rafmacalaba/datause-encoder-data huggingface dataset:
Intercepting migrants or rare elements in public places provides a cost-efficient method of surveying, and may allow surveying of individuals who are seldom found in their homes. By ex-post weighting of the sample, one can obtain a sample representative of any person in the reference group who has visited at least one of the locations during the sample period. This method is appealing in that it is likely to offer a sample which is more representative of the underlying population of interest than can be found through the first few referral chains of a snowball sample, with less time and cost than a censusbased screening and listing exercise. However, a disadvantage of interviewing in public locations is that individuals will generally have less time to answer the survey than during a home visit. As a result, on location surveys of this type will have to use a much shorter questionnaire, thereby collecting less extensive data on the population of interest.<sup>3</sup> A fourth method occasionally used to sample ethnic minorities, including migrants, is to use the telephone book to select individuals with minority names. In our application, this would involve using the Brazilian phone books to select individuals with Japanese surnames. Such an approach presents several potential problems, which we can measure the magnitudes of by identifying which of the households found in the stratified random survey also appear in the phone book with a Japanese surname. The first potential problem is that households may not have a landline telephone. This is likely to be more of an issue with poorer households, and as cellphones increasingly substitute for landlines. 90.1 percent of Nikkei households in our stratified sample have a landline phone – of those without a landline, 79.2 percent have a cellphone. The second potential issue is that not all members of the ethnic minority will have a minority surname, especially with intermarriage. 74.8 percent of Nikkei households have a recognizably Japanese surname. A final problem is that households may opt-out of having their telephone number listed in the phone book. Only 24.1 percent of Nikkei households in our sample appear in the phonebook, and only 20.1 percent appear in the phone book and

> 3 In some circumstances one may be able to use the intercept location survey to construct a sampling frame of migrants along with contact details, and subsequently follow-up with longer questionnaires at home or in another location. However, it appears likely that many people approached on the street will refuse to provide follow-up contact details, particularly if they are concerned about crime, or are of illegal migration status.

8

-  -
26 ——. 1989. "Economic Reform in the USSR, Eastern Europe, and China: The Politics of Economics." American Economic Review, Papers and Proceedings 16-20.

Inotai, Andreas, (editor). 1986. "The Hungarian Enterprise in te Context of Intra-CMEA Relations; Hungarian Scientific Council for World Economy." World Economy 52.

Ivanov, Ivan D. 1987. “Restructuring the Mechanism of Foreign Economic Relations in the USSR" Soviet Economy 3:/192-218.

Koeves, Andras. 1985. The CMEA Countries in the World Economy; Turning Inwards or Turning Outwards. Budapest: Akademiai Kiado.

Lavigne, Marie. 1988. “Intra-~CMEA Relations and Domestic Reforms in the USSR and Eastern Europe: Some Interactions." Paper presented at an International Confererence, Seoul.

Machowski, Heinrich. 1988a. "Aussenwirtschaft.” In G. Buetow, editor, Laenderberichte Sovietunion,; Studien zur Geschichte und Politik, Vol. 263. Bonn.

--- 1988b. "Grundzuege der neuen Sovietischen Aussenwirtschaftspolitik.” Osteuropa: 657-71.

--—--. 1988c. “Der COMECON in der Krise." Neue Zuericher Zeitung" July 5.

Marer, Paul, and Wlodzimierz Simiski, (editors). Creditworthiness and Reform in Poland. Bloomington: Indiana University Press.

Marrese, M. and J. Vanous. 1983. Soviet Subsidizing of Trade with Eastern Europe: A Soviet Perspective. Berkeley: University of California.

McKinnon, Ronald. 1979. Money in International Exchange; the Convertible Currency System. York: Oxford University Press.

# New

Murrell, Peter. 1986.°A Framework for Analysis of Bilateral Trade and Countertrade: the Case of the CMEA Countries.” World Bank CPD-Discussion Paper No. 1986-16. World Bank, Washington, D.C.

Nordhaus, William D. 1990. "Soviet Economic Reform: The Longest Road." Brookings Papers on Economic Activity 1:/287-318.

Oblath, G., and P. Pete. “Institutional System of Finnish-Soviet Economic Relations.“ Vienna Institute for Comparative Economic Studies, No. 111.

Pecsi, Kalman. 1981. “The Future of Socialist Economic Integration." Eastern European Economics XIX (2-3).

Pecsi, Kalman. 1985. “Intra-CMEA cooperation.“ Hungarian Scientific Council for World Economy.

Racz, Margit. 1986. "A Summary Analysis." In A. Inotai, editor, "The Hungarian Enterprise in the Context of Intra~-CMEA Relations; Hungarian Scientific Council for World Economy." World Economy 52.
# **What Has Driven the Decline of Infant Mortality in Kenya?**

Gabriel Demombynes Sofia Karina Trommlerová World Bank University of Göttingen JEL Codes: I10, I38 Keywords: Kenya, infant mortality, malaria, bednets __________________________________ The authors thank Michael Clemens, Andrew Dabalen, Wolfgang Fengler, Jed Friedman, Abdisalan Noor, Bo Pedersen, and Robert Snow as well as participants at a session of the 2012 Annual Conference of the Centre for the Study of African Economies at Oxford University for helpful comments. They express particular appreciation to Ms. Noor and Mr. Snow for their guidance and for providing malaria prevalence estimates for Demographic and Health Survey clusters. The views expressed in this paper are the authors‘ alone. They do not necessarily reflect the views of the World Bank or its Executive Directors. Mr. Demombynes is part of the Poverty Reduction staff of the Poverty Reduction and Economic Management (PREM) unit of the World Bank‘s Kenya office. Corresponding email: gdemombynes@worldbank.org
# **Figure 3: Factors Affecting CO2 Emissions Growth in LAC Countries**

|Country|1981 1982|1983 1984 1985|1986 198|7 1988 1989 1990|1991 1|99|2 1993 1994 199|5 1996 1997 199|8 1999 2000 2001|2002 2003 2004 2005|<br>Overall|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Argentina|||||||||||EA|
|Bolivia|||||||||||EI|
|Brazil|||||||||||EA|
|Caribbean|||||||||||EI|
|Chile|||||||||||EI & EA|
|Colombia|||||||||||EI & EA|
|Costa Rica|||||||||||EA|
|Cuba|||||||||||EI|
|Ecuador|||||||||||EI|
|El Salvado|r||||||||||EI & EA|
|Guatemala|||||||||||EI|
|Honduras|||||||||||EI|
|Mexico|||||||||||EI & EA|
|Nicaragua|||||||||||EI & EA|
|Other L.A.|||||||||||EI|
|Panama|||||||||||EI|
|Paraguay|||||||||||EI|
|Peru|||||||||||EA|
|Uruguay|||||||||||EA|
|Venezuela|||||||||||EI & EA|
||EI|EA||EI-EA|FM-E|A|FM-EI|||||

Note: Overall factor selected if it is a main factor in at least 40% (10 out of 25 years) of the study period.

The modal mix effect, as defined in this study, considers only four modes: road, rail, water and air, and does not appear to influence emission growth. If necessary data is available to further disaggregate road transportation into auto, bus etc., modal mix might be found to influence CO2 emission growth.

20
# Table 9

# PAKISTAN' PUBLIC EXPENDrrURE IN AGRICULTlURE (Development) Total Condant Rupees -million

|Pxunlab|1982.83|1983-84|1984-85|1985-46|1986-87|1987-88|1988-89|19S9-90|1990-91|1991-92|1992-93|1993-94|Averagc|Growth R-al|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture&Credit|215 92|211.79|184.16|225.38|226.99|273.86|124.76|135.57|157.18|138.77|171.21|70.58|187.79|-3.95|
|Irrigation|424.67|288.29|322.24|272.59|402.51|350.38|182.13|237.11|595.62|545.90|410.55|186.77|36654|-1.47|
|<br>Total Punjab|7S5.-?|61127|62429|60174|75210|739.76|398.90|460.78|S3792S|76.73|649Z51|309f04|656S.6||
|Slndh|||||||||||||||
|Api and Agri Credit|88.85|64.37|71.79|76.51|97.66|13S.05|78.58|73.58|89.70|50.88|59.22|4S.08|30.83||
|<br>Irrigation|193.57|172.17|189.74|193.97|284.91|262.34|181.10|255.91|313.04|273.46|238.53|22831|232.61|2.51|
|<br>Tetal Sindh<br>NWFP|D7.65|26963|29838|3300|_440-61_|_45S.91_|3fU6|393.96|_49066_|37260|1592|324L84|367.74|I|
|Agriculture|40.24|48.08|4S.43|47.05|58.77|72.21|90.19|164.71|_58.26_|142.40|56.64|64.78|75.23|3.41|
|<br>Irigation|34.22|50.48|55.52|51.82|64.16|92.37|60.92|10539|124.50|145.22|189.46|172.00|82.64|29.70|
|<br>Total NWFP<br>Balochistan|11|2|**2**<br>1 41244|5|152.04|12242|12242|316-S1|_Z22AI_|J335|302 08|311 57|202-32|142fi|
|Crop Agriculture|59.91|53.62|56.56|43.15|56.51|41.01|42.45|41.33|60.24|24.16|41.56|56.23|4732|-1.26|
|<br>Inrigption|1S.50|122.54|100.83|186.52|156.37|154.57|177.35|16t 12|199.44|36S.59|415.75|243.52|196.96|15.77|
|<br>Total Balochistan<br>Federal Governnumnt|243Q|2137.71|221.90 6|24.|2763|ZM|2649246|2|2462|_423.04Q_|_A_49135|36037|29223||
|Food&Agriculture|1879.07|1713.52|1551.57|2349.41|1579.56|1337.06|_1181.60_|1163.S7|S09.71|1130.17|71168|47931|1400.66|-6.08|
|Irigation|873.31|<br>894.11|<br>544.85|S67.91|1011.44|<br>608.12|363.69|920.79|775.46|312.07|334.15|507.59|6S236|4.76|
|Food Storage|219.58|118.76|175.74|47.64|69.83|29.92|331|50.94|31.82|5.28|0.00|0.00|71.17|-9.09|
|Federal Subsidies|||||||||||||||
|FertilizerSubsidy|1623.00|1220.00|1137.12|1766.69|788.77|1389.24|1428.66|674.84|678.41|1224.00|_33323_|309.00|_104531_|**_.0.%_**|
|<br>Tubewell Subsidy|2.00|13.33|<br>13.33|<br>11.74|<br>12.59|6.40|4.71|4.42|na|0.00|0.00|na|rna|na|
|Total Federal|46039|4129.90|366512|4Q-<br>4|31665.1|5162140|3149.95|29 29411|2AU|27cL05|173129|7 1448 19|32417|.2|
|Total National Dcv.|||||||||||||||
|Expenditures<br>Total National Agri.|6062-00|538900|49SI.OQ|_66Q255_|5298.52|525956|433740|413A41|42552|468227|3534.13|2954.02|_49T705_|.42|
|<br>Expenditures|3200|7960295|_116605_|_7_12512.3|21063157|3 LS01Q1|1342955|992641|10602.1|21102.2Q|994428|781349|107292<br>2i|94<br>2|

Source: Economic Survey of Pakistan, 1993-94. _The_ growth rates for Federal items are calculated from 1984-85 to 1992-93. All growth 'igures are calculated based on the first and last periods, except for National Expendinure growth, which is calculated from a regression trend line on a tD-period moving aveage. Other subsectors like fisheries, veterinary etc. are not shown individually but are included in the total figures.

23
of Development, Industry and Foreign Trade. These data include information on firm-level export status in each year over 2009-2012, as well as the share of industry-level exports to each destination in 2008. To construct industry-specific real exchange rate instruments, we have supplemented these data with information on the consumer price index from the World Development Indicators of the World Bank and bilateral nominal exchange rates from the International Financial Statistics of the International Monetary Fund.

We further use detailed administrative records on training provision collected by the largest training provider in the manufacturing sector– _Servi ̧co Nacional de Aprendizagem Industrial_ (SENAI), the training arm of the national confederation of industry.<sup>3</sup> From these records, we extracted the set of workers trained in each year, worker-level information on demographics, occupation before starting the course, training category and course duration, as well as enrollment and completion dates. The data cover around 270,000 trainees per year.

SENAI provides five categories of job training: (1) technical upgrading; (2) apprenticeship; (3) initiation; (4) habilitation; and (5) qualification courses. The various course types have different objectives. In particular, technical upgrading courses differs considerably from the other training categories.

Technical upgrading courses are aimed at workers seeking to update, expand or complement the professional skills they have acquired in a specific occupation or field of work. Their duration varies between 40 and 120 hours. The curriculum is advanced and occupation-specific. They focus mainly on the skill needs resulting from technological innovations and new production and management processes. For instance, for industrial electricians, SENAI offers a course on electrical controls which lasts 80 hours.

> 3SENAI is the national service for training in the Brazilian industrial sector. It consists of a network of professional schools established and maintained by _Confedera ̧c ̃ao Nacional da Ind ́ustria_ (CNI), the national confederation of industry. SENAI is part of _Sistema S_ which is a set of vocational training institutions managed by CNI. While SENAI is the manufacturing arm, CNI also manages similar institutions for other sectors, including wholesale trade, agriculture and transport. SENAI is one of the 5th largest training providers in the world and the largest in Latin America. It is financed by a tax charged to firms equal to 1% of their wage bill and is the main provider of job training in manufacturing (Silva, Almeida and Strokova, 2015).

6
<!-- Start of picture text --> Common rocks Ores<br>—_—— "><br>3 MINERALOGICAL S<br>CURRENT / URRENT<br>RittmnltiNING INING<br>GRADE ————> GRADE ———><br><!-- End of picture text --> <!-- Start of picture text --> 500<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Chile DRC Zambia<br><!-- End of picture text --> <!-- Start of picture text --> 70<br>6.0<br>5.0<br>40<br>30<br>2.0<br>1.0<br>oo nz<br>Chile BRC Zambia<br><!-- End of picture text -->
- 40 -

Figure 7. Financial Deepening and the Real Rate in the Philippines <!-- Start of picture text --> 0.6*.<br>0.5 , a<br>0.4<br>0.3 -- 2<br>0.21 \<br>0.1<br>1g6197¶97197197197197¶97197197197¶gSlgSl9819lgeglgelgs¶98<br>9 0 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7<br><!-- End of picture text --> Source: IFS.
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE and are around 15 percent of GDP—roughly equivalent to what China has been investing on its own domestic infrastructure development in recent years.

**Figure 28. Sudan’s infrastructure spending needs in the regional context, as share of GDP**

Estimated infrastructure spending needed to meet targets, as percentage of GDP <!-- Start of picture text --> SSA<br>LIC-Fragile<br>COMESA<br>LIC-NoFragile<br>Resource-Rich<br>MIC<br>Congo, Dem. Rep.<br>Ethiopia<br>South Sudan<br>Kenya<br>North Sudan<br>Uganda Capex O&M<br>South Africa<br>Botswana<br>% of GDP 0 10 20 30 40 50 60 70<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009. _Note:_ Values excludes irrigation.

LIC = low-income country; MIC = middle-income country; COMESA = Common Market for Eastern and Southern Africa; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

Sudan already spends a sizable amount—$1.5 billion per year, or 7 percent of GDP—to meet its infrastructure needs (table 19). In terms of the economy’s size, the aggregates are comparable to what middle-income countries spend and perhaps slightly higher than what other resource-rich countries spend (figure 29). About 60 percent of the total infrastructure spending is allocated to capital expenses and 40 percent to operating expenditures. Operating expenditure is entirely covered from budgetary resources and payments by infrastructure users. The composition of financiers indicates that Sudan has been able to capitalize on the interest of non–Organisation for Economic Co-operation and Development (OECD) investors and the private sector.

**Table 19. Financial flows to Sudan’s infrastructure, average 2001 to 2005**

|$ millions per year|O&M<br>Public<br>sector|Public<br>sector|Ca<br>ODA|pital expenditure<br>Non-OECD<br>financiers|<br>PPI|Total<br>CAPEX|Total<br>spending|Total<br>spending as<br>share of GDP|
|---|---|---|---|---|---|---|---|---|
|ICT|4|5|0|35|209|249|253|1.18|
|Power|281.9|104|0|304|0|409|690|3.23|
|Transport|282.0|183|22|22|0|227|509|2.38|
|WSS|2|6|12|5|7|31|32|0.15|
|Total|570|298|35|366|215|915|1,485|6.95|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development; WSS = water supply and sanitation; ICT = information and communication technology; GDP = gross domestic product.

> <sup>40</sup>
assess how the vulnerability and resilience of rural households vary by household types, geography, and wealth (Jain, 2007; Thurlow, et al., 2012; Wineman and Crawford, 2017; Al Mamun, et al., 2018; Braimoh, et al., 2018; Alfani, et al., 2019; Ngoma, et al., 2019; Hamududu and Ngoma, 2019). It is equally not well understood the extent to which exposure to different climatic shocks affects vulnerability and resilience at national and subnational levels. There is also a dearth of information on the relative importance of CSA practices such as minimum tillage, improved inputs (fertilizer and seed), and crop diversification to mitigate the effects of climate shocks on livelihoods. This paper contributes to filling these gaps and evaluates the common types of climate shocks Zambian households face and how these shocks affect household vulnerability and resilience. It also assesses the types of strategies households use to cope with shocks, particularly assessing if CSA practices can mediate damaging climate shock effects on vulnerability and resilience.

This paper extends the literature on climate change and poverty in Zambia in four ways:

- (i) It uses detailed longitudinal household-level data to analyze the types of climate shocks households face in rural Zambia, how these shocks differ by geography and household types, and how these shocks affect vulnerability and resilience. This builds on several papers that focus on explaining poverty dynamics in Zambia. See for example Chapoto et al., (2011) covering the period 2001 – 2008, Ngoma et al., (2019) for the period 2012 – 2015 and Diwakar et al., (2020) for the period 2012 – 2019. The last two papers use the same RALS data sets as in this paper.

- (ii) It incorporates unique exogenously defined drought and flood risks to measure vulnerability and exposure to climate risk using detailed three-wave householdlevel panel data.

- (iii) It assesses how exposure to climate risk affects vulnerability and resilience.

- (iv) Finally, the paper assesses whether adopting common CSA practices mediates the effects of climate shocks on vulnerability and resilience in Zambia, and if so, by how much.

The multidimensionality of poverty, resilience and vulnerability and their causes make measuring any of these indicators a daunting task. For this paper and given our focus on the last two metrics, we follow Barrett and Constas (2014) and define resilient households over three waves of data as those that were never poor or those that escaped poverty by the third wave. We classify vulnerable households as those that are poor in all the three waves, or poor in the first and third waves but not in the second wave, or those poor in the second and third waves but not in the first wave, or indeed those that were not poor in the first and second waves but fell into poverty in the third wave.

Vulnerable households are more likely to be adversely affected due to either exposure or lack of capacity to cope and adapt. Exposure refers to the presence of rural households or 3
**Table 3. Comparison of propensity score matched (PSM) and treatment regression** **<u>(TRE) of average participation effect on the participating households in GMA (ATT)</u>**

||PSM<br>|T<br>|reatment <br>|Regressi|on|
|---|---|---|---|---|---|
|Outcome:per capita consumption|Estimates|Estim|ates|||
||(1)|(2|)||(3)|
|Bangweulu|-0.074<br>**|0.858|***|-0.56|***|
|Kafue|-0.240|-0.286||0.13||
|Lower Zambezi|-0.118|0.494||0.37||
|Luangwa|0.034|0.530|***|-0.42|**|
|Overall|0.083|0.438|***|-0.30|*|

Significance: *=Significance at 10%; **=Significance at 5%; ***=Significance at 1% Source: Authors’ calculations, Data from the IGMAW survey, 2006 Among the households living in GMAs, some households participate in CRB/VAGs. If CRB/VAGs provide broad social infrastructures from the hunting licensing revenue, we expect all households in the GMA to gain in welfare, on average. However, if active participants in these institutions restrict benefit sharing to themselves, we would expect participants to gain more in welfare relative to non-participants.

Table 3 shows the impact of participation in CRB/VAG activities on household welfare. Households in the GMA areas of Kafue and Lower Zambezi park systems obtain no welfare gains from participating in CRB/VAGs. The residents of the GMAs near the other two park systems, Bangweulu and Luangwa, seem to obtain significant benefits from participation in CRB/VAGs. Overall, GMA households participating in CRB/VAGs consume 44 percent more per than their non-participating counterparts. The complete estimation results in Tables A1 and A2 show various factors and their effects on household welfare.

# _3.2 Determinants of being in GMA and participation in VAGs and CRBs_

The decision of a household to live in a GMA and, once in the GMA, the decision to participate in natural resource management decisions through CRB/VAGs are influenced by the circumstances that the household faces both within the household and in and around the community. Several factors were considered and the significance of their contributions towards explaining these decisions tested with the joint estimations of equations (1) and (2).<sup>4</sup> In this section we focus on the equation (1).

The results indicate that an average household’s probability to reside in a GMA is directly and significantly related to its average distance to the nearest main road, the number of social projects in the area, and the viability of the CRB/VAG as measured by its ability to generate resources and number of participating households (Column 1; Table A1 in the Annex). Female-headed households are also 19 percent more likely to reside in GMAs than are their male-headed counterparts. The results further indicate an inverse and significant relationship between the household’s GMA status and age of the household head, education of the most educated member, value of consumer durable assets, and participation in other area cooperatives. All these clearly suggest that households that reside in GMAs are relatively more disadvantaged than are those in non-GMAs.

> 4 The estimation results of the full models are in the annex tables.

9
# Figures and Tables

_Table 1. Outcome and main explanatory variables of interest_

||**Dependent variable**(Y�)|**Unit of**<br>**measurement**|**Level of analysis**|**Main independent**<br>**variable**(T�)|
|---|---|---|---|---|
|Output/|Maizequantity produced|kg|Plot-level|Recall length|
|<br>Production|Harvest<br>Maizequantitysold||Household/farm-|(months) to harvest|
||disposition<br>Maize in storage||level|end|
|Inputs –|Agricultural plots and parcels listed and cultivated by the<br>household|number|Household/farm-<br>level|Recall length<br>(months) to lantin|
|Land|Difference between self-reported and GPS-measured plot<br>land area|hectares|Plot-level|pg<br>of plot*|
||Total labor<br>inputs by type,<br>Total labor<br>Household member labor|person-days|Plot-level|Recall length<br>(months) to planting|
||household and<br>external<br>Use of hired labor|binary||of plot*|
|Inputs –|Labor by<br>Plotpreparation andplantinglabor|||Recall length|
|Labor|activity<br>Person-days in plot maintenance|erson-das|Plot-level|(months) to planting<br>ofplot*|
||Person-days in harvesting and post-<br>harvest activities.|py||Recall length<br>(months) to harvest<br>end|
||Organic fertilizer use|||Recall length|
|Inputs –|Inorganic fertilizer use|binary|Plot-level|(months) to planting<br>ofplot*|
|other|Inorganic fertilizer application quantity|kg||Recall length<br>(months) to first<br>fertilizer application*|

* In the Tanzania NPS 2012/13 survey, these variables are not available at the plot-level. Regional sowing season timing information is used instead.

20