rafmacalaba/datause-encoder-data huggingface dataset:
then eliminating only the latter bias will increase aggregate bias. While this is possible in theory, replication studies (comparing NX evaluations with experiments for the same programs) do not appear to have found an example in practice; I review lessons from replication studies below.

The variables in _Z_ may well differ from the covariates of outcomes ( _X_ in section 2); this distinction plays an important role in the methods discussed in section 8. But what should be included in _Z_ ?<sup>29</sup> The choice should be based on knowledge about the program and setting, as relevant to understanding the economic, social or political factors influencing program assignment that are correlated with counterfactual outcomes. Qualitative field work can help; for example, the specification choices made in Jalan and Ravallion (2003b) reflected interviews with participants and local administrators in Argentina’s _Trabajar_ program (a combination of workfare and social fund). Similarly Godtland et al. (2004) validated their choice of covariates for participation in an agricultural extension program in Peru through interviews with farmers.

Clearly if the available data do not include a determinant of participation relevant to outcomes then PSM will not have removed the selection bias (in other words it will not be able to reproduce the results of a social experiment). Knowledge of how the specific program works and theoretical considerations on likely behavioral responses can often reveal likely candidates for such an omitted variable. Under certain conditions, bounds can be established to a matching estimator, allowing for an omitted covariate of program placement (Rosenbaum, 1995; for an example see Aakvik, 2001). Later in this chapter we will consider alternative estimators that can be more robust to such an omitted variable (although requiring further assumptions).

Common practice in implementing PSM is to use the predicted values from a logit or probit as the propensity score for each observation in the participant and the non-participant samples, although non-parametric binary-response models can be used; see Heckman et al., (1997).<sup>30</sup> The comparison group can be formed by picking the “nearest neighbor” for each participant, defined as the non-participant that minimizes _P_<sup>ˆ</sup> ( _Zi_ ) − _P_<sup>ˆ</sup> ( _Z j_ ) as long as this does

> 29 For guidance on this and the many other issues that arise when implementing PSM see the useful paper by Caliendo and Kopeinig (2005).

> 30 The participation regression is of interest in its own right, as it provides insights into the targeting performance of the program; see, for example, the discussion in Jalan and Ravallion (2003b).

22
Table 15: Spouse control over harvest revenues

||Amount of total<br>harvest revenue<br>spouse partakes in<br>control of($)|Amount of rubber<br>harvest revenue<br>spouse partakes in<br>control of($)|Proportion of plots<br>for which spouse<br>partakes in income<br>decisions|Proportion of rubber<br>plots for which<br>spouse partakes in<br>income decisions|
|---|---|---|---|---|
|Seedling Subsidy|7.97|0.24*|-0.004|0.010**|
||(15.27)|(0.13)|(0.020)|(0.005)|
|Couples' Training|-8.01|0.15|-0.014|0.007|
||(15.05)|(0.20)|(0.023)|(0.007)|
|Seedling Subsidy + Couples'<br>Training (beta)|-0.04|0.39***|-0.018|0.017***|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|1.00|0.01|0.400|0.006|
|Observations|1435|1435|1435|1435|
|Mean for control group|45.66|0.05|0.102|0.001|
|Mean for individual training|51.74|0.30|0.098|0.011|

Robust and clustered standard errors in parentheses. Revenue amounts winsorized at 99%.

* p<0.1 ** p<0.05 *** p<0.01

Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

What changes does all this imply for the couple? We see no significant shift in either the planter’s or the spouse’s perception of teamwork, or their satisfaction with the relationship (Appendix Table 13). Appendix Table 14 shows a modest increase in the planter’s involvement in housework in the individual training group, specifically in the preparation of food and feeding of children. However, these results disappear when restricting results to households where both the planter and their spouse respond to the question. We see zero changes in housework arrangements among the couples’ training group compared to the control group. Finally, in Appendix Table 15, we look at gender attitudes to see if the changes in production decisions might be related to a shift in gender dynamics. The results are not conclusive. In the couples’ training group, both the planter and the spouse are likely to recognize that housework is really a job, but they are also both more likely to agree that a woman who earns money independently is unruly. All of these effects are fairly small and most are significant at the 10 percent level.

3
# Food Price Spikes, Price Insulation, and Poverty<sup>*****</sup>

Kym Anderson,<sup>†</sup> Maros Ivanic<sup>‡</sup> and Will Martin‡ Keywords: Domestic market insulation, Loss aversion, International price transmission, commodity price stabilization, trade and poverty JEL codes: F14, Q17, Q18, O24 ARD Sector Board

* This paper is a revision of one presented at the NBER conference titled “The Economics of Food Price Volatility” in Seattle, WA, USA on 15–16 August 2012. Particular thanks are due to Signe Nelgen for assistance with the price distortions database. The paper reflects the views of the authors alone and not those of the World Bank or any other organization. Forthcoming in _The Economics of Food Price Volatility_ **,** edited by J.-P. Chavas, D. Hummels and B. Wright, Chicago and London: University of Chicago Press for NBER.

† University of Adelaide and Australian National University, kym.anderson@adelaide.edu.au

> ‡ World Bank, mivanic@worldbank.org and wmartin1@worldbank.org
<!-- Start of picture text --> 2 months - 0 months 6 months - 2 months 6 months - 0 months<br>0.06<br>c<br>2<br>5 0.04<br>fom<br>‘J<br>a 0.02<br>0.00<br>-20000 0 20000 -20000 0 20000 -20000 0 20000<br>Difference in Sales in past month<br>2 months - 0 months 6 months - 2 months 6 months - 0 months<br>0.06<br>c<br>2<br>< 0.04<br>fe)<br>foe<br>‘J<br>A (0.02<br>0.00<br>-10000 0 10000 -10000 0 10000 -10000 0 10000<br>Difference in Profits in past month<br>2 months - 0 months 6 months - 2 months 6 months - 0 months<br>c 0.03<br>2<br>5 0.02<br>roe<br>‘]<br>a 0.01<br>0.00<br>-0.5 0.0 -0.5 0.0 -0.5 0.0<br>Difference in Index of business practices<br>Treatment [ Control [| Treated<br><!-- End of picture text -->
10 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity surface lights. Nighttime lights products have high correlation to human activities (Hsu et al., 2015), and have been previously used for regional and global analysis of population modeling (Anderson et al., 2010; Lo, 2001), economic performance (Cao et al., 2016; Forbes, 2013), and urbanization (Cheng et al., 2016; Pandey et al., 2013; Sutton et al., 2006; Zhang and Seto, 2011; N. Zhou et al., 2015; Zhou et al., 2015).

There are two different nighttime light products from DMSP-OLS that can be used to delineate urban areas: the stable or ordinary product (NTL), and the radiance-calibrated (NTL RC) product. We decided to use the latter, since it is aimed to correct the saturation issue in bright areas such as city centers, where the NTL might be brighter, but the recorded digital number (DN) values are truncated at 63; and the RC product gives better correlations with socioeconomic variables than the stable products (Hsu et al., 2015; Ma et al., 2014). Another known issue of the DMSP-OLS products is the “overglow” effect: dim lighting detected from light in surrounding areas of cities because of the scattering of lights in the atmosphere (Wu et al., 2014). A novel deblurring process was applied to address the issue of over glow in the radiance-calibrated products. This process involves the use of two sequential filters, a standard deconvolution and the frequency of illumination maxima, to withdraw the light from the surroundings back and restacking it vertically on its source pixels at city centers (Abrahams et al., 2016).

Deblurred DSMP-OLS RC annual composites for the years 1996, 2000 and 2010 were previously inter-calibrated and corrected for a multi-temporal analysis of urban form and city productivity in Latin America (Duque et al., 2017). In that work, the three nighttime images were used to delineate urban extents in each year for most of the Latin American and Caribbean cities that had more than 50,000 people in 2010. We used those delineated urban extents for the year 2010 to identify the metropolitan areas in the region. We consider the presence of a metropolitan area when more than one municipality or equivalent administrative unit intersects a single urban extent with more than 500,000 people in 2010. We use the administrative unit boundaries from the World Bank Latin American and the Caribbean Spatial Framework Database (Branson et al., 2016) for this purpose. Metropolitan area boundaries were obtained by aggregation of all of the administrative units that intersected the same urban extent. We verified each obtained metropolitan area with ancillary information from official sources to include those municipalities that are part of the official metropolitan area denomination but were not intersected by the urban extent. Figure 2 presents some
# **Table 12 Implications of Tunisian unilateral and bilateral services trade reform initiatives for consumer and producer prices and real resource costs in Tunisia (percentage deviation from baseline)**

||_Consumer_|_prices_|_Producer_|_prices_<sup>**a**</sup>|_Real resour_|_ce costs_<sup>**a**</sup>|
|---|---|---|---|---|---|---|
||_Domestic_<br>_goods_|_Imports_|_Tunisian_<br>_owned_|_EU-owned_|_Tunisian_<br>_owned_|_EU-owned_|
|Electricity|0.4|0.0|0.4|0.0|0.4|0.0|
|Gas and water|0.3|0.0|0.3|0.0|0.3|0.0|
|Construction|0.3|0.0|0.3|0.3|0.3|0.3|
|Trade|-0.2|0.0|-0.2|-0.2|-0.2|-0.2|
|Other transport|0.4|0.0|0.4|0.4|0.4|0.4|
|Air transport|5.1|-8.1|5.1|5.0|5.1|5.0|
|Communications|-4.8|0.0|-4.5|-5.3|2.0|9.3|
|Other financial services|-1.2|0.0|-1.1|-1.7|1.3|8.1|
|Insurance|0.1|0.0|0.1|0.1|0.1|0.1|
|Other business services|-0.5|-8.4|-0.4|-1.8|-0.4|9.9|
|Other services|0.8|0.0|0.8|0.8|0.8|0.8|

> **a** Producer prices are inclusive of rents from services trade barriers, while real resource costs are not.

_Source_ : FTAP model projections.

42
so we follow previous studies in pre-treating the data in ways that reduce the volatility of the outcome variables. Inherent variability in clearance times and in firms’ monthly import activity generates a high degree of volatility in the outcome variables even in logs. We further smooth the fluctuations in the outcome variables by calculating the 3-month moving average (i.e., the average of the current and previous two months) for each of the outcome variables a)-e) for both treated and control firms. These moving averages are the actual outcome variables we use in the impact evaluation of the IHC program.<sup>35</sup> These variables nonetheless remain more volatile than is common in most SCM studies.

The SCM uses a weighted combination of non-IHC firms to construct a synthetic control firm that resembles in the most relevant economic characteristics the IHC firm before the outset of the IHC program. We rely on the administrative trade data to define the vector of conditioning variables that are used to construct the synthetic control firms. For each firm and month we calculate the share of imports originating in the EU, the share of imports cleared under a special customs clearance code (other than IHC), and the share of imports in each of 10 broad commodity groups or sectors. Appendix Table 1 provides the detailed definitions of the conditioning variables. In particular, note that the use of sector data allows us to account for seasonal patterns in imports across sectors and for supply or demand shocks that would apply to firms trading in the sector. The vector of conditioning variables also includes the relevant outcome variables in specific pre-treatment periods that are discussed below.

The purpose of the conditioning variables is to ensure that the synthetic control firm not only matches the treated firm in terms of the dynamics of the outcome variable, but also matches important characteristics of the treated firm in the pre-treatment period. The share of imports originating in the EU is useful for conditioning the construction of the synthetic control firm if shocks to the outcome variable are related to the origin of the goods that firms import. The sectoral import shares ensure that the synthetic control firm imports broadly the same commodity mix as the treated firm. A special customs clearance variable matches the synthetic control firm to the treated firm on the basis of their exposure to special import regimes. Ideally our vector of conditioning variables would include a larger number of source region and product categories, but we find that the inclusion of larger numbers of conditioning variables generates computational

> 35 Transformation of the outcome variable via a moving average has been done in previous applications of the SCM.  Cunningham and Shah (2014), for example, smooth their state-level crime and public health outcomes using a moving average before they apply the SCM. Use of the moving average further reduces the role of noise arising from fluctuations in the outcome variable.

23
the same, the poor save much less across each age. The poor also tend to display negative saving after the age of 45 until 65, while the non-poor have positive save rates in this period of their lives. This matter a great deal for the weighted average of the aggregate saving rate since a large fraction of the population in Brazil is between 45 and 65. The negative saving rates among the poor may be difficult to explain, but there may be informal credit possibilities for the poor which are not part of the established financial sector (“agiotas”), which may explain the possibly of negative saving.

**Figure 2a. Saving Rates for the Non-Poor Figure 2b. Saving Rates for the Poor are Lower follow LCH Except if Public Pensions are but Displays the Same Pattern as the Non-Poor** **<u>Included</u>**

<!-- Start of picture text --> 60% 100%<br>40%<br>50%<br>20%<br>0%<br>0%<br>0 10 20 30 40 50 60 70 80 90<br>-20% 0 10 20 30 40 50 60 70 80 90 -50%<br>-40% -100%<br>-60%<br>-150%<br>-80%<br>-200%<br>-100%<br>-120% -250%<br>-140% Saving rate, incl. public pensions (non-poor) -300% Saving rate, incl. public pensions (poor)<br>Saving rate, excl. public pensions (non-poor) Saving rate, excl. public pensions (poor)<br><!-- End of picture text --> Source: Author’s estimations based on POF 2008.<sup>5</sup> The simple division of age-specific saving rates across people above and below the poverty line would therefore suggest the saving rate would be positively affected in the future if the share of the population below the poverty line were to falls with its recent trend. In fact, it does not make sense to speculate about future saving behavior without taking income inequality into consideration. Such dynamics are therefore incorporated into the partial equilibrium model employed in the next section.

In 2008, 41 percent of Brazilian families have negative savings, which is 8 percentage points less than in 2002 (Figure 3a). What lies behind this change in saving patterns? Positive savings started at the 74<sup>th</sup> percentile in 2002 and at the 61<sup>st</sup> percentile in 2008. This increase in savings for low income percentiles is mainly the result of the increase in income of the poor. Or put differently, the main driver of the increase in saving rates is the decrease in the inequality of Brazilian income distribution which occurred between 2002 and 2008. Figure 3b supports the argument above by illustrating the share of the population below the poverty line for each age-group.

Clearly, there are fewer poor elderly than young. Since the poor save less than the non-poor, we can expect aging to increase the weighted average of the saving rate — all else equal.<sup>6</sup> Such dynamics are also incorporated into the partial equilibrium model to be presented below.

> 6 As Figure 3b illustrates, the trajectories for the population across different age-groups will display parallel shifts when the poverty headcount is reduced by, for example, by 1 percent.

10
typically goes fishing alone or accompanied by another household member or relative. The beach-landing fiber boat is a relatively new technology, with the first such boats appearing in Tamil Nadu in the mid-1990s. Fiber boats typically have a crew of four, some of which can be relatives of the boat owner and the rest are laborer-fishermen. Every crew member earns a daily minimum wage and a percentage of the value of catches.

On a typical day, boats leave the shore around 1 am and land back at the market place on the beach between 7 and 11 in the morning. There, local fish auctioneers and staff from the fishermen association market the catches to a group of buyers, which comprise local traders as well as agents of nation-wide operating fish-processing companies. Prices are fixed in advance every week depending on the season and variety of fish, so most of the variation in the value of catches on a day to day basis comes from quantity and variety rather than price. Fish that are too small under international legal minimum size standards are sold at the local market at a discount.

Our data come from three different sources. Boat owner characteristics for all 272 boat owners come from a household survey conducted in November 2007. We then fielded our methodological experiment in July 2009 to elicit expectations about August catches. Actual daily catches for August then come from the hand-written records kept by both the fishermen association and the auctioneers. Daily records for five individuals show that they did not go fishing in August, as they decided to work as laborers for large mechanized boats. In addition, we do not have daily catch data for two boat owners because the auctioneer they work for refused to share his records. Two individuals also refused to answer the expectations survey. As a result we have expectations data for 270 boat owners, and realizations of August catches daily for 263 boat owners.

Table 1 provides some basic demographic characteristics of the sample of boat owners. All boat owners are male, with an average age of 41 years. Mean years of schooling is 5.5, with 90 percent having 8 or fewer years of education. The average years of schooling for male adults 25 and over in the developing world in the year 2000 was 6
3. Data sources and stylized facts from the data

# **_Data sources_**

This paper uses monthly series spanning the period from July 1998 to September 2020. The data are obtained from the National Bank of Ethiopia (NBE), except for the import prices which are sourced from World Bank Commodity Price Data (The Pink Sheet) and the Federal Reserve Bank for the U.S. manufacturing producer price index (as a measure of import prices of non-food items other than oil). The fiscal variables are transformed into monthly frequency from annual series using interpolation. The list of variables used in the regressions as well as the data sources are provided in Table A1 in the Appendix.

Before moving to the empirical methodology, some highlights from the data and salient relationships among the key variables of interest are provided below.

# **_Trends of monetary aggregates_**

The trends of money stocks show three phenomena. The first is a divergence of broad money (M2) from narrow money (M1) and base money (M0), which became increasingly pronounced starting from 2010/2011 as M2 grew faster than the two other aggregates. The rapid growth of M2 was driven by rapid growth in savings deposits, which grew from 35.4 percent of M2 in 1997 to 60.7 percent in 2020, and it is also reflected in an increase in the money multiplier from 2011.

The second and third phenomena are trends in the velocity of money, both of which reflect important developments in Ethiopia’s financial system. Specifically, the second phenomenon is an increase in the velocity of money starting in 2002 (Figure 1), which reflects the acceleration of monetization of the economy. The velocity of money was declining for all three money stocks until mid-2002 (end 2004 for M0). The velocity of M0 and M1 then rose steadily, while that of M2 peaked in October 2008 then went on a downward trend. The third phenomenon is a decrease in the velocity of broad money starting from the end of 2008, which reflects the beginning of deepening of the financial system from the early 2000s. Financial deepening is reflected in the rising share of savings and time deposits in the public’s portfolios and the declining velocity of M2, reflecting its faster growth relative to nominal GDP.

**Figure 1: Trends of velocity of money**

<!-- Start of picture text --> 7.50<br> 7.00<br> 6.50<br> 6.00<br> 5.50<br> 5.00<br>M1 velocity M2 velocity M0 velocity<br>Jul-97 May-98 Mar-99 Jan-00 Nov-00 Sep-01 Jul-02 May-03 Mar-04 Jan-05 Nov-05 Sep-06 Jul-07 May-08 Mar-09 Jan-10 Nov-10 Sep-11 Jul-12 May-13 Mar-14 Jan-15 Nov-15 Sep-16 Jul-17 May-18 Mar-19 Jan-20<br><!-- End of picture text --> _Source_ : Authors’ construction using data from NBE’s database.

7
# **Who Benefits from Promoting Small and Medium Scale Enterprises? Some Empirical Evidence from Ethiopia**

Bob Rijkers<sup>1</sup> , Caterina Ruggeri Laderchi<sup>2</sup> and Francis Teal<sup>1</sup> * 1Centre for the Study of African Economies Department of Economics University of Oxford 2World Bank, Washington DC We would like to thank the staff of the Ethiopian Economic Association, in particular Ato Kibre Moges, Ato Daniel Aklilu and Ato Assefa Adamassie and the supervisors and enumerators of the Addis Ababa Construction Enterprises Survey. Discussions with Dr Wubshet Berhanu, City Manager, Addis Ababa City Government, and Woz. Tsedale Mamo, Manager, Addis Ababa Housing Development Project Office and other staff at the Addis Ababa Housing Development Project Office have been most helpful to frame the analysis of this study. We also owe gratitude to Justin Sandefur at Oxford University as well as Emily Kallaur, Rumana Huque and Louise Fox at the World Bank. Seminar participants at a CSAE seminar at Oxford University, the CSAE conference on economic development and the IZA/World Bank conference on employment and development provided useful feedback. Bob Rijkers gratefully acknowledges financial support from the Prins Bernhard Cultuurfonds and the Herbert and Ilse Frankel Memorial Fund at Oriel College, Oxford. All errors are our own. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, its Board of Executive Directors, or the countries they represent.

*Authors’ names are in alphabetical order. Corresponding authors e-mail addresses: <u>brijkers@worldbank.org</u> and <u>francis.teal@economics.ox.ac.uk</u>