rafmacalaba/datause-encoder-data huggingface dataset:
Given our focus on data-scarce environments, we emphasize methods with operationally feasible implementation, which can be practiced at scale.

We discuss a typology of data scarcity situations, including the overall analytical framework and data source in the next section. This is subsequently followed by more detailed discussion for key data shortage situations, which range from the cross-sectional consumption data being completely missing (Section III) to being partially missing (Section VI). We then discuss the situation of missing panel data in Section V and other related topics in Section VI before concluding in Section VII.

# **II. Typology of Data Scarcity Situations and Imputation Methods**

Given the conceptually broad meaning of household welfare, it is useful to lay out in the beginning the practical strategy that we will employ in this paper. More precisely speaking, we sidestep the theoretical debates about the various approaches to measuring household welfare, and are mostly concerned with the most common practice with measuring household welfare. We will focus accordingly on the money-metric aspect of household welfare that can be measured with household consumption (or income) data that are widely collected from household surveys. In particular, our central premise is that household consumption (or income) data provide the benchmark measure of household welfare.<sup>7</sup> This key assumption helps operationalize and better focus the measurement of household welfare, whereby a household’s position in the welfare space can be quantitatively identified by their consumption level. For example, households can be identified as poor if their consumption levels are below a specified threshold (i.e., the poverty line).

As another example, since we can rank different households’ welfare against each other on a

> 7 There is perhaps some strong consensus among policy makers regarding this as well. For example, the United Nations most recently decided to monitor the global poverty rate as the proportion of the population living below the international poverty line of US$1.90 per day. But see also Alkire _et al._ (2015) for a comprehensive discussion of the alternative approach of multi-dimensional poverty.

7
Table 5: Data to be calibrated

|Frequency of lagged shipments in bin|0|1|2|3-5|6-10|11-100|100+|
|---|---|---|---|---|---|---|---|
|Share of total 2016 shipments|0.189|0.051|0.038|0.084|0.089|0.374|0.176|
|Inspections per 100 shipments|14|6.7|6.4|6.0|5.6|5.7|6.0|
|Mean no. of lagged shipments|0|1|2|3.8|7.6|26.9|205|
|Total inspections per 100 shipments||||7.3||||

Table notes: Author calculations using Serbian customs data from 2016.

made up entirely of observations with exactly zero, one, or two lagged shipments, respectively. In the last four bins, we average over _ofk_ triplets that have varying levels of lagged shipment frequencies. The mean for each bin is reported in row 3 of Table 5.

# **Parameterization**

In order to emphasize the role of ambiguity, we shut down by assumption other possible sources of cross-bin variation that might otherwise affect search behavior and/or compliance. We restrict the optimism parameter to be equal across bins, _αl_ = _α_ , _∀l_ . Likewise the firm’s loss parameter, _L_ , is assumed to be common across bins.

The parameter _L_ represents the loss to the importer from having non-compliant goods seized by the border authority. This parameter can be important in determining overall levels of compliance, and our model fits best with generally high levels of compliance. In our exercise we set _L_ = $200.<sup>56</sup> We also choose the Pareto distribution scale parameter to generate relatively high levels of aggregate compliance; _b_ = 1.<sup>57</sup> The magnitude of the loss parameter is most relevant for the cost of compliance since their relationship determines the compliance decision of the importer.<sup>58</sup> Therefore, it is

> 56 The right-hand side of inequality (14) is decreasing in both _sl_ and _L_ (recall _p_ ( _sl_ ) _≡ F_ ( _slL_ )). With the relatively low levels of search activity observed in Serbian customs (which results in low levels of search intensity), we need a high level of _L_ for the calibration to satisfy inequality (14). In other words, we need to have high levels of compliance with low levels of search activity, which can be achieved with a hefty non-compliance cost.

> 57In an earlier draft of the paper, we set _b_ = 0 _._ 01 and _L_ = 10. these are both scale parameters, and any ratio of _b/L_ that is constant produces the same model outcomes. We choose to set _b_ = 1 here for clarity on some subsequent calculations.

> 58Note that if the loss parameter is related to the value of goods, the relationship between the loss parameter and the cost of compliance also _implicitly_ defines the relationship between the value of goods and the cost of compliance.

38
**Figure 6: Fiscal Multipliers by Business Cycles only**

<!-- Start of picture text --> 4<br>Recessions Expansions<br>3<br>2<br>1<br>0<br>-1<br>-2<br>On Impact 1 year 2 years Long run<br><!-- End of picture text --> _Note:_ The graph shows the conditional fiscal multipliers during recessions at select horizons. These are based on estimates from the IPVAR model, where model coefficients are conditioned only on the phase of the business cycle. Recessions are determined by the Harding-Pagan (2002) business cycle dating algorithm. Bars represent the median, and error bands are the 16-84 percent confidence bands.

22
Policy Research Working Paper 10736

# **Abstract**

The data for estimating monetary poverty in the Syrian Arab Republic are outdated. In the context of data scarcity, this paper aims to propose a methodological approach to address the knowledge gap regarding welfare in Syria over the past decade. In particular, the analysis provides (i) updated pre-conflict poverty baseline estimates based on grouped data from the 2009 Household Income and Expenditure Survey; (ii) supporting evidence on the viability of using Humanitarian Needs Assessment Programme Demographic and Water Supply, Sanitation, and Hygiene 2022 survey data for the estimation of monetary poverty in 2022; and (iii) supporting theoretical and empirical evidence to identify growth in per capita gross domestic product in current prices deflated by Consumer Price Index as the best metric to project poverty using a nowcasting approach. Based on this analysis, the paper proposes to use 2022 Humanitarian Needs Assessment Programme–based poverty estimates to anchor the most recent estimates to the best available evidence, and to interpolate the poverty evolution obtained from back-casting 2022 and nowcasting 2009 poverty estimates over 2009–22 using the growth rate of per capita gross domestic product in current prices, deflated by the Consumer Price Index with a passthrough of 0.7.

This paper is a product of the Poverty and Equity Global Practice. 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 sredaelli@worldbank.org.

_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
- Watmough, G. R., Marcinko, C. L., Sullivan, C., Tschirhart, K., Mutuo, P. K., Palm, C. A. and Svenning, J. C. (2019). Socioecologically informed use of remote sensing data to predict rural household poverty. _Proceedings of the National Academy of Sciences_ , **116,** number 4, 1213–1218.

- Weiss, D. J., Atkinson, P. M., Bhatt, S., Mappin, B., Hay, S. I. and Gething, P. W. (2014). An effective approach for gap-filling continental scale remotely sensed timeseries. _ISPRS Journal of Photogrammetry and Remote Sensing_ , **98,** 106–118.

- Winsemius, H. C., Jongman, B., Veldkamp, T. I., Hallegatte, S., Bangalore, M. and Ward, P. J. (2015). Disaster risk, climate change, and poverty: assessing the global exposure of poor people to floods and droughts. Technical Report.

- Wu, J., He, S., Peng, J., Li, W. and Zhong, X. (2013). Intercalibration of dmsp-ols night-time light data by the invariant region method. _International journal of remote sensing_ , **34,** 7356–7368.

- Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C. and Wu, J. (2019). Estimation of poverty using random forest regression with multi-source data: A case study in bangladesh. _Remote Sensing_ , **11,** number 4.

- Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L. and Myneni, R. (2013). Global data sets of vegetation leaf area index (lai) 3g and fraction of photosynthetically active radiation (fpar) 3g derived from global inventory modeling and mapping studies (gimms) normalized difference vegetation index (ndvi3g) for the period 1981 to 2011. _Remote sensing_ , **5,** number 2, 927–948.

- Zomer, Robert J, Trabucco, Antonio, Bossio, Deborah A and Verchot, Louis V (2008). Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. _Agriculture, ecosystems & environment_ , **126,** number 1-2, 67–80.

59
(2009), Sahoo and Dash (2012), and Sahoo et al. (2012) find a positive correlation between infrastructure investment and economic growth in India, South Asia and China, respectively.

A few studies are carried out for South America <mark>(German-Soto</mark> & <mark>Bustillos</mark> 2014; Urrunaga and Aparicio 2012). Following Holtz-Eakin (1994), <mark>German-Soto</mark> and <mark>Bustillos</mark> (2014) estimated the production function using panel data with fixed effect and found positive links between infrastructure and growth. Urrunaga and Aparicio (2012) find that public infrastructure is important in explaining temporary differences in regional output.

# **_3.2 Economic Growth Effects of Sector-Specific Infrastructure_**

Instead of investigating the impact of aggregate public infrastructure investment, a few papers aimed at specific sectors, studying the economic growth effects of sector-specific infrastructure investment (Zou et al. 2008; Herrerias 2010; Czernich et al. 2011; Banerjee et al. 2012), mostly for the case of China. Specifically, Zou et al. (2008) use both panel and time-series data along with fixed effect and Granger causality test to examine the effect of transportation infrastructure on economic growth, suggesting that the greater growth level is, to some degree, attributed to the better transportation system. A meta-analysis conducted on the effect of transport infrastructure investment on economic growth (Melo et al. 2013) suggests that transport’s impacts are larger for the United State than Europe; the effects are also larger when using roads compared to other modes of transportation. It also shows that if the study is not controlling for unobserved heterogeneity and spurious correlations, then it will obtain higher estimates. Herrerias (2010) investigates whether an investment in equipment/machinery impacts the economic growth effect in China and finds that it does significantly. Banerjee, Duflo and Qian (2012) also study the effect of the transportation system on regional economic performance in China. They find that the transportation infrastructure could increase sectoral output, but it does not necessarily boost GDP growth.

Czernich et al. (2011) study the effect of broadband infrastructure on the economic growth in the panel of OECD countries and find positive and significant impacts. Czernich et al. address endogeneity concerns using an IV approach where pre-existing voice telephony and cable TV networks predict maximum broadband penetration. However, since the model derives its first stage 22
consumption per person based on a common “mixed reference period” for categories of consumption as obtained by Sundaram and Tendulkar (2003). The other uses the alternative method of estimating a “common reference period” made by Deaton (2001). In all three cases, the rural and urban distributions are aggregated assuming urban-rural cost-of-living differentials of 33% and 38% for 1993/94 and 1999/00 respectively; these are based on updated poverty lines as used in Ravallion and Datt (2002). Using either adjustment method, the GIC for the 1990s tends to show a U shape, with lowest growth rates for people around the 20<sup>th</sup> percentile. Overall, the Gini index rose in the 1990s, by either adjustment method. However, it is too early to say if this is going to be the similar to the trend increase in inequality that China has experienced. Looking back over time, rising inequality in India is a recent phenomenon (Figure 9). (Longer term comparisons are only possible using the Deaton method of correcting for the comparability problem in the 1999/00 data.) As in China, India’s recent rise in inequality appears to have stemmed in part from geographic and sectoral imbalances in the growth process, evident as regional divergence and a lagging rural economy. As in the case of China, one finds that growth in mean rural incomes has been far more effective against poverty in India (Table 1) and that the sectoral composition of growth has been important (Table 4), though tertiary sector growth was relatively more important in India than we find for China. This could well reflect the difference between the two countries in the distribution of agricultural land, which is clearly more unequal in India, which naturally attenuates the impact of agricultural growth on poverty relative to that found in China.

By one estimate, if not for the sectoral and geographic imbalance of growth, the national rate of growth since reforms began in full force in the early 1990s would have generated a rate of poverty reduction that was double India’s historical trend rate (Datt and Ravallion, 2002). States 25
the Foster–Greer–Thorbecke family, and the associated elasticities with respect to growth and inequality. Any member of this family for a given group can be written as a weighted sum of poverty within all the subgroups. The weights are equal to the population shares. Based on this fact, SimSIP Poverty provides decompositions of poverty outcomes in three components. The first component represents change in within-group poverty. The second term measures the effects of population shifts between groups. The last component captures the interaction between inter- and within-group effects (see Ravallion and Huppi 1991 for details). It is also possible to decompose changes in poverty over time into the following contributing factors: growth, inequality, and a residual. These decompositions require two sets of observations on the distribution of income or expenditure. This is the same approach discussed above in the case of POVCAL.

# **_2.2. Unit-Record-Based Approaches_**

# **_PovStat_**

PovStat is an Excel-based tool primarily designed to simulate the poverty implications of alternative growth paths. This simulation tool arose out of the basic idea that the rate and pattern of economic growth determine the evolution of poverty over time. In particular, _it is assumed that per capita consumption for a household grows at the same rate as per capita output in the sector of employment of the head of the household_ . Households are classified into four sectors on the basis of the employment status of the head: (1) agriculture, (2) industry, (3) services, and (4) residual. The residual sector amalgamates households with unemployed or inactive heads and those with unknown occupational status.

A major advantage of PovStat over SimSIP Poverty stems from the use of unitlevel data. This has the potential to improve the precision of the estimates of the poverty and inequality measures. Otherwise, the tool shares in the major limitations that we have flagged for POVCAL and SimSIP Poverty: (1) there is no explicit modeling of the macroeconomic framework, and (2) there is a loss of information about the heterogeneity of households due to that fact that the sectoral classification of households is based on the 13
# **1. Background**

Uruguay is a relatively small middle-income country (GDP per capita was US$ 4,800 in 2005, IMF 2005). Although Uruguay is an early starter in the process of development, over the second half of last century the country has grown at a slow rate. While per capita GDP in 1870 was approximately equal to the contemporaneous per capita income in the USA, in 1920 this figure was about 50% and by the end of the last century this was around 30% (Maddison, 2004).

Uruguay boasts a long tradition of social inclusion and publicly provided education. Primary schooling was made compulsory in 1877. Universal primary schooling was achieved in the 1950s leading to high current adult literacy rates (97% among men and 98% among women). In terms of its education system, compulsory education comprises primary education ( _Educación Primaria_ , ages 6-11) and junior high school ( _Ciclo Básico_ , ages 12-14). Public provision of schooling also extends to pre-primary education ( _Nivel Incial_ , ages 3-5), supplied through both kindergartens ( _Jardines de Infantes_ ) and increasingly so through primary schools ( _Clases Jardineras en escuelas primarias con Educación Inicial_ ). Private fee-based education is also common particularly in Montevideo, where it is estimated that around one third of children in primary education attend private institutions.<sup>1</sup> In general, children in public pre-primary and primary educational institutions attend school four-hours a day during a 180 day school term. Most of these institutions operate in two daily shifts (morning and afternoon).

Two of the most notable inefficiencies of the system are widespread grade retention and early drop out (Manacorda, 2006). Both features are common to other Latin American countries (Urquiola and Calderon, 2004). Data from a specific education module administered in conjunction with the National Household Survey ( _Encuesta Continua de Hogares_ ) of 2001 illustrates a long delay in the transition through the primary school system due to widespread grade retention. Despite normal entry into school (average age at entry is 5.82 versus a theoretical entry age of 6),

> 1 Private schools do not receive subsidies from the government besides national and municipal tax exemptions.

5
**Table 6: Indirect Questions about Informal Payments referring to “establishments like this one”**

||N|N<br>Reticent|Coefficient<br>Reticence<br>Dummy|t‐statistic|Uncond.<br>Mean|
|---|---|---|---|---|---|
|**We've heard that establishments are**<br>**sometimes required to make gifts or**<br>**informal payments to public officials to**<br>**"get things done" with regard to customs,**<br>**taxes, licenses, regulations, services etc.**<br>**On average, what percentage of total**<br>**annual sales do establishments like this**<br>**one**<br> **pay in informal payments/gifts to**<br>**public officials for this purpose?**<br>(0= 0% of sales for bribes;<br>1= more than 0% of sales for bribes)|4641|610|0.001|(0.05)|0.566|
|**Whenestablishments like this one**<br> **do**<br>**business with the government, what**<br>**percentage of the contract value would**<br>**typically be paid in informal**<br>**payments/gifts to secure the contract?**<br>(0= 0% of contracts for bribes;<br>1= more than 0% of contract for bribes)|4641|610|-0.024|(-1.08)|0.521|

t statistics in parentheses

* p<0.10, ** p<0.05. *** p<0.01

22
Table 4: Effect of savings default on bank transactions

||(1)<br>Day of<br>transfer|(2)<br>+3 days|(3)<br>+7 days|(4)<br>+14 days|
|---|---|---|---|---|
||Panel A: Depo|sits|||
|Savings Default|23452.552***<br>(378.585)|23680.378***<br>(621.169)|23932.107***<br>(698.161)|22586.859***<br>(1256.521)|
|Observations|474|474|474|474|
|Mean of dependent variable in control group<br>R-squared|1636.75<br>0.90|2190.82<br>0.78|2741.40<br>0.81|5122.17<br>0.80|
||Panel B: With|drawals|||
|Savings Default|-17937.270***<br>(649.696)|-19966.633***<br>(565.638)|-20456.032***<br>(703.387)|-21086.793***<br>(1264.565)|
|Observations|474|474|474|474|
|Mean of dependent variable in control group|-9.02|-254.79|-886.54|-2426.92|
|R-squared|0.68|0.81|0.75|0.50|
||Panel C: Net D|eposits|||
|Savings Default|5524.446***<br>(721.615)|3738.687***<br>(821.783)|3437.037***<br>(704.077)|1479.645<br>(1057.334)|
|Observations|474|474|474|474|
|Mean of dependent variable in control group|1627.74|1936.03|1854.86|2695.25|
|R-squared|0.28|0.24|0.13|0.63|

All specifications include village and week-of-first-survey fixed effects, and the value of deposits, withdrawals, net deposits, respectively, in the 90 days prior to survey 1. Withdrawals are represented as negative numbers. Exchange rate: MK 420 <u>per</u> USD. Robust standard errors in <u>parentheses.</u> * _<u>p <</u>_ 0 _._ 10, ** _<u>p <</u>_ 0 _._ 05, *** _<u>p <</u>_ 0 _._ 01.

32
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**Table 9: Ranks amongst 242 institutions based on RePEc data**

|Institution|Journal<br>pages|No.<br>works|Journal<br>pages<br>adjusted for<br>impact<br>factor|No. works<br>adjusted<br>for impact<br>factor|Total<br>citations|h-index|Abstract<br>views|Down-<br>loads|
|---|---|---|---|---|---|---|---|---|
|Berkeley|7|9|7|3|6|6|6|7|
|Brown|44|67|26|34|31|48|44|38|
|Chicago|10|28|4|9|2|14|7|4|
|Columbia|46|65|28|29|22|33|38|34|
|Cornell|125|154|73|77|94|44|128|135|
|Harvard|4|5|1|1|1|2|2|2|
|LSE|73|69|34|35|35|33|63|55|
|MIT|13|15|3|4|5|10|8|8|
|NYU|11|18|8|8|7|12|10|9|
|Oxford|2|4|6|7|10|15|4|5|
|Princeton|9|11|5|5|4|5|9|10|
|Stanford|36|47|13|19|16|27|34|31|
|UCL|26|25|23|24|33|23|30|34|
|Yale|79|72|41|42|54|22|65|63|
|<br>IADB|<br>259|<br>121|<br>300|<br>194|<br>259|<br>121|<br>89|<br>91|
|IMF|5|3|19|11|5|3|3|3|
|<br>World Bank|<br>1|<br>1|<br>9|<br>6|<br>3|<br>18|<br>1|<br>1|

Sources: RePEc. Numbers are ranks among 242 institutions. Data not available for other finance/development institutions besides IADB and IMF. RePEc defines h-index of _x_ as saying that _x_ authors affiliated to that institution had an h-index of _x_ or higher (Zimmerman, 2007).

47