rafmacalaba/data-use-mentions-extended huggingface dataset:
position decline. These patterns of upward and downward mobility across population groups have shown some attenuation over time but have not disappeared. Mobility patterns are more heterogenous across the states, but are qualitatively similar to the national trends within the states.

We offer a review of the literature in the next section. In Section III we provide a brief overview of the methods employed for the construction of synthetic panels, and for specifying the vulnerability line. We subsequently gauge in Section IV the validity of the synthetic panels methodology by implementing it in the IHDS survey. Section V reports our main findings on mobility trends at the national and state levels and for different population and consumption groups. We offer concluding remarks in Section VI.

# **II. Literature Review**

The last quarter-century has seen gross domestic product (GDP) per capita rise significantly in India. As seen in Figure 1, the study period can be readily divided into two periods: in the first, extending up to around 2002, GDP per-capita growth rates were generally below 6 percent per annum (with two exceptions); thereafter, growth accelerated and generally ranged between 6 and 8 percent per annum. As documented by Datt and Ravallion (2011) and Ravallion (2011), there was considerable attention to poverty trends in India following economic reforms introduced in the early 1990s. However, the evidence suggests that the pace of poverty decline started to increase dramatically only during the 2000s, after the 2004/05 round of the NSS, and in particular between 2009/10 and 2011/12.<sup>4</sup> Interestingly, the dramatic falls in poverty between 2009/10 and 2011/12 occurred when per-capita growth rates were in fact lower than in the 2004/05–2009/10 interval (Figure 1).

> 4 Dang and Lanjouw (2018) explore the possibility that the marked acceleration in measured poverty decline between the last two survey years might have been driven by changes in survey methodology, but they conclude that this
Ravallion, M. (2004). “Pro-Poor Growth: A Primer”. _World Bank Policy Research Working Paper_ 3242. Washington DC: The World Bank.

Ravallion, M. (2012). “Why Don’t we See Poverty Convergence”. _American Economic Review_ 102(1): 504-523.

Ravallion, M. and S. Chen. (1997). “What Can New Survey Data Tell Us About Recent Changes In Distribution And Poverty?” The World Bank Economic Review, 11, 357–82.

Ravallion, M. Datt, G. and Van De Walle, D. (1991). “Quantifying absolute poverty in the developing world.” _Review of Income and Wealth_ 37(4): 345-361.

Sundberg, R., and Melander, E. (2013), “Introducing the UCDP Georeferenced Event Dataset”, _Journal of Peace Research_ , vol.50, no.4, 523-532 Shimeles, A., and Nabassaga, T. (2018). Why is inequality high in Africa? Journal of African Economies, 27 (1), pp. 108-126 Thorbecke, E. (2023). The Interrelationships Among Growth, Inequality and Poverty in Africa _. Journal of African Economies_ 32, ii81–ii86.

Thorbecke, E. and Ouyang, Y. (2022). “Towards A Virtuous Spiral Between Poverty Reduction And Growth:Comparing Sub Saharan Africa With The Developing World”. World Development 152: 1- 18.

World Bank. (2018). “ _Poverty and Shared Prosperity Report: Piecing together the poverty puzzle_ ”. Washington DC: The World Bank.

World Bank. (2022a). “ _Poverty and Shared Prosperity 2022: Correcting Course_ ”. Washington DC: The World Bank.

World Bank. (2022b). “ _Poverty and Inequality Platform (version 20220909_2017_01_02_PROD)._ World Bank Group. www.pip.worldbank.org. Accessed Septermber 2022.

World Bank. (2022c). “ _Poverty and Inequality
development goals (SDGs) will require infrastructure investment in LAC of around 4.5 percent of GDP annually at least ( fi gure 4.12; World Bank 2019c). Extrapolating from data from 2008-15, roughly 70 percent of such needed infrastructure investment (over 3 percent of GDP annually) is likely to be public, but in the years leading up to the pandemic, public infrastructure investment in LAC countries was about 1 percent of GDP, suggesting a sizeable public investment gap (Infralatam database; Serebrisky et al. 2018). Past estimates of the total infrastructure investment gap in LAC are in the range of 3 to 4 percent of GDP (Brichetti et al. 2021; Kohli and Basil 2011).

Inadequate infrastructure provision is likely to be a key contributor to high levels of urban congestion. This is an important challenge because LAC is projected to be the most urbanized EMDE region by 2050. Rising congestion costs may o ff set otherwise bene fi cial returns to scale in urban environments, representing one potential cause of an apparent lack of agglomeration bene fi ts in productivity growth in LAC cities (Gómez- Lobo et al. 2022). The annual cost of tra ffi c congestion alone is estimated to be worth more than 1 percent of production in the cities of Buenos Aires, Sao Paulo, Montevideo, and Santiago (Calatayud et al. 2021).
# **Management Practices**

The existing empirical literature on informality has yet to focus on management practices as a driver of firm performance. This line of research may be important since in the context of formal firms, better management practices are associated with higher firm productivity (Bloom and Van Reenen, 2007, 2010). The limited studies that focus on small businesses in developing countries also show that business practices play an important role in the growth and survival of these businesses (McKenzie and Woodruff, 2017). Higher productivity, in turn, is viewed for informal businesses as a prerequisite for being able to compete with existing firms in the formal sector. Indeed, a lack of managerial skills has been recognized as a potential bottleneck in the slow transition to formality observed in many developing countries (La Porta and Shleifer, 2014). Recent work suggests a role for governments and development agencies in this regard: training informal business owners in management skills as part of broader education campaigns on the benefits of formally registering (Kanbur, 2017; Benhassine et al., 2018; Campos et al., 2018).

An entire section of the Informal Sector Enterprise Surveys is dedicated to questions about basic management practices. As noted in Section 2.3, the battery of questions was adapted and shortened from the original set developed for smaller businesses by McKenzie and Woodruff (2017). Table 6 provides descriptive statistics of the full set of questions included in the Informal Sector Enterprise Survey on this topic. Eleven out of 13 questions take binary Yes/No responses and are coded as one if the business engages in the corresponding practice and zero otherwise. The question on inventory management allows four options, of which the options other than frequently are coded as 100, and the rest as zero. The question on comparison of the achieved sales with target allows four options, of which monthly or more often is coded as 100 and the rest as zero, including when the business does not have a target for sales.

19
from also using baseline data, under a variety of assumptions about the autocorrelation in firm outcomes (ρ). Firm data exhibit considerable variability, much of it seemingly genuine (Fafchamps et al. 2010a), and available data suggest that the autocorrelation of profits or sales over periods of 6 months to 1 year is likely to be 0.5 or less (McKenzie, 2011). As a result, adding baseline data improves power slightly, but not dramatically – with ρ = 0.5, we would still need treatment and control groups each of 3547 to detect a 20 percent increase in sales with 90 percent power.

Carrying out multiple rounds of post-treatment follow-up surveys and pooling these multiple measures can be used to further increase power (McKenzie, 2011). For example,

> 6 Specifically, I assume mean sales of 1000 with a standard deviation of 3000, mean employment of 29 with a standard deviation of 21, and that 12% of firms in the control group export.

10
replace these variables with instruments such as parents' education, inherited wealth, etc.

Unfortunately, such variables are not available in the data sets and our regressions include productive asset variables on the RHS. In interpreting the regression results, some caution will thus be necessary, not to view the estimated coefficients as measuring strictly one-way causality from assets to welfare or poverty.

Using a one-year time frame, we consider as exogenous the following sets of variables:

- household assets: education, physical capital (house, household enterprises), land;

- demographic household characteristics: household size and composition and characteristics of the head of household;

- labor market connections: unemployment, and share of wages in total income;

- economic environment: location.

The _human capital_ of the household is embodied in its members and hence their numbers (by sex and age group) are introduced as regressors. Since it is likely that the education of the head of household has a greater influence on welfare and poverty outcomes than that of other members, the education level of the head was introduced as a separate regressor, by means of a series of dummy variables reflecting the highest level of education achieved (primary or less, secondary, vocational/technical, university). The earlier cited country poverty studies have indeed found strong bivariate correlations between poverty incidence and the level of education of the head of household. The data at hand do not provide information on work experience, but this can be proxied by age.

16
# **Figure II-2: Spectral Density Function of Filtered Series**

<!-- Start of picture text --> Saudi Arabia: Spactral Density Function of Filtered Series Egypt: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50 0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency Frequency<br>Evaluated at the natural frequencies Evaluated at the natural frequencies<br>USA: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6 6<br>6.00 6.00<br>4 4<br>4.00 4.00<br>2 2<br>2.00 2.00<br>0 0<br>0.00 0.00<br>log Periodogram-2 log Periodogram -2<br>-2.00 -2.00<br>-4 -4<br>-4.00 -4.00<br>-6 -6<br>-6.00 -6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> In the literature on credit cycles, the primary variable under observation is the real private credit growth. To compute credit growth, we start with a series of stocks of credit. The WDI data
10,000 for two years (US$15.95, $38.95 PPP). Yearly benefits correspond to about 11 percent of household consumption for the targeted poor, rural households.<sup>6</sup> The program is financed by the World Bank has had high visibility as it is managed by a safety nets unit in the office of the prime minister. It reached approximately 100,000 beneficiary households between 2012 and 2019 and has continued to expand. The cash transfers are unconditional but delivered together with parenting and child development promotion activities, as well as with complementary interventions to diversify livelihoods. The program did not have a specific objective to foster peace,<sup>7</sup> but questions on the linkages between cash transfers and conflict have become salient over time with the deterioration of the security situation in the Sahel.

The cash transfer program reached all eight regions of the country, and the selection of villages was randomized through public lotteries before using poverty targeting to select beneficiary households. In particular, geographical targeting was first applied within each region to select “communes” (middle-sized geographical units containing several villages) with the highest poverty rates. Within each selected commune, public lotteries to select beneficiary villages took place in the presence of village chiefs and commune and regional authorities.<sup>8</sup> Next, to determine beneficiary households within the selected villages, poverty-targeting methods were applied.<sup>9</sup> Finally, within the selected households, the recipient of the cash transfers was an adult woman, typically the (first) wife of the household head.

Our main explanatory variable is the assignment of a given village to the cash transfer treatment through the public lotteries within communes. To construct this variable, we rely on the national locality registry and an administrative database from the national cash transfer program. Specifically, we obtained a geo-localized list of the 1,190 villages that benefited from cash transfers at any point between 2012 and 2019.
a preference for ILO data if both data sets are from the same year.

24 The three large countries/regions without GNIPC data are Somalia, the Syrian Arab Republic, and Taiwan, China. They also do not have HCI data, and so I cannot calculate either UHCI or the full utilization rate.

> 25 Downloaded from https://www.ilo.org/shinyapps/bulkexplorer7/ on 13 December 2019. In cases where there are multiple sources for a country for the latest period (e.g. Thailand, Costa Rica and Armenia), I use the labor force survey (LFS).

26 The reported “year” of the ILO data reflects the average of the years of different data sources used in the construction of the full utilization measure. Generally, this is the average of the year of the employment rate data (basic utilization) and the data used to construct the SEBJ. In countries with interpolated ILO data, the year of the SEBJ data is itself an average of the year of the agricultural employment data and the all-sector employee/employer data. This averaging process explains why the reported year is not always an integer. For countries missing the full utilization rate, the year is only that of the employmentto-population data. The only ILO data from before 2010 are for St. Kitts and Nevis, which come from the 1990s.

27 That is, they are calculated by ILO staff based on microdata from labor force surveys or living standards surveys – though there are some from other sources. They are similar to the ILO’s “National Estimates” reported in the World Development Indicators. The ILO also produces “Modelled Estimates” that are highly interpolated and are not used in this paper. 28 For example, the Labor Force Survey underlying the Peruvian data covers the “main city of metropolitan area.” 11
the actual panel comprised those who had been poor in 2004/05 but were non-poor but vulnerable in 2011/12. This compares with an estimate of 16.7 percent in the synthetic panel. Overall, the impression one is left with is that the synthetic panels do a reasonably good job of reproducing the true panel estimates. Certainly, broad-brush qualitative conclusions derived from the synthetic panels would seem to be rather robust. Based on these reasonably encouraging results, we report below our synthetic panel-based estimates of consumption dynamics over the 1987–2012 period derived from NSS data.

# **V. Welfare Dynamics Analysis**

# **V.1. Mobility Levels and Trends**

We begin by enquiring into the extent of consumption mobility and assessing how this has evolved over time. Table 2 reports consumption dynamics across our three population groups between the 1987/88 and 1993/94 survey years. During this interval, overall poverty remained largely unchanged at roughly 46 percent of the population.<sup>15</sup> We employ two different approaches to setting the vulnerability line. The first refers to an arbitrarily set vulnerability line equal to twice the national poverty line. In panel B we reproduce our estimates, based now on a vulnerability line associated with an average risk of falling into poverty by the vulnerable of no less than 20 percent. The two panels differ primarily in that panel A is associated with a somewhat higher vulnerability line, corresponding to a larger share of the population found in the vulnerable group.

In both panels, a majority of the population can be found in the cells along the diagonal of the transition matrix, indicating that they were either poor, vulnerable, or secure in both years.

> line from the data as described in Section 3. Mobility patterns associated with our estimated poverty line are examined when we turn to our description of mobility patterns based on the NSS-based synthetic panels. 15 Recall that our reference population comprises only that population that belonged to households in which the head was
13

**Figure 5** : _FDI per capita in Bulgaria, Romania and CEEC-8 population weighted average in 1990-2003 (in US dollars)_

<!-- Start of picture text --> 250<br>Romania<br>200<br>150<br>Bulgaria<br>100<br>CEEC-8<br>50<br>w eighted<br>average<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>(FDI per capita)<br><!-- End of picture text --> Source: IMF balance of payments statistics, EBRD data for data missing in the IMF statistics, and Bulgarian national statistics. Data for 2003 include the upward revision (48%) of the BCB (see the footnote below).

The contradiction is more apparent than real. Consider first that countries bordering the EU-15 (except those bordering Greece) have also moved fast in transition to competitive markets. In consequence, proximity has also somehow influenced the pace of transition, although the presence of potential foreign investors also probably had an influence on reforms. Second, richer countries are, to paraphrase Fisher, Sahay and Vegh (1998), closer to Brussels. Richer countries have also better institutions and are thus more attractive to FDI. Third, openness to trade influences the extent to which domestic markets are competitive. Contestability of domestic markets, in turn, reduces the extent to which distortions might affect the developments in unit labor costs. Both EBRD-based indicator and governance indicator indirectly captures contestability of domestic markets and thereby developments in unit labor costs.

While policy makers cannot do much about the size of a market or proximity to the EU, they can do quite a lot in designing institutions and changing policies. The latter also usually influence both unit labor cost and openness to trade and thus boost country’s attractiveness to foreign investors. More importantly, there is evidence that institutional quality towers over other determinants
a credible signal of trustworthiness from the state, cash transfers are also likely to be perceived as carrying the highest risk of capture.

Our paper contributes to several strands of research. Much of the existing literature on preferences for redistribution is based on opinion survey data (for example, Alesina and Angeletos 2005; Alesina, Di Tella, and MacCulloch 2004; Alesina and La Ferrara 2002). We add to this literature by providing rigorous evidence from a field experiment that elicits preferences on redistribution in a setting where participants face real tradeoffs, while maintaining the national representativeness of the results. In doing so, we also complement and extend the existing behavioral economics literature that investigates altruistic behavior using samples of higher-education students in laboratory settings (e.g., Charness and Rabin 2002; Fehr and Schmidt 2003). While also using real trade-offs to evaluate subjects’preferences, inference from laboratory studies explores human behavior through selected samples that may not be representative of the population of interest. Field behavioral experiments testing altruistic behavior have been less common, have never been based on nationally representative samples, and have not tested the effects of enhanced transparency (Parra 2011; Johansson-Stenman, Mahmud, and Martinsson 2009).

Finally, the causal estimates we offer based on a nationally representative sample contribute to inform pressing policy issues in developing countries, where many governments are deliberating whether and how to shift resources away from costly universal subsidies, which benefit the middle and upper classes the most, and toward more effi cient forms of social safety nets (Silva, Levin, and Morgandi 2013, World Bank 2015).<sup>6</sup> Besides the redistributive implications, one of the main challenges of a policy shift away from subsidies is the high deficit of trust that governments face as they attempt to replace an easyto-monitor price subsidy for everyone with a targeted social safety net that can deliver better impacts for the poor and vulnerable at a lower cost. Indeed, important episodes of civil unrest in Jordan in the past decade were linked to attempted
87]|[-3.18]|[-2.77]|
|Constant|-19.39|162.28**|67.74|0.31|291.20***|143.07***|56.29***|
||[-0.33]|[2.05]|[1.44]|[0.00]|[3.97]|[3.83]|[3.27]|
|Observations|44|44|44|44|44|44|44|
|R-squared|0.00|0.20|0.06|0.00|0.46|0.35|0.16|
|NTL Data|DMSP|DMSP|DMSP|DMSP|DMSP|VIIRS|VIIRS|

Notes: The average annual DMSP or VIIRS values are emitted by the core and periphery grid cells defined by the methodology described in section 3.2. See Figure 6 for a map showing the core-periphery delineations across the country. The change (dependent variable) is based on the value in the last year of a period and the value in the year before the period started (which is designated the base year). For example, for 2010-2011, the percentage change is between 2011 and 2009 and the base year is 2009.

25
we simulate a partial registry by drawing a random sample of villages from the census extract. This sample, consisting of all households in the sampled villages, is used as the hypothetical partial registry in the two-step procedure. The first step involves utilizing the parameters from the census model used to predict benchmark welfare (described above) to predict per capita consumption into the simulated partial registry. The predictions are based on 39 independent variables common to both the census and survey. The resulting predictions for households are then aggregated into a measure of village welfare.

The second step of the prediction process entails estimating a second model, the geospatial model. The dependent variable in this model is predicted village welfare from the simulated partial registry, and the independent variables are village geospatial indicators, also estimated using extreme gradient boosting. This “geospatial model” is used to predict out-of-sample welfare predictions for villages not included in the partial registry. The predictions from the two models are combined, by using the census model predictions from the simulated partial registry in the villages for which they are available, and the geospatial model predictions for the remaining villages not included in the registry. In other words, in villages covered by the simulated partial registry, we use predictions from the census model rather than those from the geospatial model. This is because the simulated partial registry provides more accurate predictions than geospatial data, which are in fact exactly equivalent to the benchmark welfare measure by construction. We show below, however, that predictions from this procedure become 14