rafmacalaba/data-use-mentions-extended huggingface dataset:
somewhat. Informal salaried workers now show no significant standard deviation in any specification. By contrast, civil employment shows a modest rise in heterogeneity in job, life and financial satisfaction relative to the previous exercise. The standard deviation for self-employment with employees is also higherthan was the case in the previous exercise both in the job and financial satisfaction specifications, but not in the life satisfaction regression. This appears to be due to the fact that the random effect is only significant in the life satisfaction case and we know from table 4 that this reduces, by roughly the difference observed here, the estimated variance. The same phenomenon is seen for the self-employed with no employees which lose the significance of the standard deviation in the case of life satisfaction.

Despite the weaker power resulting from the smaller sample size, the results with the life and financial satisfaction variables broadly confirm the findings with the larger sample using just job satisfaction. Self-employment of both types appears more favorable than before. Overall, the self-employed and informal salaried are certainly no less satisfied with their lives and their financial situation, ceteris paribus, than those in the formal sector. Further, there does not appear to be a strong tradeoff between financial and life satisfaction on the one hand, and job satisfaction on the other. Since satisfaction measures are strongly correlated and because work satisfaction has been argued to be the most important determinant of life satisfaction (?), these findings should perhaps not come as a surprise.

# 5.3 Survivor/Attrition Bias

Finally, we address the potential biases due to attrition from the panel. Each year approximately 30% of the respondents drop out, but many of these drop-outs are temporary, as a significant share of the dropouts re-appears in later waves.<sup>26</sup> Moreover the attrition does not seem systemic, but instead appears driven largely by difficulties associated with tracking people as addresses were not recorded and the initial waves of the survey did not use GIS mapped data. Simple probit models (not presented here to
2 demand curve with constant elasticity yields a model in which both domestic and foreign offer curves lie on top of one annother. 3/ They are dissatisfied with this specification and go on to propose a third external closure: a model with price taking behavfir and no product differentiation for tradables, plus the inclusion of non-tradables. In essence, this closure corresponds to a multi-sector version of the well-known dependent economy (Australian) trade theoretic model. They show that in this formulation, there is an exchange rate variable -- or "parameter," as they call it -- that measures the relative price between composites of traded and non-traded goods. Tibey show that, in this model, the foreign offer curve is a straight line, while the domestic offer curve has some elasticity (thereby following conventional trade theory). However, they feel that this price taking assumption will be unpalatable in empirical models of large countrieo. More generally, they note that this model will not allow two-way trade, or cross hauling, which is widely observed in trade statistics at the aggregation levels used in all CGE models, and so is not a desirable specification.

Several points about the WY analysis deserve comment. First, the role of the exchange rate in computable general equilibrium models has received attention for some time and we can find no case in which modelers interpret it as a "financial variable." 4/ Second, the two-good model with both goods traded that IY use in their first discussion of external-sector closure does not represent well arty of the applied CGE trade models, which invariably include some non-traded goods. 5/ Third, an external closure using a price-taking formulation for all tradables in a model with perfect substitution will be unpalatable for stronger reasons than those mentioned by WY. If the price-taking formulation is not accompanied by some product
the development of, and pressures from, domestic factors. However, the indication is that a supportive legal and regulatory environment encourages the factoring industry to grow.

A weak information infrastructure may also be problematic for factors. The general lack of data on payment performance, such as the kind of information that is collected by public or private credit bureaus or by factors themselves, can discourage factoring. Since the credit risk of the transaction is the aggregate credit risk of all the supplier’s customers, the cost and time required to collect information on many customers may discourage factoring in countries with weak credit information.

Finally, an advantage to factoring is that it’s generally linked on a formula basis to the value of the underlying assets, which allows quick credit approval and disbursement. However, this depends on a good technology infrastructure and supporting electronic security laws that allow the electronic sale and transfer of electronic securities (accounts receivable). Furthermore, there must be a supportive regulatory environment for electronic security, so that factors and borrowers are assured that their transactions are confidential and secure. As discussed in the following section, the success of reverse factoring requires a legal environment that facilitates safe and easy electronic transactions.<sup>4</sup>

# **4.  The Determinants of Factoring**

In this section we test what country-level characteristics are associated with a greater use of factoring. We use a 10-year panel dataset on total factoring turnover from Factor Chain International (2005) for 48 countries from 1993 to 2003. The data include

> 4 For additional information see Glaessner, Kellermann, and McNevin (2002) and Claesssens, Glaessner and Klingbiel (2001).

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## **3.4 Other Control Variables**

We include in our regressions also several country-level and industry-level variables from the real economy. The real _GDP per capita_ is reported in constant 2005 US dollars. _Real exchange rate_ is computed as the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs. The source for both variables is the World Development Indicators of the World Bank. We also control for countries’ endowments with physical and human capital, relying on data from the Penn World Table (version 8.1). The stock of _physical capital_ per worker in a given country is constructed according to the perpetual inventory method. _Human capital_ per worker is calculated from the average years of schooling in a given country using attainment data. These time-varying economic characteristics of exporting countries are measured at the beginning of a given export spell. We also include two time-invariant industry-level characteristics - _physical capital intensity_ and _human capital intensity_ . Both variables are from Braun (2003) and are measured at the ISIC 3-digit level.

The direct effects of these real-economy variables are absorbed by the country-time and product fixed effects included in our empirical specifications. However, these control variables do explicitly enter the regressions as parts of various interaction terms.

# **4 Banks vs Stock Markets**

This section relies on standard measures of well-developed banks and stock markets when examining their relative importance for export survival at different time horizons. Subsection 4.1 provides preliminary graphical evidence, both by capturing the overall impact of banks and stock markets on export survival, and by further exploring the transmission channels related to high investment needs, high liquidity needs, and asset intangibility. Subsection 4.2 reports full results from additional regressions focusing on these three channels from finance to export survival (Equation 1).

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# 1. Introduction

Russia’s invasion of Ukraine has had far-reaching consequences through various social, economic, and other channels, creating global ripple effects. The economic impact of the invasion has been felt through multiple channels, including commodity and financial markets, trade and migration links, and business confidence and investor uncertainty. These effects have added to existing concerns about a potential global economic slowdown, increased inflationary pressures, rising levels of debt, and a surge in poverty rates. Russia’s invasion of Ukraine (the invasion) has heightened economic uncertainties and underscored the need for careful analysis and response to mitigate its adverse effects on the global economy and local businesses alike.

Central Asian (CA) countries also felt major consequences of the invasion through multiple channels, including the sanctions imposed on Russia and Russia’s recession. Closed trade and payment system links curbed trade, remittances, investment, and tourism, adversely affecting economic growth, inflation, and external and fiscal accounts shortly after the invasion.<sup>1</sup> As a result, the growth of CA economies slowed to 4.1 percent in 2022, from 5.3 percent in 2021. Economic growth is set to remain mediocre in 2023, with the real GDP of CA projected to grow by 4.0 percent (World Bank, 2023a). And businesses in CA countries report that their imports from Russia or Ukraine have decreased or they have stopped sourcing from these countries altogether. They are responding to the resulting growth in production costs by raising their sales prices or coping with reduced profitability (Iootty and Melecky, 2022).

A few papers use microeconomic data to examine the effects of Russia’s invasion of Ukraine on business. For example, EBRD (2023) explores the compounding effects of the global pandemic and the invasion on businesses in EBRD countries, focusing on trade and finance transmission channels. The findings suggest that trade disruptions prompted firms to enhance their supply chain resilience by stoking up inputs and diversifying their supplier base.<
12 _“_ Quality _”_ is, at the outset, a comparatively arbitrary term. In previous postal studies it has been measured using indicators such as number of letters delivered on time. It may also encompass the linked concept of reliability—are letters delivered at all? Again, we do not have such data at the cross-country level. Instead, we use a measure of trust in the postal system (survey answers to a question regarding willingness to send an object worth $100 through the posts) as a proxy for quality. This trust measure might plausibly be related to measures of efficiency—bloated public monopolies frequently provide a lower quality of service than efficient ones (World Bank, 1995). Trust, and a broad definition of _“quality”_ might also be related to the extent of service provision.

In order to empirically examine our posited relationships, a range of relevant variables were selected from the UPU database as well as information from Kirkman et. al. (2002), and the World Bank’s _World Development Indicators_ database. These are described in Table 4, with descriptive statistics in Table 5.<sup>19</sup> There is considerable variation across countries in most of the indicators—for example, the number of letters delivered per year per employee varies from 196 to 174,930, receipts as a percentage of post office expenditures varies from 11 percent to over 400 percent, privately operated post offices as a percentage of all offices varies from none to 95 percent and the average geographic coverage of post offices varies from less than one square kilometer to nearly 30,000 km<sup>2</sup> .

> 19 It should be pointed out that much of the data is subject to significant error.  For example, the UPU collects its data from the incumbent postal operator and it has limited capacity to check the reliability of this information. Further, while such operators usually have an official monopoly over at least parts of the postal delivery system
Pijanowski and B. K. Pekin. 2013. "Evidence for Increased Monoculture Cropping in the Central United States." _Agriculture, Ecosystems & Environment_ , **_165_** , 50-59. Porter, P. M., et al. 1997. "Environment Affects the Corn and Soybean Rotation Effect." _Agronomy Journal_ , **_89_** (3), 442-448. Qiao, F., J. Huang and X. Wang. 2017. "Fifteen Years of Bt Cotton in China: Results from Household Surveys." _World Development_ , **_98_** , 351-359.

Regis Mauri, G. 2019. "Improved Mathematical Model and Bounds for the Crop Rotation Scheduling Problem with Adjacency Constraints." _European Journal of Operational Research_ , **_278_** (1), 120-135. Seifert, C. A., M. J. Roberts and D. B. Lobell. 2017. "Continuous Corn and Soybean Yield Penalties across Hundreds of Thousands of Fields." _Agronomy Journal_ , **_109_** (2), 541-548. USDA. 2019. "2017 Census of Agriculture Highlights: Farms and Farmland." United States Department of Agriculture, National Agricultural Staistics Service, Washington DC. Wang, H. and A. Ortiz-Bobea. 2019. "Market-Driven Corn Monocropping in the U.S. Midwest." _Agricultural and Resource Economics Review_ , **_48_** (2), 274-296.

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in the last month in total? (amount in Somoni)”. With these two questions, we created the Income and Consumption Index using the methodology of Kling, Liebman and Katz (2007). It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again.

# **B.B Treatment Variables**

Treatment variables were constructed using the online data from the Foundation for Defense of Democracies’ Long War Journal (LWJ). They map the districts controlled by Taliban, by U.S. allies, and contested territories between 2014 to 2021, when the Taliban’ took control over the total territory. The classifications are based on open-source information, such as press reports and information provided by government agencies and the Taliban. With this information, they defined the following:

- Taliban and U.S allies controlled districts: places where the Taliban or the U.S. allies

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findings on the differential impacts of Covid-19 along caste lines, driven by the overrepresentation of marginalized castes in informal work and their lower levels of human capital (Deshpande and Ramachandran, 2020).

Our paper also adds to emerging evidence in developing economies on how labor markets fared during the “recovery” period that followed the first wave of Covid-19 and associated lockdowns in developing countries. In the early days of the pandemic between April and June 2020, the recession inflicted by the pandemic caused severe labor market disruptions in developing countries. The World Bank’s High-Frequency Phone Survey (HFPS) data collected in a large sample of developing countries show that work stoppage, pay-cuts and income loss were common consequences (Khamis et al., 2021) and occurred at a disproportionately high rate among those with low education, informal jobs in urban areas, women and youth (Bundervoet et al., 2022). Sectors hit hardest by Covid included accommodation and food services, non-food manufacturing, retail and wholesale, and travel and transport (ITC Business Impact Survey 2020).

Evidence on labor market recovery in developing countries since the summer of 2020 is rather sparse. The limited and indicative evidence that exists suggests that even as economic activity started reviving in many countries since May/June 2020, the job market recovery was slow and uneven in most countries. In a sample of 17 developing 3
); LAC (Latin America & Caribbean); MENA (Middle East & North Africa); SA (South Asia); SSA (Sub-Saharan Africa).</mark> _<mark>Note:</mark>_ <mark>All three variables from Worldwide Governance Indicators, scores between [-2.5, 2.5].</mark> Petty corruption is quite prevalent in public administration and services, and negatively affects the quality of and access to public services. According to the 2019 GCB, one in four individuals surveyed had paid a bribe in the previous year for access to public services, such as health care or education; in practice, this means that about 130 million people were likely to have paid money or done favors in exchange for access to services.<sup>10</sup> Petty corruption skews politicians’ and bureaucrats’ incentives to deliver public services: it is indeed associated to poorer management and lower quality of public services (World Bank 2004).<sup>11</sup> Petty corruption also has regressive effects, as poor users pay a larger share of their incomes on bribes and are also more likely to be discouraged from seeking public services.<sup>12</sup> De facto in SSA, the 2019 GCB survey data indicates that the poorest are twice as likely to pay a bribe as the richest individuals. This is particularly delegitimizing when such corruption is embedded in security agencies such as the police and military as it represents a failure of the very basis of any social contract, which is basic security.

> 9 State capture is perpetrated by interconnected corrupt economic and political actors targeting state assets and (ab)using weak institutions for their private benefit. Raballand et al. 2022.

> 10 Transparency International, 2019, _Global Corruption Barometer: Africa 2019_ .  Berlin: Transparency International.

> 11 In a similar vein, vote-buying tends to be associated with lower levels of investments in public services. Khemani,
.3 percentage points in growth (from 7 to 4.7 percent) would be associated with an increase in unemployment of about 0.7 percentage points (annex 4).<sup>15</sup> Given the relatively low value of the “Okun coefficient”, as well as the low value of the elasticity of employment to output, we expect a jobless recovery.

The labor market is a cause of concern given the lower job creation rates, higher job destruction and increases in the size of the informal labor market. In addition to the effect of the sudden stop on job flows, the World Bank rapid survey (World Bank, 2009c) shows that large manufacturing firms are reducing employment more than small firms, making it likely that informal employment will increase. In emerging economies, the informal labor market expands as economic activity contracts, and hence it is countercyclical in nature (Perry et al. 2008). The expected increase in informality is

> 13 El-Ehwany and EL-Megharbel, 2008 found that a change of 1 percentage point in economic growth would lead to a change of 0.53 percentage points in employment in the same direction.

> 14 The 0.3 elasticity of employment with respect to output is estimated by means of an Autoregressive Distributed Lag (ARDL) model. Using both yearly data for 1980-2009, or quarterly data for 2003-2009, the long run elasticity decreases in value and turns statistically insignificant when a deterministic trend is introduced, and the short run elasticity is 0.1. In the quarterly model, the long run elasticity is 0.3, with insignificant short run elasticity. This elasticity is lower than that reported in the previous footnote and generally used in studies of the labor market in Egypt.

> 15Annex 4 presents the estimation of Okun’s Law, which captures an empirical relationship between changes in unemployment and changes in output growth. The estimated coefficient used in this paper is slightly lower than the .36 average reported by the IMF (2010) for a group
model’s estimate of entry barriers against the World Bank’s DBI for the cost of starting a firm. This indicator, which is based on legislation and regulations, has been used widely in the literature to quantify the aggregate effects of barriers to firm entry. While the DBI comprises various elements, we focus on those that more directly affect the costs of starting a formal manufacturing firm: the actual time cost of getting permits and licenses to operate a new business plus the costs of getting access to electricity. The DBI translates these costs into fractions of GDP per capita, which we then turn into labor units to make them comparable with _fe ∗ τ_<sup>_E_</sup> in the model.<sup>31</sup> Figure 5: Model-Based Estimate of Entry Barriers and the World Bank’s Doing Business Indicators

**Model's Barrier vs Doing Business Indicator**

<!-- Start of picture text --> Ghana<br>Ethiopia<br>Kenya<br>Peru ColombiaSalvador India<br>Spain LatviaItaly F inlandrance BulgariaHungaryPortugalBelgiumRomaniaChile<br>−0.5 0.0 0.5 1.0 1.5 2.0<br>log(1+TauE Model)<br>Note: According to the model, the total cost of entry in units of labor is EC = fe (1 + τ e ). To get at<br>a model equivalent entry cost from the Doing Business Indicator (DBI), we proceed as follows. We start<br>adding the total cost of starting a firm and acquiring electricity as a proportion of income per capita from the<br>Doing Business Database, DB = ( start + ( Y/L electricit ) y ) . Multiplying the DBI by the inverse of the labor share,<br>which we take from the Penn World Table version 9.0, we get the level of the cost of entry in units of labor:<br
.54|-0.11|75.46|0.13|0.15|0.08|
|1997|25.00|-0.11|75.00|0.11|0.14|0.06|
|1998|43.03|-0.10|56.97|0.09|0.14|0.04|
|1999|44.68|-0.09|55.32|0.09|0.14|0.03|
|2000|37.08|-0.09|62.92|0.10|0.13|0.03|
|2001|36.86|-0.10|63.14|0.11|0.16|0.06|
|2002|52.80|-0.09|47.20|0.11|0.15|0.02|
|2003|43.80|-0.10|56.20|0.09|0.14|0.00|
|2004|42.69|-0.10|57.31|0.12|0.16|0.00|
|2005|32.02|-0.10|67.98|0.14|0.18|0.05|

Notes: Column (6) shows price change for the for Transport Equipment Industry using the industry price deflator from the Government of India Price Statistics (WPI) from the 1993-94 price series. Columns (1)-(5) are constructed from firm level price data.

Information on prices allows me to estimate (25). I construct firm specific price deflators, which vary across firms within an industry, unlike industry level deflators that are common to all firms in an industry. I thus recover estimates of the production function coefficients and _ωit_ that do not suffer from
13</sup> The base OLS estimates with the cross-section data show a loss of -1.8 percent in GDP per capita between 2005 and 2018 due to the fall in SCI. This is larger than the -0.4 percent decrease in GDP per capita due to the decline in trade openness (between 2005 and 2018), and the 0.01 percent increase due to the increase primary school enrollment (between 2005 and 2017). Using the OLS with year fixed effects estimates (panel data), the loss in GDP due to the fall in SCI is slightly higher at -1.9 percent. This is still larger than the loss of -0.4 percent and gain of 0.4 percent due to the decrease in trade openness and the increase in schooling respectively.

> 12 Source: How should governments spend on data <u>https://blogs.worldbank.org/opendata/how-much-shouldgovernments-spend-data</u> (accessed July, 25<sup>th</sup> 2021, 10:45 pm).

> 13 We use the 2017 value for primary school enrollment rate due to the sparse data in MENA for 2018. All trends are for developing economies in the MENA region (i.e. high-income economies in the region are excluded).

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