rafmacalaba/datause-encoder-data huggingface dataset:
26

|AR (1)|-4.13***|-4.08***|-3.93***|-4.29***|-2.99***|
|---|---|---|---|---|---|
|AR (2)|0.73|0.93|1.09|0.37|1.40|
|Number of||||||
|observations|1,035|1,010|1,012|1,128|1,144|
|Number of<br>countries|67|65|63|65|65|

Note: Base sample is the extended sample from Table 1. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. System-GMM specification analogous to explanation in table 1. The sample in column 5 includes all available observations, independent of whether hyperinflation was present or not.

The column 5 specification removes the hyperinflation adjustment that was applied to all expenditure data to avoid spurious effects induced by the timing of elections under hyperinflation. Consistent with the earlier discussion, column 5 shows that after reversing this adjustment and putting these observations back in the data, the magnitude of the party effect actually increases. One explanation for the increased magnitude of the estimate in column 5 is that pre-electoral outlays occurred late in the fiscal year. In high-inflation environments, the nominal value of outlays at the end of the fiscal year is a much larger fraction of national income than outlays at the beginning of the fiscal year.

Table 3 displays results using the two alternative measures of party institutionalization, the age of the largest government party less the leader’s years in office, _ruling party age,_ and the survey measure of citizens’ confidence in parties, _party confidence_ . As usual, the table reports only results using the most appropriate specification, System-GMM, as in column four in Table 1. Again, the key variable is the interaction effect of the party variable with the election dummy. Columns one and two examine (age of largest government party – ruler years in office) in our base sample and in the original Brender-Drazen sample. Using either sample, political budget cycles are significantly smaller when ruling party age is larger.
**Table 2: Coverage**

||**Number of coun**<br>With<br>|**tries covered**<br>|**Percent ofpopul**<br>With<br>|**ation covered**<br>|
|---|---|---|---|---|
|**Income group/region**|retrospective<br>data|Total|retrospective<br>data|Total|
|**High-income countries**|38|38|93%|93%|
|**Developing countries**|76|115|86%|98%|
|East Asia and Pacific|8|18|92%|99%|
|Europe and Central Asia|20|20|99%|99%|
|Latin America and the Caribbean|15|16|95%|97%|
|Middle East and North Africa|6|10|51%|83%|
|South Asia|5|8|89%|100%|
|Sub-Saharan Africa|22|43|72%|97%|
|**World**|114|153|87%|97%|

_Note:_ The table shows the number of countries our database covers, and the population share that these countries account for.

# **3.3. Coding of education data**

To improve comparability of estimates between surveys, a globally harmonized categorical measure of educational attainment is constructed. This measure represents the lowest common denominator across surveys, thus invariably reducing the amount of detail exploited in some countries. With minor exceptions, all surveys contain the following five categories, which are based on UNESCO’s International Standard Classification of Education (ISCED): (i) less than primary (ISCED 0), (ii) primary (ISCED 1), (iii) lower secondary (ISCED 2), (iv) upper secondary or postsecondary nontertiary (ISCED 3–4), and (v) tertiary (ISCED 5–8).

The categories refer to the highest education level completed by the respondent. A few highincome countries do not have data on the first of these five categories. Since these countries have mandatory schooling at the primary level, it is assumed that all individuals have completed at least primary in these cases.<sup>11</sup> Respondents who are younger than 21 years old or are still enrolled in school are excluded. The only exception to this rule is respondents who are enrolled in school, have completed upper secondary, and are 21 years or older. It is assumed that these individuals complete the lowest tertiary degree (ISCED 5).<sup>12</sup>

> 11 This concerns some economies where LITS or ESS is used, as well as the United States; Taiwan, China; the Republic of Korea, Japan, Canada, and Australia. In these economies, no category below primary exists. For a few other countries, it was unclear how to code respondents who have completed Koranic or other religious schools. In general, we assume that these respondents completed education corresponding to the primary level.

> 12 This assumption may matter for the estimates. If most individuals who are enrolled in tertiary and 20 years or older end up completing a master’s degree (ISCED 7), they are assigned too little education. If these individuals, simultaneously, tend to have highly educated parents, mobility is underestimated. If these individuals instead were dropped from the analysis, representativeness would likely decrease.

12
worker upskilling and alternative models for revenue sharing in the economy (Guellec and Paunov, 2019; Autor et al., 2020).

The main contribution of our study relative to the growing literature on the pandemic’s effects on trade is the emphasis on the role of product resilience. We provide empirical evidence to inform two debates: whether reliance on inputs from abroad, and China in particular, caused more vulnerabilities and how social distancing measures affected global production and exports. Thereby, our paper adds to the studies of the impacts of COVID-19 on trade and GVCs (e.g., Bonadio et al. 2021; Cerdeiro and Komaromi, 2020; Crozet et al. 2021; Demir and Javorcik 2020; Espitia et al. 2022; Fujiy, et al., 2021; Lachitew and Socrates, 2020). Using cross-country-productmonth level data, Berthou and Stumpner (2021) show that lockdown measures implemented by exporter and importer countries impacted trade. Bricongne et al. (2021), Pimenta et al. (2021), and Lucio et al. (2021) also show that lockdown stringency in destination markets reduced, respectively, French, Portuguese, and Spanish firms’ exports at the onset of the COVID-19 crisis.

Closer to our work, Liu et al. (2021) show better performance in the early phases of the pandemic of Chinese exports of medical products, products with a high share of work from home, high contract intensity, and capital goods, while Lafrogne-Joussier et al. (2021) look into one production vulnerability, the reliance on inputs from China, showing that French firms with higher such reliance suffered more input shortages that translated into a decline in their exports through input-output linkages. These two papers differ from ours in that they focus on single countries’ exports (China and France) and single or few specific dimensions of how production was affected by COVID-19, while our analysis covers exports by all countries to the 29 largest markets and an encompassing set of product vulnerability dimensions. Differently from these studies, our paper 5
# **Korea's Trade in Auto Parts**

<!-- Start of picture text --> 7,000,000,000<br>6,500,000,000<br>6,000,000,000<br>5,500,000,000<br>5,000,000,000<br>4,500,000,000<br>Imports<br>4,000,000,000<br>Exports<br>3,500,000,000<br>3,000,000,000<br>2,500,000,000<br>2,000,000,000<br>1,500,000,000<br>1,000,000,000<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> 9
Policy Research Working Paper 7599

# **Abstract**

This article examines how a policy oriented toward a specific group within the population can have collateral effects on the economic decisions of other groups. In 1996, the Chilean government approved the extension of the school day from half- to full-day school. This article exploits the quasi-experimental nature of the reform’s implementation by time, municipality, and age targeting of the program in order to examine how the maternal labor supply is affected by the childcare subsidy implicit in the lengthening of the school day. Using data from the Chilean socioeconomic household survey and administrative data from the Ministry of Education for 1990–2011, the authors estimate that, on average, there is a 5 percent increase in labor participation and employment rates of single mothers with eligible children (between 8 and 13 years old) with no younger children, who are the group that would be mainly affected by the policy. No significant labor supply responses are detected among others mothers with eligible children.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dcontrer@fen.uchile.cl and pausepulv@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Data Sources

Inflation: consumer price index (4th quarter average:<sup>IFS, line 64). CPI</sup> for 1989 are World Bank staff estimates Bank reserves: IFS, line 14-14a (14-14a-14e for Ghana).

Currency outside banks: IFS, line 14a.

Government loans: IFS, line 22a.

Non-government loans: IFS, lines 22a+22d+22f for Cote<sup>d'Ivoire and</sup> Nigeria, 22c+22d for Ghana, 22a+22d for Zambia,<sup>and 22a+22b+22c+22d+22f</sup> for Kenya.

GDP: IFS, line 99b. For late years World Bank<sup>staff estimates are used</sup> (1987 Cote d'Ivoire, 1988 Ghana and Cote<sup>d'Ivoire, 19'</sup> all five countries).

M2: IFS, line 34+35.

International Interest Rate: LIBOR US$, IFS line<sup>60ldd.</sup> Exchange Rate: IFS, line ae for Cote d'Ivoire<sup>and Kenya and line ag for</sup> Ghana, Nigeria, and Zambia.

Treasury bill rate: IFS, line 60c (4th quarter<sup>average).</sup> This was augmented for Cote d'Ivoire by the money market<sup>rate from La Zone Franc</sup> and World Bank files; for Ghana by the Bank<sup>of Ghana Annual Report and</sup> for Nigeria by the Central Bank of Nigerial Annual Report.<sup>As explained</sup> in the text, assumed gap between actual and market<sup>lending rates were</sup> measured by comparing actual Treasury bill rates with estimates<sup>of market</sup> clearing rates.

Parallel market exchange rate: World Currency Yearbook<sup>for 1978-86 and</sup> Currency Alert for 1987-89.

Government Revenue: calculated from the government<sup>revenue to GDP ratio,</sup> in African Economic and Financial Data (1989) Cote<sup>d'Ivoire 1980-85,</sup> Ghana 1980-86, Kenya, Nigeria and Zambia,<sup>1980-87.</sup> Other years extrapolated using the somewhat different series in<sup>Trends Ln Developing</sup> Economies (1989).

Other data including the TPS for Cote d'Ivoire before<sup>1986 and the rate</sup> of remuneration on bank reserves were based<sup>on World Bank files and some</sup> extrapolation.

37
# **1. Introduction**

The financing of small and medium enterprises (SMEs) has been a topic of keen interest in recent years because of the key role that SMEs play in economic development and their potentially important contribution to economic diversification and employment (Ayyagari et al., 2007; Beck et al., 2005a). In developing economies including Sub-Saharan Africa, SMEs are typically more credit-constrained than large firms, severely affecting their possibilities to grow (Beck et al, 2005b; Beck and Demirguc-Kunt, 2006; Beck et al, 2006; Ayyagari et al, 2008; Beck et al, 2008a; Ayyagari et al, 2012). As regards extending financing to SMEs, banks have an important role to play in Sub-Saharan Africa due to their dominance in the financial systems and the limitations of informal finance, especially as regards serving the higher end of the SME market (Ayyagari et al, 2012). Other external financing options such as corporate bond and organized securities markets are typically only accessed by larger firms requiring longer-term funding (Beck et al, 2008a).

The extent to which commercial banks lend to SMEs depends on a range of country- and bank-specific factors. Among the main factors impacting bank financing for SMEs are _inter alia_ the macroeconomic environment, the legal and regulatory framework, the state of the financial sector infrastructure, bank-internal limitations in terms of capacity and technology, and SME specific factors, particularly the SME landscape in terms of number, size, and focus of operation, as well as the opaqueness of information (Berger and Udell, 1998; Beck et al, 2008b; De la Torre et al, 2010; Beck et al, 2011). Due to the opaqueness of SMEs, i.e. the challenges associated with ascertaining the reliability of information provided, it has conventionally been assumed that small and domestic banks applying relationship lending are better equipped to lend to SMEs (Berger and Udell, 1995; Berger et al, 2001; Mian 2006; among others). Recently, however, this view has been challenged with the argument that large and foreign banks are also able to lend to SMEs effectively by using arms-length technology and centralized organizational structures (Berger and Udell, 2006; Berger et al, 2007; de la Torre et al, 2010; Beck et al, 2011). Supply side factors that have been shown to positively contribute to bank financing of SMEs are improvements in credit information sharing, better property and creditor rights and increased judicial efficiency, as well as a stronger collateral infrastructure, for instance through movable credit registries (Love and Mylenko, 2003; Miller, 2003; Jappelli et al, 2005; Quian and Strahan, 2
# **1. BACKGROUND AND MOTIVATION**

_Why are some cities more successful than their peers?_ Is the ‘success’ of individual cities driven by factors mostly external to any city’s immediate control (location, growth in market potential, being a port in a period of national trade growth, national level decentralization and improved governance), or do individual city policies and politics influence growth and development? Disentangling the relative contribution of regional and local efforts is important for understanding the potential of alternate policy interventions for stimulating growth of cities across the national urban system. At this time, there is very little research examining the effectiveness of local and national policy environments on urban growth in developing countries.

Brazil is a highly urbanized country – 80 percent of its population lives in urban centers and 90 percent of GDP is created in cities. According to estimates by the UN Population Division for Brazil, the entire growth in population that is expected over the next three decades will be in cities where the national urbanization rate is expected to rise to over 90 percent (UN 2003). This will add about 63 million people to Brazil’s cities, and total urban population will be over 200 million. This population growth is occurring across the Brazilian urban system (Table 1; see also Lemos et al. 2003). Of the 123 major urban agglomerations in Brazil, only three were above 2 million people in 1970 versus ten in 2000. In the middle of the size distribution in 2000, there were 52 agglomerations with population between 250,000 and 2 million people compared to 25 in 1970. Thus, not only is the scale of urbanization a major concern, but the distribution of population across the urban hierarchy will also challenge policy makers to devise appropriate policies for 2
# **Beyond Baseline and Follow-up:**

**The Case for More T in Experiments**<sup>*****</sup>

David McKenzie, _World Bank_ _Keywords:_ Randomized Experiments; Multiple Measurements; Program Evaluation.

_JEL codes:_ O12, C93.

> * I thank Chris Woodruff and participants at the Warwick University summer workshop on Firms in Development for conversations and questions which lead to this article; Chris Blattman, Miriam Bruhn, Jishnu Das,  John Gibson and Berk Özler for helpful comments; Matthew Groh for research assistance; and Miriam Bruhn, Jishnu Das, and Markus Goldstein for sharing their data.
# **Figure 15. Sample Means by Usage of Credit Products**

<!-- Start of picture text --> Sample means by Usage of Credit products<br>No acces to financing<br>1<br>Access to financing<br>0.87<br>0.9<br>0.78<br>0.8<br>0.68<br>0.7 0.64<br>0.6<br>0.5<br>0.41<br>0.4<br>0.3<br>0.3 0.24<br>0.2<br>0.2 0.15 0.17<br>0.1<br>0<br>Operating Income Any Investment IK Retained Earnings Retained Earnings<br>to sales Variables (Investment) (Working Capital)<br>Companies which have overdaft or line of creadit or loan<br>Sample Mean<br><!-- End of picture text --> Source: ICA data The usage of credit products is not significantly related to our proxy for cash flow, but, as shown above, it clearly is an important determinant of investment. While our results cannot be interpreted in a casual manner, they are suggestive of the positive impact increased usage of financial products might have on stimulating investment into productive assets. While we cannot test for this directly, these results are also consistent with a possibility that firms without access to external finance will need to accumulate financial savings before they can make investment.

Firms that claim they have no demand for loans have higher cash flows. This may explain why these firms claim that they have no demand – their cash flows are adequate for their financing needs. They do not make any more investment relative to firms that have demand for more loans.

Firms that applied and were rejected for loans have lower cash flows. This might be a reason for the loan rejection. Surprisingly, these firms also have higher investment than other firms, perhaps suggesting that they have strong investment opportunities and that is why they have applied for a loan.

26
# Effects of external and domestic lockdowns on intermediate imports that were not exposed to NTMs

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than by providing inter-island service. There is not much island-to-island tourism. Tourists tend to arrive, stay in their chosen resort, and leave. The inter-island system offered by LIAT, therefore, is more targeted to government, business, and friends & family travel.

**Table 10: Airlines serving** **<u>passengers within the  small island states (2012)</u>**

|**IATA**<br>**Code**|**Airline**|**2012**<br>**Estimated**<br>**Seats**|**Share**<br>**2012**|
|---|---|---|---|
|LI|Leeward Islands Air Transport(1974)Ltd.(LIAT)|550,563|80.0%|
|BW|Caribbean Airlines|108,186|15.7%|
|5M|Joint Stock CompanySIAT(Sibaviatrans)|27,170|3.9%|
|V0|Conviasa|2,288|0.3%|
|Total||688,207|100%|

_Source: Authors’ calculation on data provided by Diio (2014). Note: Estimates exclude British Airways._ The airline operates a fleet mix including a series of Dash-8 turboprop aircraft that are now on average over 20 years old ( <mark>Planespotters.net, 2014)</mark><sup>15</sup> , and some recently acquired new ATR 42s and 72s acquired from France. Admittedly, LIAT operates in a challenging environment with thin markets, and by its own accounts (Government of St. Kitts and Nevis 2012) states that 35 percent of its 112 daily flights are not profitable. LIAT currently is not able to interline with large, intercontinental carriers. This means that the airline does not act as a re-distributor of incoming tourists from outside the Caribbean (except for two islands), but rather as a carrier for Caribbean residents. The airline has admitted that 39 of its daily routes can be considered “social”, i.e. they are not sustainable.

Two islands, though, currently depend heavily on the regional network for their tourist arrivals: Dominica and St. Vincent. Runway lengths on both islands limit operations to turboprop aircraft. The failure of the inter-island network is most likely one of the reasons St. Vincent is now building a large, new international airport, and is no longer planning any additional investments in LIAT.

The internal traffic of the small island states has shown a steady decline since its recent peak in 2007 (Figure 15a). This trend is highly correlated with LIAT’s history of flights and capacities (Figure 15b).

> 15 The timeliness of the web site is not known. The site reports there 15 aircraft and one stored, however, a recent fire that consumed the maintenance hangar in Antigua on June 10, 2012 also destroyed an aircraft being serviced.

40