rafmacalaba/data-use-mentions-extended huggingface dataset:
1 in the <u>longer version of this</u> paper.

To complement our findings and assess the crisis impact on workers who are outside our model (i.e., not formally employed in the private sector), we utilize real-time surveys on labor markets. For Jordan, the Economic Research Forum (ERF) conducted the COVID-19 MENA Monitor Household Survey (CMMHH), a nationally representative panel survey conducted among mobile phone users aged 18-64. The baseline wave of this dataset was collected in February 2021. This is a reliable and detailed survey that is more similar in scope and design to the LFS than other surveys conducted during the pandemic, such as the World Bank’s high-frequency phone survey. For Georgia, the World Bank conducted a COVID-19 (Georgia) High-Frequency Phone Survey (GHFPS) that cover a national random sample of mobile phone users aged 18-64. Both surveys aim to collect data on the socioeconomic impacts of COVID-19 on households and individuals, including information on job and income loss.

3.2 Pre-COVID Country Context Both Jordan and Georgia had experienced relatively stable growth over the years prior to 2020. Jordan’s real GDP growth averaged 2.4 percent between 2012 and 2020 and GDP per capita reached USD 4,405.5 in 2019.

> 5 Given our purpose to predict job losses in 2020, we used LFS 2019. With a full year of repeated cross-sectional data, it contains sufficient observations, and the impacts of COVID-19 are already reflected in the 2020 LFS. Given one purpose of our paper is to develop a method for predicting jobs lost in absence of post-COVID household level data, using the 2020 dataset from Georgia would undermine this objective.

> 6 <u>https://www.weforum.org/agenda/2020/04/occupations-highest-covid19-risk/. The three factors are equally weighted.</u>

8
fails to diagnose a disease correctly, the data do not allow further investigation on why providers are unable to correctly diagnose the disease or propose a correct treatment option. To address this, the future SDIs will record the incorrect diagnosis or treatment proposed by the respondent. Furthermore, to better adapt to the evolving burden of disease, future SDI surveys will expand to include vignettes of other non-communicable diseases and chronic conditions including hypertension and depression. At the same time, they will expand beyond clinical interaction to include an evaluation of interpersonal aspects of care delivery, and patient education and counseling. Another key aspect of the SDI survey is the measurement of provider absence. A careful review of the data across nine surveys has revealed a need for redesigning the questions informing this indicator. For example, in our data, a large fraction of absent workers are classified as “other excused” which does not provide adequate information on the nature of the absence. In response, it will be important for future SDI surveys to provide better clarity and granularity for correctly classifying provider absence from the facility during their expected hours of clinical duty.

There are some limitations of the SDI survey’s approach to measuring key inputs to care, including infrastructure and availability of medicine and supplies. As the analysis here has indicated, the data do not allow an investigation of the quantity or stock of medicine, equipment or other medical supplies. Likewise, there is limited information on service readiness as it pertains to the growing burden of non-communicable diseases globally and recently on important aspects of pandemic preparedness, such as infection control or surge capacity. The SDI is not intended for rapid measurement of facility readiness for pandemics but it does provide some insight on hospitalization capacity, WaSH measures and effective communication and coordination, summarized in a previous paper.<sup>47</sup> These measures of infrastructure do not adequately capture the basic requirements for access for differently abled individuals, or gender dimensions of accessing basic services and amenities at health facilities.<sup>48<
1996 and 2002. Court quality is measured through the share of the court’s decisions appealed. Using panel data regressions and matching firms and courts, the authors find that creditors make more loans when their rights are protected by courts with lower appeal rates.

Visaria (2009) evaluates a mechanism to improve the functioning of credit markets, India’s Debt Recovery Tribunal (DRT). The author uses staggered roll out in the establishment of the Debt Recovery Tribunal–an alternate contract enforcement system to the existing judicial system of courts–across India and a minimum claim amount by the lender to study the effect on debt recovery, delinquency and, finally, the cost of credit for Indian firms using difference-in-differences. The author finds that DRT increases the probability of timely payment of loan installment by 28 percentage points.

Nonetheless, as previously mentioned, the credit market consequences of justice may be heterogeneous. For instance, Kranton and Swamy (1999) study the impact of the introduction of civil courts in colonial India on agricultural credit markets. The introduction of courts increased competition among lenders. However, it lowered farmers’ welfare by reducing the lenders’ ability to underwrite farmers’ investments during bad shocks. Horioka and Sekita (2011) use a panel survey of consumers in conjunction with judicial data by court district in Japan to estimate that better judicial enforcement increases the probability of being rationed in some cases and decreases loan size (contrary to expectation) but increases the probability of bankruptcy (as expected). Thus, the authors argue that better judicial enforcement facilitates the recovery of loans but may sometimes be socially harmful.

In addition, Besley, Burchardi, and Ghatak (2012) examine the effects of improving property rights to facilitate the use of fixed assets as collateral, as has been posited in much of the finance and credit markets literature. Using a structural model on Sri Lankan data, they find that the effects are nonlinear and heterogeneous based on borrower wealth, which in turn, also depends on competitiveness in the credit markets.
)|(27.159)|(27.138)|(5.482)|(5.264)|
|=1 if district controlled by<br>Maoists (Definition 1)|0.042|0.058|0.123|0.129|0.304|0.321|
|=1 if district controlled by<br>Maoists (Definition 2)|0.102|0.122|0.357|0.337|0.541|0.554|
|Age at interview|19.983|20.376|19.020|19.659|18.873|19.928|
||(5.286)|(5.327)|(5.018)|(5.098)|(5.032)|(5.220)|
|=1 if DHS 2006|1.000|1.000|1.000|1.000|1.000|1.000|
|Observations|1264|1454|904|1122|887|1247|
|Panel B - Sample used to estimate E|q. (3) in theye|ars of educatio|n and enrollmen|t analysis (all ch|ildren aged 5 t|o 18)|
|Years of education|2.887|2.405|2.841|2.372|2.763|2.338|
||(3.060)|(3.002)|(2.900)|(2.787)|(2.856)|(2.744)|
|=1 if enrolled in schooling|0.758|0.631|0.806|0.678|0.798|0.664|
|District casualties before survey|0.132|0.134|0.316|0.321|0.680
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C9.3. SOSD: Social Sustainability Index C9.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.9*** 1***<br>H<br>Kendall tau-b 0.89*** 0.78*** 1***<br>Spearman 0.98*** 0.95*** 0.78**<br>A<br>Kendall tau-b 0.94*** 0.89*** 0.67**<br>Spearman 0.98*** 0.92*** 1***<br>M0<br>Kendall tau-b 0.94*** 0.78*** 1***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1<br>.2<br>.8 .15<br>CCDF .6 .1<br>.4 Difference .05<br>.2 0<br>0 -.05<br>.4<br>.3<br>.2
06***|0.01|(0.10)|1,422|
|Total Labor and Chores Hours Last Week|-10.62***|(3.24)|[0.01]|-22.95***|-6.61**|25.54|(23.39)|1,422|
|IP Assistance in Last 12 Months (=1)|0.08**|(0.03)|[0.14]|-0.11***|0.09***|0.04|(0.19)|1,422|
|IP Assistance in Last 12 Months (USD PPP)|128.25***|(46.50)|[0.10]|-88.67***|145.45***|28.74|(198.87)|1,422|
|General Happiness (Scale 1-3)|-0.22***|(0.08)|[0.02]|-0.51***|-0.04|1.76|(0.61)|1,422|
|**Panel B: Follow-up**|||||||||
|Child SDQ score, std|-0.56***|(0.17)|[0.01]|-0.72***|-0.33*|-0.01|(1.00)|859|

_Notes:_ The table shows the regression results on statistically significant results from the endline survey. Each row is its own dependent variable. Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers. The regressions also have month-by-year fixed effects, community-level controls
_“Ever since Adam Smith and David Ricardo, economists have explained and extolled the gains in living standards that derive from international trade. Those arguments are just as valid for trade in services as for trade in goods.”_ Alan Blinder, 2006

# **1. Introduction**

What drives growth has been a perennial question of enquiry in economic literature, and recent rapid growth of China and India has rekindled this debate. These two countries have taken two different routes to achieve growth rates of nearly 7 percent per annum. While China has followed a more traditional manufacturing led growth strategy, India‘s growth has been driven by growth in the services sector. The Indian experience has led researchers to challenge the conventional notion that industrialization is the only plausible route to rapid economic development (Ghani and Kharas, 2010; Felipe et al., 2007; UNCTAD, 2003). Services have grown as a share of the world‘s GDP in the last decade accounting for 70 percent of global GDP, and service exports in developing countries have almost tripled between 1997 and 2007.<sup>1</sup> Even though manufacturing continues to be a dominant driver of growth, recent developments suggest that we must include the service sector in the debate. Services are no longer exclusively an input for trade in goods but have become a "final export" for direct consumption. In view of these changes in the nature of services and their growing importance, the objective of this paper is to examine whether growth in service exports and its sophistication can provide an additional route for rapid economic growth in developing countries. Sophistication has a multitude of interpretations but it broadly aims to capture the productivity level associated with a country‘s production, empirically mirrored in exports data. We develop a new index of ―service exports sophistication‖ and examine its relation with growth.

As countries develop, they undergo structural transformation. Traditional theories have focused on growth as transition from agricultural to industrial production, with manufacturing being the prime ―
productivity growth.” However, rapid credit growth might also have a reverse, negative, impact on productivity. For example, Ghani and Suri (1999) argue that the rapid growth of bank credit was associated with negative productivity growth in Malaysia because allocation of credit was inefficient.

We use information from a cross section of Bulgarian firms to estimate whether access to credit is associated, and causally so, with higher productivity. To our knowledge, there are only a few firm-level studies that attempt to evaluate the effect of access to credit on total factor productivity. Bernstein and Nadiri (1993) estimate the effect of financial structure on productivity growth in US manufacturing companies. Their focus is on estimating the impact of agency cost of debt and the signaling benefits of dividends on productivity growth. Nickell and Nicholitsas (1999) find that financial pressure (defined as the ratio of interest payments to cash flow) has a positive effect on productivity. They deal with endogeneity of financial pressure by using instruments of lagged debt burden and yield on treasury bonds. Using data from the UK and Italy, Schiantarelli and Sembenelli (1999) show that firms with a larger proportion of long-term debt in their capital structure have improved subsequent performance measured as profitability, sales growth and total factor productivity. Similar patterns are found in Schiantarelli and Jaramillo (1999) for Ecuador and Schiantarelli and Srivastava (1999) for India. However, due to data limitations, all the previous studies focused on the effect of leverage on productivity.<sup>2</sup> In our paper we are able to use more direct measures of access to credit, such as presence or absence of overdrafts and lines of credit. Several recent papers estimated TFP in transitional economies. Hoekman and Djankov (1999) estimate the effects of trade liberalization

> 2 A related strand of literature focused on the relationship between productivity and leverage in firms that have undergone a leveraged buyout. (See Lichtenberg and Siegel (1990), Ravencraft and Scherer (1987)).

3
diversification are, we would argue, due in no small part to its policy-driven transition program, and is unique in the region, insofar as rapid diversification is concerned.

Based on the Herfindahl, most of the export diversification achieved by Syria appears to be due to changes in its larger export sectors, with a decline in oil exports as the most likely driver.<sup>12</sup> As such, it is uncertain whether the moderate diversification levels achieved in 2006 and 2007 are likely to persist, especially if oil prices rise in the medium run.

The 78 percent decline in the Herfindahl-Hirschman is not, however, mirrored in the (aggregated) Theil index (which better tracks changes in the share of smaller export sectors); in fact, the latter increases rapidly from 2001, peaks in 2003, before declining to lower levels that are nonetheless higher than that in 2001. This suggests that, in the 2002/03 and 2004/06 periods, the rapid expansion of export varieties—as evidenced by the number of products—has been mostly skewed toward larger lines; equivalently, export diversification has been due less to new product discovery than export declines along the intensive margin. While part of this result may be an artifact of the degree of disaggregation—the Theil declines more systematically over the period when measured at the 2-digit level<sup>13</sup> —the results nonetheless suggest that substantial degrees of diversification have yet to be achieved in newly emerging export lines.

This result comes into sharper focus when examining concentrated indices corresponding to diversification within sections (columns 5–8). Herfindahls remain fairly stable over the period, with a mean of 0.32 (and standard deviation of 0.09). Theils within sections are similar to their values between all product lines—the correlation coefficient is 0.92—which imply once again that smaller export sectors are not responsible for export diversification patterns. Taken together, the relatively stable Herfindahl and varying Theil indices are indicative of
percent. The regression analysis indicates much smaller price elasticities with respect to calories than with respect to food consumption, suggesting that households traded off quality for quantity of calories consumed. As additional evidence of this trade-off, the regression estimates show that the price increases are associated with changes in the composition of food consumption, namely decreases in dietary diversity and a movement towards staple foods. This result is similar to that of McKenzie (2006) who finds a shift in food consumption towards staples after the devaluation of the Mexican Peso in 1994.<sup>11</sup> The analysis in this paper also documents the statistical relationships between commonly used nutrition indicators and various measures of food security, and thereby contributes broadly to work on the efficacy of household-level indicators like dietary diversity.<sup>12</sup> This is in line with the growing recognition of the importance of household- and individual-level consumption data in developing sub-national interventions to reduce chronic food insecurity (Barrett, 2010).<sup>13</sup> In addition to contributing more broadly to the food security literature, we also believe that this analysis fills a critical gap in the knowledge base for policy makers. The dearth of data and analysis available on Afghanistan poses acute challenges to political leaders, lawmakers, and humanitarian organizations interested in creating programs and policies to alleviate poverty and food insecurity.

The next section in this paper briefly describes the Afghan economy and its ties to international markets during the period of the food crisis. Section 3 provides details on the survey, the methodology and the sample. More findings on the household response to the price effects are described in section 4, along with discussion of robustness tests, and section 5 concludes.

# **2 Afghanistan**

Over the past decade, the Afghan economy has experienced strong growth, with real GDP growth averaging approximately 10.8% per year between 2003 and 2009. But after decades of war and political instability, landlocked Afghanistan remains one of the world’s

> 11
6-7%. The scenario using historical growth rates from 1997-2017 is a bit more optimistic, due to the high growth rates at the turn of the century. While the different growth scenarios do not matter much at a global level, there are starker differences at the regional level. In Latin America & the Caribbean, using growth rates from the past 10 years results in almost no decrease in poverty – a pattern mostly attributable to poor recent growth in Venezuela -- while the other two scenarios decrease poverty substantially towards 2030. In the Middle East & North Africa, both historical growth scenarios yield increasing poverty rates towards 2030 while using the WEO growth projections suggests that poverty will be halved by 2030. The global poverty rate is largely driven by Sub-Saharan Africa, which in all three scenarios has poverty rates at or above 30% in 2030, while the other regions of the world have rates below 7% (the vertical axis differs across regions in Figure 5).

> 9 The projections and estimates are available at <u>https://datacatalog.worldbank.org/dataset/population-estimates-andprojections.</u>

> 10 See Ferreira et al. (2016) for a description of how the $1.90 international poverty line has been defined.

13
.3|0.0|0.0|0.0|3.9|0.1|1.2|8.0|
|Other consumer services|0.0|0.0|0.0|0.5|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.4|0.0|0.5|
|Other services|0.0|0.0|0.0|16.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|15.9|16.0|
|Backward/ Demand|5.6|10.3|17.2|66.9|0.8|14.5|16.2|5.4|0.7|0.3|0.3|4.8|1.3|22.5|100.0|

Source: World Bank Trade Value Added Database and Francois et al, 2013. Note: Column headings are: (1) Primary; (2) Energy; (3) Manufacturing; (4) Services; (5) Electricity, Gas, and Water; (6) Construction; (7) Trade, Distribution, and Hotels; (8) Transport; (9) Communication; (10) Finance; (11) Insurance; (12) Other Business Services; (13) Other Consumer Services; (14) Other Services; (15) Forward/Supply **.**
social standards.

While we obtain interesting insights, this is only a first step with ample scope for expansion in a number of possible directions. First, it would be worthwhile to explore alternative outcome variables, especially prices. Second, there seems ample potential to deepen understanding of the channels through which spillover effects may materialize. Ways to do so would be to link survey to administrative data, or to exploit impacts of exogenous variation in large farms’ contractual terms or their interaction with local communities. Third, to interpret and contextualize the magnitudes of effects found here, it will be useful to compare their size to those from other types of public goods such as irrigation or technology transfer as well as private investment, including establishment of up-stream agro-processing facilities to integrate small producers into value chains. This is a challenging agenda of great importance for rural development and poverty reduction. The fact that, after the 2008 boom, many countries are left with a large number of non-viable large farms could provide a unique opportunity in this respect.

16
Justice (2006), “Global distribution of agricultural fires in croplands from 3 years of moderate resolution imaging spectroradiometer (modis) data.” _Global Biogeochemical Cycles_ , 20.

- Kremer, Michael, Jessica Leino, Edward Miguel, and Alix Peterson Zwane (2011), “Spring cleaning: Rural water impacts, valuation, and property rights institutions.” _The Quarterly Journal of Economics_ , 126, 145–205.

32
# **Appendix III. Evidence on U.S. Monetary Policy Transmission**

In this section, we focus on the effects of U.S. monetary policy shocks on the financial markets and macroeconomic variables in EMEs. Our empirical framework enables us to investigate the transmission of U.S. monetary policy shocks as U.S. monetary policy instruments and the VIX index are incorporated as control variables. We use shadow short-rate point estimates, constructed by Wu and Xia (2016), to address zero lower bounds (ZLB) and unconventional monetary policies implemented during the period after the global financial crisis. However, since these rates encompass both endogenous (anticipated) and exogenous (unanticipated) components of monetary policies, we also implement the analysis using the pure monetary policy surprises estimated by Jarocinsky and Karadi (2020). Additionally, we present the responses of the variables following an increase in VIX as a proxy for global financial uncertainty for the purpose of comparison. We then compare the responses of domestic variables to U.S. monetary policy shocks to those induced by domestic monetary policy shocks.<sup>43</sup> Figure A11 presents the impulse responses of domestic macroeconomic and financial variables in EMEs to U.S. monetary policy rates (“SHADOW”), U.S. monetary policy surprises (“CJKMP”), and the VIX index (“VIX”).

**Foreign exchange rates.** We first discuss the dynamics of foreign exchange rates per US dollar, a primary channel for international monetary spillovers. Initially, following a contractionary U.S. monetary policy shock, the domestic currencies of these economies experience immediate depreciation, followed by a gradual appreciation over several months. This pattern aligns with the predictions of the overshooting theory by Dornbusch (1976). Interestingly, there is no evidence supporting the delayed overshooting or foreign exchange rate puzzle proposed in previous studies, corroborating recent findings (Rogers et al. 2018; Bjørnland 2009).

**Equity prices
., 2018), the characteristics of the informal businesses (Amin and Islam, 2015; Islam, 2019), and the interlinkages of the informal and formal sectors (Benjamin and Mbaye, 2012; Jolevski and Aga, 2019; Amin and Okun, 2020; Amin, 2021).<sup>3</sup> The size and persistence of the informal sector, and the wealth of research studying informal business activity reflects the need for high-quality, representative firm-level data collected in an efficient manner.

Businesses operating informally are frequently small and less productive, but widely abundant (see La Porta and Shleifer, 2014), implying that the typical (modal) business often does not appear in official registrar records, tax rolls, or most often, both. This very nature of informality

> 3 Several of these analyses rely on data reported by formal firms on informal firms’ activity.

2