rafmacalaba/datause-encoder-data huggingface dataset:
12 <!-- Start of picture text --> Heat Gasifier Retrofit Economics<br>Heat Gasifiers vs Fuel Oil Boiler Costs<br>Cost of Steam (S/GJ) Fuel Oil Price ($/bbl)<br>a Economic Zone1<br>6 30<br>2- - Non-Economic Zone -to<br>0 10 20 30 40 50 60<br>Wood Price ($/tonne)<br>1 7 = z ~~~~GASIFEER cosTsI<br>| 115.000 per GJ/hr '-- $25.000 per GJ/br --- M35000 per GJ/br|<br>Source: See Annex<br><!-- End of picture text -->

Figure 4 Heat Gasifiers vs. Fuel Oil Boiler Costs 36. As with fuel alcohol programs above, the primary economic benefits of heat gasifiers accrue from savings in imported petroleum fuels. Given the wide range of possible heat gasifier applications, it is difficult to generalize on the economics of these systems. Nonetheless, a brief cost analysis of a generic heat gasifier retrofit to an oil-fired boiler illustrates the sensitivity of this technology to lower oil prices. Figure 4 shows the economic sensitivity of such a system to changes in oil prices, wood fuel prices, and gasifier capital cost estimates.'/ For a drop in fuel oil price from US$ 30/bbl to US$ 15/bbl411 the break-even fuelwood price for a moderately priced heat gasifier system (US$ 25,000/GJ/hr) drops from US$ 30/tonne to near zero.

37. As the market for large scale heat gasifiers is generally in populated urban or rural areas, where fuel oil is commonly available and alternate demands for wood fuels exist, the fuelwood price is usually above US$ 20/tonne. Moreover, as large scale heat gasifiers tend to be fully mechanized, they commonly have capital costs at the high end of the range shown in Figure 4. Conversely, small scale heat gcsifier systems such as those used for tea and copra drying, are usually located in more remote areas where the economic cost of fuel oil is relatively high while the Source: **Foley,** G. and Barnard, G., Biomass Gasification in Deeloping Countries, Eanhscan, 1983. Gasifier **cost** estimates summarized in **Annex.** 14/ Avert **border prices** for fuel oil dropped from S30435/bbl in November 1985 to **S10-SlS/bbl** in July 1986 for **countries with** or **near a por.**
|||(0.030)|(0.043)|
|---|---|---|---|
|Commerce sector dummy*Treatment|||-0.073<br>(0.133)|
|Services sector dummy*Treatment|||-0.075<br>(0.120)|
|Full-time paid employees*Treatment|||-0.006**<br>(0.002)|
|Age of principal decision maker (years)*Treatment|||0.001<br>(0.005)|
|Male principal decision maker dummy*Treatment|||0.146<br>(0.110)|
|Business age (years)*Treatment|||0.005<br>(0.004)|
|Log (Avg. sales)*Treatment|||0.008<br>(0.035)|
|Profits*Treatment|||-0.000|
||||(0.000)|
|Return on assets (ROA)*Treatment|||0.010<br>(0.057)|
|Constant|0.567***|0.329***|0.332**|
||(0.030)|(0.121)|(0.149)|
|R-squared|0.000|0.113|0.144|
|N|432|427|427|
|F-test p-value: joint significance of interaction terms|||0.122|
|Average of dependent variable|0.574|0.569|0.569|

Note: All explanatory variables are measured at baseline. Binary control variables included for when covariate is missing, and then missing covariate coded as zero. Variables with *Treatment at the end are interacted with a treatment group dummy. Robust standard errors in parentheses. Significance levels: *10 percent, **5 percent, ***1 percent.

Appendix Table 3: Number of Enterprises Surveyed Each Month (Follow-Up Survey)

|Surveymonth|# treatment|% treatment|# control|% control|
|---|---|---|---|---|
|Mar-09|95|70.37|152|62.55|
|Apr-09|29|21.48|68|27.98|
|May-09|10|7.41|20|8.23|
|Jun-09|1|0.74|3|1.23|

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<!-- Start of picture text --> Submission period Procedure type<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>S= QS2SztSaadS2S=S2S2S=SQS2SztS2S2S=S2S2S<br>NN N N N N N N N N N N N N N N N N<br>Decision period Single bidding<br>1.0 0.5<br>0. 86 ——— oa<br>0.3<br>0.4<br>ala N ise) vt 2) © N ce for] 0.2 TM N ise) st wD © N v0) fo>)<br>S 7“S iadS -S 7S -S >S éS éS S >S éS aS S S aS 3S 6S<br>N N N N N N N N N N N N N N N N N N<br>No call for tender Buyer-level market shares of suppliers per year<br>0.5 0.25<br>0.4 0.20<br>0.30.2 0.150.10 i eee<br>0.1 0.05<br>0.0 0.00<br>=22=22=22=x2x22=22<br>SS S S S S S S S S S S S S S S S S<br>N N N N N N N N N N N N N N N N N N<br><!-- End of picture text -->
# **4.4 Measurement of child costs**

The modeling framework outlined in Section 4.2 can be applied to the analysis of the costs of children. The standard literature on measuring child costs (see Browning 1992, for a survey) is based on the idea that the cost of a child is given by the compensating variation in income required to restore the parent’s utility level<sup>40</sup> to that she would enjoy in the absence of the child. The glaring deficiencies of this approach have been well criticized by Pollak and Wales (1979). One problem is econometric: costs thus defined cannot be estimated from the usual household income and expenditure datasets. The other is conceptual: if a household has children, it must be because they increase utility, therefore the compensating variation must be negative.

In Apps and Rees (2002) the exchange model in (23) to (26) is extended to include children and developed as an alternative approach. The cost of a child is defined as the value of consumption of market and of domestically produced goods, especially child care produced with parental time, that the child receives at the household equilibrium, as well as of any increment in amounts of household public goods (housing space, heat, light) the child occasions. We are also interested in the incidence of these costs on the respective parents. The data from time use studies show that the advent of a child leads to a larger fall in the leisure time of the mother than of the father and, in the model we specify, this implies that the income share of the mother also falls by more, which in turn implies a heavier incidence of child costs on the mother than on the father. The key element in the approach is the evaluation of the effects of the arrival of children on the equilibrium allocation of the multi-person household with domestic production as well as on outside labor market opportunities.

The procedure adopted for estimation is as follows. Two samples of households are selected from the ABS 1993 Time Use Survey (TUS), one containing families with two children and the other, those who have not yet had children. Following Gronau (1991),

> 40 The underlying utility and expenditure function framework is that of a single person household - see for example Deaton and Muellbauer (1980).

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# **International Trade and Inclusive Growth: A Primer for Busy Policy Analysts**

Daniel Lederman<sup>1</sup> Lead Economist, PRMTR Keywords: Trade, firms, workers, households, inclusive growth JEL codes: F10, F13, F15, F16 1 This paper was partially funded by the multi-donor Diagnostic Facility for Shared Growth (DFSG), established to support the development and dissemination of methodological tools and approaches to better determine the binding constraints to shared (inclusive) growth in different country contexts. The findings reflect the views of the author, and do not represent the views of the World Bank or any of the countries contributing to the DFSG. The author gratefully acknowledges comments on earlier drafts of this note by Bernard Hoekman, Jennifer Keller, and Sebastian Saez, as well as from participants in the DFSG Inclusive Growth Conference held in Nairobi, Kenya, September 2011.
in the literature on the best indicator to measure droughts (Trenberth et al., 2014) and weather shocks, in general.<sup>7</sup> Our paper also contributes to this literature by showing that temperature and NDVI are likely more appropriate drought measures in arid and semi-arid climatic conditions over rainfall-based measures, such as in Afghanistan.<sup>8</sup>

# **3 Data**

## **3.1 Household Data**

We use the 2019-2020 Integrated Expenditure and Labor Force Survey (IE-LFS) collected by the National Statistics Information Authority (NSIA) of Afghanistan, with the assistance of international organizations. This nationally representative household survey is the fifth in the series, a repeated cross-section collected as a follow-up to the Afghanistan Livelihoods Conditions Survey (ALCS) conducted in 2007-08 2011-12, 2013-14, and 201617, supported by the World Bank and other international organizations.<sup>9</sup> There are various advantages of using this survey. First, this survey contains geo-tagged locations of surveyed households, which we leverage to combine with the satellite-based remote sensing data on weather and vegetation for the given region. Second, this survey collects detailed information on (i) household demographics, (ii) household’s asset holdings and access to basic facilities, (iii) labor market participation of every individual in the household, and (iv) consumption expenditure. This survey is nationally and provincially representative and is used to compute welfare measures, including poverty estimates. The results are intended to inform the government’s and development partners’ policy making.

Unit non-response in IE-LFS arises from security concerns, and road access led to undersampling in some provinces. The final sample was below planned coverage (<70 percent) in three provinces (Faryab, Badghis, and Kapisa). This under-sampling can be a potential source of bias. Various checks, including comparisons with the 2016-17 survey data and sensitivity analysis to see if poverty trends are dramatically different in under-sampled and other provinces, find little cause for concern.

A second issue arises from poorly identified geographical locations for 19 percent of the

> 7For a comprehensive review of data sources and weather indicators (satellite and non-satellite) used in economic development literature, please refer to Fisker et al. (2021); Dell et al. (2014).

> 8While land surface temperatures are shown to have a negative relationship with NDVI, Rousta et al. (2020) shows that lagged precipitation deficits (or surpluses), soil moisture, and other factors also affect NDVI.

> 9The 2007-08 survey was called the National Risk and Vulnerability Assessment (NRVA).

6
Agriculture share of total wealth <!-- Start of picture text --> Kyrgyz Rep<br><!-- End of picture text --> <!-- Start of picture text --> 40.9% TFP growth with natural resource 2.6%<br>TFP growth excluding natural resource 2.6%<br><!-- End of picture text --> <!-- Start of picture text --> Kyrgyz Republic: Total Factor Productivity<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐2.00%<br>‐4.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text -->

# _TFP growth in countries with high dependence on petroleum_

Turning to the petroleum producers, Figure 10, the general picture is of extreme volatility in TFP growth measures, which is unsurprising considering the boom and bust nature of the petroleum market. We present the top three most petroleum-dependent economies in our database. The case of **Iraq** is particularly extreme given the advent of the Iraq War in 2003, which led to a steep decline in economic activity followed by a sharp recovery. In **Kuwait** TFP growth is negative on average, but growth measured with natural resources is generally higher (or less negative), suggesting that growth in fixed capital is higher than growth in petroleum extraction. In **Saudi Arabia** TFP growth is highly volatile and almost trendless, as the low estimates of average TFP growth over 1996 to 2014 indicate.

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# **Interest Rate Pass-Through:**

# **_A Meta-Analysis of the Literature_**<sup>**_*_**</sup>

Jiří Gregor<sup>a</sup> , Aleš Melecký<sup>a</sup> , Martin Melecký<sup>b</sup>

**Keywords:** Interest Rate Pass-Through, Literature Survey, Monetary Policy Rates, Money Market Rates, Bank Lending Rates, Meta-Analysis.

**JEL Classification:** E4, E5, C23

> * We thank Martin Cihak, Erik Feyen, Alfonso Garcia Mora, Mario Guadamillas, and the participants of the World Bank Finance, Competitiveness and Innovation Seminar for useful comments and suggestions on earlier drafts of this paper. Financial support from the Czech Science Foundation “16-13784S – Financial sector policy and institutions: Current challenges in balancing financial development and stability” is gratefully acknowledged.

> a Department of Economics, VSB-Technical University of Ostrava, Czech Republic.

> b Finance, Competitiveness, and Innovation Global Practice, World Bank, Washington DC, USA.
# **D. F Heterogeneous Effects**

<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure D.6. Parallel Trend Assumption – Mayoral Elections<br>Continuous Treatment Variable – Tercile 1<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.01 .02<br>.01<br>.005<br>0<br>0<br> (95% CI) (95% CI)<br>β β -.01<br>-.005<br>-.02<br>-.01 -.03<br>.04 .04<br>.02<br>.02<br>0<br>0<br> (95% CI) (95% CI)<br>β -.02 β<br>-.02<br>-.04<br>-.06 -.04<br>.04<br>.02<br>0<br> (95% CI)<br>β<br>-.02<br>-.04<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation for the tercile 1 of the PEP holders intensity. We interacted the standardized values of the variable PEP holders with mayoral electoral years excluding the election of 2015, the year the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

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# **Using Satellite Imagery and a Farmer Registry to Assess Agricultural Support in Conflict Settings: The Case of the Producer Support Grant Program in Ukraine**

# **1. Introduction**

The Russian Federation’s invasion of Ukraine in February 2022 started a war that has to date displaced more than 8 million individuals, destroyed the productive capacity and infrastructure of vast stretches of territory, and resulted in marked decreases in agricultural productivity. To help small and medium-size farmers, who are most likely to be credit constrained, to cope with this shock, Ukraine’s Ministry of Agriculture established a digital registry platform, the State Agrarian Register (SAR) in August 2022 with the goal of implementing support programs, including an EU-supported US$ 50 million Producer Support Grant (PSG) and improve farmers’ ability to access markets. We use the SAR’s link to administrative data from the registry of property rights and the cadaster at parcel-level to assess the targeting and impact of this program, compare results to those from a phone survey, and draw out implications for future research.

At the start of the war, most of the 2021 harvest was still in storage but traditional export routes had been blocked, requiring development of logistics through alternative and more costly overland routes. Similarly, fertilizer imports from Russia were cut off. Once immediate military challenges had been fought off, an immediate challenge was to secure market access and essential support (with reconstruction, generators, essential inputs, grain drying facilities) to many farmers in a setting with restrictions on physical movement, limited capacity, and a tradition of weak governance. While power supplies and cyber-attacks posed challenges and many doubted farmers who traditionally remained in informality to avoid taxes in a cashbased economy would be willing to abandon their anonymity, a digital registry platform emerged as the most viable solution and the SAR, interoperable with registry and cadaster, was launched by August 2022.

The PSG, which provided a cash grant to small farmers (below 120 ha) proportional to the amount of land cultivated in 2022, was one of the first programs using the SAR. Although it provides interesting qualitative insights, high rates of non-response and attrition reduced the representativeness and power of a phone survey. Combining administrative data with use of the universe of eligible SAR registrants thus provides an alternative way to explore PSG targeting and impact. As resources were made available on a first come, first served basis, analysis of impacts on area cultivated is more challenging methodologically. We use the pool of farmers whose digital application was lodged too late to be processed before PSG resources had been exhausted as a control group and use weights from entropy matching to establish comparability in observables between treatment and control. Parallel pre-trends cannot be rejected and insignificant coefficients from a placebo test allay concerns about differences in unobservables driving our results.

2
In Figure 21, we plot the proportion of employment in financially developed states in each bin (EM-developed) and the proportion of employment in financially underdeveloped states in each bin (EM-Underdeveloped). We find marginal differences between financially developed versus underdeveloped states. Across both categories of states, the 5-9 aged firms have the highest proportion of employment after which it declines. So older plants have smaller employment shares though the oldest age group of 40+ have higher employment shares than middle-aged plants between 20-39 years of age Next, for each age bin in financially developed states we take the ratio of total number of employees to total number of establishments to give us the intensive margin (IM-developed) and repeat the same for the underdeveloped states to obtain IM-Underdeveloped. While older firms have higher employment/ establishment ratio, we don’t find significant differences across financially developed versus underdeveloped states. All our results hold if we were to take sampling weights into account and examine the intensive and extensive margins in the population.

# **_D. Informal Manufacturing Sector_**

In this section we study the size-age gap in the informal manufacturing sector in India. In a recent paper using World Bank Enterprise survey data for the informal sector, La Porta and Shleifer (2008) show that informal firms look very different from formal firms in terms of size, productivity, and education level of managers and find little evidence that growth occurs by informal firms eventually becoming large formal establishments. They interpret their findings as being consistent with the _dual economy_ view (e.g. Harris and Todaro, 1970) which predicts that in developing countries, in addition to the formal sector, there is an informal sector that employs a large section of the labor force.

36
frequency data can supplement infrequent HBSs to help identify rapid political and social-economic changes. For example, Lebanon and the West Bank and Gaza could benefit from this type of data collection given the high frequency of shocks people in each country face.

# **Public Opinion Surveys (Gallup, Arab Barometer, World Values surveys)**

# <u>Method</u>

Some firms and organizations perform household surveys at a higher frequency than national statistical offices. The Gallup World Poll (GWP) conducts surveys every year in nearly every country, for example. Importantly, this includes surveys in some very challenging settings, such as Yemen. These surveys can supplement traditional household surveys to better understand how welfare is evolving in data-scarce environments.

Each type of organization has its own sampling strategy in each individual country and year. For example, the GWP outsources sampling and actual surveying to survey firms in each country. Depending on the capacity of the firm and the security situation, the surveys can be either face-to-face or conducted via mobile phone. In each year, the GWP surveys 1,000 nationally representative households. Importantly, each survey is comparable across countries and can be particularly useful for region-wide analysis.

One problem with public opinion surveys is that they are short, with questions typically focusing on subjective welfare and household opinions. However, each survey collects information on employment and access to food and shelter, all of which strongly correlate with poverty. We can use these data to identify welfare trends in a country over time, even in settings with no traditional welfare metrics.

# <u>Data requirements and pitfalls</u>

There are three primary drawbacks to using public opinion surveys as a source of welfare information. First, similar to phone surveys, one cannot construct traditional welfare metrics, such as poverty. Furthermore, the survey collects very little information on household characteristics, location, and living conditions. Given these important gaps, these surveys are best used to supplement household surveys. That said, in countries with little-to-no information, these surveys can provide at least some welfare information to policy makers and researchers.

Second, given the lack of uniform sampling both across countries and within a country over time, it is difficult to identify how representative each survey is of the entire population. Related to this, the quality of the survey firms subcontracted to perform the surveys is unknown. Combined, these two factors can make welfare estimates “noisier” (less accurate and varying more) compared to welfare metrics from traditional data sources, and subsequently make it difficult to interpret welfare levels and how they change over time.

20