rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4 The Data**

The data used in this paper come from surveys conducted in Albania in four different moments in time: the 2002 Living Standard Measurement Survey (LSMS), the 2005 Living Standard Measurement Survey (LSMS), the 2008-09 Demographic Health Survey (DHS) and the 2012 Living Standard Measurement Survey (LSMS). The LSMS surveys were undertaken by the Albanian National Institute of Statistics (INSTAT) with the technical assistance of the World Bank. The 2008-09 DHS was conducted by the INSTAT but also by the Institute of Public Health (IPH). These surveys correspond to different wave rounds, but it is important to note that they do not form a panel database. They are nationally representative surveys for the whole country, as well as at regional and at the urban/rural level. Each survey contains a wide range of information on individuals’ demographic and socioeconomic characteristics (i.e. fertility, health, employment, migration and education among many others). In each one of these databases, the country is stratified into four regions (strata), Tirana, Coastal, Central and Mountain. All the surveyed individuals belong to the 36 districts in the country, with all regions nearly equally represented.

These surveys interviewed both women and men with respect to their fertility decisions, thereby permitting the unique opportunity to incorporate information in my study regarding the fertility history of each household. In other words, each one of these surveys contains detailed information which helps identify children by birth of order and match them with their biological mother and father. For instance, in the 2008-09 DHS survey, all the children ever born are already matched to each one of their corresponding mother and father, which makes the DHS a perfect database for studying the fertility history of the parents. In contrast, in the LSMS surveys, the fertility history information is not as clearly identified as in the DHS, which means that the children are not already linked to their corresponding parents. However, the advantage of the
**Table A-11: Distribution of formal and informal jobs and workers.**

|||**number o **<br>formal|**fjobs, thsd**<br>informal|share of<br>informal, percent|
|---|---|---|---|---|
||employed|41,961|10,337|19.8|
|first job|self-employed|2,545|4,329|63.0|
||total|44,506|14,666|24.8|
||employed|914|723|44.2|
|second job|self employed||501|100.0|
||total|914|1,224|57.3|
|occasional job|self-employed|625|6,861|91.6|
||employed|42,875|11,060|20.5|
|all jobs|self-employed|3,171|11,190|77.9|
||total|46,046|22,250|32.6|
|||**number of **<br>formal|**people, thsd**<br>informal|share of<br>informal, percent|
|any job||43,340|21,706|33.4|
|all jobs||45,037|20,009|30.8|
|primary job||44,506|14,666|24.8|

Source: Authors calculations using RLMS-HSE-2016 45
our benchmark calibration due to the actual dominant role played by � and �. Some parameter calibration values deserve special attention. A key mechanism in the literature on ...scal multipliers is the reaction of the nominal interest rate to in‡ation and output gaps by central banks (i.e., parameters �� and �Y in the Taylor rule of equation 11). We follow Iacoviello (2005) in choosing a value of �� = 1:27, �Y = 0:13, and �R = 0:73. The persistence parameter � = 0:86 is based on our own estimates shown in Section 2. The rest of parameter values are selected as follows: Frisch elasticity '<sup>�1</sup> = 1 (as in Christiano et al., 2005), discount rate � = 0:995, Calvo probabilities �p = �w = 0:75 (from Barattieri et al., 2014), sticky wage elasticity "w = 20:50 (matching 5% unemployment rate), steady-state government purchases Gss = 0:20 (from World Development Indicators), steady-state capital adjustment cost S<sup>00</sup> = 1:5 (from Altig et al., 2011) as well as depreciation rate � = 0:016, average mark-up X = 1:10, and capital share � = 0:30 (in order to match K/Y from KLEMS). Measurement and sources of data regarding parameters � and � were discussed in detail in Section 4. In particular, we take the average � and � for each sample in the Latin American and developed

> 28 Based on a simple regression of STT against �, we cannot reject the hypothesis that such a coe¢ cient equals statistically zero for the whole sample of countries (as well as for developed and emerging markets separately).

> 29 Appendix 3 shows the share of HtM individuals (�), the share
in Afghanistan’s economy in recent years. It is thus relatively straightforward to estimate the change in informal sector activity as a weighted difference between the change in nighttime light intensity and the change in cell phone traffic among registered firms.

Non-traditional sources of information are also used to measure the intensity of conflict at the local level, and to control for potentially confounding circumstances. The US Pentagon has made available data on conflict-related casualties with a high level of spatial disaggregation, as well as information on the presence of international and Afghan troops at the district level. Based on information from international donor agencies, it is also possible to estimate the level of foreign aid at the district level.

The analysis is conducted with the district as the unit of observation, as this is the lowest level of spatial disaggregation for which all the relevant data are available. The estimates are for the period 2012-16. The initial year is the starting point of a rapid deterioration of the security situation in Afghanistan. The final year is the last one for which the Pentagon made spatially disaggregated data on conflict-related casualties available.

The high frequency of the data also allows estimating the response of each of the three sectors to conflicts with varying time lags. In this paper, the impacts from conflict are estimated over each of the four quarters following a surge in the number of casualties. The exercise reveals very different time patterns across sectors. The impact of local surges in conflict on overall economic activity is relatively modest, but their effect on the composition of output is dramatic.

# **The Afghan context**

Following the fall of the Taliban regime in 2001, Afghanistan received massive international security and development assistance. More than 100,000 international troops were stationed in the country until 2012, supporting operations against the ongoing insurgency and assisting in building and training a national defense force. To ensure a path toward self-reliance, the government of Afghanistan and NATO partners agreed in 2010 on a gradual handover of security responsibilities. By 2018, there were only 16,229
other studies, transition cost in this paper focuses on the transition of pillar 2 from notional to fully funded individual accounts. We assume that starting in 2001, current notional individual accounts will be kept only for prereform pensioners and workers who have contributed to the notional individual accounts (middle men), and that it will be phased out as the prereform pensioners and middle men die. Fiscal resources will be injected to finance deficits in the transition accounts. Using this model the financial flows over the next 50 years could be traced and estimates can be made of the financial resources needed over time to pay the current pension and accrued pension rights of current workers.

The equilibrium data set of the model at base year is constructed around a Chinese social accounting matrix for social security reform estimated for 1995. It provides a consistent framework for organizing the relevant flow-of-value statistics for China's economy to satisfy the requirements of a benchmark data set for CGE modeling, as outlined in Whalley (1985). Data on production and exports are divided among three types of ownership. The sources and structure of the base year data set are described in annex 2. The key parameters used in the model, and their averages, are listed in annex 3.

# **Baseline Calibration and Simulation Design**

# **Major assumptions in baseline calibration**

To calibrate a baseline of the model, we introduce two economy-wide, ownership-specific (one for SOEs, and one for IPEs and NSEs) TFP variables into the model. These are solved endogenously in calibration to match a pre specified path of real GDP growth and the decline of employment in the state sector. The growth rate of real GDP is set to the actual growth rate during 1996-99. The growth rate then declines linearly from 7.5 percent in 2000 to 3 percent in 2050. There were 113 million workers in the state sector in 1995. This figure had decreased to about 95 million by 1999. It is assumed to decrease further by 5 million annually from 2000 to 2020
# Figure 2

# Panel A

<!-- Start of picture text --> % Employment in Agriculture vs. GDP per capita, 2008<br>Sri Lanka + Region Trends & Comparator Countries<br>IN - 1983<br>NP - 2008<br>BGD - 1984 IN - 2008<br>PK - 1980<br>BGD - 2008<br>PK - 2008<br>SL - 1981 Thailand<br>Philippines<br>SL - 2008<br>Brazil<br>Malaysia<br>KoreaRep<br>Unit e dKingdomUnitedStates<br>Singapore<br>.5 5 10 15 20 25 30 40 50<br>GDP per capita, 000s (in constant 2005 international $ at PPP)<br>Data source: ILO (KILM 3rd & 4th eds. & LABORSTA) & World Bank World Development Indicators, 2010. India data from National Sample Survey.<br>2008 employment share regressed on log(2008 GDP per capita) & log(2008 GDP per capita) squared. Excludes ECA countries in transition.<br>Panel C<br>% Employment in Services vs. GDP per capita, 2008<br>Sri Lanka + Region Trends & Comparator Countries<br>UnitedKingdomUnitedStates<br>Singapore<br>KoreaRep<br>Brazil Malaysia<br>Philippines<br>BGD - 2008 PK - 2008 SL - 2008 Thailand<br>PK - 1980 SL - 1981<br>NP - 2008<br>BGD - 1984 IN - 2008<br>IN - 1983<br>.5 5 10 15 20 25 30 40 50<br>GDP per capita, 000s (in constant 2005 international $ at PPP)<br>Data source: ILO (KILM 3rd & 4th eds. & LABORSTA) & World Bank World Development Indicators, 2010. India data from National Sample Survey.<br>2008 employment share regressed on log(2008 GDP per capita) &
Zambian farmers. But before doing that, we review the poverty trends observed in Zambia during the 1990s.

# **1.3 Poverty Trends**

In spite of the significant reforms adopted by the Zambian government and of the significant intervention in international agricultural markets, Zambia is one of the poorest countries in the world. Furthermore, poverty rates tended to increase during the 1990s.

Our description of the poverty trends is based on three household surveys, the 1991 Priority Surveys, and the 1996 and 1998 Living Conditions Monitoring Surveys.<sup>6</sup> The Priority Survey of 1991 is a Social Dimension of Adjustment (SDA) survey. It was conducted between October and November and covered a total of 9,886 households. Sample sizes were increased to 11,750 and 16,800 households in the 1996 and 1998 LCM Surveys.

Table 6.1 reports the poverty dynamics. In 1991, the poverty rate at the national level was 69.6 percent. Poverty increased in 1996, when the head count reached 80 percent, and then declined towards 1998, with a head count of 71.5 percent. In rural areas, poverty is widespread; the head count was 88.3 percent in 1991, 90.5 percent in 1996 and 82.1 percent in 1998. Urban areas fared better, with a poverty rate of 47.2 percent in 1991, 62.1 percent in 1996 and 53.4 percent in 1998. In what follows, our focus is on rural areas.

Poverty trends by provinces are reported in Table 6.2. Zambia is a large country, and provinces differ substantially in basic characteristics like land quality, distance to the capital, roads, etc. In particular, cotton, the commodity under investigation here, can be produced (due to soil characteristics) in only the Southern, the Central, and the Eastern provinces. At the national level, poverty increased from 1991 to 1996 and then declined in 1998. Poverty trends in Lusaka
the gender, socio-demographic and country of origin characteristics. We also examine how the inflow of asylum seekers affects inequalities among the hosts, by comparing labor-market outcomes with the native population. Our study consists of original empirical analysis. We employ a unique dataset based on the Swiss administrative records and social security data provided to us by the Swiss Federal Statistical Office (FSO). Our dataset includes the universe of refugees and migrants in Switzerland over 1998–2018. Our dataset presents the longitudinal dimension, which allows us to follow individuals over time and examine their labor-market outcomes and career trajectories over the life-cycle, comparing refugees to the host population and other migrants.

Our study complements the other papers that are part of this World Bank project on forced displacement. Specifically, it is closely related to the studies that examine the effect of forced displacement on inequalities. These studies focus on both low and middle-income countries, which have different traditions of hosting refugees (e.g., Albarosa and Elsner (2021) focusing on Germany; Kovac et al. (2021) on Bosnia and Herzegovina and Croatia; Murard (2021) examining the case of Greece and Aksoy and Ginn (2021) focusing on a cross-country comparison). On the other hand, we focus on Switzerland, an advanced economy with a multicultural society, and a long historical tradition of hosting refugees.

Our paper makes three main contributions to the existing literature. First, the specific context of Switzerland where refugees are randomly allocated across cantons that are different in terms of local economic and social conditions, provides us with a ‘natural experiment’. This allows us to rigorously evaluate the causal effect of these determinants on refugees’ labor market outcomes and economic integration. In this context, we examine the role of initial local conditions such as natives’ attitudes towards refugees at the time of arrival in the host location, and their subsequent effects on the trajectory of economic integration. Second, we examine in a horserace model the relative importance of three fundamental initial local conditions, namely
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2009. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

- Helio International. 2009. ―Energy Systems: Vulnerability—Adaptation—Resilience (VAR) 2009: Regional Focus Cameroon.‖ Paris.

- Husband Charles, Gary McMahon, and Peter van der Veen. 2009. ―The Aluminum Industry in West and Central Africa: Lessons Learned and Prospects for the Future.‖ Extractive Industries and Development Series #13, World Bank, Washington, DC.

- Nkama, Arsène Honoré Gidéon. 2007. ―Analyzing the Impact of ICT Investments on Productivity Growth in Developing Countries: Evidence from Cameroon.‖ Paper presented at the African Economic Conference in Addis Ababa.

- www.uneca.org/aec/documents/Arsene%20Honore%20Gideon%20Nkama.pdf.

- Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

- USAID (United States Agency for International Development). 2010. ―Cameroon Country Profile. Property Rights and Resource Governance.‖ Washington, DC.

- World Bank. 2008. ―Republic of Cameroon: Energy Sector Development Project.‖ Project Appraisal Document as of May 29, World Bank, Washington, DC.

- ———. 2009c. ―Enterprise Surveys for Cameroon 2009.‖ Washington, DC: World Bank.www.enterprisesurveys.org/ExploreEconomies/?economyid=34&year=2009.

- ———.
.. share from crop prod|0.15|0.38|0.32|0.47|0.15|0.01|
|..share from self-employment|0.13|0.12|0.07|0.09|0.07|0.15|
|.. share from livestock prod|0.02|0.03|0.01|0.03|0.02|0.01|
|.. share from other sources|0.05|0.05|0.03|0.03|0.04|0.06|
|**Productivity**|||||||
|<br>Annual gross revenue (Rs./ac)|20408.59|21628.00|13216.82|18167.36|17611.82|-|
|Annual net revenue (Rs./ac)|8652.84|9112.45|5554.71|8178.62|7606.50|-|
|**Agricultural market participation**|||||||
|Ever rented out land|0.01|0.03|0.00|0.01|0.01|0.00|
|Would like to rent in land|0.64|0.64|0.78|0.70|0.78|0.62|
|Would like to rent out land|0.03|0.04|0.04|0.03|0.04|0.01|
|No. of observation|95926|36053|2740|3094|6457|46609|

_Source:_ 2008/9 West Bengal listing survey.

17
# Appendices

# Appendix 1. Data de...nitions and sources

All series are quarterly and seasonally adjusted. If they are not seasonally adjusted from the original source, we apply a seasonal adjustment following the U.S. Census Bureau’s X-13ARIMA-SEATSX13. All series are expressed in millions of national currency, in constant prices, de‡ated using the implicit GDP de‡ator derived from quarterly national accounts for each country.<sup>38</sup> GDP, private consumption, private investment (gross ...xed capital formation to be precise), and GDP de‡ator sources: Argentina (Dirección Nacional de Cuentas Nacionales, INDEC), Brazil (Instituto Brasileiro de Geogra...a e Estatística, IBGE), Chile (Banco Central de Chile), Colombia (Departamento Administrativo Nacional de Estadística, DANE), Mexico (Instituto Nacional de Estadística, Geografía e Informática, INEGI), Peru (Instituto Nacional de Estadística e Informática, INEI, Banco Central de Reserva del Perú, BCRP), United States (Economic Research Division, Federal Reserve Bank of St. Louis), and European countries (Eurostat [Last update: 16.09.19. Extracted on: 17.09.19]).

Social transfers sources: Argentina (Dirección Nacional de Política Fiscal y de Ingresos. Subsecretaría de Programación Macroeconómica. Secretaría de Política Económica, Ministerio de Hacienda), Brazil (STN, IBGE, IPEA e BCB), Chile (Dirección de Presupuestos (DIPRES) / Banco Central de Chile), Colombia (Ministerio de Hacienda y Crédito Público), Mexico (Dirección General de Estadística de la Hacienda Pública. Unidad de Planeación Económica de la Hacienda Pública.), Peru (Government Finance Statistics (GFS) - IMF), United States (Economic Research Division, Federal Reserve Bank of St. Louis), and European countries (Eurostat [Last update: 19.07.19. Extracted on: 17.09.19]).<
areas that were a subject to Soviet forced resettlement policies where population was brought into valleys from the mountains to increase available labor. Since the areas with more resettlement were more heterogeneous, they suffered from a higher level of conflict argues Tadjbakhsh (1996). Many resettled families lived in their new locations for generations but were considered to be outsiders as they continued to marry within their own community.

> 12 I also used several alternative specifications of the conflict variable such a count measure of events and a dummy variable – catch-all definition of conflict affected area by region, where all raions (districts) in Dushanbe, Khatlon and RRS were defined as areas affected by conflict and all raions in Sugd and GBAO were defined as not affected. The count measure of conflict has no significant effect, while the “catch-all” measure has a similar if not stronger impact as the “reports of conflict activity measure” defined above on the dependent variables used in this study. In my future research, I plan to evaluate the effect of particular events, e.g. fighting in a raion, presence of military groups, attacks on civilians, on the dependent variables of interest by using sub-sets of the count event data.

12
Nor has the geographic pattern of agricultural growth in India been particularly pro-poor. The states with higher growth in agricultural yields were not the key states with higher shares of India’s poverty; indeed, there is a mild negative correlation, although not statistically significant (Datt and Ravallion, 2002). Agricultural growth as a whole has also lagged relative to India’s (primarily urban) non-farm economy.

# **_Which country has had more pro-poor growth?_**

In addressing this question we must first confront a semantic point that has been a source of some confusion in recent development policy discussions. “Pro-poor growth” has sometimes been taken to mean that poverty falls more than it would have if all incomes had grown at the same rate (Baulch and McCullock, 2000; Kakwani and Pernia, 2000). This definition focuses on the distributional shifts during the growth process; roughly speaking, for growth to be deemed “pro-poor” the incomes of the poor should grow at a higher rate than those of the nonpoor. By this definition, growth has not been pro-poor in either China or India (after adjusting for the comparability problem in the survey data noted above). In both countries, a distribution-neutral growth process would have had more impact on poverty than actually observed in the 1990s.

However, it is surely problematic to identify a growth process as not being “pro-poor” when the poor benefit as much as they have in India and (especially) China. As the experience of both countries has exemplified, rising inequality during a period of overall economic expansion can come with large absolute gains to the poor. Similarly, a recession will be deemed pro-poor if poor people lose proportionately less than others, even though they are in fact worse off. An alternative definition of “pro-poor growth” proposed by Ravallion and Chen (2003) avoids this problem by focusing instead on what happens to poverty. By this definition “pro-

> and the term in ( ) is the empirical elasticity of non-farm output
MALAWI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 6. Benchmarking ICT indicators**

||Unit<br>Lo|w-income countries<br>**Malawi**||Middle-income<br>countries|
|---|---|---|---|---|
|GSM coverage|% population|48.2<br>**93.3**||97.2|
|International bandwidth|Mbps/capita|5.8<br>**0.19**||30.2|
|Internet|subscribers/100 people|0.1<br>**0.15**||2.0|
|Landline|subscribers/100 people|0.8<br>**1.3**||9.4|
|Mobile phone|subscribers/100 people|15.1<br>**5.2**<br>**Malawi**<br>Countries wit<br>access to subm<br>cables|hout<br>arine|86.7<br>Other developing<br>regions|
|Price of monthly mobile basket||**11.0**|11.12|9.9|
|Price of monthly fixed-line basket||**16.5**|13.58|—|
|Price of 20-hour Internet package||**46.6**|67.95|11.0|
|Price of call to the United States||**1.33**|2.59|2.0|
|Price of intra-African calls, mean||**1.10**|0.72|n.a.|

_Source:_ Minges and others 2009. Derived from AICD national database, http://www.infrastructureafrica.org/aicd
(0.037)<br>Observations<br>1023<br>1023<br>Mean control group<br>6.47<br>6.23<br>SD - control group<br>1.75<br>1.76<br>***, **, * signifcant at 1%, 5%, and 10%, respectively. B<br>outcomes. Positive attitudes, time spent on homework, pa<br>using collected self-reported data at follow-up. Absenteeis<br>tained from school administrative data. Bad behavior rep<br>level. Panel B presents results on academic outcomes. Re<br>up. Score is an average of the three courses. See Appendi<br>nomial of student’s IVV, and school-by-education-level blo<br>bad behavior reports, we also include the corresponding i<br>of noncognitive outcome observations are due to variation <br>multiple hypothesis testing of means and FWER following<br>ing, (ii) violence and misbehavior, (iii) academic performa|

42