rafmacalaba/datause-encoder-data huggingface dataset:
a large divergence between CPI data for Bangladesh, survey-based measures of inflation for the poor, and implicit measures of inflation as assessed through official poverty lines. Nevertheless, the decision to deviate from the use of official CPI data is taken very conservatively. For this round, we have taken it only for seven countries, and in each case only after detailed consultations with the relevant national statistical bodies and the country poverty economists of the World Bank.

For the 2012 global poverty estimates, such deflators are used in Bangladesh, Cambodia, Ghana, Lao, Iraq, Malawi and Tajikistan. Annex 1 provides an overview of these countries and the deflators used. For these countries, we have made the assessment that the alternative deflators provided a more accurate picture of changes to prices, particularly to those faced by poor people. As one example of the importance of these differences, in the case of Tajikistan using the official national CPI would imply an 18% annualized growth of household survey mean from 2004 to 2009, a growth rate that was considered to be unreasonably high. The alternative inter-temporal deflator used for national poverty measurement implies 6% annualized growth.

# **_3.3 Purchasing Power Parity – Updating measures from 2005 PPPs to 2011 PPPs_**

Income and consumption measures available from national household surveys, as well as national poverty lines, are denominated in local (national) currency units (LCU). To be able to compare the standard of living between countries, one needs to express the measure of consumption or income in common units. One solution would be to use market currency exchange rates to express all values in a common currency, but these exchange rates fail to accurately reflect relative purchasing power, particularly for poorer countries. One reason for this is that non-traded goods, and especially services, are typically cheaper in poorer countries at market exchange rates (Balassa, 1964; Samuelson, 1964). This implies that using market exchange rates to convert consumption or income underestimates the real standard of living in poor countries.

To address this issue, international poverty comparisons use exchange rates based on PPP conversion factors for private consumption from the ICP. These are essentially exchange rates that attempt to adjust for price differences and to ensure that a dollar has the same purchasing power across countries. PPP factors convert the value of consumption from the LCU into a common currency (i.e. USD) in a manner that allows for comparability across countries. PPPs 18
in more remote rural areas and has recently expanded lending after a credit crunch (Clarke, Cull and Fuchs, 2006).

Following the crisis in the late 1990s, the Ugandan authorities have significantly strengthened bank regulation and supervision, tightening loan classification and provisioning standards. The closure of Cooperative Bank, Greenland Bank, ICB and Trust Bank in 1998 and 99, the UCB privatization, the introduction of a risk- based approach in the banking supervision as well as reforms in the regulatory environment have made the Ugandan banking sector less fragile, resulting in falling loan loss provisions.

Uganda’s banking system is small, both in absolute terms as in relation to its GDP, as we illustrate using private sector lending. With $200 million of liquid liabilities, Uganda’s banking system is smaller than many mid-sized banks in developed economies (Figure 1). With Private Credit to GDP at 5% in 2004, Uganda is significantly below the average for low-income and Sub-Saharan African countries and neighboring Kenya and Tanzania (Table 1). Further, Uganda has a very low loan-deposit ratio, suggesting that the limited resource mobilization by the banking system is accompanied by even more limited intermediation into private sector loans. On the other hand, and as reported above, interest margins in Uganda are significantly higher than in other countries, even than in the average low-income and the average Sub-Saharan African country.

While Uganda’s banking system is small, it has always had a relatively large number of banks, even before financial liberalization. As of 2004, there were 15 banks, 12 of them foreignowned and the remainder owned by domestic private shareholders. During the sample period of our empirical analysis, the Ugandan banking system has undergone quite dramatic changes in its market structure. Take first concentration, as measured by the _Herfindahl indices for deposits_ 7
# **3.5 Inference**

As already alluded to, the error term _εd_ is likely to be correlated across neighboring districts, if only because geographically proximate regions experience similar productivity shocks over time. I use a nonparametric covariance matrix estimator or spatial HAC (Conley, 1999) to account for heteroskedasticity and spatial dependence. A familiar alternative to the spatial HAC is the clustered covariance estimator. But clustering standard errors by state or region assumes independence of errors across state or regional boundaries, a serious lacuna given the large fraction of districts bordering an adjacent state.<sup>25</sup> Bester et al. (2011) show that the asymptotic normal distribution, typically used to obtain critical values for inference in HAC estimation, is a poor approximation in finite samples. I thus follow their suggestion of bootstrapping the distribution of the relevant test-statistics. For this reason, inference should be guided by _p_ -values rather than standard errors alone, although I will follow convention and report both. In particular, bootstrapped _p_ -values are much less sensitive than standard errors to choice of the tuning or bandwidth parameter (i.e., the degree of kernel smoothing).<sup>26</sup> Both numerator, ∆ _wd_ = _<u>wd,</u>_ 09 _−_ _<u>wd,</u>_ 04, and denominator, _ψd_ , of the dependent variable in equation (8) are district-level summary statistics derived from micro-data. This gives rise to a particular form of heteroskedasticity and renders least-squares estimation inefficient. The standard solution is to use weighted least-squares, taking the inverse of the estimated sampling variances as weights. While the sampling variance of ∆ _wd_ is _σ_<sup>2</sup> <u>(</u> _<u>wd,</u>_ 09)+ _σ_<sup>2</sup> <u>(</u> _<u>wd,</u>_ 04) (see above), there is no equally straightforward ‘plug-in’ estimate of the sampling variance of _ψd_ . I, therefore, bootstrap this variance as well by drawing 1000 random samples of individuals from each district’s original sample and computing _ψd_ repeatedly. From these

> 25Also note that with only a single (five-year difference) observation per district, serial correlation is not an issue in my set-up.

> 26Bandwidth here is the distance cutoff, in degrees of lat/long, beyond which spatial dependence is assumed to die out. Based on simulation evidence from Bester et al. (2011), I choose a bandwidth of 16; i.e., given the area of my ‘sampling region’ (the 18 major states of India), this choice should yield minimal test-size distortion across a range of possible spatial correlations. I find these _p-_ values to be highly robust to bandwidth deviations of at least _±_ 4.

17
y��� is the outcome for health facility/health provider/consultation/individual _i_ in district _d_ in period _t_ . _PBFd_ takes value 1 if the observation belongs to a PBF district and zero otherwise. Post� takes value 1 if the observation is from the follow-up survey and 0 if it is from the baseline survey. X��� is a vector of control variables, including catchment area fixed effects. The assignment into the PBF intervention was done at the district level. Because there are only 16 districts in the sample, clustering standard errors at that level is likely to lead to over-rejection. We, therefore, follow the methodology suggested by Cameron et al. (2008, 2011). We use a two-way standard error clustering by district and time, calculated using a wild-cluster bootstrap-t procedure.

For some outcomes of interest, we cannot employ the difference-in-difference approach. Some variables were not measured, or not measured correctly during the baseline survey. With respect to direct clinical observations, the baseline sample was too small, as described above. To assess the impact of the PBF program on the indicators for which we only have follow-up data, we employ a propensity score weighting approach (Hirano et al. 2003). In appendix 4, we provide more information on the procedure and show that the weighting achieved balance concerning baseline characteristics of the PBF and control groups.

# 4. Results

## Utilization of health services

Panel A of Table 6 presents the results of vaccination and growth monitoring for children under5. No significant impacts on vaccination rates are observed but coverage at baseline was already relatively high. There is also no measured impact on growth monitoring, despite low levels at baseline. In the follow-up survey, only about 30 percent of mothers reported that the growth of their child was measured in the past 6 months. This rate is surprisingly low given that in the direct observations of curative consultations in children, height and weight was measured in 85 percent of consultations. We cannot rule out that providers were more likely to conduct these measurements when they knew they were being watched. It could also be that the phrasing of the question was not clear to women.

In Panel B of Table 6, we show results on utilization of reproductive health services by recently pregnant women. In the baseline survey, 90 percent of women reported to have received at least 9
# **1. Introduction**

The purpose of this paper is to introduce GEM‐Core, a dynamic computable general equilibrium (CGE) model designed for medium‐ and long‐run policy analysis. GEM stands for General Equilibrium Model. GEM‐Core is a core model in the sense that it can address the issues that typically are relevant for CGE analysis for developing countries, including fiscal space (with its spending, tax, and foreign aid aspects), public investment, social safety nets, trade, jobs, demography, poverty, and inequality. The model may be applied to databases with different disaggregations, ranging from highly aggregated to highly disaggregated. It may be extended when this is needed for special topics. GEM‐Core comes with a user‐ friendly Excel‐based interface, ISIM (which stands for “I simulate”).<sup>1</sup> It permits the analyst to choose between alternative country databases and, for the selected database, develop an application, something that involves choosing from a menu of pre‐programmed assumptions, adjusting selected data, and defining and analyzing simulations. The interface lowers entry barriers to CGE modeling and provides a platform around which training may be organized, making it possible to focus courses on economics instead of computer programming.

The fact that the model may be applied to relatively aggregate databases means that it can be applied on short notice to virtually any country, including fragile and low‐income countries. In a companion paper, Cicowiez and Lofgren (2017a) show how the core component of such a database, a macro SAM, may be constructed at low cost from cross‐ country data; that paper is accompanied by empirical macro SAMs for 133 countries.

In outline, this paper first provides a non‐technical overview of GEM‐Core and a macro database for an archetype developing country using a SAM from the companion paper (Section 2). After this, it presents and analyzes a set of simulations that demonstrate the use of the model with this macro database (Section 3), winding up with some concluding

> 1 The interface is linked to the model program, written in GAMS (the General Algebraic Modeling System; see <u>www.gams.com). Both are available on request from the authors. GEM‐Core draws heavily on MAMS</u> (Maquette for Millennium Development Goal Simulations; Lofgren et al. 2013) for which the IFPRI Standard Model provided the starting point (Lofgren et al. (2002). Other country CGE models in GAMS include Décaluwé et al. (2013) and McDonald (2015). The interface developed for MAMS has been adapted to work with GEM‐ Core. An extension of GEM‐Core, named GEM‐Trade, has been developed for analysis of preferential trade agreements (Cicowiez and Lofgren 2016).

‐2‐
# **Table 6: Robustness Checks for the IV Estimates**

||**Regional Fix**<br>|**ed Effect**<br>|**Indiv. and H**<br>**Contr**|**ousehold**<br>**ols**|**IV base**<br>**1999-2005**|**d on**<br>**Rainfall**|<br>**Control for Ag**<br>**Spatial**|**ri Wage &**<br>**Price**|
|---|---|---|---|---|---|---|---|---|
||Propensity<br>to Pay|Amount<br>Paid|Propensity<br>to Pay|Amount<br>Paid|Propensity<br>to Pay|Amount<br>Paid|Propensity<br>to Pay|Amount<br>Paid|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Per capita income**|**-0.149**|**0.003**|**-0.153**|**0.003**|**-0.153**|**-0.020**|**-0.153**|**-0.003**|
||**(2.46)****|**(0.04)**|**(2.52)****|**(0.03)**|**(2.03)****|**(0.21)**|**(2.33)****|**(0.03)**|
|Indiv. and Household Controls|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Regional Fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Sign and Significance of the**|**Instrument in**|**First stage**|||||||
|Heavy Rainfall Dummy|-0.707|-0.601|-0.702|-0.599|-0.564|-0.503|-0.636|-0.568|
||(3.21)***|(3.40)***|(3.19)***|(3.36)***|(2.71)***|(2.90)***|(3.18)***|(3.16)***|
|Angrist-Pischke F Statistic|10.29|11.53|10.18|11.31|7.33|8.44|10.09|9.99|
|Observations|3760|1832|3760|1832|3760|1832|3718|1810|

Notes: (1) All regression includes regional dummmies. Standard errors are clustered at primary sampling unit (PSU) level. (2) Indiv. and Household Controls: Household head's age, gender and religion; Household size, No. of school age children (3) Robust z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
# MLYTICAL 5~ OF THIRTY ENT SiWIES

IINDEPBD VMIMULES GRnrH RATE ----------------;;;;;W W ................................... CHARACTERCALOUTATIN RU BITAL SECTO. CF v.MI*LES PRMtJCT ISTICS OF PERICD AmI A EIONICIERSITIIG ;cioPIIIIIPISE TYPE AID SlM ANALYSIS IOIVITAL POLICY PUCITION POLITICAL _OF SMY_ SXJ NTRIES PBIRIl VBLARL4 S UUI FACTRS ARIA RS VEIERIS lqmct of Per capita 10K 1 ar: 19S7 Invwstt SocoR-qr - 0.25 wnd UPTA sociqol itial insataility GNP (tMited bsre + political inmt tility DhAOle qFAres leot (C193) (si **Adtuneu** Naticns) PopAction apatio) gh, .................... ...................................... .................................................................... ................................................................................................. HEElfR StuW r*eaos CDP 25 SurSdahren 10 yers SiLent Av. arnul S lent (1964) for Africa's (World Africen Av. growth varidtbe rate of groith variable poor growth San) countries rate eoqAl to I if of real cq.uL to 1 performean , 1970-80 v. wenal effective if cartry distinwgishing term of trait exdhuan rate has had lees betwueen growth rate calculated by then 4 fuduent l or exceeds 1 .5X _IW_ stete wrs umiro,mntL - (no cap vfriahas wd Silent Indicator of d'etat or acroic policy variable oqul weight of narfd vrieblas to 1 if total cutomry violence) abesnc of practices in out of 11 minifg wpmorts foreign-exch. (MM"e of in 1970 alLoention for political + copital goods irstability) Export ard nm-food diverifficn. cauaer gooCd indicators iaports **I.** uaarow other Difference of nonsignif. rwgir l vwriable ieaort (rainfall, prap nitiec extarnul aid, in period of migrcnt rising nd workers falling export remittane, receipts export price (_maure of instability) inoapacity to wuae trade balence qlilbrius) ..... ..

w ........... ...................... .. .. . .. . .. .. . .. .. . .. .. . .. .. . .. . .. .. . .. .. . .. .. . .. . .. .. . .. .. . .. .. . .. .
Desmet, Klaus, Ejaz Ghani, Stephen O’Connell, and Esteban Rossi-Hansberg, “The Spatial Development of India”, Working Paper (2011).

- Dhaliwal, Spinder, “Asian Female Entrepreneurs and Women in Business—an Exploratory Study”, Enterprise and Innovation Management Studies 1:2 (2000), 207-216.

Diamond, Charles, and Chris Simon, “Industrial Specialization and the Returns to Labor”, Journal of Labor Economics 8 (1990), 175-201.

- Doms, Mark, Ethan Lewis, and Alicia Robb, “Local Labor Force Education, New Business Characteristics, and Firm Performance”, Journal of Urban Economics 67:1 (2010).

- Drucker, Joshua, and Edward Feser, “Regional Industrial Dominance, Agglomeration Economies, and Manufacturing Plant Productivity”, CES Working Paper 07-31 (2007).

Duflo, Esther, “Gender Equality in Development”, MIT Working Paper (2005).

Dunlop, John, and Victoria Velkoff, “Women and the Economy in India”, U.S. Department of Commerce Report WID/98-2 (1999).

- Duranton, Gilles, and Diego Puga, “Micro-Foundations of Urban Agglomeration Economies”, in Vernon Henderson and Jacques François Thisse (eds.) Handbook of Regional and Urban Economics, Volume 4(Amsterdam: North-Holland, 2004), 2063–2117.

Ellison, Glenn, and Edward Glaeser, “The Geographic Concentration of Industry: Does Natural Advantage Explain Agglomeration?”, American Economic Review Papers and Proceedings 89 (1999), 311-316.

- Ellison, Glenn, Edward Glaeser, and William Kerr, “What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns”, American Economic Review 100 (2010), 1195-1213.

- Estrin, Saul, and Tomasz Mickiewicz, “Do Institutions Have Greater Effects on Female Entrepreneurs?”, IZA Working Paper 4577 (2009).

- Evans, David, and Linda Leighton, “Some Empirical Aspects of Entrepreneurship”, American Economic Review 79 (1989), 519-535.

- Fernandes, Ana, and Ariel Pakes, “Factor Utilization in Indian Manufacturing: A Look at the World Bank Investment Climate Survey Data”, Working Paper (2010).

- Fernandes, Ana, and Gunjan Sharma, “Together We Stand? Agglomeration in Indian Manufacturing”, Working Paper (2011).

- Field, Erica, Seema Jayachandran, and Rohini Pande, “Do Traditional Institutions Constrain FemaleEntrepreneurship?A Field Experimenton Business Training in India”, American Economic Review Papers and Proceedings 100 (2010).

- Figueiredo, Octávio, Paulo Guimaraes, and Douglas Woodward, “Home-Field Advantage: Location Decisions of Portuguese Entrepreneurs”, Journal of Urban Economics 52:2 (2002), 341-361.

Florida, Richard, Cities and the Creative Class (New York, NY: Routledge, 2005).

23
Table 8 presents the values for P(a,<sup>z). I note, first, that if the</sup> CNSE is accepted as the appropriate reference<sup>point, 81.2X of the population</sup> (and 72.8X of all households) would<sup>be classified as moderately-poor. While,</sup> as argued in section III.1, moderate-poverty<sup>is a subjective concept, sucha</sup> large number calls into question<sup>the components of the CNSE (and of other</sup> studies that have also used the CNSE<sup>as a reference point)45. Second, when</sup> a - 0 I find that the urban areas now account<sup>for the largest share (58%) of</sup> national poverty. When a - 2 these proportions are reversed,<sup>with the rural</sup> areas accounting for 53% of moderate-poverty.<sup>As with extreme-poverty,</sup> when account is taken of the depth and<sup>distribution of moderate-poverty the rural</sup> areas come to the forefront.

To sum up, based on the 1984 IES:<sup>(i) at most 19% of the population was</sup> below the line of extreme-poverty, although it is probably the case<sup>that this</sup> is an over-estimate, (ii) the extremely-poor<sup>are mostly located in rural</sup> areas, (iii) the poorest of the<sup>extremely-poor</sup> are also found mostly in rural areas, (iv) the extremely-poor have very large families, have<sup>the largest</sup> share of children, the highest<sup>dependency ratio and the lowest educational</sup> levels, (v) not even the extremely-poor allocate more than 60X of total monetary expenditures to food,<sup>(vi) most of the extremely-poor are in</sup> agricultural activities, (vii)<sup>the urban extremely-poor are relatively better</sup> off than the rural, but have similar<sup>demographic, expenditure and educational</sup> characteristics.

4 5The complete basket is found in Coplamar, 1983, pp. 134-45; a careful look shows that an important part<sup>of the basket is made up of items like</sup> refrigerator, T.V., automatic<sup>laundry and dry cleaning, vacations and personal</sup> entertainment, etc.. Recall that<sup>this basket was formed on the basis of the</sup> expenditure patterns of the seventh<sup>decile; not surprisingly, the value for</sup> P(O,z) calculated on the basis<sup>of household incomes is, as mentioned in the</sup> text, 0.728.
# **Demand Analysis of Multiple Goods and Services in Vietnam***

Mauro Vigani†, Hasan Dudu‡

**JEL** : C03; D01; O12;

**Keywords** : price elasticity; quality substitution; QUAIDS; instrumental variable; endogeneity; household survey.

* We would like to thank Andrew Burns, Quang Hong Doan, Doerte Doemeland, Stephen Ling, Jacques Morisset, Carolyn Turk and Judy Yang for their support and valuable comments. This paper relies on research conducted under “Supporting Systematic Action to Implement Vietnam’s NDC” project led by Stephen Ling and “COVID19 and the Future of Work” project led by Jacques Morisset. We are also grateful to the National Center for Socioeconomic Information and Forecast for supplying some of the data used in the study. Financial support from the Government of Australia, through the Australia – World Bank Group Strategic Partnership in Vietnam Phase 2 (ABP2) is greatly appreciated.

- University of Gloucestershire; mvigani@glos.ac.uk

- The World Bank; hdudu@worldbank.org
transmitted to people and animals by the tsetse fly. Approximately 30% of Africa’s 160 million cattle population and comparable numbers of small ruminants are at risk (ILRI 2005). This disease is known to be climate sensitive (Ford & Katondo 1977).

This study uses a cross-sectional method, the Ricardian model, to estimate the sensitivity of African livestock to climate (Mendelsohn et al. 1994; Mendelsohn & Dinar 1999; Mendelsohn et al. 2001). Two models are estimated. A single-equation model regresses livestock net revenue per farm on climate, soils and other control variables. A two-equation model regresses the value of livestock owned per farm and the net revenue per value of livestock owned on the same independent variables. These two models both detect whether climate affects net revenue per farm, but the second provides more detail about what is changing on the farm. The Ricardian model is estimated on data from a survey collected as part of the GEF/WB Climate Change and African Agriculture Project (Dinar et al. 2006). Over 5000 farms with livestock are analyzed from ten countries across Africa. The estimated model is used to predict the impact of a set of uniform climate change scenarios. These scenarios test the importance of Africa-wide changes in climate such as a 2.5 ° C change in temperature or a 14% change in precipitation. Finally, a set of climate predictions from Atmospheric Ocean General Circulation Models (AOGCMs) are analyzed. These three scenarios provide a realistic range of future climate outcomes. The estimated model is used to test the magnitude of the impacts on both small and large livestock farms in each scenario.

Section 2 discusses livestock husbandry in Africa to provide a background for the study; Section 3 presents a theoretical model of a profit-maximizing livestock farmer; Section 4 discusses the data, which includes an extensive survey, the relevant information on climate, soils and water, and the empirical specification; Section 5 examines the Ricardian results; and Section 6 explores the impacts of two sets of climate scenarios: the uniform climate change scenarios and the AOGCM climate scenarios. The paper concludes with some general observations and policy implications.

# **2. Animal husbandry in Africa: Background**

Using the data collected from the ten-country survey, a few basic facts about Africa animal husbandry become clear. For example, Table 1 summarizes livestock ownership by two different 6