metadata: authors: 0: author: Amendola,Nicola
1: author: Belotti,Federico
2: author: Etang Ndip,Alvin
3: author: Mancini,Giulia
4: author: Vecchi,Giovanni
count: Bhutan
docna: 0: docna: Poverty Dynamics in Bhutan, 2017–2022 :
            Evidence from Synthetic Panels
docty: Policy Research Working Paper
theme: Inclusive Growth,Gender,Human Development and Gender,Economic Policy,Food Security,Youth Employment,Social Protection,Social Development and Protection,Economic Growth and Planning,Private Sector Development,Labor Market Policy and Programs,Active Labor Market Programs,Urban and Rural Development,Jobs,Nutrition and Food Security,Flood and Drought Risk Management,Disaster Risk Management,Social Safety Nets
abstracts: cdata!: This paper examines the dynamics of
            poverty in Bhutan between 2017 and 2022, utilizing
            cross-sectional data from the Bhutan Living Standards
            Surveys. The paper constructs synthetic panels and estimates
            poverty transition probabilities. Three main findings
            emerge. First, poverty turnover in Bhutan is low overall
            (not many people, as a share of the population, moved in and
            out of poverty during the period considered). Second,
            chronic poverty, defined as the probability of remaining
            poor in both years (2017 and 2022), was also low, both in
            absolute terms and compared to other countries for which
            similar estimates are available. The probability of being
            poor in both years is 6 percent of households in Bhutan,
            compared to over 15 percent in India and 17.5 percent in
            Pakistan. Third, upward poverty mobility (the probability of
            escaping poverty between 2017 and 2022) is 20 times higher
            than downward poverty mobility.
display_title: Poverty Dynamics in Bhutan,
            2017–2022 : Evidence from Synthetic Panels
listing_relative_url: /research/2025/01/34444980/poverty-dynamics-bhutan-2017–2022-evidence-synthetic-panels
topicv3: Poverty m1326687 695
publishtoextweb_dt: 2025-01-10T00:00:00Z
dois: 10.1596/1813-9450-11031
no_of_pages: 28
author_keys: 0: author_key: 998611
1: author_key: 1831729
2: author_key: 1193617
3: author_key: 1317880
4: author_key: 458686
model_extractions: Policy Research Working Paper 11031 Poverty Dynamics in Bhutan, 2017–2022 Evidence from Synthetic Panels _Nicola Amendola Federico Belotti Alvin Etang Giulia Mancini Giovanni Vecchi_ Poverty and Equity Global Department January 2025
Policy Research Working Paper 11031

# **Abstract**

This paper examines the dynamics of poverty in Bhutan between 2017 and 2022, utilizing cross-sectional data from the Bhutan Living Standards Surveys. The paper constructs synthetic panels and estimates poverty transition probabilities. Three main findings emerge. First, poverty turnover in Bhutan is low overall (not many people, as a share of the population, moved in and out of poverty during the period considered). Second, chronic poverty, defined as the probability of remaining poor in both years (2017 and 2022), was also low, both in absolute terms and compared to other countries for which similar estimates are available. The probability of being poor in both years is 6 percent of households in Bhutan, compared to over 15 percent in India and 17.5 percent in Pakistan. Third, upward poverty mobility (the probability of escaping poverty between 2017 and 2022) is 20 times higher than downward poverty mobility.

This paper is a product of the Poverty and Equity Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aetangndip@worldbank.org; nicola.amendola@uniroma2.it; federico.belotti@uniroma2.it; giulia.mancini@uniroma2.it; and giovanni.vecchi@uniroma2.it.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Poverty Dynamics in Bhutan, 2017–2022:**

# **Evidence from Synthetic Panels**

Nicola Amendola<sup>*</sup> Federico Belotti<sup>*</sup> Alvin Etang<sup>**</sup> Giulia Mancini<sup>***</sup> Giovanni Vecchi<sup>*</sup>

**Keywords:** poverty dynamics, economic mobility, synthetic panel methods. **JEL:** I30, I32, C10, C23

> * Department of Economics and Finance, Tor Vergata University of Rome – nicola.amendola@uniroma2.it, federico.belotti@uniroma2.it, and giovanni.vecchi@uniroma2.it

> ** The World Bank - aetangndip@worldbank.org

> *** Department of Economics and Management, University of Sassari – gmancini@uniss.it
# **1 Introduction**

Traditionally, the study of poverty dynamics requires tracking the same individuals or households over time. However, in many developing countries, panel data is scarce, which prevents analysts from observing the economic trajectories of individuals or households over an extended period of time.

Following the path opened by Deaton (1985), papers by Bourguignon, Goh, and Kim (2004), and Guell and Hu (2006) explored the use of synthetic panels at the household level. However, these two approaches require certain assumptions that may not be easily satisfied in available cross-sectional data. The former method requires at least three rounds of cross-sectional data, and assumes a first-order auto-regression process where past household or individual incomes (earnings) can affect present outcomes; the latter is exclusively restricted to duration analysis. To overcome these limitations, Dang et al. (2014) constructed synthetic panels from as few as two rounds of household-level crosssectional data, providing lower-bound and upper-bound estimates of poverty transitions. More recently, Dang and Lanjouw (2023) proposed a method that delivers point estimates of poverty transition probabilities (households shifting in and out of poverty, or preserving their status) in the absence of longitudinal data.

This paper takes advantage of these new developments, as well as some critiques that have recently been raised regarding some aspects of the methodology (e.g., Herault and Jenkins, 2019; Garcés‐Urzainqui, Lanjouw, and Rongen. 2021, Colgan, 2023), to delve deeper into the characteristics of poverty dynamics in Bhutan during the five-year period from 2017 to 2022. According to Amendola et al. (2023), during this time the stark decrease of poverty in Bhutan at the national and sub-national levels seems to be linked mainly to redistributive processes in the monetary metric of expenses, rather than to economic growth. The fact that the key driver of poverty reduction is a reshuffling of households along the distribution curve, rather than a movement of the curve itself, justifies the need for tools capable of identifying who changes their position within the income (or expenditure) scale during the period considered. Methods based on pseudo-panels allow for precisely this kind of analysis, and significantly contribute to our understanding of the relative importance of the underlying drivers of poverty dynamics. In the case of Bhutan, the results are interesting and, hopefully, useful to policy makers: education level and residence in rural areas are associated with greater overall mobility, both upward and downward, but only residence in rural areas is informative regarding the “structure” of mobility. Families living in rural 2
areas are more likely than average to escape poverty, while people living in urban areas are more likely to fall into poverty. Such dynamics suggest that the poverty trend in Bhutan during the years considered is somehow linked to productivity or price shocks in the agriculture sector, which have benefited rural families compared to urban ones, independently from other household characteristics. This hypothesis is also supported by the analysis of available macroeconomic indicators and other extra-survey sources, which also show a sectoral shift away from agriculture: the share of employment in the primary sector decreased steadily, with marginal producers shifting to the (mainly urban) tertiary sector. Rural migration outflows, combined with a favorable increase in food prices, might explain why living in rural areas can increase the likelihood of escaping poverty (Amendola et al., 2023).

The rest of the paper is organized as follows. Section 2 presents the data used throughout the paper, which are drawn from the Bhutan Living Standards Survey (BLSS). Section 3 discusses methodological aspects in detail: it describes a solution to the problem of estimating utility-consistent poverty lines in the presence of partially reliable CPI (section 3.1) and illustrates the estimation methodology based on synthetic panels (section 3.2). Section 4 contains: estimates of transition probabilities, both conditional and unconditional (section 4.1); a profile of households exhibiting characteristics of chronic poverty, as well as those that have managed to escape poverty (section 4.2); and a general analysis of the statistical robustness of the results (Section 4.3). Section 5 presents a summary of the main findings.

# **2 Data**

The Bhutan Living Standards Survey (BLSS) is a nation-wide cross-sectional household survey conducted by the National Statistical Bureau, which has been carried out every five years since 2003. It is the main source of information on household living conditions in Bhutan, and focuses on household expenditures, within the framework of the World Bank’s Living Standard Measurement Surveys (LSMS).

Many key features of the survey have been kept the same over time for comparability purposes. The two most recent BLSS waves, 2017 and 2022, are no exception: the questionnaires are close in structure, content, and level of detail (Amendola et al. 2023). Both surveys were administered through computer-assisted personal interviewing (CAPI) 3
and all expenditure modules used a recall approach. The 2017 sample counts 48,639 individuals representing a population of 692,895, while the sample size in 2022 is 52,822 individuals, out of a population of 650,118.<sup>1</sup> Both surveys are representative at the district level (there are 20 districts, or _Dzongkhags_ , in Bhutan) (Bhutan National Statistics Bureau 2017, 2022).

The intertemporal analysis of poverty requires the welfare measure of choice – in Bhutan’s case, household consumption – to be computed in a consistent way across surveys. The comparable consumption aggregate is constructed following the principles outlined in Deaton and Zaidi (2002) and Mancini and Vecchi (2022), and includes all required expenditure components, save for the consumption flow from durable goods, for which consistent information is not available across BLSS waves (Amendola et al. 2023). Finally, it is worth remarking that the 2017 and 2022 questionnaires do have a couple of discrepancies that affect the analysis presented in this paper: some variables that would be relevant in explaining households’ living standards and mobility, namely household income sources and individual labor market characteristics, were unfortunately not collected in both waves, and therefore cannot be used within the synthetic panel framework.

# **3 Methodology**

The study of the dynamics of poverty in Bhutan between 2017 and 2022 poses two distinct challenges. The first one is virtually unavoidable when performing this kind of analysis, and it concerns comparability: even when survey instruments are sufficiently similar, one cannot assume that poverty comparisons are accurate in the absence of consistent poverty lines, that is, monetary amounts that correspond to a reference level of utility that remains constant in the two periods (Ravallion and Bidani 1994, Ravallion 1998). This issue turns out to require special attention in the case of Bhutan: the process used to obtain consistent poverty lines is summarized in section 3.1. The second challenge concerns the absence of panel data: section 3.2 discusses the methodology employed for the construction of synthetic panels, following recent developments in the literature.

> 1 The 2017 sample counts 11,660 households (of 164,011 in the population), and the 2022 has 13,416 households (of 164,331).

4
# **3.1 Estimation of consistent poverty lines**

To identify poverty trends, it is common practice to select a poverty line for a base year and project this line into subsequent years using an appropriate temporal consumer price index (CPI). Alternatively, the reference poverty line can be held constant by expressing all monetary variables, previously adjusted for spatial cost-of-living differences, at the base year's constant values. Thus, the temporal price index plays a crucial role in dynamic poverty analysis. In the context of Bhutan, there is evidence that the official CPI for Bhutan underestimates the inflation faced by the poor over the period at hand: inflation recorded by the true cost of living index, based on the ratio of the nominal food poverty lines, is 87 percent, while the official food CPI only records 39 percent inflation (Amendola et al. 2023). For this reason, in this section we develop an analytical framework for identifying the poverty trend with no reliance on the official CPI. The proposed method anchors the trend to the official food poverty line for 2022: this means that the endpoint of the poverty trend will be close to the official point estimates for 2022 (NSB 2022).<sup>2</sup> The method consists in calculating the change in the monetary value of a _fixed_ reference food basket, which can be interpreted as a basic-needs basket consistent with the consumption pattern of poor households in Bhutan. The cost of such a basket can be used as the basis for calculating a food cost-of-living index. As detailed below, the construction of such an index is equivalent to the calculation of a Laspeyres-type price index based on the consumption basket of poor households. The index is then used to “move” the 2022 food poverty line back in time, which provides an anchor for estimating a new upper-bound poverty line.<sup>3</sup> _h_ : h h h h 0 1 2 � JJ qq = �qq , qq ,∙∙∙, qq Let us define a reference food basket for household _h_ : where _J_ denotes the number of food items included in the basket. The reference basic needs food basket is identified by averaging h0 over the first decile of the expenditure qq qq

> 2 There is, in fact, a small discrepancy between the 2022 poverty estimates presented in this paper and the ones published in NSB (2022): the reason is that the consumption aggregate and upper bound poverty line used for trend estimation have been computed to maximize comparability with 2017.

> 3 For a complete and detailed description of the method, see Amendola et al. (2023).

5
distribution.<sup>4</sup> We denote by E the number of calories provided by the reference food basket . The ratio between E and the minimum energy requirement _ER_ , identified (qq) based on a normative criterion, provides the coefficient ⁄E needed to rescale qq (qq) the basket to return a basket yielding exactly the normative energy requirement _ER._ λλ= E (qq) 00 qq qq =<sup>qq</sup> 00 qq Finally, to value , a price vector is needed. Consistently with the choice underlying λλ quantities, we take the median values of the unit values for households belonging to first 00 qq decile of per capita expenditure as a reference group. Denoting by the vector of prices of the food items included in the bundle for period _t_ , the food poverty line for period _t_ pp<sup>tt</sup> is given by: 00 qq F 00 Similarly, the food poverty line at _t+_ 1 prices can be computed as: FF<sup>tt</sup> = pp<sup>tt</sup> ∙qq Similarly, the food poverty line at _t+_ F 00 FF<sup>tt+1</sup> = pp<sup>tt+1</sup> ∙qq The ratio between the food poverty lines provides an estimate of the true-cost-of-living index between _t+_ 1 and _t_ , as a Laspeyres-type price index based on the period _t_ subsistence/reference basket: The identification of the food basket can be based on period _t+_ 1 data instead of period _t_ . That would imply repeating the steps above designing the basket with reference to the consumption pattern of period _t+_ 1, creating a Paasche-type price index.

In our application _t_ = 2017 and _t+_ 1 = 2022. Table 1 compares the food poverty lines based on the 2017 and 2022 food baskets, respectively. If we assume that the energy requirement correctly identifies a minimum and unique standard of living over time, we clearly see that the poverty lines based on the 2022 food basket are _not_ utility consistent: the 2017 basket allows to achieve the minimum energy requirement at a lower cost both in 2017 and 2022. This is why we decided to base our implicit food price index on the 2017 food basket.

> 4 The exact composition of the reference food bundles is detailed in the Amendola et al. (2023).

6
**Table 1. Cost of a basic-needs food basket (current Nu/person/month)**

||2017<br>prices|2022<br>prices|
|---|---|---|
|Basket defined on 2017 consumption pattern|1,431|2,349|
|Basket defined on 2022 consumptionpattern|2,035|2,455|

Notes: The reference group for both baskets is households in the first decile of per capita household expenditures; we select items that make 80 percent of total food expenditure (this amounts to 45 different items for 2017 and 53 items for 2022); average quantities are rescaled so that each basket yields 2,200 kcal/person/day.

f f The index 2017 can be used to project food poverty lines back in time, from 2022 to 2017, and therefore to identify the poverty trend. To preserve consistency with the 2022 CCFFCC official poverty estimates, we use F as an anchor for the projections. Accordingly, the 2017 food poverty line becomes: FF<sup>2022</sup> F =<sup>F</sup> 2017 f f<sup>FF2022</sup> FF<sup>2017</sup> CCFFCC The upper-bound CBN poverty line (UBPL) is defined as: U F = F Following the same procedure, we can estimate<sup>2022</sup> FF<sup>2022</sup> U+ NNFFNNF<sup>20172022</sup> .

Table 2 shows the estimates for both food (FPL) and total (UBPL) poverty line values.

**Table 2. Comparable poverty lines (current Nu/person/month)**

||**2017**|**2022**|
|---|---|---|
|FPL|1,737|2,852|
|UBPL|3,448|5,786|

Note: The official 2022 FPL was projected back to 2017 by using the food CPI estimated based on the 2017 basic-needs basket.

7
The poverty lines illustrated in Table 2 can be used to estimate the poverty measures summarized in Table 3 (top panel). The decrease in poverty between 2017 and 2022 is extremely pronounced, both in terms of headcounts and poverty gaps. However, it primarily affects rural areas, where the incidence of poverty in 2022 decreased by more than 20 percentage points.

**Table 3. Poverty (total expenditure < upper bound poverty line)**

||||**H**||**PG**||**PG2**||
|---|---|---|---|---|---|---|---|---|
|**2017**|**Bhutan**||28.0||7.6||2.9||
||**Urban**||6.2||1.2||0.4||
||**Rural**||38.9||10.8||4.2||
|**2022**|**Bhutan**||11.6||2.4||0.8||
||**Urban**||3.7||0.7||0.2||
||**Rural**||16.5||3.5||1.1||
|**2022/2017**|**Bhutan**||0.41||0.32||0.26||
||**Urban**||0.59||0.54||0.50||
||**Rural**||0.43||0.32||0.27||
|||**Gini**|**GE(0)**|**GE(1)**|**GE(2)**|**A(0.5)**|**A(1)**|**A(2)**|
|**2017**<br>**urban**||31.9|16.9|18.8|32.8|8.4|15.5|27.4|
|**rural**||34.6|19.5|21.7|32.8|9.7|17.7|30.1|
|**within com**|**ponent**||18.6|20.3|35.6|9.1|16.7|28.8|
|**between c**|**omponent**||3.9|4.1|4.3|2.1|4.2|8.1|
|**2022**<br>**urban**||26.2|11.3|12.6|17.4|5.8|10.7|19.1|
|**rural**||28.4|13.2|13.6|16.3|6.5|12.4|22.7|
|**within com**|**ponent**||12.5|13.2|17.1|6.2|11.6|21.1|
|**between c**|**omponent**||0.6|0.6|0.6|0.3|0.7|1.7|

Inequality also decreases significantly (Table 3, bottom panel), which appears consistent with the standard Datt and Ravallion (1992) decomposition shown in Table 4. The reduction in poverty is primarily explained by the redistribution component, while economic growth plays a relatively minor role.

8
**Table 4. Datt-Ravallion decomposition, 2017-2022**

||Baseyear 2017|Baseyear 2022|Average effect|
|---|---|---|---|
|Poverty rate (P0)|28.0|11.6||
|Change in P0|-16.39|-16.39|-16.39|
|Growth component|-4.00|-3.47|-3.74|
|Redistribution component|-12.92|-12.39|-12.66|
|Interaction component|0.53|0.53|0.00|

Note: Expenditures underlying the decomposition are expressed in 2022 prices, using the implicit deflator implied by the new 2017 and 2022 upper bound poverty lines.

# **3.2 The synthetic panel approach**

We closely follow the methodology of Dang and Lanjouw (2023) which, based on standard assumptions rooted in asymptotic theory, delivers point estimates and related standard errors of a set of probabilities that provide different measures of poverty dynamics, across both survey periods. These probabilities can be defined as follows, where the ~ symbol indicates either the greater sign (>) or the smaller or equal sign (≤), and the letter _z_ denotes the comparable poverty lines for each survey year (as in section 3.1): first, we are interested in the _joint_ probabilities _P_ ( _yi_ ,1 ~ _z_ 1 and _yi_ ,2 ~ _z_ 2 ), that is, for example, the probability that a household is poor in both survey periods, considered together; secondly, we are interested in the _conditional_ probabilities _P_ ( _yi_ 2 ~ _z_ 2 | _yi_ 1 ~ _z_ 1 ), that is, the probability that, for instance, household _i_ remains poor in the second period, _given that_ it was poor in the first period.

As pointed out by Dang and Lanjouw (2023), these probabilities can also be interpreted as estimates of population shares: for instance, _P_ ( _yi_ ,1 < _z_ 1 and _yi_ ,2 < _z_ 2 ) estimates the share of individuals that are poor in both years, as a proportion of the total population observed in _both_ survey periods, considered jointly; on the other hand, _P_ ( _yi_ ,2 < _z_ 2 | _yi_ ,1 < _z_ 1 ) estimates the share of individuals that are poor in the second period, as a proportion of the number of individuals that were poor in the first period.

9
If panel data is available, the joint and conditional probabilities defined above can be computed directly. If not, one can obtain estimates of these quantities by constructing synthetic cohorts based on repeated cross-sectional data. Using the data from BLSS 2017 and 2022, we estimate 2017 and 2022, as well as the mean squared errors and , using OLS on the following linear regression ββ̂ ββ̂ model: εεii,2017 εεii,2022 σσ� σσ� where _yij_ denotes the real per capita consumption in survey round _j_ for household _i_ , with _i_ = _1_ , ..., _n_ , and _j_ = _2017,2022_ . _xij_ is a vector of time-invariant household characteristics observed in both survey rounds, which include the age, gender and education of the household head, the district of residence of the household, and whether the household lives in a rural or urban area. Household heads’ ages are restricted to between 25 and 55 for the first period, and the age group is then adjusted for the second period according to the time elapsed between survey rounds ( _i.e._ household heads aged 30 to 60), so as to focus on a consistent group of households. The estimation of joint and conditional probabilities rests on the assumption that the population underlying the 2017 and 2022 BLSS samples is the same. If verified, this assumption allows to exploit the time-invariant household characteristics used to define the cohorts as the connectors of household consumption between the two periods. This is a strong and crucial assumption in the context at hand, due to the unprecedent shock generated by the COVID epidemic. In particular, we are concerned with any migration from rural to urban areas (between 2017 and 2022, the share of population living in rural areas decreased from 59.8 to 56.3 percent; Amendola et al. 2023). Even though the Monte Carlo simulations presented by Dang and Lanjouw (2023) show that results remain reasonably robust when this assumption is violated, we extend their framework by using a simulation based on actual survey data for 2022, to provide evidence of robustness. More details are provided in section 4.3.

The second step of the empirical strategy proposed by Dang and Lanjouw (2023) consists in computing the unconditional measures of poverty mobility as: 10
where _dj_ is an indicator function that equals 1 if the household is poor and equals −1 if the household is non-poor in period _j, j_ = _2017, 2022_ , and 1 2 .<sup>5</sup> As can be noted, the application of (2) requires that ε _i,_ 2017 and ε _i,_ 2022 have a bivariate normal distribution with ff ρρ� = dd dd ρρ� zero mean, (the partial) correlation coefficient ρ, and standard deviations σε,2017 and σε,2022, respectively. This assumption is standard for most analysis of household consumption data. Furthermore, we exploit a logarithmic transformation to normalize the dependent variable. We are also implicitly assuming that no cohort-specific effects exist; neither do we explicitly assume individual level heterogeneity. While these could result in more restrictive analysis, we are here forced by the limited availability of data for Bhutan. A final crucial step in this framework is represented by the estimation of the correlation coefficient . Given equation (1) and the aforementioned assumptions, and further assuming that the number of cohorts (C) constructed from the survey data is large enough, ρρ� the partial correlation coefficient ρ is estimated from a modified version of equation (1) where all the variables are aggregated at the cohort level: where the error term εcj includes now a cohort fixed effect τc and the error νcj.<sup>6</sup> All predictions from model (1) are obtained by applying the estimated parameters from the 2017 and 2022 survey rounds to 2022 data. Following Appendix 1 in Dang and Lanjouw (2023), standard errors are obtained adjusting for sampling errors and model errors � according to V 2) , assuming that these two errors are uncorrelated with each other. The variance for the sampling errors ss mm has been estimated using the bootstrap VV(ΦΦ<sup>�</sup> = ΣΣ<sup>�</sup> + ΣΣ<sup>�</sup> method while the Delta method has been exploited to estimate ss . ΣΣ<sup>�</sup> mm ΣΣ<sup>�</sup>

> 5 We consider household sampling weights to obtain population-level numbers.

> 6 The formula used to obtain the correlation coefficient is ~~�~~ ccffcc(εεcc1, εεcc2) ) ~~,~~ where and are defined as in equation (3). ρρ= ccv (εεcc1) ccv (εεcc2 εεcc1 εεcc2

11
# **4 Results**

The results of the poverty mobility analysis are organized in three parts. Section 4.1 presents estimated poverty transition probabilities, both joint and conditional, at the national level. The analysis unveils what lies beneath the poverty levels observed in Bhutan in 2017 and 2022, by estimating the volume of the flows that add up to cross-sectional estimates. Transition probabilities reveal whether and to what extent, in the period under consideration, households experienced poverty as transient status or a chronic one, that is, whether poverty mobility was high or low. Even though the overall poverty rate decreased in the period under consideration, these questions remain relevant: different conclusions as to the static or transitory nature of poverty typically lead to different policy implications (Dang et al. 2014). Section 4.2 investigates the determinants of poverty mobility, by analyzing household characteristics that correlate with the probability to move in and out of poverty, or to maintain one’s status over time. Finally, section 4.3 discusses the robustness of the estimates.

# **4.1 Poverty transitions**

The model described in section 3.2 yields two types of statistics: joint and conditional probability distributions. Both provide insights on poverty dynamics, but their interpretation is subtly different.

Table 5 shows the joint probability distribution of different patterns of poverty statuses, _P_ ( _yi_ ,1 ~ _z_ 1 and _yi_ ,2 ~ _z_ 2 ). Each of these indicates the probability (on average, across Bhutan) that a household belongs to one of four possible patterns: a household may be poor in both periods, non-poor in both periods, poor first and non-poor later, or non-poor first and poor later. The cells of Table 5 sum up to 100, and each probability can be interpreted as the share of Bhutan’s population that experienced a given combination of poverty statuses over time.

All estimated probabilities are significant at any level. The main diagonal of Table 5 reports what might be called “immobile” patterns: these are individuals who either did not experience poverty in any of the two periods, or who were poor in both years. The probability of the former pattern is reassuringly high, almost 80 percent: this means that the vast majority of people in Bhutan were never touched by poverty during the time span considered here. Conversely, only 5.8 percent of the population was chronically poor: 12
taking Bhutan’s 2022 population as the base, this corresponds to about 45,100 people.<sup>7</sup> On the other hand, the probabilities reported in the off-diagonal refer to what we might call “mobile” patterns: people who experienced different poverty statuses in the two periods, either because they became poor or because they escaped poverty. Although the two trajectories are opposite in terms of social desirability, their combined probability provides an aggregate measure of mobility: overall, fewer than one in six people in Bhutan, or 15.7 percent, crossed the poverty line in either direction between 2017 and 2022, which indicates that overall poverty mobility was relatively low. However, most of it was upward mobility: only 3 percent of the population ended up falling into poverty over the period considered.

**Table 5. Poverty dynamics based on synthetic panel data, joint probabilities (percent)**

||Poor 2022|Non-poor 2022|
|---|---|---|
|Poor 2017|5.77***|12.71***|
|se|(0.47)|(0.60)|
|Non-poor 2017|3.00***|78.52***|
|se|(0.47)|(0.66)|

Note: *** p<.01, ** p<.05, * p<.1. Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

Table 6 offers a different set of measures of poverty mobility: conditional probabilities, _P_ ( _yi_ 2 ~ _z_ 2 | _yi_ 1 ~ _z_ 1 ), represent the likelihood of an individual’s status in the second period, _given_ their status in the first period. The first line of Table 6 refers exclusively to people who were classified poor in 2017: out of this group, how many remained poor in 2022, and how many managed to escape? A similar interpretation can be applied to the second row, which focuses on people who were not poor in 2017: out of this (much larger) group, how many fell into poverty in 2022, and how many remained above the poverty line? Clearly, this implies that the row-wise sum of probabilities in Table 6 is 100.

The takeaways from Table 6 go along with those discussed for Table 5 – after all, the trajectories underlying the measures presented are the same – but they provide a different point of view, one that allows for a sharper focus on the poor. In fact, while the share of chronically poor individuals is relatively low as a proportion of the country’s population,

> 7 National population from the World Bank World Development Indicators.

13
it corresponds to about one in three individuals who were poor in 2017 (31.2 percent). Still, this implies that almost 70 percent of individuals who started as poor managed to escape poverty; this is 20 times as high as the proportion of people who were not poor in 2017 that fell into poverty in 2022 (3.7 percent). Overall, the picture emerging from these results is a decidedly positive one.

**Table 6. Poverty dynamics based on synthetic panel data, conditional probabilities (percent)**

||Poor 2022|Non-poor 2022||
|---|---|---|---|
|Given Poor 2017|31.25***|68.75***|100.00|
|se|(2.55)|(2.66)||
|Given Non-Poor 2017|3.68***|96.32***|100.00|
|se|(0.57)|(0.64)||

Note: *** p<.01, ** p<.05, * p<.1. Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

In a recent paper, Hérault and Jenkins (2019) argue that non-parametric estimates of bounds of transition probabilities should always be reported alongside point estimates. Bound estimates are shown in tables 7 (joint probabilities) and 8 (conditional probabilities). Each table reports two distributions: “lower bound” distributions show what would be the estimated set of transition probabilities if one were to pick, among all possible estimates, those that yield the lowest values for poverty mobility (poor to non-poor and non-poor to poor); conversely, “upper bound” distributions provide the highest possible estimates of poverty mobility. Therefore, lower bound (upper bound) estimates provide the maximum underestimation (overestimation) of poverty mobility. Tables 7 and 8 provide some reassurance as to the validity of the findings presented so far: first, the widths of the bounds are reasonably narrow (Hérault and Jenkins 2019); second, point estimates shown in Tables 5 and 6 are all included within the bounds.

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# **Table 7. Poverty dynamics based on synthetic panel data, bound estimates for nonparametric joint probabilities (percent)**

||Lower Bound|Upper Bound|
|---|---|---|
|Poor, Poor|10.59|3.21|
|Poor, Non-Poor|12.48|15.15|
|Non-Poor, Poor|0.08|7.46|
|Non-Poor, Non-Poor|76.85|74.18|
|All|100.00|100.00|

Note: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

**Table 8. Poverty dynamics based on synthetic panel data, bound estimates for nonparametric conditional probabilities (percent)**

||Lower Bound|Upper Bound|
|---|---|---|
|Poor to Poor|45.91|17.49|
|Poor to Non-Poor|54.09|82.51|
|Non-Poor to Poor|0.10|9.13|
|Non-Poor to Non-Poor|99.90|90.87|
|All|100.00|100.00|

Note: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

Finally, the conclusions derived from tables 5 and 6 may be substantiated by taking a comparative view of Bhutan’s case. Estimates of joint transition probabilities based on synthetic panels are available for a number of countries: a selection of the most relevant to the context at hand is shown in Table 9. Bhutan’s recent experience emerges almost unequivocally as the most favorable in the bunch. A natural comparison is with earlier mobility estimates for Bhutan itself (World Bank 2014): the picture that refers to 20072012 is remarkably similar to the one obtained for 2017-2022 – if anything, the country’s performance improved, as the probability of chronic poverty (poor to poor) decreased, and upward mobility (poor to non-poor) increased. Compared to other countries, Bhutan’s overall mobility (the sum of the two cross-state transitions, poor to non-poor and non-poor to poor) appears low: Viet Nam 2006-2008 is the only example where aggregate mobility is lower (though the shorter time period considered should be factored into the comparison). However, mobility in Bhutan is overwhelmingly positive, that is, it is mostly 15
directed upwards, poor to non-poor. This is not true for Pakistan in 2010-2016, for instance; even among countries where “good” mobility is also prevalent, the ratio of upward to downward mobility is everywhere less favorable than in Bhutan; this includes India in 2009-2011, which does have the highest upward mobility among the examples provided. Even when looking at the proportion of the poor population in period one who remained poor in period two, Bhutan’s recent experience emerges as the best case scenario among those included in Table 9.

**Table 9. Poverty dynamics in selected countries**

||**Bhutan**<br>**2007-2012**|**Bhutan**<br>**2017-2022**<br>(this paper)|**India**<br>**2009-2011**|**Pakistan**<br>**2010-2016**|**Viet Nam**<br>**2006-2008**|
|---|---|---|---|---|---|
|Poor - poor|8.3|5.8|15.0|17.5|9.9|
|Poor - non-poor|10.5|12.7|15.2|2.5|5.9|
|Non-poor - poor|4.1|3.0|8.9|19.0|4.9|
|Non-poor - non-poor|77.2|78.5|54.1|61.0|79.3|
|All patterns|100.0|100.0|100.0|100.0|100.0|
|Poor|Poor|44.1|31.2|50.0|87.5|62.6|

Sources: Bhutan, 2007-2012: World Bank (2014); India: Dang and Lanjouw (2018); Pakistan: Shabnam et al. (2023); Vietnam: Dang and Lanjouw (2023). Estimates for Vietnam are based on actual, not synthetic, panels.

# **4.2 Profiling of population groups**

This section explores the determinants of poverty dynamics, by identifying the characteristics of the population that are associated with, for example, a higher chance to move out of poverty, or to remain trapped below the poverty line. To do so, we first follow the approach that is conventional in the literature, and present estimates of joint probabilities, _i.e. P_ ( _yi_ ,1 ~ _z_ 1 and _yi_ ,2 ~ _z_ 2 ), computed by population group (Figure 1). These compare with the average population-level probabilities displayed in Table 5 (which are also reported as horizontal dotted lines in the figure, for convenience). The interpretation of each estimate is as follows: taking the top left panel of Figure 1 as an example, we can say that living in urban areas is associated with a lower-than-average probability of remaining poor in both periods, and vice-versa, living in rural areas correlates with a higher-than-average chance of staying poor; belonging to a household where the household head has no education is also associated with a higher chance of staying poor, and vice16
<!-- Start of picture text --> Poor-Poor Poor-NonPoor<br>10 20<br>8 z i i i<br>15<br>10<br>ad cy<br>z<br>2z5z<br>e s>rs & ras Ra * &> ra oN Ra *<br>& os<br>SS & SS &<br>NonPoor-Poor NonPoor-NonPoor<br>3.54 i go}85 =£ cy<br>Be 75<br>2 i 70 z }<br>Ss & > > & oe Ss e > » & oe<br>ws & ~ © s e SS & so © sg x<br>~¥ & ~¥ &<br><!-- End of picture text --> <!-- Start of picture text --> Poor-Poor Poor-NonPoor<br>25 40 i<br>20 i<br>15 30 6<br>105 = x z x By- = Pe = EEEs zr 200}_ zr tisx x * ci = ni 7 x z<br>0 - }<br>eSSo”FED&MEEOe NyLOEPMSeLBSPS LK@OP ¥SOOOFSoGLH EEANQP EEOHSLSCIOEEEMHasOD ODESOD eS)No”EEQP BOPMe LMSY)ie”LOLLBSES Seee OD¥ELEOF LPSOLOSEEOSQP EEOH EEEPFessOO&ESOH<br>QSS& “wse Ssv QSsS “ws€ Sve<br>= we s &<br>NonPoor-Poor NonPoor-NonPoor<br>8 =<br>6 tya 90 z z i z i z<br>47, ? oe caeer ae 70 i c] it,<br>2 - x os -t 60 ry t ;<br>0 . 50 i<br>sOOPEEDO _@KEPSF &RSLOE%PEPLFSESESODSCSEPF PF FILESLLELOOPFCSVIFELEOSSF PLS SGSE& EES OOO sheROCEESDP mcsPLPEERSPEFL SEESBODFS PFISF OFSCPCOELH EEOOLIFEEE SVSO,.% ie& EESOOO<br>QSk& «Ke& RSe gSk& axC3 &sf<br>sf we Sf we<br><!-- End of picture text -->
A potential limitation of such an approach is that these claims cannot be made independently from each other: for instance, one might suspect that the “immobilizing” effect of residing in a rural area is a spurious one, potentially explained away by the fact that household heads in rural families tend to _also_ have low education; or the other way around (the effects of rural residence and low education might reinforce each other). Ideally, one would like to evaluate the impact of each characteristic while keeping all others constant. This requires going beyond bivariate analytical tools. Figure 2 presents estimates of joint probabilities by population group, _after controlling for other characteristics_ . These estimates are obtained as conditional means from a regression that includes area and district of residence, education and gender of the household head. By so doing, each estimate can be interpreted as the probability of belonging to a certain pattern, for a given population group, _net of_ the effect of observable confounding factors.<sup>8</sup> For example, the probability of being poor in both periods for households living in urban areas is about 3.8 percent, keeping all other observable characteristics constant.

Figure 2 delivers a few stylized facts on the determinants of poverty mobility. First, the gender of the head of the household does not seem to have a strong association with the household’s trajectory, compared to other factors (even though including controls does make a difference with respect to Figure 1, pushing the effect of gender further away from the national mean). Second, all else being equal, having no education is positively correlated with experiencing mobility in either direction. This means that households with an uneducated head are more likely than average to have experienced changes of state between 2017 and 2022, regardless of other characteristics. However, education does not help to discriminate between upward and downward mobility, and therefore fails to explain a household’s “success story” of escaping poverty. On the other hand, living in a rural area (regardless of other characteristics) is positively associated with upward mobility, and negatively associated with downward mobility; naturally, the opposite is true for urban areas. Therefore, the distinguishing characteristic of upwardly mobile households seems to be their rural location, net of the effect of education or district of residence.

> 8 Naturally, the effect of characteristics we cannot measure, either by nature ( _e.g._ ability) or because they are not included in both surveys (most importantly, labor market variables) cannot be controlled for here, and may still be clouding results.

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findings. It does so by repeatedly simulating 1,000 new samples for 2022, where households are randomly assigned to districts and areas (urban vs. rural), and the resulting district/area distribution reproduces the one observed in 2017. Such an experiment allows to repeat the analysis presented in section 4.1 under controlled conditions that allow to claim that the geographic location of households has no effect on country-level estimates of transition probabilities.

The resulting estimates are presented in Table 10 (joint probabilities) and 11 (conditional probabilities). The comparison with baseline estimates presented in Tables 5 and 6 indicates a remarkable stability of estimates.

**Table 10. Simulation of poverty dynamics assuming no migration between districts between 2017 and 2022 (joint probabilities).**

||Mean|Min|Max|
|---|---|---|---|
|Poor, Poor|5.93|5.85|6.05|
|Poor, Non-Poor|10.86|10.46|11.26|
|Non-Poor, Poor|3.70|3.59|3.78|
|Non-Poor, Non-Poor|79.51|79.12|79.91|

Notes: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.73).

**Table 11. Simulation of poverty dynamics assuming no migration between districts between 2017 and 2022 (conditional probabilities).**

||Mean|Min|Max|
|---|---|---|---|
|Poor to Poor|35.31|34.76|35.86|
|Poor to Non-Poor|64.69|64.14|65.24|
|Non-Poor to Poor|4.44|4.33|4.52|
|Non-Poor to Non-||||
|Poor|95.56|95.48|95.67|

Notes: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.73).

As an additional sensitivity check, following Hérault and Jenkins (2019), we examine the impact of varying the poverty line and the age range of household heads used to define synthetic panel cohorts. Specifically, we adjust the poverty line by ±20 percent of its official value, and alter the age range from 25-55 to 25-65. The results are displayed in 20
Table 12 (the first column reports baseline estimates). The main takeaways from the comparisons are that changing the definition of the age range of household heads has very little effect on the findings, while shifting the poverty line does make some difference in the configuration of poverty mobility. This is, in a way, expected, given that a 20 percent shift of the poverty line certainly qualifies as major; however, the overall picture that emerges from the results does not change drastically. In particular, the magnitude of chronic poverty is affected, but the country still appears as having more upward than downward mobility, with the vast majority of the population being unaffected by poverty.

**Table 12. Sensitivity of estimated joint transition probabilities to the poverty line and the age of household head**

||**PL**<br>**25<age<55**|**PL**<br>**25<age<65**|**PL-20**<br>**percent**<br>**25<age<55**|**PL-20**<br>**percent**<br>**25<age<65**|**PL+20**<br>**percent**<br>**25<age<55**|**PL+20**<br>**percent**<br>**25<age<65**|
|---|---|---|---|---|---|---|
|Poor - oor|5.8|6.7|2.3|2.8|11.0|12.4|
|p|(0.17)|(0.16)|(0.07)|(0.08)|(0.29)|(0.27)|
|Pr nnr|12.7|12.9|8.5|8.9|15.7|15.6|
|oo - o-poo|(0.37)|(0.35)|(0.28)|(0.26)|(0.41)|(0.39)|
|Non-oor - oor|3.0|2.5|1.5|1.2|4.8|3.9|
|p p|(0.07)|(0.05)|(0.04)|(0.03)|(0.1)|(0.08)|
|Non-poor - non-|78.5|77.9|87.7|87.2|68.6|68.0|
|poor|(0.54)|(0.49)|(0.36)|(0.33)|(0.69)|(0.62)|

Notes: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Standard errors in parenthesis.

# **5 Conclusions**

The paper applies a methodology recently developed by Dang and Lanjouw (2023), that delivers point estimates of poverty transition probabilities in the absence of longitudinal data. The analysis focuses on the dynamics of poverty in Bhutan between 2017 and 2022. This case study is interesting because the observed and documented reduction in poverty appeared particularly significant (the poverty headcount ratio was 28 percent in 2017, and only 11.5 percent in 2022) and, as recorded by the results obtained with the decomposition _à la_ Datt and Ravallion (1992), unusually little of this decrease is explained by the role of economic growth, but rather by redistributive effects. In light of this, an analysis based on synthetic panels can help identify the flows responsible for observed changes in poverty.

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The analysis leads to three main conclusions. The first concerns the nature of poverty in Bhutan: first, poverty mobility is low overall. This is evident from the analysis of the probability that an individual manages to escape poverty, or conversely, falls into poverty within five years, starting from a condition above the poverty line. Second, the chronic nature of poverty, defined as the probability of remaining poor in both years (2017 and 2022), is low, both in absolute terms and compared to other countries for which we have similar estimates: the probability of being poor in both years is 6 percent of households in Bhutan, compared to over 15 percent in India. The third characteristic that emerges is consistent with the two main results, a strong downward trend in aggregate poverty and a low turnover in the condition of poverty: the probability of escaping poverty between 2017 and 2022 is twenty times higher than that of falling into poverty.

The profiling of population groups provided deeper insights into the factors influencing these dynamics. Education and rural residence emerged as significant determinants of mobility. Households with lower education levels were found to have higher rates of both upward and downward mobility, suggesting a greater susceptibility to economic fluctuations. Conversely, rural residence was positively associated with upward mobility and negatively associated with downward mobility, underscoring the benefits accrued by rural populations during the period under study. This nuanced understanding of poverty dynamics in Bhutan has important implications for policy. The results suggest that while the country has made substantial progress in reducing poverty, efforts need to be targeted towards sustaining the gains made by rural households and addressing the vulnerabilities faced by low-education households. Additionally, the findings highlight the importance of policies that support education and skill development as means to enhance social mobility and economic stability.

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