rafmacalaba/datause-encoder-data huggingface dataset:
The results presented also highlight the need to upgrade the knowledge production function used in endogenous growth models by allowing not just knowledge complementarities, but also substitution or competition effects. The evidence supports these considerations and highlights the need for new theoretical research that could investigate the growth and welfare implications of international knowledge spillovers. By the same token our results highlight the pertinence of studying different types of innovation as the nature of international knowledge spillovers could be different, depending on the type of innovation, be they product-quality upgrading, the introduction of new product varieties by firms, or patentable commercial inventions.

# **References**

- **Aghion, P. and P. Howitt** , “A Model of Growth Through Creative Destruction,” _Econometrica_ , 1992, _60_ (2), 323–51.

- **, C. Harris, and J. Vickers** , “Competition and Growth with Step-by-Step Innovation: An Example,” _European Economic Review_ , 1997, _Papers and Proceedings_ , 771–782.

- **, M. Dewatripont, and P. Rey** , “Competition, Financial Discipline, and Growth,” _Review of Economic Studies_ , 1999, _66_ (4), 825–852.

- **Allison, Paul and Richard Waterman** , “Fixed-Effects Negative Binomial Regression Models,” _Sociological Methodology_ , 2002, _32_ , 247–265.

- **Almeida, R. and A.M. Fernandes** , “Openness and Technological Innovations in Developing Countries: Evidence from Firm-Level Surveys,” _Journal of Development Studies_ , 2008, _44(5)_ , 701–727.

- **Audrestsch, D. and M. Feldman** , “R&D Spillovers and the Geography of Innovation and Production,” _American Economic Review_ , 1996, _86(3)_ , 630–640.

- **Backus, D., P. Kehoe, and T. Kehoe** , “In Search of Scale Effects in Trade and Growth,” _Journal of Economic Theory_ , 1992, _58_ , 377–409.

- **Bernard, A. and J. Bradford** , “Exceptional Exporter Performance: Cause, Effect, or Both?,” _Journal of International Economics_ , 1999, _47(1)_ , 1–25.

- **Bernstein, J. and I. Nadiri** , “Research and Development and Intra-industry Spillovers: An Empirical Application of Dynamic Duality,” _Review of Economic Studies_ , 1989, _56(2)_ , 249–267.

- **Blundell, R., R. Griffith, and F. Windmeijer** , “Individual Effects and Dynamics in Count Data Models,” _Journal of Econometrics_ , 2002, _108_ , 113–131.

21
# **Table A26:  Political and Economic Risk Consultancy Corruption in Asia Survey (PRC)**

**Data Provider** Political and Economic Risk Consultancy **Description** Commercial business information firm headquartered in Hong Kong, China **Website** www.asiarisk.com **Data Source** Corruption in Asia **Type** Survey **Respondents** Expatriate business people **Frequency** Annual since 1998 **Coverage** Asian countries **Public Access** Commercially available **Description** This survey asks respondents to rate severity of corruption, attitudes towards corruption, and effectiveness of efforts to reduce corruption, on a 10-point scale.

<!-- Start of picture text --> 2006 2005 2004 2003 2002 2000 1998 1996<br>Voice and Accountability<br>NA .. .. .. .. .. .. .. ..<br>Political Stability<br>NA .. .. .. .. .. .. .. ..<br>Government Effectiveness<br>NA .. .. .. .. .. .. .. ..<br>Regulatory Quality<br>NA .. .. .. .. .. .. .. ..<br>Rule of Law<br>NA .. .. .. .. .. .. .. ..<br>Control of Corruption<br>To what extent does corruption exist in a<br>way that detracts from the business X X X X X X X ..<br>environment for foreign companies?<br>Country Coverage 13 13 14 14 14 12 12 ..<br>Year of publication 2006 2005 2004 2003 2002 2000 1998 ..<br><!-- End of picture text --> 64
Figure 2: Days of Cash in Hand across Industries <!-- Start of picture text --> Multiline & specialty retail<br>Restaurants<br>Consumer staples<br>Textiles, apparel, & luxury goods<br>Auto & components<br>Other transport<br>Utilities<br>Industrials<br>Hotels, resorts, & cruise lines<br>Other consumer discretionary<br>Airlines<br>Materials<br>Information technology<br>Communication services<br>Health care<br>Energy<br>0 30 60 90 120 150 180<br>Number of days<br><!-- End of picture text -->

**Note** : This figure shows the number of days of operating expenses covered by cash held across U.S. listed firms. The figure shows 2000-2016 averages by industry. **Source:** Authors’ calculations based on Compustat data.

The resilience of the corporate sector is also tightly linked to the duration of the pandemic shock. The exogenous health emergency is transitory. Eventually a vaccine or an effective treatment will be developed, or the disease will run its course, and the source of the crisis will basically disappear. Nonetheless, the timeline for these exits from the pandemic are still unknown and hard to predict. Hence, the duration of the pandemic crisis remains a crucial source of uncertainty. The longer the pandemic lasts, the tougher it would be for firms to withstand the economic downturn, survive, and recover. To the extent that the shock does not persist for too long, a significant proportion of firms are likely to remain viable. That is, their net worth will still be positive as the economy returns to pre-pandemic levels. However, not all firms can survive a prolonged crisis, with extensive lockdowns and other containment measures. While industries themselves will weather the shock and survive (e.g., the restaurant or airline industries will not disappear), the same cannot be said about individual firms. Those that do survive might be severely impacted, possibly losing lines of business or customers, and may in fact need to transform themselves (or close) to thrive in the post-pandemic world.

The heavy costs that the COVID-19 outbreak has imposed on the world economy have been born not only by firms themselves, but rather by all parties. In fact, the pandemic crisis has disturbed a wide range of economic relationships. Shock-hit firms have suffered a collapse in revenues. Shareholders of firms negatively affected by the pandemic have 7
**Table 2. Income and Consumption regressions using NLSS 95/96**

||||||||**Distr**<br>|**ict Level P**<br>|**remium fo**<br>|**r**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||**Ag**|**e**|**Age Square**|**d/10000**|**District Fixe**|**d Effect**|**Educati**|**on**|**High ca**|**ste**|
|**Absolute:**|coef|t-stat|coef|t-stat|Mean|SD|Mean|SD|Mean|SD|
|Log income|0.042|6.456|-3.055|-4.479|10.289|0.340|0.218|0.200|0.145|0.405|
|Log Consumption|0.038|7.916|-2.974|-5.873|10.325|0.340|0.196|0.138|0.184|0.304|
|**Relative:**|||||||||||
|Relative log income|0.004|6.388|-0.292|-4.422|n.a.||0.021|0.020|0.014|0.039|
|Relative log consumption|0.004|7.860|-0.285|-5.826|n.a.||0.019|0.014|0.018|0.030|
|**Consumption Adequacy Ind**|**ex:**||||||||||
|Food|0.008|1.922|-0.345|-0.801|1.496|0.213|0.130|0.121|0.120|0.249|
|Clothing|0.006|1.593|-0.293|-0.752|1.357|0.196|0.052|0.101|0.059|0.217|
|Housing|0.007|1.685|-0.220|-0.549|1.404|0.184|0.105|0.103|0.096|0.264|
|Healthcare|0.004|0.990|-0.063|-0.164|1.412|0.198|0.077|0.112|0.063|0.237|
|Children's Schooling|-0.006|-1.426|0.900|2.069|1.444|0.201|0.051|0.120|0.043|0.302|
|Total Income|0.006|1.944|-0.307|-0.920|1.251|0.156|0.069|0.098|0.067|0.195|

Each line corresponds to a different regression. The estimator is weighted least squares, using sampling population weights.
# **3b. Predicted prevalence at the district level**

Based on the regression results discussed above we predict HIV prevalence for women aged 15-49 in the DHS 2000 survey. The actually observed district-level prevalence rates (in the 2001 sentinel data), predicted prevalence rates based on the 2001 sentinel survey (in-sample), and SAE-based predicted prevalence rates in the 2000 DHS survey (out-of-sample) are shown in Table 5. The reported estimated (partial) standard errors provide a sense of the accuracy of our SAE based district-level predictions. Note, however, that these standard errors are understatements of the true standard errors (in the same way that the sentinel-data based estimates are also subject to some kind of sampling error).

We first notice that although the regressions are estimated at the regional (North, Central, South) level, the estimates of HIV prevalence based on in-sample predictions into the 2001 sentinel survey data are generally quite close to the observed prevalence.<sup>23</sup> This indicates that the regression model estimated at the region level is able to provide a good fit at the district level as well.

The out-of-sample predictions (those that use 2000 DHS sample) indicate lower prevalence than sentinel survey estimates. The sentinel survey actual prevalence at the national level of 19.4% drops to 13.2% for the 2000 DHS-based predictions. The lower predicted prevalence for the 2000 DHS sample is to be expected given the selective nature of the sentinel sample.

It is noteworthy that predicted HIV prevalence rates in the DHS sample at the regional level mask substantial heterogeneity across districts – even for the districts belonging to the same region. For instance, in the South region the estimated prevalence varies from 11% for Mwanza district to 25.6% for Mulanje (Table 5).<sup>24</sup> How good are our predictions using the small area estimation (SAE) methodology compared to our “gold standard” of HIV prevalence rates derived directly from the 2004 Malawi DHS? Table 6 reports estimates of HIV prevalence derived directly from the

> 23 With the exception of Chiradzulu, Machinga, Mangochi, and Mulanje districts in the South.

> 24 One would generally expect the prevalence for the sample of all women age 15-49 to be generally lower than the prevalence for the sample of ANC attendees or currently pregnant women age 15-49. We find that this is indeed the case for most districts in Malawi. Those results are available from authors upon request.

14
come through changes in demand for services (and the effect on demand for services of reduced price is exactly offset by the effect on demand of reduced per person income). Ambiguity arises in the general case in (10), if _y_  _ps_  0 .

Total urban income from (12) is more consistently increased by rainfall. Given _Z_  0 , if   _y_  _ps_  0 , we can unambiguously show that _dy_ ( _N N A_ ) / _dR_  0 . Increased rainfall raises local farm productivity and all local incomes.<sup>13</sup> With city population modestly affected, total city incomes must rise. However, if  _y_   _ps_ , so that city population declines a lot, we cannot rule out the possibility that urban incomes decline as well.

**Proposition 2.** _If the city has a traded good manufacturing sector that is tiny or non-existent so that_ ( _Lm_   _Ls_  _ps_ ) _< 0, the effect of a decline in moisture on city population is ambiguous and tends to zero as Lm_  0 _when_  _y_  _ps_  0 _. However total city income declines, assuming_  _y_  _ps is not strongly negative_ .

This strict difference between the effect of moisture changes on local city incomes depending on whether manufacturing has a noticeable local presence will inform the empirical work in Section 6.

Whether a city has manufacturing is of course endogenous. The absence of manufacturing implies that the wage the first worker in manufacturing would receive in the city, _c_ , is less than the equilibrium wage in the service sector (<sup>_p b_</sup> _s_<sup>). Manufacturing arises if either local (potential) productivity,</sup> _c_ , rises with, for example, enhanced education, or the price of the manufactured good rises relative to the other goods. This latter case could be driven by changes in international prices or changes in the cost of transporting products between the local city and a port.<sup>14</sup>

# **4. Data on urbanization and climate**

Scarcity of demographic and economic data hampers empirical research on climate effects in Africa. Many countries carry out censuses only irregularly, and sample surveys such as the DHS are infrequent and provide little information before 1990.<sup>15</sup> While there are now a number of geographically detailed climate data sets that are increasingly used by economists (see Auffhammer, Hsiang, Schlenker, and Sobel 2013), most studies have employed national level population and economic data sets which are

> 13 See the expression for changes in per capita income in fn. 11 above.

> 14 Other work such as Atkin and Donaldson (2013) and Storeygard (2014) considers the transport cost story in Africa directly.

> 15 The World Fertility Surveys of the late 1970s and early 1980s (DHS precursors), are less consistently available to researchers.

12
# **1 Introduction**

in countries is than in most rich countries and has re- Inequality developing higher mained high over the past 30 years (Alvaredo and Gasparini, 2015). To what extent can tax systems redistribute income in these countries? In this paper, we combine microdata and theory to shed new light on the redistributive potential and optimal design of consumption taxes, the main source of government revenue in developing countries. We find that consumption taxes are redistributive, lowering inequality by as much as income taxes. These effects are primarily due to different informal (untaxed) consumption patterns along the household income distribution. In addition, we show that, once informal consumption is accounted for, commonly used redistributive policies such as reduced rates on necessity goods have a limited impact on inequality. Our results stand in contrast to the existing literature, which does not take into account the existence of informal consumption, and concludes that consumption taxes have a limited or negative redistributive effect in developing countries (see for example Lustig, 2018).

A major constraint in studying informality is that, by definition, informal sector purchases are hard to observe and to link to consumers’ incomes. We innovate by using the places of purchase reported by households in expenditure surveys to proxy for the share of consumption purchased from the informal sector. Thus, our starting point is the creation of a new micro database that combines expenditure surveys from 31 lowand middle-income countries and contains information on the place of purchase for each transaction (such as street stall or supermarket). To assign each place of purchase to the formal or informal sector, we build on micro evidence from retail censuses and on the literature showing that large modern retailers are much more likely to remit taxes than smaller traditional retailers (Kleven et al., 2016; Lagakos, 2016).

Our makes three contributions. The is to new facts paper first produce stylized on consumption patterns across the income distribution and over development. We document the existence of a downward-sloping Informality Engel Curve (IEC): within each country, the informal budget share declines steeply with household income. The IECs have a stable, log-linear functional form in all countries and their slopes remain negative even after controlling for household location and narrow categories of goods. We provide suggestive evidence that the residual IEC slope may be explained by richer households valuing quality more and formal firms selling higher-quality products, in line with evidence in Faber and Fally (2017) and Atkin et al. (2018b). The shape of the IECs implies that the de facto exemption of the informal sector from taxes makes consumption taxes progressive (the taxed budget shares increase with income). Indeed, 1
# **1 Introduction**

Empty miles are a common feature in many global trucking markets, with ongoing policy interest in ways to decrease the amount of empty driving. Empty trucking miles directly generate unnecessary negative externalities such as pollution and congestion. Beyond these direct effects, empty trips may increase the cost of freight transportation, as future empty trips are internalized by trucking carriers when negotiating prices. Finally, empty trips may affect the returns to other infrastructure policies, such as cost-reducing road investment, and the effectiveness of other public policies. Understanding the mechanisms which generate empty trips is therefore important for informing policies to reduce trade costs and improve the performance of transportation systems.

This paper studies the incidence of empty miles in freight trucking and explores the economic frictions which can generate them. I begin by collecting studies of empty trucking in different contexts, and I synthesize this existing literature in a meta-analysis for a bigpicture estimate of the prevalence of empty miles. After establishing the empirical level of empty trucking, I survey the empirical literature on three main mechanisms: geographic imbalances in demand, search and matching frictions, and regulatory barriers. Finally, I develop a single stylized model which captures these three potential sources of empty trips, and I use this model to evaluate potential policies and interpret historical case studies.

To document empirical patterns of empty shipping, I collect 54 estimates of empty trucking across the economics and transportation literatures spanning 40 years and 27 countries. These studies may be conducted at the trip, truck, or firm level, and differ in measuring empty trips, empty miles, or backhaul probabilities. Using a set of assumptions, I construct a common measure of empty miles and conduct a univariate meta-analysis. I find an average estimate of 29 percent empty miles, or a 42 percent backhaul probability, with a significant degree of variation across the study settings. In a meta-regression, I find that high-income countries have significantly lower empty trip rates compared to non-high-income countries. In a smaller sample of European countries, carriers are more likely to be empty when traveling domestically. In a sample where I observe multiple studies of empty mile rates over time, some countries have experience significant long-run decreases in the empty mileage share, on the order of 5 to 10 percentage points.

Having established the level of empty miles, I develop a conceptual framework to understand the potential mechanisms generating empty miles. I focus on three main mechanisms, and review the empirical evidence behind each mechanism. First, a geographic imbalance in demand for shipping can generate empty miles as carriers serving the route with more 2
# **_Introduction_**

For young women and men, decisions regarding school attendance, sexual and marital partnerships, and fertility shape one’s life-course and have long-term implications on well-being. Such decisions typically involve a trade-off between short-term and long-term utility in the face of much uncertainty. The AIDS epidemic in Sub-Saharan Africa further complicates the framework within which these kinds of trade-offs are considered. First, these decisions directly affect exposure to risk of HIV infection. Young people are well aware that some avenues toward adulthood (for example, leave school, marry soon after, have a child soon after) lead to different risk of exposure to HIV infection than others (Clark, Poulin, and Kohler 2009; Poulin 2007). Second, young people may face these pivotal decisions with uncertainty about their current HIV status and expected life horizon.

Young people in Sub-Saharan Africa are coming of age at a time when AIDS policy emphasizes HIV counseling and testing. International and local public health communities view HIV testing and the counseling that accompanies it as the gateway to treatment. But even for those who test negative, it is also hoped that testing will result in preventive behaviors that slow the spread of the epidemic.<sup>2</sup> There has been a rapid expansion of Voluntary Counseling and Testing (VCT) coverage in Malawi, achieved through outreach and mobilization initiatives such as Malawi’s annual “Testing Week” and increased supply of such services (Angotti 2010; Angotti et al. 2011). Data from the 2004 Malawi Demographic and Health Survey (MDHS) show that only 15 percent of men had been tested during the previous year, whereas data from the DHS 2010 show that 51 percent of men had ever been tested and 31 percent of men reported having been tested in the past 12 months (NSO and ICF Macro, 2011). Women were more likely

> 2 For example, the Government of Malawi (2003) national policy document strongly motivates testing as a general prevention policy through theoretically reducing risky behaviors, as well as a mean to improve access treatment and mother-to-child prevention.

2
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# FIGURE 2

THE SOCLALIST CITY COMPARED TO THE MARKET CITY Moscow Compared to Paris Figure 2.a <!-- Start of picture text --> COMPARATIVE POPULATION DENSITY GRADIENT<br>Between Moscow and Paris Built-up Area<br>300_<br>250-<br>Oituce Ira Cit cets a<br>Source: Instut of Master Plan of Moscow 1992<br><!-- End of picture text --> Figure 2.b <!-- Start of picture text --> ..<br>MOSCOW - LAND USE ANALYSIS<br>Comparative Population Distribution<br>9 7,000,000- , ......... .. . : j,X.; ........ ;.;<br>000000- ............. . .<br>Fi0u00 2.b<br>6,000,000- .<br>4,000,000 ,000,000 - Usyldt' - a. n frtajnii ,i - Ba~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.... .,..; . .. - ......<br>a2 6 . .<br>3,000,000-<br>1,000,000 Souse:Infitte f astr Pn f Msco 192,StPetersburg -.- Isiueo raitcTel<br>Pari Cenus 0 99<br>1 3 5 7 '11 3 1'5 1?7 1'9' 21 3 25 2~7 29~ '31 33 5<br>2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34<br>Distace from City cenmer (kmi)<br>-8~- Moscow -*r- St Petesburs -- w*- PwS<br><!-- End of picture text --> Souac: Institute of Master Plan of Moscow 1992, St Petersburg lnstitute of Urbanistic Theory Paris Census 1990
and medium sized (SMEs), in 104 developing and developed countries, including many low-income countries.<sup>1</sup> We use this database to study what types of financing are important for new firms, relative to older firms. Furthermore, we exploit the large crosscountry variation in our sample to test the effect of differences in institutions on new and young firms. Specifically, our paper addresses two questions: (1) What is the relationship between firm age and the usage of external financing?; and (2) What is the differential impact of the business environment on the usage of financing by younger versus older firms?

To address the first question we investigate the relationship between firm age and usage of different sources of financing, including local and foreign bank financing, leasing, trade credit, credit cards, family and friends, and informal lenders. We specifically focus on the use of formal versus informal finance over the lifecycle of the firms, controlling for other firm characteristics.

To address the second question we look at the interactions of the country-level institutional characteristics and firm age. These interactions show whether the institutional environment has differential impact on new and young firms. We specifically focus on two factors that are important for formal financial contracts - the quality of the legal system (i.e. the rule of law) and availability of credit information. Previous literature shows that the level of development and institutional environment in a country can significantly impact the type of financing that firms are able to access (Beck, Demirguc-Kunt, and Maksimovic, 2007, Brown, Chavis, Klapper 2008). We extend this research and investigate whether particular features of the institutional environment are more or less important to young firms, relative to older firms.

> 1 The complete questionnaire and database is available at http://www.enterprisesurveys.org.

4