rafmacalaba/data-use-mentions-extended huggingface dataset:
networks. Finding an exogenous source of variation is challenging. A good example is Martincus and Blyde (2013) who exploit the partial destruction of the Chilean road network in the 2010 earthquake as a natural experiment. Using establishment-level export flows from geo-referenced origins to ports, they first impute distance and time components of road travel costs before and after the earthquake with a methodology similar to the one described in the previous subsection. In this imputation, they utilize a survey on the operational transport costs of land cargo services ( _Encuesta de Servicio de Transporte de Carga por Carretera_ ) conducted annually by the Chilean statistical office INE. The exogenous variation in these imputed costs induced by the earthquake provides a basis for estimating the gravity equation (2) in differences.

An alternative in addressing endogeneity concerns related to road placement is to use the historical routes as an instrument for present day investments. Under the exclusion restriction assumption that historical networks affects current trade only through the determination of current roads, this approach helps correct the bias that may plague OLS estimation. In an example from the U.S. context, Duranton et al. (2014) use exploration routes between 1528 and 1850 and railroad routes in 1898 as instruments for the Interstate Highway System. Banerjee et al. (2020) use historical transportation corridors connecting large old cities and 19th century Treaty Ports in China as instruments for modern paved roads.

To sum up, the gravity estimation relies on the presence of high quality domestic trade data which may be hard to obtain in developing countries. An alternative method is to infer trade costs from interregional gaps in prices, which is more feasible since national statistical agencies typically collect prices of various commodities with the primary intent of calculating the inflation rate. I describe this methodology next.

# **5.2 Estimation Using Price Differentials**

Spatial price gaps are informative about trade costs. Suppose the price of a good in location _o_ is _po_ , and its price in location _d_ is _pd_ . Assuming that there is a competitive trading sector
Figure C1: DID estimates <!-- Start of picture text --> 1st assessment Baseline Endline<br>ULGSP<br>Control<br>Year<br>(a) Means and 95% CI (2000-2018)<br>Note: Sample size is N = 3054<br>15<br>10<br>NTL<br>5<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1992/1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> (b) difference-in-differences estimates _Note:_ Panel A presents mean NTL (with 95% CI) for wards in ULGSP treatment and comparison districts. The sample is restricted to wards included in the household and government officials surveys. Nighlight data excludes pixels with NTL < 7. Panel B presents results from DiD estimates. Coefficients obtained estimating equation ntlwy = α + γw + θy +<sup>�2018</sup> y=1999<sup>βy(ULGSP</sup><sup>_∗_year)wy +Xwy +εwy.Standard errors are clustered at the</sup> LGA, the level of treatment.

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# **1. Introduction**

Obtaining comparable measures of household incomes across households of different sizes and composition—or converting these incomes on a common (equivalence) scale—is a crucial task for welfare measurement. Indeed, a large body of literature has demonstrated that there are substantial effects of scale adjustments on poverty and profiles of the poor for various countries at different income levels (Lanjouw and Ravallion, 1995; Peichl _et al._ , 2012; Bishop _et al._ , 2014). Equivalence scales are often estimated based on expenditure data; one major disadvantage of this method is that it requires strong identifying assumptions (Deaton and Paxson, 1998).

In this paper, we make several contributions to the literature on equivalence scales and poverty measurement. First, we estimate equivalence scales using an alternative source of data, subjective well-being data. While a growing literature has followed this approach using panel data, these studies mostly investigate data on life satisfaction and income satisfaction.<sup>1</sup> We analyze instead a subjective well-being question where individuals are asked to evaluate their own level of material welfare on a nine-point scale from "poor" to "rich". This question arguably better captures the multidimensional nature of welfare and is more related to household welfare than satisfaction variables (Ravallion and Lokshin, 2001 and 2002).

Second, we offer new and interesting findings regarding the dynamics of poverty given equivalence scale adjustments (scaling) on long-run household panel data from the Russian Longitudinal Monitoring Surveys (RLMS). It is well-known that policies to address short-term

> 1 Two main types of subjective well-being data have been analyzed in the economic literature. The first type asks respondents about a hypothetical minimum income level that is required to reach a specified level of well-being (e.g., Garner and Short, 2004). Since this method assumes that people know what their true minimum income level is, the hypothetical assessment of the situation may lead to interpretation issues of minimum income questions (Steiger _et
7|Hotels and restaurants|444|244|35.5|3.9|
|8|Transport, storage and communications|91|102|52.6|1.6|
|9|Financial intermediation|186|6|3.1|0.1|
|10|Real estate, renting and business activities|22|9|29.0|0.1|
|11|Public administration and defense; social security|57|13|18.6|0.2|
|12|Education|281|106|27.4|1.7|
|13|Health and social work|45|30|39.5|0.5|
|14|Other community, social and personal service activities|76|22|22.4|0.3|
|15|Activities of private households|13|19|59.4|0.3|
|16|Extraterritorial organizations and bodies|3|0|0.0|0.0|
|17|Not elsewhere classified|371|83|18.3|1.3|
|18|Total employees (in thousand)|5845|6316|51.9|100.0|

Source: ILOSTAT database, International Labor Organization In addition, when it comes to paid employment a very significant gender pay gap exists in Nepal as women are paid less than their male counterparts for the same job in mostly all the sectors, owing to such circumstances women are poorer than men in the country.<sup>25</sup> A very skewed gender pay gap exists in the agriculture, hunting and forestry with women earning 40 percent less as compared to men for the same work. Other major sectors where majority of women are employed show similar trends (Table 10).

24 CBS. 2009. Report on the Nepal Labor force survey 2008. Kathmandu, National Planning Commission.

25 Published 18 December 2015. The
shown. See Appendix for all countries.The variable control list for LITS and FINDEX varies. _Sources_ : LITS Differences in income and level of education are the main sources of dissimilarity in having a bank account. The importance of education is a potential reason for the drop in coverage at the household level. This drop could have occurred because households with high levels of income and education have multiple individuals with bank accounts while households with lower income and education have none. Compared to benchmark countries, gender and income seem to account for a high level of dissimilarity in Turkey while the urban and rural populations have more equality in terms of bank account ownership.

An analysis of the individuals that do not own an account shows that not having enough money (50 percent of respondents) is the main reason for not having an account in Turkey (Figure 10). A lack of trust in financial institutions, and the high costs associated with banking are the two largest subsequent reasons at 27 percent apiece. This trend is similar to the rest of ECA while in other regions the cost of owning an account seems to be the distinctive second biggest reason of not having one. One interesting aspect of survey answers in Turkey is that a significantly lower portion of individuals listed lack of money when compared to the rest of ECA and the BRICS, and a slightly lower level than even the developed world. Considering the income levels of these different country groups, individuals in Turkey seem to have a different perspective regarding the budget necessary to use a bank account which may explain the higher usage of accounts in the country.

11
“=5<br>| 6 =<br>1 NS.<br>| S<br>fi s<br>I ‘<br>I . .<br>° ! see? °o<br>7 8 9 1<br>Irreversibility (Gamma 3)<br><!-- End of picture text -->

<!-- Start of picture text --> 2 |<br>, '<br>Ss !I|<br>I<br>a |<br>aseSs I|I<br>2 |<br>vt |<br>= 1<br>iSog. I<br>*8 |<br>I<br>'<br>|<br>I<br>S 1<br>1 2 3 4 5 6<br>Mobility across sectors (C1)<br><!-- End of picture text -->

<!-- Start of picture text --> N ! rafvar 3<br>S I1 !’ 1,1, t1<br>1I ni1 1;YW ianwe<br>1 1 .Y A<br>1<br>io}=# \I] H’ NoC5><br>2 1 Hn ° 3s<br>oS a 1 ot e<br>5 n iy =<br>= nN la S<br>mn ly =<br>Jt 1 d<br>wi meu\" " 'y'<br>Vio1 LMad wnt 7<br>° ‘;* i rae , °<br>2 3 4 5 6 7<br>Mobility in and out of Unemployment (C2)<br><!-- End of picture text -->
**Table 2 Wage Levels for Cambodia and Sri Lanka**

||**Cambodia**||||**Sri Lanka**|||
|---|---|---|---|---|---|---|---|
||**2004**|**2007**|**2008**|**2009**|**2002**|**2006**|**2008**|
|**Mean log wage in T&G**|12.24|12.45|12.53|12.45|8.35|8.75|8.86|
|**Mean log wage in agriculture**|11.05|11.6|11.72|11.61|7.82|8.31|8.68|
|**Mean log wage**|11.74|12.31|12.47|12.22|8.34|8.83|8.94|

_Source:_ Authors’ calculationsbased on Cambodia Socio-Economic Surveys and the Sri Lankan 2002 and 2006 Household Income and Expenditure Surveys and the 2002 and 2008 Labor Force Surveys _Note:_ T&G = textiles and garments.

Tables 3 and 4 present the results of wage regressions for Cambodia and Sri Lanka, respectively. The regression analysis was carried out using the methodology described in section 3.a. For our analysis, we used six rounds of the Cambodian Socio-Economic Surveys that covered the 1996–2009 period, the Sri Lankan 2002 and 2006 HIESs, and the 2002 and 2008 Sri Lankan Labor Force Surveys.

In both countries, working in apparel pays a positive premium compared to the economy average; however, the premium dipped immediately after the MFA phase-out. This result is in line with theoretical predictions that a decrease in price for
# **Appendix B: Estimated impact for floods** **_or_ droughts, and for all shocks, in the LSMS-ISA**

Table B1. Estimated impact of all adverse shocks captured in the LSMS-ISA

|Country|Year|Estimate number of<br>individuals affected<br>by all shocks|Estimate share of<br>total population<br>affected by all<br>shocks|Estimate damages<br>from all shocks|
|---|---|---|---|---|
||2011-2012|14 million<br>[ 12 ; 17 ]|15.00 %<br>[ 12.40 % ; 17.60 % ]||
|Ethiopia|2013-2014|34 million<br>[ 30 ; 39 ]|34.20 %<br>[ 29.80 % ; 38.60 % ]<br>|34 million<br>[ 30 ; 39 ]|
||2015-2016|52 million<br>[ 46 ; 57 ]|49.30 %<br>[ 43.90 % ; 54.60 % ]|52 million<br>[ 46 ; 57 ]|
||2018-2019|31 million<br>[ 27 ; 36 ]|27.60 %<br>[ 23.60 % ; 31.60 % ]|31 million<br>[ 27 ; 36 ]|
||2009-2010|6 million<br>[ 5 ; 6 ]|39.00 %<br>[ 34.20 % ; 43.90 % ]||
|Malawi|2012-2013|6 million<br>[ 5 ; 7 ]<br>|36.40 %<br>[ 32.00 % ; 40.80 % ]<br>||
||2015-2016|9 million<br>[ 7 ; 11 ]|52.30 %<br>[ 41
these criteria alone could cause vast differences between district councils‟ allocations, 25% of the National Village Fund (NVF, TASAF IIs main spending vehicle) was first distributed equally to all councils. The remaining amount was then distributed using the composite index. We now turn to more details of the data sources used in the analysis.

# **3. Data**

# _3.1. Institutional Data from TASAF_

This paper utilizes two main databases from TASAF II. The first of these datasets documents every application received by TASAF II between May of 2004 and October of 2007, for a total of 102,606 applications. More than 95% of the 2,542 wards in mainland Tanzania submitted at least one application, with the median ward submitting 14. The second institutional database describes every TASAF II project funded through August 2008, and gives details of the beneficiaries, project type, and budgets for each of the 4,037 projects funded. This database also shows the composition of funding provided by the NVF, local government authorities, and the community itself. NVF spending typically makes up about 80% of total project costs, and is never below 50%. We merge these datasets at the ward level to calculate the number of applications, the percentage of applications funded, and the total amount spent from each source per ward.

# _3.2.Poverty Maps_

The institutional data on applications, approvals, and funding are overlaid with poverty maps, also at the ward level, created by the authors using the method described in Elbers, Lanjouw, and Lanjouw (2003). This exercise uses the household surveys from Tanzania‟s 2000/01 Household and Budget Survey (HBS) and the 2002 Population and Housing Census, both conducted by the National Bureau of Statistics (NBS). The HBS is a nationally representative sample of 22,178 households sampled between May 2000 and June 2001. The HBS is a much richer survey than the census, containing information on a wide range of outcomes including
# **Appendix A: Trade Data and Tariff Rates for Armenia’s Trade Partners**

# **Trade Data by Regional Partner and Sector**

To obtain the shares of imports and exports from the different regions of our model, we used trade data published by the National Statistical Service of the Republic of Armenia<sup>21</sup> . The data is for the year 2007 and shows exports and imports by country and commodity.

The regions of our model are Armenia, the European Union, the CIS, and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. For the CIS, we include Azerbaijan, Belarus, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan. Rest of the World is the residual.

The data is reported according to the Harmonized System (HS) classification at the two digit level. We mapped the HS-commodities into the sectors of our model. The exact mapping is defined in the first table below. Results for both exports and imports are reported in the subsequent two tables.

# **Tariff Data.**

We use detailed collected tariff data from the Armenian Customs Authority. That is, we received data on collected import duties (tariffs) and import values at the four digit tariff line level (again using the Harmonized System classification). The collected tariff rates for the sectors in our model are obtained by first aggregating the four digit tariff line level tariff collections and import values to the sectors of our model. The ratio of tariff collections to import values for each sector of our model is then calculated to give estimates of the collected tariff rates, which in turn are incorporated into our dataset. The tariff rates are shown in Table 4. Applying these tariff rates across all sectors implies that tariff revenue in the revised database is about 1% of GDP, which is consistent with collected revenues in Armenia.<sup>22</
# **Poverty Measurement in the Era of Food Away from Home: Testing Alternative Approaches in Vietnam**<sup>**1**</sup>

_Gabriela Farfan, Kevin Robert McGee, Julie Perng, Renos Vakis The World Bank_ <u>Keywords: survey methodology, food consumption, welfare measurement, poverty, data collection. JEL codes: O1, C81, C93, I31, I32.</u> 1 This was joint work with the World Bank’s Living Standards Measurement Study (LSMS) and in response to the UN Statistical Commission’s call for improved guidelines on data collection. We are grateful to colleagues at the General Statistics Office of Vietnam, especially Lo Thi Duc, for their continuous support during design, implementation, and analysis of this study. We appreciate the support from Michael Lokshin, Zurab Sajaia, and Michael Wild who assisted with the retrieval and analysis of the paradata; as well as Tu Chi Nguyen, Linh Hoang Vu and Ha Thi Ngoc who helped us throughout the implementation of the project. The study also benefited from comments from Gero Carletto, Alberto Zezza, Gabriel Demombynes, Talip Killic, Obert Pimhidzai; as well as participants of various workshops at the WB. Financial support for this research has been received from the World Bank’s Knowledge for Change window as well as the Food and Agriculture Organization’s funding through the “Global Strategy for Improving Agricultural and Rural Statistics” initiative. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank or any of its affiliated organizations. All errors and omissions are our own.
that many economic and behavioral decisions are made at the household level in the study setting, this measure gauged the extent to which beneficiaries felt aligned with and supported by members of their household in decision-making or experienced tensions (reversed). It also assessed the Inclusion of Other in Self visual scale and sense of trust as indicators of intrahousehold closeness and positive relationality (Aron et al., 1992).

# **<mark>Study 3: Mechanism experiment</mark>**

This study was pre-registered in the AEA RCT registry: https://www.socialscienceregistry.org/trials/3570. It received approval from Stanford University Institutional Review Board (#44074).

_Participants_ . The study includes 2,628 women in 33 villages across six communes (see Appendix B for a map of the commune locations). Study respondents were recruited from the pool of program recipients in villages assigned to the Psychosocial and Full arms of the policy experiment, i.e., those program modalities that included psychosocial components. Women participants were from low-income households within each village. Eighty-five percent of these households were found to fall below Niger’s national poverty line (Bossuroy et al., 2022). Women in this sample were on average 34 years old (SD=14.11) and had 0.64 years of education (SD=1.97). The average household size was 9.54 members (SD=4.64). Most participants were not heads of their household (88%) and were not nomadic (89%). The average distance to the nearest market from each village was 73 minutes (SE=3.52) and to the nearest water source was 12 minutes (SE=1.14). Among those who participated in the endline survey, a minority (16%) of women respondents owned a personal cellphone. While religious identification was not asked, a vast majority of Nigeriens practice Islam.

36
of women’s role as economically dependent rather than as economic agents, potentially limiting the extent to which interventions target their needs. Consider the case of the community in the study in which women collected and sold blackberries to market intermediaries. The interviews and focus group discussions with these communities suggested that this is an important yet seasonal source of household income where interventions could increase earnings from the activity, by connecting these groups of women directly to markets in the larger cities, for example. The extent to which this type of activity is invisible in the official statistics limits the inclusion of women’s activities in interventions designed to increase productivity.

Because mismeasurement has important policy implications and may not be fully addressed with survey modifications, it is important to encourage countries and other providers of economic statistics to report all types of work according to the ICLS 19 rather than focusing only on the headline indicators: labor force participation rates, and employment and unemployment rates. Rather, engagement across all types of work, including unpaid activities for own-consumption, should be included and discussed when designing interventions and policies.

26
number of low value transactions routed through the FMIS is revealing. If, as was the case in Cambodia, these are few in number it raises the question of how these are actually executed. Large project or program advances routed through the FMIS will still give expenditure control at the aggregate level, but the FMIS does not provide for the integrity in expenditure data it otherwise would and cannot guarantee funds were actually spent according to appropriations. Pakistan and Cambodia do not appear to be isolated cases. Low budget capture was also identified as a problem in Bangladesh and Ghana, and the issue relating to advances in Somalia and Sierra Leone.

_Table 9 Transactions Data for a Select Group of Countries_

|**Country**|**Transaction**<br>**type **|**Threshold**|**Share of**<br>**Transactions**|**Share of the**<br>**Budget**|
|---|---|---|---|---|
|China, Central|High value|> US$ 78,250|2.1%|69.0%|
|Government (2015)|Low value|< US$ 1,564|69.0%|2.7%|
|Vietnam (2015)|High value|>US$ 43,800|1.8%|75.0%|
||Low value|< US$ 2,190|78.5%|4.3%|
|Philippines (2017)|High value|> US$ 10,000|7.8%|93.0%|
||Low value|< US$ 2,000|79.8%|2.2%|
|Afghanistan (2017)|High value|> US$ 25,000|8.6%|86.0%|
||Low
allowing easy cross-checking of available information. Despite significant recent progress Tajikistan lags behind on many accounts its neighbors (Table 2).

**Table 2. ICT Performance in Kazakhstan, the Kyrgyz Republic, and Tajikistan, 2007**

|**Item**|**Kazakhstan**|**Kyrgyz Republic**|**Tajikistan**|
|---|---|---|---|
|Mobile cellular subscriptions (percent)|80|41|35|
|Telephone lines (percent)|21|9|5|
|Internet users (percent)|12|14|7|
|Digital subscriber lines (monthly payment, USD)<sup>a</sup>|31|265|104|
|Internet traffic (incoming + outgoing, terabytes)<sup>a</sup>|67,539|n.a.|56,000|

_Source:_ World Development Indicators database; countries’ statistical agencies. a Data are for 2008.

9
then the threshold yield for the insurance unit is given by kg/ha.

Under NAIS, premium rates paid by farmers in respect of food crops are determined by the following rule.<sup>4</sup> For Kharif crops the farmer premium rate is 3.5% for all oilseed crops and bajra and 2.5% for all other food crops. For Rabi crops the farmer premium rate is 1.5% for wheat and 2% for all other food crops. Premium rates paid by farmers in respect of commercial and horticultural crops are determined at the state level for each crop using a Normal Theory Method, under which yields are assumed to be normally distributed with mean and variance calculated using ten years of data for that insurance unit. For all crops, small and marginal farmers receive a 10% premium rate subsidy, and therefore only pay 90% of the above rates.

> 2 See Clarke et al. 2011b for a description of the WBCIS.

> 3 The coefficient of variation is the ratio of the standard deviation to the mean. Kharif and Rabi calculations are performed separately, with Kharif calculations using historic Kharif data only and Rabi calculations using historic Rabi data only.

> 4 A premium rate is the ratio of the premium to the sum insured, where the sum insured is the maximum possible claim payment. Under NAIS, the maximum possible claim payment occurs when the actual yield is zero.

4