rafmacalaba/data-use-mentions-extended huggingface dataset:
.86)|1,422|
|Durable Gifts (USD PPP)|12.58**|(6.08)|[1.00]|5.61|(37.30)|1,422|

**_Notes_** :

- The table shows the regression results on pre-specified consumption and expenditures using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Total Consumption is log of the sum of food consumed in last seven days (Cereals and cereal products, Live animals, meat, and other parts of slaughtered land animals; Fish and other seafood; Milk, other dairy products, and eggs; Oils and fats; Fruits and nuts; Vegetables, tubers, pulses; Sugar and desserts; Ready-made food and other food products (baby food, spices)) multiplied by number of months consumed in last 12 months, home-produced goods multiplied by months produced, annualized food received as gifts and annualized non-food purchases (utilities; water; infant needs; household and hygiene items; linens; clothing and footwear; basic household items; and school costs). Food Gifts and Assistance are annualized by multiplying the monthly estimate by 12 since total value of food consumed that was received as gift (from friends, neighbors) or in-kind assistance is asked in the past 30 days. Annual Own Food Production is defined as the food that the household grew or produced and consume in a typical week multiplied by number of months in which the household consumed food that the household grew or produced. Overall Food Consumption Index is defined a mean effects index of the sum of the aforementioned nine food types, home-produced goods, and assistance last week and then in a typical week. The index is standardized by subtracting the control group’s mean and dividing by its standard deviation. Food Diversity is defined as the number of food categories consumed during the past 12 months. Reduced Coping Strategy Index is a measure of food insecurity and is defined
health outcomes: a lower proportion of households participating in WWS reported reducing their food intake (quantity and frequency), or reducing doctor visits (preventive and during illness) than households in the control group. This further confirms that the WWS program acts as an important safety net for WWS beneficiary households.<sup>27</sup>

# **5 Conclusion**

Latvia was one one of the hardest hit countries during the 2008-2010 global financial crisis. GDP contracted by 18 percent in 2009; poverty rates increased by about 8.0 percentage points in 2009; and in 2010 Q1, the unemployment rate was almost triple the pre-crisis unemployment rate. To mitigate the impact of the crisis on vulnerable households, the Government of Latvia established an emergency public works program targeting registered unemployed people who were not receiving unemployment benefits. Between 2009 and end2011, the WWS program created over 110,000 temporary jobs. This paper assesses the targeting performance and welfare impacts of the WWS program using a unique household survey collected between December 2010 and March 2011.

We find that the WWS program was successful at targeting poor and vulnerable people, and leakage to non-poor households was small. The relatively low stipend and labor-intensive works helped ration the program and deterred non-poor people from participating. To measure the WWS program impact, this paper exploits excess demand for WWS to construct a counterfactual group. Using a propensity score matching (PSM) method, we find that the WWS program raised short-term household incomes by 37 percent relative to similar households not benefiting from the program. The WWS program increased household income by LVL 67 while the actual WWS payment was LVL 100 per month. Thus, participants forego some income (about LVL 33) to participate in WWS; some of this foregone income is

> 27Households reported that the WWS program is useful as a safety net and as a program to uplift the local community. Most participants view the WWS program as an important safety net, and 96 percent believe that WWS projects are beneficial to the community.

17
1 of the ES COVID-19 Follow-up Surveys), as well as support received later (during the second half of 2020 or early 2021, as reported in Round 2 of the surveys). While initial support may be effective, if it continues over an extended period, it can also distort markets and firms’ incentives (Pop and Amador 2020; World Bank 2021). Thus, the effects of initial support may differ from the effects of subsequent support.

We analyze data for 15 emerging markets and developing countries in Europe and Central Asia (ECA), to focus on a homogeneous environment. First, we document that 42 percent of firms received government support by Round 1 of the survey, and 23 percent received support between Rounds 1 and 2, giving a cumulative 50 percent of firms receiving support. The support measure reaching by far the largest share of firms was wage subsidies, with 8 out of 10 firms that received any type of support receiving such subsides. The second most wide-reaching measure was cash transfers (less than 4 out of 10 firms), followed by fiscal relief, payment deferrals, and new credit (less than 2 out of 10 firms each).

Then, we ask how receiving government support by Round 1 and Round 2 of the ES COVID-19 Follow-up Surveys affected firm performance in Round 3 of the survey. Here, we examine the relationship between Round 3 performance and support in Rounds 1 and 2 to minimize reverse causality. To reduce omitted variable bias, we control for a host of firm characteristics that may have affected both the probability of receiving support and firm performance.

We find that firms that received government support in Round 1 performed better in terms of Round 3 sales compared to firms that did not receive any support, but only if they did not have continued 1 https://dataviz.worldbank.org/authoring/SME-COVID19/Overview 2
|5 = Highly competitive|||(0.03)|(0.03)|
|---|---|---|---|---|
|State of personal finances|||-0.007|-0.002|
|5 = Very good|||(0.02)|(0.02)|
|Cognitive Reflection Test||||-0.037**|
|3 = High||||(0.02)|
|Clarity of instructions||||-0.019|
|5 = Always clear||||(0.02)|
|Gender||||0.027|
|1 = Female||||(0.04)|
|Age (in years)||||0.016***<br>(0.00)|
|Constant|0.617***|0.666***|0.637***|0.394**|
||(0.04)|(0.04)|(0.13)|(0.17)|
|R-squared|0.015|0.031|0.063|0.088|
|P|0.051|0.004|0.000|0.000|
|Observations|650|650|650|650|

Notes: OLS regressions. The dependent variable is the proportion of total endowment that was spent. * 10%, ** 5%, *** 1% significance level. Clustered standard errors (by group) in parentheses.

Model 1 in Table 3 is the simplest model, controlling for treatment effects, and broadly confirms what we observe in Figure 1 above. When expenditure is observable, but endowments are equal (and hence, expenditure carries no signal of ability), expenditure is
10 evidence that short-term budget loans to local enterprises were partially reflected in 1993-94 as 'bther expenditures" instead of being reported as 'loans" Second, and even more importantly, some remaining forms of government assistance are deliberately excluded from the regional budget documents. The main types of these off-budget transfers are spending from extrabudgetary funds and various tax benefits, granted by local authorities'<sup>2</sup> . In addition, as mentioned earlier, the Russian budget data are reported on a cash basis, and therefore do not reflect the volume of accumulated government payables to local producers with regard to budgeted subsidies. The existing budget statistics provide neither data on accrual budget expenditure nor separate information on the accumulated stock of budget arrears.

(iii) An additional caveat regarding the data has to do with peculiarities of the Russian reporting on the federal budget transfers to regions. The existing reporting system reflects only conventional budget transfers and does not capture various regional benefits allocated through the preferential tax agreements between some of the regions and the Federal Government. As the case of Tatarstan shows, a few regions, which enjoyed a special fiscal regime within the federation, received most of their federal support through such off-budget channels.

All budgetary data used in this paper were taken from Russian Ministry of Finance sources. For all years data were deflated by regional CPIs to remove the influence of the variation in price levels in the different regions of Russia. Note, however, that some variation was already present in the regional price levels by 1991. The absence of a suitable deflator for this year makes it impossible to correct for this variation. But the fact that price controls were, by and large, still in effect in 1991 heavily restricted price variation and renders this a good base year.<sup>13</sup> In 1995, after the introduction of new budget classifications, the term 'hational economy" was no longer in Russian Ministry of Finance usage. The term is used in this analysis in relation to 1995 for
Households in the two highest MIPP quintiles pay most of the taxes calculated in this exercise. In fact, the richest 20% of the population pays close to 95 of the observed income tax. This group also pays more than half of indirect taxes (Figure 8). Notably, the observed low concentration of indirect taxes among the poorest households can be consistent with a high incidence of indirect taxes. Although the poorest 20% pay about 4% of these taxes, their share of MIPP is only 1.4%. In addition, indirect taxes are, on average, equivalent to 16.7% of households’ market income plus pensions.

**Figure 7. Incidence of taxes by quintile of per capita market income plus pensions (% of market income plus pensions)**

<!-- Start of picture text --> 0<br>-10<br>-20<br>-18.3 -18.1<br>-19.9<br>-30 -22.7<br>-40<br>-50<br>-52.0<br>-60<br>1 2 3 4 5<br>Quintiles of per capita market income plus pensions<br>Indirect Taxes Direct Tax IPTU/ITR Direct Taxes Total<br>pensions)<br>Incdence (% of Market Income plus<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: IPTU stands for Imposto sobre a Propriedade Territorial Urbana, while ITR stands for Imposto sobre a Propriedade Territorial Rural. Notes: the bars represent the distribution of payments within each quintile.

**Figure 8. Concentration of taxes (% of fiscal interventions)**

<!-- Start of picture text --> 100<br>
survey waves.

In Uganda, we use the data from the 2009/10, 2010/11, 2011/12, 2013/14, 2015/16, and 2019/20 rounds of the Uganda National Panel Survey (UNPS) (UBOS, 2012; UBOS, 2014a; UBOS, 2014b; UBOS, 2016; UBOS, 2019; UBOS, 2021). The original households were followed for three waves and then starting in 2013/14, a subset of households were cycled out and replaced with new households. This continuous, partial refresh process means that households remain in the sample for different lengths of time, though by 2019/2020 all of the original households have been replaced. The original sample, as well as the refresh, was designed to be representative at the national-, urban/rural- and regional-level. Focusing on rural, crop producing households we have 11,692 observations from 4,003 distinct households across six survey waves.

For the analysis, we combine data from the six countries and all waves to generate a single cross-country panel dataset which includes 54,237 observations from 23,705 distinct households. For estimation, we include two measures of agricultural productivity: yield (kg/ha) of the primary cereal crop and the value (2015 USD/ha) of all seasonal crop productivity on the farm. Summary statistics for these outputs as well as measured input are in Table A3 in the Appendix.

# **3.3 Descriptive Statistics**

Our pre-analysis plan specifies that we will examine 22 different ways to measure precipitation and temperature. These variables and definitions are presented in Table A1 in the Appendix Section 6. For parsimony, we focus all of our analysis in the paper on only four of these 22 variables: (1) total 11
considerable underreporting by recall households of the number of plots cultivated and the number of people farming. Table 7 shows that an average of 1.5 (or roughly 33 percent) fewer household members report they worked in farming in the recall NPS and the recall ALT than in the weekly visit group. The number of plots in recall is underestimated by roughly 35 percent, or 1.6 plots. As was the case in hours, the number of people and plots reported as active in agricultural labor is essentially identical between the two weekly survey designs and between the two recall designs. In section 4.2, we investigate which people and which plots may be systematically underreported in recall surveys and why.

Returning to table 3, we can see that the significant difference in the number of active workers between recall-surveyed and weekly surveyed households plays out in an important way in aggregating labor at each stage. If hours are aggregated to either the person or the plot, the total season hours remain higher in recall than in the weekly surveys, but the gap in person-plot hours 17 Additional comparisons can be made with our survey experiment data and the data from the three waves of the Tanzanian NPS. This is a national panel survey in which sampled households are interviewed once during each survey wave (randomly across 12 months). We can compare the NPS with our weekly data because, in each NPS interview, members were asked if they worked in agriculture, livestock, or fisheries in the previous seven days. This is a broader set of activities than the set here, which is restricted to time spent on the plot. For the NPS subsample of rural households in or near the Mara Region, both participation and hours are significantly higher in the NPS than in our weekly data (results not reported). Hours conditional on working are closer: approximately 26 to 20 hours for the NPS and our weekly data, respectively. This suggests that the respondents in the NPS are interpreting the question not literally about hours in the previous seven
users or, in more technical jargon, the price elasticity of demand. Traffic engineers have developed very sophisticated tools for estimating these elasticities, even when users have never confronted real tolls for the use of the infrastructure. However, these estimates may not be very reliable, not just because of data problems or the inference difficulties encountered with such estimates. There may be some cases where a toll road presents a strategic opportunity for some organised groups.<sup>24</sup> In Chile, of the projects currently in full service, the one that has faced the most difficulties is the El Melon Tunnel, partly because in this particular case there is a very good substitute. The tunnel has to compete with the old route that goes over the mountain, and which increases travel time by about thirty to ninety minutes depending on the vehicle's size and weight. Due to the tendering 22 In the case of Chile, EFG were the first to recognise the importance of uncertainty-especially traffic risk-for private toll roads. Some of their ideas are presented in this and the next sections. However, readers are encouraged to consult their articles (see also Tirole (1997)) for a fuller account of some of these topics.

23 This summary and data from the Mexican experience are taken from G6mez-Ibafiez (1999). See also G6mezIbafiez y Meyer (1993) for an analysis of other world experiences, including the early Mexican experience.

24 Whereas a bus or trucking lobby may have reacted passively to an increase in fuel prices in the past-perhaps because it was perceived that they had no power to influence that decision-they may have more negotiating power to pressure a concessionaire to lower the toll of a newly constructed road. Their power resides in their ability to boycott the road if there are good free alternative routes.

27
of wholesale textile trade in Rio, and foreigners soon dominated the manufacturing activities closely linked to their commercial activities. In both S ̃ao Paulo and Rio, the first electricity generating companies were founded by foreigners. Birchal (1999) documents that Juis de Fora, Minas Gerais, a major steel and manufacturing area from 1858-1912, immigrants were responsible for 66% of industries (Birchal, 1999, p.26).

In Chile, 70% of steam powered businesses were started by immigrants, roughly 12 times their share in the male population. Again, this conforms with other historical accounts. Silva Vargas (1977a) notes that “the lack of entrepreneurs and of qualified national workers

> 6We are very grateful to Richard Sutch for the census sample data and to he and Larry Neal for extremely helpful discussions.

7
# **<u>Annex: Rethinking Power Sector Reform Indices</u>**

The ‘Rethinking Power Sector Reform’ study employs various indices to measures reforms. As the main global index, a simple Power Sector Reform Index aggregates four dimensions of power sector reform consistent with the standard package of reforms prescribed by international donors in the 1990s. These dimensions are: restructuring (vertical and horizontal unbundling of power utilities); private sector participation; creation of an independent regulator; and competition in power generation. The index gives each country a score from 0 to 100 on each of the above four dimensions of power sector reform. The scores are based on giving equal weight to each step on each dimension of the reform continuum (see tabulation below). The average of the four 0-100 scores is used to provide an overall summary of the extent of reform. _The score of 100 for the reform index is not intended to signify ‘good practice’ but merely alignment with the 1990s model._

## **A. Global reform index**

<!-- Start of picture text --> Regulation No regulator Regulator<br>= 0 = 100<br>Restructuring Vertically integrated Partial vertical Full vertical Vertical & horizontal<br>unbundling unbundling unbundling = 100<br>= 0<br>= 33 = 67<br>Competition Monopoly IPPs Single Buyer Bilateral Competitive<br>Model Contracts market<br>= 0 = 25<br>= 50 = 75 = 100<br>Private Sector 0.5*(Percentage of generation capacity with private sector participation)<br>Participation<br>+<br>0.5*(Percentage of distribution utilities with private sector participation)<br><!-- End of picture text --> Separately, a set of distinct indices are used to measure regulatory and governance frameworks in the power sector. The scores for Vietnam and a selection of other case study countries, are presented below
-2-

Determinants of private investment have been studied more extensively for developed than developing countries, but even there it remains a relatively under-researched field.<sup>3</sup> The available studies stress different factors in explaining private investment behavior; but most indicate as the more important determinants of private investment (a) expected demand, typically proxied by sales or output (Kuh (1971), Jorgensen and Siebert (l16M) and measured indirectly as profits or cash flow (Elliott (1973), Bischoff (1971); (b) cost of capital relative to wages (Bischoff (1969), Eisner (1970)); and (c) the level of capacity utilization (Feldstein and Foot (1971) and Eisner (1972)). A more recent survey by Chirinko (1986) shows that the differences in results from various neoclassical investment models can largely be traced to differences in assumptions about the dynamics of investment behavior.4 Studies of private investment behavior in developing countries are scarce. Some have attempted to adapt neoclassical models of investment to study private investment behavior in individual developing countries (Conway (1987), Sundarajan and Takur (1980), Tun Wai and Wong (1982) and van Wijnbergen (1982)). A recent, more wide ranging s;udy by Blejer and Khan (1984) has attempted a more explicit analysis of the impact of government policies on private investment for 24 developing countries, with data pooled over the period 1971-79. They attempt to incorporate variables which could

- For surveys see Eisner and Strotz (1963), Jorgensen (1971) and Nickell (1978).

- i/ One class of models, based on among others Eisner's work, stresses the importance of expectations, whereas the studies iaspired by Jorgensen's work stress intertemporal aspects of technology.
One of the main findings is that the prevalence of non‐functional water and sanitation facilities is a significant issue. More information is necessary to establish what the secondary sources are, and if they are adequate for effective health service delivery. There is also a need for more comprehensive data to be collected not just on where arsenic testing has been carried out, but what the results were and whether proper treatment was administered to mitigate the issue before usage.

In relation to our health indicator for human development, the degree of overlap between upazilas with high levels of stunting and low levels of WASH is remarkably high and calls for a closer look at the determinants of stunting in those regions.

Despite low income levels, the west of Bangladesh had relatively higher levels of WASH in its community clinics. As noted in the above sections, there are two main caveats to keep in mind. One, that the index is a relative measure and a perfect score of 1 is by no means indicative of adequate WASH. Two, it is worth entertaining the possibility that there could have been measurement errors in the collection of data for the rapid assessment. If that is the case, we can expect to see over‐ or under‐estimated rates of WASH at community clinics.

While the lowest income areas are not necessarily the ones where WASH is the poorest, it is worth noting that the Union Parishad’s limited revenues, particularly own resource mobilization, will most probably be a constraint to achieving adequate level of WASH by the SDG standards. Therefore, the local governments who are accountable for the improvement of community clinics need support at the national level through dedicated budget accounts. This pattern where WASH levels are higher in poorer regions could also reflect that more Bangladeshis in poorer regions use community clinics whereas those who live in richer regions bypass community clinics and may prefer to use private health services or hospitals even for the first line of care, causing greater neglect of community clinics in richer regions. If this happens to be the case,
br>Other manuf. Industries|Fishing, forestry and logging|2.01%|
|0.23%<br>Wood|Non-metallic minerals|1.34%|
|0.20%<br>Paper|Wood|0.64%|
|0.16%<br>Non-metallic minerals|Paper|0.58%|
|**Pakistan**|**Sri La**|**nka**|
|56.65%<br>Textiles and apparel|Textiles and apparel|33.81%|
|18.09%<br>Food, beverages and tobacco|Agriculture and mining|20.89%|
|6.86%<br>Agriculture and mining|Chemicals|18.05%|
|6.61%<br>Chemicals|Food, beverages and tobacco|12.80%|
|4.46%<br>Fabricated metals|Fabricated metals|6.88%|
|2.92%<br>Non-metallic minerals|Paper|2.24%|
|1.87%<br>Fishing, forestry and logging|Fishing, forestry and logging|1.59%|
|1.30%<br>Basic metal industries|Other manuf. Industries|1.28%|
|0.93%<br>Paper|Wood|0.99%|
|0.65%<br>Wood|Non-metallic minerals|0.77%|
|-0.35%<br>Other manuf. Industries|Basic metal industries|0.69%|

Source: Artuc et al., (2019)

# Labor Market Data and Characteristics

Our main source of labor market data is the labor force survey (LFS) provided by Bangladesh Bureau of Statistics. Bangladeshi labor force surveys are cross-sectional containing detailed household and individual information about key labor market characteristics, household characteristics, and individual demographic characteristics with units of analysis