rafmacalaba/datause-encoder-data huggingface dataset:
where IlcI (respectively Ilul) is the determinant of Qc (respectively _Du), T_ the number of observations, and c the number of parameters (equal to the number of lags times the number of variables, plus one for the constant term) estimated in each equation of the unrestricted system. This statistic has a x<sup>2</sup> distribution with degrees of freedom equal to the number of restrictions in the systern, equal to one times the number of lags.

# **IV. An Application to Brazil, 1981-99**

The above procedure was applied to Brazil, using annual data over the period 1981-99. Brazil is an interesting case to test the methodology described in the previous section because of the availability of sufficiently long time-series data to ensure adequate degrees of freedom. It is also one of the few countries for which several ongoing studies have focused on assessing the impact of macroeconomic variables on poverty. Paes de Barros et al. (2000), for instance, in a study based on micro-simulation techniques, found that unemployment has a major impact in relative terms on the behavior of poverty rates.<sup>9</sup> The variables included in the VAR, in addition to the (log of the) output gap, are the cyclical components of the (log of the) aggregate unemployment rate, the real minimum wage (which plays a key role in the distribution of wages in Brazil, as noted for instance by Neri and Thomas (2000)), and the poverty headcount index. Capacity output (with output measured by real GDP) is measured using a univariate technique, the Hodrick-Prescott (HP) filter (see Hodrick and Prescott (1997)).1° More precise definitions of these variables are

> 9 Neri and Thomas (2000) analyzed the effects of aggregate shocks on the poor in Brazil, using incomes rather than consumption expenditures. See De Ferranti et al. (2000, pp. 76-77) for a discussion of their results.

> 10 It should be kept in mind, however, that because of the difficulties involved in measuring capacity output, the output gap is likely to be subject to a significant margin of error.

16
data comparisons over time suggests that business exits were much less severe than were expected early in the crisis, likely due to swiftly enacted government policies (Crane, Decker, Flaaen, Hamins-Puertolas, and Kurz, 2021).

Thus, we argue that the swift, widespread, and relatively quickly acknowledged turnarounds in GDP growth and unemployment rates and employment, as well as the improvements in personal incomes, poverty rates, credit scores, consumer debt, and business exits clearly describe a favorable “economic surprise.” To be sure, the U.S. economy may not be completely out of the woods as of this writing in mid-2021. Supply bottlenecks, the withdrawal of economic and monetary stimuli, and most importantly the spread of the very infectious Delta variant could still lead to a deceleration and undermine recovery. However, we argue that the ultra-fast economic recovery to date qualifies as the “economic surprise” of this crisis.

# **4. The U.S. Banking “Surprise” during the COVID-19 Crisis**

The very steep slide in the U.S. and global economies during COVID-19 might have been expected to result rapidly in banking crises due to large credit losses and uncertainty about future prospects. However, more than a year later, we are surprisingly unaware of banking crises to date anywhere in the world, although there are areas of significant concern (e.g., OECD, 2021).

In this section, we dig deeper to explore the impact on the U.S. banking system primarily using raw data from regulatory Call Reports from 2019:Q4 to 2020:Q4, as well as other information. What we see supports our claim of a favorable “financial surprise” for the U.S. banking system during the COVID-19 crisis. Not only was there no banking crisis, but the banks performed very well and did not suffer any noticeable financial distress.

This is not an econometric analysis, but a rough look at how the industry performed during some very volatile economic circumstances from before the crisis to well into the economic 16
In this paper, we employ instrumental variable estimation to disentangle demand-side from supply-side effects in explaining the relation between capital inflows and equity issuances. We make use of the fact that, for a given amount of capital inflows to emerging markets as a whole, positive shocks to _other_ countries’ attractiveness to foreign investors constitute negative shocks to the subject country’s supply of funds. Our instruments capture changes in the attractiveness to foreign investors of other emerging market countries (for a given total amount of inflows, which we capture by a time fixed effect). Valid instruments should be strongly correlated with capital inflows and should also satisfy the exclusion restriction that they are not correlated with demand-side influences within the subject country.

We employ three alternative measures as instruments. To start, we instrument the equity inflows received by a country with the lagged weight of that country in the MSCI Emerging Markets stock index. The MSCI weight of a country depends primarily on the market capitalization of that country’s stock market, relative to the global market capitalization of 25 emerging economies. The weight also depends, to a lesser extent, on MSCI’s adjustments to country weights for factors that they regard as relevant to foreign investors. Because all country weights sum to 100%, variation in a country’s weight in the MSCI index should primarily reflect shocks to the market values of the other 24 countries’ stock markets (and to MSCI’s adjustments to country weights), which are plausibly exogenous to subject country demand-side shocks. That should be especially true for small countries.

Changes in MSCI weights should affect capital inflows not only as an indicator of market value changes in other countries, but also because some foreign investors, such as emerging market mutual funds, follow closely the MSCI index when setting their portfolio holdings (Raddatz et al., 2017). When those investors receive funds from their ultimate fund suppliers, they invest those funds into emerging economies’ equity markets according to the 14
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These policies could be implemented by furloughing workers, who then receive unemployment insurance benefits, or transferring resources to firms so that they pay the wages of employees who are “unemployed” within the firm. This sort of policies were adopted by New Zealand and the United States, among other countries. For example, the Small Business Pulse Survey conducted by the US Census Bureau reports that over 70% of small businesses received assistance from the PPP and other programs. During the shutdown, their cash-on-hand increased and the arrears in servicing loans and other costs decreased.

The cross-country differences in the speed of recovery could be the result of weaker balance sheet support policies, longer lockdowns, or reallocation shocks. In our interpretation, the slow recovery of employment and output in the United States relative to New Zealand is the consequence of a more prolonged lockdown, which could also have induced a reallocation shock that would not have materialized under a shorter lockdown. Concerning the latter, there is mounting evidence in the US that this is indeed the case. Barrero et al. (2020) report evidence on reallocation from an online survey of businesses, showing that excess job and sales reallocation across firms rose sharply since the pandemic struck. The Business Formation Statistics (BFS) reported in Figure 3 and Dinlersoz et al. (2021) also suggest a reallocation shock, manifested by a surge in new business applications, following a temporary contraction in the first weeks of the pandemic.<sup>8</sup> We assess the implications of a reallocation shock in our model in Section 5.

# **3 Modeling the Great Shutdown**

Building on Buera et al. (2015), we model an economy with entrepreneurial production subject to financial and labor market frictions. There is a continuum of individuals, who are heterogeneous in their entrepreneurial productivity, employment opportunity, and wealth. Furthermore, an unanticipated shock can shut down a fraction of them temporarily, but since it is completely unanticipated, it has no impact on peoples’ behavior ex ante.

Access to capital is determined by entrepreneurs’ wealth through a simple collateral constraint, motivated by the imperfect enforceability of capital rental contracts. One entrepreneur can operate only one production unit in a given period. Entrepreneurial ideas are inalienable, and there is no market for managers or entrepreneurial talent.

We assume that there is a centralized labor market where hiring entrepreneurs compete for available workers. The arrival of unemployed workers into this centralized hiring market is subject to frictions modeled as a simple matching function.

> 8See also Guilford and Scott (2020), who report a version of this figure and also provide real world examples.

7
# **<mark>Table</mark>** **<u><mark>S2. Knowledge of actions that can reduce exposure to COVID-19</mark></u>**

||Soap reduces<br>risk|Avoid<br>physical<br>greetings|Use masks or<br>gloves|Stay at home|Avoid<br>crowds|Maintain<br>distance of<br>one meter|
|---|---|---|---|---|---|---|
|Ethiopia|0.006|0.459***|0.299***|0.540***|0.295***|0.193***|
||(0.005)|(0.022)|(0.027)|(0.022)|(0.024)|(0.021)|
|Nigeria|-0.013*|0.298***|0.372***|0.544***|0.314***|0.058**|
||(0.007)|(0.026)|(0.026)|(0.023)|(0.024)|(0.025)|
|Uganda|0.010**|0.462***|0.585***|0.617***|0.381***|0.161***|
||(0.004)|(0.021)|(0.021)|(0.020)|(0.022)|(0.021)|
|Ethiopia-Nigeria|0.007|0.000|0.003|0.838|0.218|0.000|
|Ethiopia-Uganda|0.340|0.585|0.000|0.000|0.000|0.006|
|Nigeria-Uganda|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|9,127|9,127|9,128|9,127|9,127|9,128|
|R-squared<br>_Note_: Each column r|0.006<br>eports results from|0.122<br>a single regres|0.078<br>sion of a binary var|0.160<br>iable equal to 1 if|0.067<br>the respondent|0.038<br>was familiar with|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent was familiar with the action and 0 otherwise. The binary variable is then regressed on indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

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# Determinants of MDG Achievement

It is obvious that social services, without doubt an important component of MDG achievement, cannot be used if there is no supply. However, the above framework has illustrated that a given service supply only represents a hypothetical maximum amount of service utilization—because of the significance of demand-side factors. Therefore, the following review will mainly be concerned with evidence that sheds light on the relative importance of these two sets of service-utilization determinants. We then briefly discuss possible complementarities in service utilization that partly become effective through the influencing of relevant household characteristics, as described in equation (4) above. The focus of our review subsequently shifts to human development outcomes and the links between social service utilization and these outcomes.

On the supply-side, some of the elementary provisions need to be fulfilled. Social service facilities, particularly schools, health clinics, or vaccination posts, need to be accessible. Buildings, equipment, and trained personnel therefore need to be available. Recent evidence suggests that absenteeism of teachers and health staff is a substantial problem in many developing countries (Chaudhury et al., 2006). The presence and qualification of personnel is a decisive factor for the quality of the service offered, although the best doctor will not be able to help much without the necessary drugs. In the health sector, poor quality of either equipment or health staff may even pose a threat to those who seek treatment. Similarly, it is certainly not only the number of schools that matters, but also the quality of teaching.

The importance of these factors is well documented for the education sector.<sup>11</sup> These supplyside factors include not only the distance to a school (Glewwe and Jacoby, 1994; Bommier and Lambert, 2000; Handa, 2002; Glick and Sahn, 2006) but also a number of quality-related variables ranging from more teachers (Case and Deaton, 1999; Chin, 2005; Glick and Sahn, 2006)<sup>12</sup> to building conditions (Drèze and Kingdon, 2001; Bedi and Marshall, 2002; Handa, 2002; Glick and Sahn, 2006). Using a school construction program as a natural experiment, Duflo (2001) also documents the positive effect of supply-side interventions, namely, the building of more schools in areas with low enrollment rates. An evaluation of the Bolivian Social Investment Fund’s school upgrading projects (Newman et al., 2002), however, finds only limited (and often insignificant) effects as a result of better building and equipment conditions in schools.

> 11 See Glewwe and Kremer (2006) for a detailed survey.

> 12 Better teachers and/or teachers who are present more often may also attract students. Direct evidence of this relationship is scarce, but there is a related literature that examines incentive schemes for teachers, for example, Glewwe at al. (2003) and Duflo and Hanna (2006).

14
Last but not least, formality is positively associated with better access to formal sources of financing at start-up. Columns 3-4 of Table 2 show that, controlling for other factors, more formal firms are more likely to have received some portion of their initial capital from formal sources, which are defined as private and public banks and micro-finance organizations.<sup>20</sup> The EMES data on access to credit shows that formal sources of financing tend to offer credit on better terms, which in turn positively affects the profitability of firms who are able to access these sources. Clearly, these results fall short of establishing causality between formality and access to finance, because we do not observe the formality status of firms at start-up.<sup>21</sup> However, as shown in Figure 10, firms who complied with regulatory requirements from the start do have a higher likelihood of having benefitted from formal sources of financing; on the other hand, due to a relatively small number of firms who actually obtained credit from formal sources, the standard errors are too large for the differences to be statistically significant. Therefore, the evidence on a positive link between formality and improved access to credit in Table 2 and Figure 10 is suggestive, but not conclusive.

**Figure 10: Formal financing and time of registration**

<!-- Start of picture text --> 0.30 0.30<br>0.20 0.20<br>0.10 0.10<br>0.00 0.00<br>Reg. from start <1 yr 1-2 yrs 3-4 yrs >4 yrs Never reg. Reg. from start <1 yr 1-2 yrs 3-4 yrs >4 yrs Never reg.<br>Time between start-up and registering for a municipal license Time between start-up and registering for a RUC<br>Likelihood of obtaining finance from formal sources Likelihood of obtaining finance from formal sources<br><!-- End of picture text --> _Source_ : Authors’ calculations with EMES data.

# **6. Conclusions**

In order to understand the links between formality and firm profitability, this paper relies on a new survey of micro and small firms in the urban areas of Ecuador collected specifically for the purposes of this study. The paper finds that most entrepreneurs face substantial difficulties in performing daily businessrelated activities, access to credit is limited, and firm revenues and profits relatively low. Defining formality as compliance with a broad spectrum of legal and regulatory requirements, the paper finds that the vast majority of firms operate somewhere along the informality continuum, with compliance varying substantially by firm size and type of regulation.

> 20 Public banks include Banco Nacional de Fomento and Corporación Financiera Nacional. The micro-finance category also includes financing received through Credito de Desarrollo Humano (CDH).

> 21 Knowing whether a firm received a loan from a formal source in the last year does not resolve the issue because, for example, more formal firms could have a lower demand for loans due to their higher profitability which could allow them to use retained earnings instead of credit.

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villages, but wholesale prices are regularly quoted in major market centers. Khandsari is traded separately from mill sugar in wholesale markets, in the 1980s and 1990s at discounts of about 10 to 20 percent from mill sugar prices. However consistent price series are only available for Bareilly in UP and Delhi. The special role of khandsari and gur production in the Indian sugar economy is discussed in World Bank ( 1981) Goldar and Gulati (1991) and Kalra and Gulati (1992).

Sugar cane is processed in the rural areas where it is grown. For mill sugar, in order of production ( Fig 8), the four most important states are Maharashtra, UP, TN , and Karnataka . The average recovery rate for mill sugar is higher in Maharashtra than in other states. In 1992 it was 11.19 percent compared to 9. 19 percent in UP and an all India average of 10.02 percent.<sup>9</sup> About half the cane grown in UP, Tamil Nadu and Kamataka is used for gur and khandsari.. In the early 1990s UP accounted for roughly 60 percent of national gur and khandsari production. Tamil Nadu about 13 to 15 percent each, and all other states combined between 10 and 14 percent.

Figure 8: **Mill sugar production by state for the crop year 1993-94** <!-- Start of picture text --> O ihar 7 6%<br>Rajasthan<br>0. I%<br>ar=LIt? ~~~~~~~~~8 ~Punjab<br>Karnataka 29<br>7.7%<br>Haryana<br>2.8% MP g||<br><!-- End of picture text -->

Source: Indian Sugar Yearbook, 1995 After textiles, sugar milling is the second largest agro-based industry in India. In the 1991-92 season there were 406 sugar mills, of which 392 were operating and employing about 350,000 people directly. On average mills receive cane from about 30-35 thousand growers. The total number of farmers growing at least some cane in this year was estimated to be about 35 million. In 1993 national sugarcane output was valued at Rs 11,439 crores, or about $US 3.7 billion. ' India is also the world's largest consumer of sugar. Consumption of mill sugar has more than doubled over the past three decades. In the first half of the 1980s consumption grew at 13.2 percent a year, but the growth rate slowed to about 5.5 percent per annum after 1985. As disposable incomes have increased consumption has shifted from gur and the cruder forms of

> 9Indian Sugar Mills Association, _Indian Sugar Yearbook(I_ 991/92) Table5. p.13. 10 Central Statistical Organisation, _National Accounts Statistics,_ 1995.

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**Table 3 Unanployment, Labor Force Participation and Employment Rates by Age and Gender** (%)

|||Allwo|rkers|||Men|||Women||
|---|---|---|---|---|---|---|---|---|---|---|
||15t64|1524|||||||||
||o|to|25to54|55to64|15to24|25to54|55to64|15to24|25to54|55to64|
|Lithuania (May2001)<br>|||||||||||
|Unemploymentrate<br>|17.2<br>|32.7|15.7|12.3|36.0|17.5|16.1|27.6|13.8|7.7|
|Laborforce participationrate<br>|68.7<br>|31.4|87.2|23.3|37.5|90.3|33.6|25.2|84.2|16.9|
|Employmentrate|56.8|21.2|73.5|20.4|24.0|74.5|28.2|18.2|72.5|15.6|
|OECD(1999)<br>|||||||||||
|Unemploymentrate<br>|6.4|11.8|5.4|5.2|11.7|4.9|5.6|11.9|6.1|4.6|
|Laborforceparticipation rate<br>|70.4<br>|53.0<br>|80.3|51.6|57.8|93.0|64.5|48.0|67.8|39.4|
|Employmentrate|65.9|46.7|75.9|48.9|51.1|88.5|60.8|42.3|63.6|37.6|

> _Source: Lithuania: Labor Force Survey, May 2001; Bank staffcalculations._

_OECD: Employment Outlook 2000._ 8
Kingdom, and the United States.<sup>9</sup> Together they accounted for more than 68 percent of total FCS in the major Latin American countries as of 2002.<sup>10</sup> The complete dataset was used in the regression analysis of section 6, where the data were also deflated by the U.S. Producer Price Index (PPI).<sup>11</sup> Although the agencies that collected the OECD and UNCTAD databases vary from one source country to another, they remain the same within the host countries, which facilitates international comparisons, especially in econometric analyses that control for source-country effects as in section 6 below.

Data for total outward stocks from the U.S. were taken from the Bureau of Economic Analysis.<sup>12</sup> These stocks are reported on a historical cost basis in millions of U.S. dollars. These data are of particular interest for our purpose due to the visible presence of U.S. multinational corporations in Latin American countries as well as in China, Hong Kong (China), and India. An advantage of these data is that it was collected by the same agency. Finally, we take the U.S. stocks in the manufacturing sectors of the host countries from the same source. Again, these data are of special interest since companies in this sector seem to be potentially more inclined to relocate production to China or India as they search for reductions in labor costs.

To make the analysis more tractable and due to data availability on bilateral stocks from the OECD, we focus on nine Latin American countries. We include the major countries (Argentina, Brazil, Chile, Colombia, Mexico and Venezuela) as well as some Central American countries that may be of particular interest (Costa Rica,

> 9 Stocks from Spain were calculated by accumulating the flows, which were taken from the OECD. Japan does not report stocks in 1995 for any Country. It does not report stocks in any year for Colombia, Costa Rica, Guatemala, Venezuela and in Argentina and Chile since 1996. 10 UNCTAD  2004. This figure does not include stocks into Mexico. However, FDI flows from these countries comprised more than 90 percent of Mexican FDI inflows in 2002.

> 11 The PPI was taken from the International Monetary Fund’s _International Financial Statistics_ database. 12 www.bea.org

6