|8.5|
|Sri Lanka|33.5|33.9|13.5|33.0|35.7|30.4|Paraguay|-2.4|7.1|37.0|-2.3|4.7|5.9|
|Nicaragua|33.3|35.7|12.5|31.3|32.5|7.3|Moldova|-2.2|24.9|61.3|-6.4|8.4|69.1|
|Nigeria|32.2|37.8|6.4|13.1|15.8|12.3|Belgium|-2.1|7.2|7.2|-0.8|5.9|4.4|
|El Salvador|32.2|33.1|2.8|33.9|34.6|3.0|Russia|-1.8|10.4|73.7|1.0|5.4|75.5|
|Malawi|32.0|36.8|43.6|41.9|50.8|30.8|Latvia|-1.7|23.8|43.8|-32.1|31.8|66.9|
|Togo|31.9|45.6|37.9|19.9|33.9|45.6|Japan|0.4|1.9|7.7|0.9|1.8|4.5|
|Cuba|31.2|31.5|2.7|32.8|32.9|2.5|Turkey|0.5|7.2|3.9|6.6|12.2|2.8|

Notes. Only countries with labor force above one million are included. Countries’ ranking is based on net emigration rates in 2000. The non-OECD share measures the share of non-OECD countries in gross emigration of college graduates.
_Table 1: The Expansion of Kenya's Horicultural Exports, 1973-1988'_

|Years|Volume<br>(O00 Tons)|Value<br>(LK Million)|
|---|---|---|
|1973|27.4|3.6|
|1974|21.6|3.8|
|1975|34.4|8.4|
|1976|47.3|13.2|
|1977|67.8|18.8|
|1978|66.7|19.1|
|1Si79|66.3|21.2|
|1980|66.3|23.1|
|1981|69.6|27.2|
|1982|70.6|31.5|
|1983|83.0|43.3|
|1984|90.0|51.6|
|1985|87.1|57.3|
|1986|93.3|65.3|
|1987|93.4|81.9|
|1988|111.1|99.9|
|Average Annual|||
|Change 1973-88|11.6|28.0|

a. Includes Standard intemational Trade Classification (SITC) categories 051 (except nuts). 053,054 ,except<sup>**cassava**and</sup> dried leguminous vegetables), 035, and 2927 (cut flowers). From 198-,. categories 051<sup>and 053 became 057 and 058.</sup> _Sources:_ Kenya Annual Trade Retums; Data from the Horticultural Crops Development<sup>Authority (HCDA))</sup> 14. The expansion of Kenya's horticultural exports has been based upon a wide range of individual commodities and provides the only significant case of agricultural<sup>export</sup> diversification in Kenya since World War II. In recent years, Kenya's horticultural<sup>trade has</sup> consisted of more than sevZr .y-five different commodities/products<sup>from the following</sup> categories:

- (i) _canned fluit and vegetables_ (including canned pineapple and french beans),

- (ii) _ftuit and vegetable juices_ (inclu -ng pineapple, passion fruit, orange, and tomato juices),

8