rafmacalaba/data-use-mentions-extended huggingface dataset:
# Social transfer multipliers in developed and emerging countries: The role of hand-to-mouth consumers<sup>�</sup>

Jessica Bracco Luciana Galeano Universidad Nacional de La Plata University of Michigan Pedro Juarros Daniel Riera-Crichton Georgetown University World Bank Guillermo Vuletin<sup>y</sup> World Bank April 13, 2021 Abstract This paper estimates the macroeconomic e¤ects of social transfer payments to individuals for a sample of 23 developed and Latin American countries. The ...ndings show that the social transfer multiplier is 0.3 in developed countries, but 0.9 in Latin American economies. The paper studies the role of hand-to-mouth consumers, who have no access to ...nancial markets and a high marginal propensity to consume, as a ...rst order factor to explain the heterogeneity in the size of social transfer multipliers. Using survey-based data from the Global Findex dataset, the paper ...nds that the average share of the population living hand-to-mouth is 23 percent in developed economies versus 60 percent in Latin American countries. This evidence is interpreted with a two-agent New Keynesian model. The ...ndings show that the di¤erence in the share of hand-to-mouth consumers explains 80 to 90 percent of the di¤erence in the estimated social transfer multipliers. The paper also documents that the share of hand-to-mouth individuals in emerging countries is in general 47 percent which suggests that a larger social transfer multiplier may be expected for this type of economy.

Keywords: ...scal transfer, social transfer, ...scal policy, ...scal stimulus, government spending, multiplier, hand-to-mouth, emerging markets.

JEL Classi...cation: E62, E63.

> �We would like to thank Martin Ardanaz, Leopoldo Avellan, Yan Carrière-Swallow, Fernando Castaño, Manuela Cerimelo, Antonio David, Guillermo Falcone, Roberta Gatti, Margaret Grosh, Chadi Habib, Alejandro Izquierdo, Norman Loayza, Nora Lustig, Jorge Neyro, Ha Nguyen, Steven Pennings, Carola Pessino, Emilio Pineda, Francisco
|2005|33|33<br>90<br>0.34|0.29|0.22<br>8<br>81|
|2006|29|38<br>89<br>0.47|0.23|0.29<br>11<br>161|
|2007|28|29<br>89<br>0.48|0.18|0.29<br>9<br>178|
|2008*|—|42<br>50<br>—|—|0.41<br>—<br>—|
|2009*|73|45<br>88<br>0.44|0.21|0.36<br>21<br>141|
|2010*|226|45<br>88<br>0.28|0.21|0.31<br>33<br>69|
|_Source_: World B<br>_Note_: — = data<br>**Figure 6. Und**<br>0<br>50<br>100<br>150<br>200<br>**% of the revenues**|ank 2005; World<br>not available. * du<br>**erpricing and n**<br>2003|Bank 2007; NPA data; and World Bank staff e<br>e to conflicting data sources estimates must b<br>**etwork losses have been major challe**<br>2004<br>2005<br>2006|stimates<br>e interpreted wit<br>**nges at differe**<br>2007|h caution.<br>**nt periods for NPA**<br>2009<br>2010|
|||Losses<br>Underpricing<br>
by legislative indicators and the change was in the same law. This is confirmed empirically. Pargal notes in a footnote that the coefficient on the regulatory variable had higher t-values when the liberalisation variable was omitted<sup>48</sup> . Unfortunately, we are not told the results of that regression nor is there any discussion of this issue in the regressions by sector. In addition, we note that the estimates of the regulatory variables may be downwards biased because of the errors-in-variables from the (unavoidable) absence of any data on regulatory practice.

The results for the individual regulatory variables in the pooled regressions are also strange and, in some cases counter-intuitive. Only the coefficients on 1), 2) and 4) are significant at the 10% level or above. The coefficient on funding is significant at the 5% level and the right sign (ie licence fee funding is associated with higher private investment, presumably because this increases the effectiveness of the regulatory

> 48 See Pargal, op cit, footnote 7, p.17.

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Table 1.2: Annual Incremental Production from Bank Financed Projects Over the Period 1974-85 (as estimdted **at** appraisal) for selected Crops

|Crop|No. of<br>Projects|Estimate<br>Annual<br>Incremental<br>Output<br>(Thousand<br>Metric Tons)|Volume**as**X<br>of World<br>Production<br>1985|Volume as Z<br>of World<br>Trade<br>1985|
|---|---|---|---|---|
|Rice|304|16,813|3.6|147.0|
|Wheat|156|12,506|2.6|11.1|
|Maize|263|12,815|2.6|18.3|
|Sorghum|121|5,910|8.8|58.5|
|Sugar|118|4,200|4.3|14.1|
|Groundnuts|125|1,969|9.7|198.0|
|Soybeans|70|2,850|2.9|11.6|
|Cotton Lint|<br>147|2,822|15.8|68.8|
|Rubber|42|2,179|50.0|59.7|
|PalmOil|36|1,832|24.3|38.0|
|Coffee|55|220|4.3|5.1|
|Cocoa|44|184|9.8|14.2|
|Tea|5|36|1.6|3.7|
|Tobacco|32|118|1.9|8.4|

Source: AGR, AGR Data Base for the estimated incremental output at full development.

FAO and USDA for Production and Trade
. A close look at the Shapley contributions reveals that rural poverty would be much higher than the national average if rural inequality were not lower than the national average. For instance in 2007, the incidence of rural poverty would have been about 21 percentage points higher than the national average if rural inequality had been at the same level as overall inequality. The observed difference stood at 15 points because the inequality effect was -6 percentage points.

> 15 Here we focus on these two years because the data from the 1996 survey are organized around 4 regions only in addition to the 2 major cities.

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(1), 157-176.

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30 Informal Employment and Worker’s Wellbeing in Russia / June 2019
Agriculture|2010/11|
||Niger|AFR|Low income|General Household Survey|2014/15|
||Nigeria|AFR|Low middle income|National Panel Survey|2015/16|
||Tanzania|AFR|Low income|Living Conditions Survey|2014/15|
||Uganda|AFR|Low income|Cameroonian Household Survey (ECAM-IV)|2015/16|
||Afghanistan|SAR|Low income|Integrated Household Survey|2016/17|
||Cameroon|AFR|Low middle income|Household Socioeconomic Survey|2014|
||Kyrgyz Republic|ECA|Low middle income|National Household Survey on Living Standards Measurement (EMNV)|2013|
|**Other multi-topic surveys**|Mongolia|EAP|Low middle income|Social and Living Standards Measurement Survey|2016|
|**(LSMS-tagged)**|Nicaragua|LAC|Low middle income|Integrated Household Survey|2014|
||Pakistan|SAR|Low middle income|Living Conditions Survey|2013/14|
||Sierra Leone|AFR|Low income|Integrated Agricultural Survey|2018|
||South Africa|AFR|Upper middle income|LivingConditions Survey|2014/15|

**World Bank database SSAPOV** The Sub-Saharan Statistical Development team harmonized the existing household surveys in the Sub-Saharan Africa region by extracting about 200 variables and unifying the definitions and variables names. SSAPOV contains the harmonized information on ownership of vehicles for 33 countries. We use data about ownership of cars, motorcycles, and bicycles. Unfortunately no other variable linked to transport expenditures has been harmonized in this database

# **2.2 Country-level datasets**

**International Road Federation (IRF).** We use vehicle ownership data included in the World Road Statistics database produced by IRF. These data are panel data covering 194 countries from 2000 to 2017. More specifically, we use data on vehicle in use including: Passengers cars, Buses and Motor coaches, Vans,
000||

- *Multiply by 100 to obtain parameter/variable values in percent share or growth terms (%). Note (source and calculation):

- _(1)_ Penn World Tables (PWT) 9: share of labor compensation in GDP at current national prices. There is no data for Syria. As a proxy, we use the 2010 average for four relatively conflict‐free middle‐income MENA economies (Djibouti, Jordan, Morocco and Tunisia).

- _(2)_ δ is PWT 8.1 data for Syria. δ<sup>�</sup> is the PWT 9 depreciation rate for non‐residential structures.

- _(3)_ δ<sup>�</sup> is derived as the residual from a weighted average calculation of δ based on δ<sup>�</sup> and K<sup>�</sup> ⁄K _. (4)_ Calibrated value based on long‐term averages of investment‐to‐output, GDP growth and the aggregate capital depreciation rate. See the discussion in Section 2.2.1.

- _(5)_ Calibrated based on average shares for lower‐middle income countries and oil‐based economies. See the discussion in Section 2.2.1.

- _(6)_ K��⁄Y� and K��⁄Y� are derived by applying K<sup>�</sup> ⁄ K to K�/Y� . _(7)_ Gross fixed capital formation (% of GDP), average for 1998‐2007 from WDI. Public investment share assumed at 40% based on World Bank (2017b) and IMF (2010).

- _(8)_ PWT 9: index of human capital per person based on years of schooling (Cohen and Soto 2007; Cohen and Leker 2014) and returns to education (Psacharopoulos 1994).  Average growth rate, 2001
part because of this, recent research has explored the use of satellite and phone data as an alternative to identify poor households.

A recent paper (Aiken et al, 2022) evaluates an innovative two-step approach to identify the poorest households in Togo, which was applied to the Novissi cash transfer program. The first step entailed using the Meta relative wealth index from Chi et al. (2021) to identify the poorest hundred Cantons, which are the third administrative level in Togo, out of 397 total Cantons. Within these identified Cantons, the team used CDR data to identify poor households, using a model trained against per capita consumption collected as part of a phone survey in September 2020. Targeting accuracy was then evaluated against a Proxy Means Test constructed from an independent phone survey representative of all cell phone subscribers in the country.

The headline result is that this two-step targeting approach outperformed two hypothetical feasible alternatives based on geographic targeting, as shown in Table 6. The first feasible alternative considered is a transfer of equal value to all persons within the poorest prefectures, which is the second administrative level in Togo, out of 40 total prefectures in Togo. The second alternative provided a transfer of equal value to all individuals within the poorest Cantons. Both of these hypothetical alternatives simulated transferring cash to all households in the poorest geographic areas, Prefectures in the first case and Cantons in the second, until 29 percent of the population was covered. This 29 percent threshold selected to cover the same percentage as the two-step approach that combined the relative wealth index with CDR data.

The Meta relative wealth index, however, is a measure of asset wealth rather than a measure of household-size adjusted consumption. This raises the question of whether targeting could be further improved if the 100 poorest cantons were identified using small area estimates of poverty, derived from combining survey and geospatial auxiliary data along the lines of the studies discussed above, instead of small area estimates of wealth. The paper does not address this question directly, because Canton-level poverty estimates derived from
13 percent among men). **Table 2** presents sample statistics pertaining to key labor market outcomes of Nepali men and women (further details provided in **<u>Table A.7, Annex A</u>** <u>).</u> Most male respondents report having worked in the past 7 and 30 days prior to the survey. On the other hand, over half of all women indicate they have not worked in the past 30 days or in the week prior to the survey (57 percent). Almost 40 percent of women are not working nor looking for work.

The main reasons for not working in the past 30 days among women include household chores, lack of trustworthy childcare options, and lack of suitable jobs, with the latter being the most common reason for not working among men. Among non-working respondents, while 59 percent of men report having worked at some point in the past (most of them in the construction sector), only 17 percent of women report the same (most of those worked in the agriculture, forestry, and fishing sector). Among non- working women

> 24 A third of all working age individuals (15 years and above) in Nepal are illiterate, and 27 percent have up to basic education (Nepal Labor Force Survey, 2017/2018).

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economic impacts.

The findings have implications for policies and interventions aimed at disaster risk reduction and resilience building. For such policies to be effective, it is important to recognize the risks from less salient and underreported adverse events and offer ways to insure households’ livelihoods against their impacts. The findings also have implications for research and measurement. Research and analysis using aggregate global data such as EM-DAT are likely missing some disaster impacts in poorer countries and among poorer people. This concerns also tracking progress towards the SDGs, in particular SDG target 1.5, which seeks to reduce the vulnerability of the poor to extreme weather events, and SDG target 13.1, which seeks to strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.<sup>15</sup> Survey micro-data such as the LSMS-ISA have more limited country and temporal coverage. Inventory and survey data offer complementary perspectives on disaster impacts and combining both sources will likely yield a more complete and nuanced understanding of the issue that will promote more effective policy design. In particular, information on disaster impacts from survey micro-data could be incorporated systematically into disaster inventories. Improving micro-data systems is key to systematically utilizing micro-data for monitoring and reporting of emergency events. More flexible and higher frequency data collection is needed to provide better temporal coverage and account for disaster impacts when they occur. Phone surveys, which have only recently become more widely adopted in lowincome settings, may provide this function, for instance as part of mixed-mode survey systems that combine traditional in-person surveys with data collection over the phone.<sup>49</sup> Integration of geospatial data can improve spatial coverage and facilitate better identification of natural hazards and disaster occurrence.<sup>50,51</sup> Sampling protocols of household surveys can be optimized to better capture disaster impacts and greater harmonization of survey methods and measurement instruments in line with best practices can benefit data quality.

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years ago between 20 and 100; being large-sized, with the number of employees three years ago exceeding 100); two firm age indicators (firm age of 6–10 years; firm age of 11<sup>+</sup> years); being an exporter; being a foreign firm; and the share of foreign ownership. We also include indicators of competition faced by a firm.<sup>12</sup> In the ES data set, firms are asked the number of competitors they face. Based on their responses, we construct two indicators of competition: the firm facing one competitor in its market, and the firm facing more than one competitor in its market, with the firm facing no competitors as the base group.<sup>13</sup>

> 12 The inclusion of this variable does not affect our main results.

> 13 The survey contains richer information about competition than we utilize here. The number of competitors can be classified as no competitors, one competitor, two to four competitors, and five or more competitors. Moreover, the number of competitors is specific for the types of market in which the firm is involved (mainly with the local market, with the national market, and with the international market). We have experimented with more nuanced classification of the number of competitors, and found that the effect of competition tends not to differ much when the number exceeds one. Moreover, controlling for the dummies for the extent of the market (being in national market or being in international market, relative to the base category of the local market) does not matter for the effect of competition. In our base specification and all main specifications, we do not control for the dummies related to the extent of the market because it is too endogenous—better-performing firms are in larger markets.

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. The remainder were other types of workers on the construction site that were classified by the company as having a low level of qualifications.<sup>12</sup> Following recruitment to the study, we administered a baseline survey in April 2019 to collect information on worker characteristics and personality traits. Baseline data was collected for 391 workers. Of these, 386 were in the final sample that was randomized for the intervention.<sup>13</sup> After the baseline, half of the workers were randomly selected to receive the behavioral activation intervention focused on conscientiousness. All workers selected for the intervention took part.<sup>14</sup>

> 11We excluded 51 workers who worked at night since the data collection was conducted at the site of work and could only be done during the day. 12Based on the total number of workers employed by the company before the training across all skill levels, about 40% of workers were part of the experiment.

> 13Five workers were removed from the study because they did not speak Wolof, which was the language used for the intervention.

> 14It is possible that there were spill-over effects between the treatment and control groups (given that workers were assigned to different locations on the construction site depending on the company’s needs, it was not possible to cluster randomize by work location). This, however, would bias the results against finding

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7 foreign investment is determined not only by the cost of labor, but also by a combination of factors including the investment climate of the country, favorable exchange rates, stable macroeconomic conditions, sound infrastructure, a sophisticated banking system and a healthy relationship between workers and management.

A thorough and comprehensive understanding of women workers in Indonesia is limited by the paucity and limitations of research in this area. This study undertaken by CSIS, in cooperation with the Department of Labor and the World Bank, attempts to rectify the situation by undertaking a study with a relatively large sample, and including a broad perspective of the situation. The survey was done between August 1995 and March 1996 and the purpose of the survey was to identify the problems faced by women workers and to indicate where attention should be focused to rectify identified problems. It is important to get an accurate picture in order to recommend policy changes, ensure effective implementation of policy and to introduce broader institutional changes.

This study was undertaken based on a survey of women workers who were asked to respond to questions revolving around the following general themes:

1. Are women workers receiving the legal minimum wage?

2. Are firms complying with Government of Indonesia labor legislation, standards and health and safety provisions for the protection of women workers ?

3. Are women workers aware of the legal Indonesian standards of working conditions, wages and benefits ?

4. Does the presence of the official labor union (SPSI) in a workplace make a difference in the implementation of the labor standards, working conditions, health and safety procedures, wages and benefits, to which women workers are entitled?

5. What are the links between women's labor market mobility in the Jakarta-West Java region, and poverty alleviation in rural and/or urban areas?

The remainder of the paper is divided into five sections. The next two sections analyze the issues faced by women workers, the growth of labor intensive industries and the labor legislation and institutional situation for women. In the fourth section a description of