rafmacalaba/datause-encoder-data huggingface dataset:
Gregorio Impavido, Alberto R. Musalem, and Thierry Tressel

# **REFERENCES**

- [1] **Aghion, P., Bachetta, P., and A. Banerjee** (2000), Currency Crisis and Monetary Policy in an Economy with Credit Constraints, Harvard University, mimeo.

- [2] **Allen,** F., **and Santomero, A. M.** (1998), The Theory of Financial Intermediation, _Journal of Banking and Finance,_ 21, 1461-1485.

- [31 **Barth, James, Gerard Caprio and Ross Levine** (2001) Bank Regulation and Supervision: What Works and What Doesn't, World Bank, mimeo.

- [4] Beclh **I., De mirguc-Kunt, A., Levine, R., and V. Maksimovic** (2000), Financial Strucrurr and Economic Development: Firm, Industry and Country Evidence, _World Bank Po. icy Research Working Paper,_ No. 2423.

- _[5]_ **Bolton, P., and X. Freixas** (2000) Equity, Bonds, and Bank Debt: Capital Structure and Financial Market Equilibrium under Asymmetric Information, _Journal of Political Economy,_ vol.108, No.2.

- [6] **Caballero Ricardo J.** (2001) Macroeconomic Volatility in Latin America: a View and Three Case Studies, mimeo MIT.

- [7] **Caballero Ricardo J. and Arvind Krishnamurthy** (2000) Dollarization of Liabilities: Underinsurance and Domestic Financial Development, mimeo MIT.

- [8] **Caprio, Gerard, Jr. and Asli Demirguc-Kunt** (1997) The Role of Long Term Finance: Theory and Evidence, Policy Research Department, The World Bank.

- [9] **Caprio, G., and L. Summers** (1993) Finance and its Reformn, Beyond LaissezFaire, World Bank Policy Research Paper 1171.

- [10] **Catalan, M., Impavido G., and A. R. Musalem(2000)** Contractual Savings or Stock Market Development: Which Leads? _Journal of Applied Social Science Studies,_ 120(3): 445-87. Paper can also be downloaded from the World Bank Pension Primer Series at http://www.worldbank.org/pensions

- [ 1] **Chang, R., and A. Velasco** (1999), Liquidity Crisis in Emerging Markets - Theory and Evidence, _NBER Working Paper,_ No 7272.

- [12] **Chen, Yehning** (1999) Banking Panics: The Role of the First-Come, First-Served Rule and Information Externalities, _Journal of Political Economy,_ Vol. 107, No.5.

- [13] **Claessens, S., Demirguc-Kunt, A., and H. Huizinga** (2001), How Does Foreign Entry Affect Domestic Bank Markets?, _Journal of Banking and Finance,_ No.25, 891-911.

- [14] **Davis,** E. **Philip** (2001) Multiple Avenues of Intermediation, Corporate Finance and Financial Stability, _IMF Working Paper._

- _[15]_ **Davis,** E. **Philip and Klaus Tuori** (2001) The Changing Structure of Banks' Income, an Empirical Investigation.

- [16] **Deckle, R. and K.** Kletzer (2001) Domestic Bank Regulation and Financial Crisis: Theory and Empirical Evidence from East Asia, _NBER working paper_ 8322.

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19 rules. They also find that the balance of payments closure is at least as important as the savings-investment closure rule for the distributional outcome.

# b. Cross-country Econometrics

Beyond CGE models, a second methodology is a cross-country econometric analysis which will yield the average effect of adjustment programs. This is the approach used in the World Bank's Report on Adiustment Lending II (1990). Countries are divided into those that received several adjustment loans beginning in the early 1980s, and those that did not. The econometric procedure isolates the impact of adjustment lending programs on selected performance indicators such as GDP growth, and investment, savings, and export ratios. This is done by separating out the effects of external shocks and pre-program conditions, accounting for policies that would have been followed in the absence of ar adjustment program, and accounting for the bias resulting from countries selecting themselves into the program.

This kind of analysis could be applied to distributional types of variables if these data were available for a sufficient number of countries. For example, the procedure was applied to real private consumption per capita growth (which reflects aggregate living conditions) and it was found that consumption growth rates improved following adjustment programs (but at the expense of investment). If cross-country data were available on poverty or quintile distributions of income, before and after adjustment, this methodology could isolate the impact of adjustment programs on distribution.

This analysis will reveal the average effect of adjustment programs on all countries receiving adjustment lending, but it is not detailed enough to provide information on how a performance indicator is affected by a single policy or by an alternative adjustment policy package. This is because all types of
interest: Forest clearing is much lower in protected areas than the other model variables would predict, and the effect has high statistical significance.

## **<u>Table 2:  Regression results:  Forest clearing in Lao PDR</u>**

**Dependent variable:  Cumulative percent cleared, 2016**

## **<u>Regression variables:</u>**

|**Log distance from nearest road segment**|**-0.208**<br>**(63.30)****|
|---|---|
|**Secondary road segment**|**-0.151**<br>**(10.67)****|
|**Tertiary road segment**|**-0.224**<br>**(18.65)****|
|**Legally protected**|**-0.720**<br>**(70.17)****|
|**Transport time**|**-0.001**<br>**(36.26)****|
|**Agricultural opportunity value**|**0.003**<br>**(52.72)****|
|**Elevation**|**-0.001**<br>**(84.07)****|
|**Constant**|**0.967**<br>**(68.68)****|
|**Observations**|**90,330**|

|**Robust t statistics in parentheses**|
|---|

*** significant at 5%; ** significant at 1%**

# **5.4  Implications for Forest Clearing**

To assess the implications of our results, we upgrade all secondary and tertiary road links to primary status and predict the resulting changes in clearing for all Hansen cells in our database. Figures 4 and 5 display predicted cumulative clearing and the change from 2016, respectively. Predicted impacts vary widely, with little or no change in areas far from the Delorme road network<sup>6</sup>

> 6 The econometric results for log (distance from road) in Table 2 quantify a continuous relationship, so upgrading secondary and tertiary roads will have small predicted effects in areas that are relatively far from those roads.

17
# **Table 3: Product-country level analysis, positive and negative GAP subsamples**

||(1)|(2)|
|---|---|---|
||GAP>0|GAP<0|
|(t-r)|0.673**|0.195|
||(0.265)|(0.319)|
|prc_sh|-9.324***|-9.184***|
||(0.392)|(0.457)|
|collpriv_sh|-22.778***|-22.515***|
||(0.403)|(0.471)|
|for_sh|-63.470***|-61.554***|
||(0.344)|(0.412)|
|HS4 fixed effects|Yes|Yes|
|Country fixed effects|Yes|Yes|
|Observations|102,567|76,742|
|R-squared|0.326|0.333|

Notes: Dependent variable is the share of indirect exports at product-country level (conservative measure), ranging from 0 to 100. The export shares by trade regime and firm ownership type variables range from 0 to 1. GAP is the trade data reporting discrepancy, defined as log(importing country reported imports from China)-log(China reported exports). *** p<0.01, ** p<0.05, * p<0.1.

35
- c. A household can be a duplicate if the head of household’s name, with the same characteristics, shows up more than once on the household list targeted to be interviewed in an enumeration area. In this case, only interview the household with the smallest number and replace the other household.

- d. A household cannot be reached after four hours. This could happen if all household members are out of town, or adult household members are too busy to meet.

- e. A household on the preprinted data listing is unknown to village authorities and villagers.

All replacements must be authorized by a supervisor.

# **5. DATA COLLECTION**

This section provides information about the datasets used in the evaluation and compares the characteristics of the treatment and control communities. We also examine the data for differential sample attrition across program and comparison villages.

A baseline survey was conducted in both treatment and control communities in August – September 2008. A total of 2,087 households in 160 sub-villages were interviewed. A health nutrition module to record recent illness for children under 5 years of age was also administered, providing a baseline sample of 2,353 children.

The follow-up data collection was conducted approximately 24 months later, between November 2010 and February 2011. The endline survey used the same field methodology as the baseline survey. The final sample size was 2,500 households. Of these, 1,908 were also surveyed in the baseline and so constitute our “panel sample” (see Table 1).

# **6. VALIDATION OF EVALUATION DESIGN**

# _6.1 Baseline Comparison of Means of Tests for Balance_

We use the 2008 baseline survey data to compare characteristics of treatment and control groups. Table 2 compares the means of selected key variables across treatment and control groups. Randomization aims to minimize systematic differences between the two groups. Table 2 shows that the means of the variables are similar in magnitude for the two groups and we cannot reject that they are equal for most of the variables. Thus the randomization successfully created groups comparable along observable dimensions. That is, mean differences between the treatment and control groups are not statistically different from zero. For the key outcome variables (household water and sanitation condition, as well as children’s health variables), balance is achieved. The 9
factors. Estimation is based on the stochastic trend model defined by equations (9), (12) and (14), depending on the seasonal specification.<sup>17</sup> For some of the series, missing data points are a potential problem. These take two forms. Some series start later or finish earlier than others. With thirteen years of data, there will be a maximum of 156 data points in each series. We only have this full number of observations for wholesale prices in Uganda and (with some exceptions) Tanzania ‐ see Table 5. Sample start and end dates therefore differ across series. The more serious problem is gaps within the series. This is most acute in the Burkinabe retail price series, where nearly one in five intermediate data points are absent.<sup>18</sup> In those cases in which gaps are present, we use the skip estimation procedure defined by equation (10).

The stochastic trend model is applied to estimate the seasonal gap and a three step procedure is followed to identify the appropriate specification (dummy variable, trigonometric, or sawtooth). In the absence of precise information for all crop‐location pairs on the existence of multiple growing seasons (and the exact month of harvest), it is a priori not clear whether parsimonious models are preferred over the dummy variable model, nor which of the two parsimonious models is more appropriate. Overall, the trigonometric and sawtooth gap estimates have correlations of 0.94 and 0.92 with the dummy variable estimates and 0.8 with each other. More particularly,

- a) The estimates of the trigonometric and sawtooth specifications, which are nested within the dummy specification (see section 4), are compared against those of the dummy variable model. If the _F_ test rejects both models, the dummy variables estimates are retained. This helps for example selecting the better (more flexible) model for location‐ crop pairs with two or more harvests.

17 In Kaminski, Christiaensen and Gilbert (2016) we apply these methods to deflated Tanzanian maize and rice data using slightly longer samples. The results are comparable to those reported here. 18 We dropped a number of series from the analysis on the basis of insufficient data. We require (a) at least 24 observations, with (b) at least one observation for each month (otherwise the dummy variables estimator ceases to be identified) and (c) a maximum of 50% missing intermediate (gap) observations. We also dropped both the wholesale and retail rice price series for Niger – these only changed intermittently, suggesting that they might be administered or official prices.

18
also significant in terms of the labor force they employ. They provide livelihood to large segments of the population and can absorb around 60 percent of the labor force engaged in non-agricultural activities in many developing countries (ILO 2012). Such high levels of informality can have an impact on growth and productivity of a country.<sup>2</sup> Informality can often generate inefficiencies in the production process since firms, in order to avoid detection limit their size to below optimal efficiency scale. They also tend to use less advanced production technologies (Dabla-Norris et al. 2007). The costs they avoid through noncompliance of regulations and tax evasion may help them stay in business despite their low productivity, potentially generating inefficient competition (Perry et al. 2007). Informal firms may also find it difficult to access finance through conventional sources, which can result in sub-optimal levels of investment in physical capital, research, technology and innovation and training programs for improving skills of their employees. Informal workers – which include people working in the informal sector and those employed by the formal sector but working informally, lack social protection, job security, on-job training and insurance. Informality can thus impact their income prospects and productivity. Large informal sectors can also have substantive fiscal implications regarding erosion of tax revenue. A high degree of informality means that for any given level of public expenditure, incomes and profits generated by the formal sector will be taxed at a higher rate (Spiro 2005). Also if public investments in infrastructure have a positive impact on growth then use of already congested public infrastructure for informal activities without contributions towards their replenishment can lower growth (Loayza et al. 2009). Large informal sectors also imply that certain transactions are omitted from official economic statistics rendering macroeconomic indicators vital for policy inaccurate and possibly leading to sub-optimal policy decisions.

On the other hand, informal economies can potentially play a positive role, especially in developing countries where they may be viewed as the nursery of future economic growth in the formal economy (IMF 2012). In economies and business environments mired by overly excessive barriers and poorly designed regulations, informality can provide an alternative to entrepreneurs and small firms. In such situations the absence of an informal sector can result in a far greater waste of resources (Loayza et al. 2009). If costs to start and run a business are binding, the informal sector can benefit firms – to the extent that they may grow to be successful formal sector firms – by helping them avoid excessive costs

> 2 Empirically, it is not very clear whether informality lowers growth and productivity or whether it is a symptom of low growth and productivity.

3
|**Table 2.1: Illiteracy and**|**Infant Mortal**<br>**Illitera**<br>**People Ov**|**ity, 1980**<br>**cy Rate**<br>**er Age 15**<br>|**and 19**<br>**Infant M**<br>**Per 1,00**<br>**Bir**|**99**<br>**ortality**<br>**0 Live**<br>**ths**|
|---|---|---|---|---|
||**1980**<br>**(%)**|**1999**<br>**(%)**|**1980**|**1999**|
|**Brazil**|25.3|13.3|85.2|34.6|
|**Northeast**|45.9|26.6|131.3|53|
|**Alagoas**|54.0|32.8|159.5|66.1|
|**Bahia**|43.1|24.7|95.4|45.4|
|**Ceará**|45.5|27.8|155.2|52.4|
|**Maranhão**|51.0|28.8|126.3|54.2|
|**Paraíba**|49.3|25.9|170.6|60.3|
|**Pernambuco**|42.2|24.7|149.8|58.2|
|**Piauí**|49.6|31.6|105.6|45.3|
|**Rio Grande do Norte**|44.4|25.5|169.3|48.7|
|**Sergipe**|46.5|23.9|110.9|45.4|

Although Paraíba has made substantial progress in reducing the share of the population living in extreme poverty, the problem remains broad and deep. The P0 measures the proportion of people below a certain poverty line but takes no account of how far they are below that line—the degree of poverty—or whether they are becoming even poorer. To address the situation of the poorest and to evaluate whether their economic situation has improved, the squared poverty gap measure (or P2) is used. This takes into account the degree of poverty, because it gives more weight to the poorest and most vulnerable. As a matter of fact, the squared poverty gap measure reveals that poverty was less deep in 1999 than in 1981 (3 percentage points), implying that the poorest became less poor. In fact, Paraíba has done better than the Northeast and Brazil—the squared poverty gap measure increased by 1.3 and 2.3 percentage points in the Northeast and the nation.

Part of the reason that Paraíba’s indicators are worse than in other countries with similar per capita incomes is because of income inequality. Both Brazil and Paraíba have extremely unequal income distribution. Paraíba’s income inequality has declined, however. In 2000, the Gini Coefficient for Paraíba was 0.60, slightly below the coefficient for the Northeast region (0.62), and Brazil as a whole (Figure 2.4). That compares to a Gini of 0.64 in 1991, a slightly better improvement than that in the rest of the Northeast and Brazil, where it improved by 0.03 percentage points.

Paraíba’s performance with reducing inequality is commendable. Changes in inequality are typically very slow, except during periods of radical social and institutional change. Where inequality has fallen it has usually happened in association with major expansion and equalization in educational attainment, as in Korea and Malaysia in the 1970s and 1980s. Paraíba’s expansion in education may have been too recent to have a significant effect on the composition of skills, and occurred during a period in which the overall returns to high levels of skills were rising and returns to basic skills were falling in Brazil.

8
# **1 Introduction**

COVID-19, a contact-transmitted epidemic that emerged at the end of 2019 worldwide, has lastingly modified and accelerated transformations and daily practices, particularly in the area of payment.<sup>1</sup> It has accelerated the shift away from cash and contact card payments, with shoppers often being encouraged to use contactless in many stores for public health reasons. Even before the pandemic, the popularity of the latest digital payment technologies was on the rise, with more and more people realizing the convenience they offer (Accenture, 2020; Bakkt, 2020; Thales, 2020).

When COVID-19 began to spread in Europe in 2020, consumers and merchants across the countries were faced with radical changes in many aspects of their lives and activities. The pandemic has increased general public and retailer concerns about the risk of coronavirus contamination in cash, resulting in visibly increased card use, with many retailers asking their customers to pay by contactless. In France, for example, although household consumption was strongly affected by the pandemic, the overall amount of payments made by bank cards and cell phones in 2020 was roughly equivalent to that in 2019. At the same time, cash use and withdrawals decreased in 2020 (Cartes Bancaires CB, 2020; European Central Bank, 2020a; INSEE, 2020a).

Thanks to the rapid evolution of financial and digital technologies, new digital payments such as contactless card payments have been growing fast in recent years. Contactless card payment improves the comfort and efficiency of purchases and sales without physical contact as opposed to contact card and cash payments, which can, in turn, increase consumption and sales. However, it is not certain that in a crisis where physical contact is prohibited, having a suitable means of payment that promotes social distancing will contribute to increasing consumption and sales. A natural question, therefore, arises on the benefits of having a digital payment technology available during an epidemic shock (e.g., COVID-19 pandemic). On the other hand, does having the digital payment technology mitigate the impact of a shock by thus increasing the resilience of users? If there is resilience, what are the origins? Is it because digital payment technologies attract more new customers? Or is it simply because of the shift of transactions with other payment methods to digital payment technologies?

This paper addresses these questions by investigating the resilience of merchants with the con-

> 1The coronavirus is a virus that causes diseases ranging from a simple cold to more severe pathologies such as respiratory distress. It is very dangerous and is transmitted by what are called droplets (like sputum): these are invisible secretions that are projected when talking, singing, sneezing, breathing heavily or coughing. Close contact with a sick person is the main mode of transmission of the disease. Contact with unwashed hands or surfaces soiled with droplets is also at risk of contamination. Some transmission also occurs through the air, mainly in a confined space (World Health Organization, 2020).

1
The surveys mentioned above have typically focused on the findings in the literature related to the effects of financial globalization on aggregate macroeconomic variables. Namely, they discuss whether the existing academic papers find that financial globalization affects positively, negatively, or has no effect on variables such as gross domestic product (GDP) growth and volatility, investment, consumption, asset prices, exchange rates, and financial stability indicators, among other macroeconomic indicators.

In this paper, we complement these previous studies by surveying empirical evidence on some of the major driving forces of financial globalization that affect the financial sector. This information is relevant because it can provide a more complete understanding of (i) how financial globalization has affected different parts of the financial system and (ii) why some of the effects of financial globalization at the macro level could have played out differently than anticipated. The three forces we study are: risk diversification, firms seeking financing abroad, and institutional investors that invest internationally. For each of these driving forces, we review the initial expectations and how they have worked once globalization took place. We show that these forces have affected the financial system differently than originally expected.

The main arguments of the paper can be summarized as follows. First, theoretical models posit that two main channels would foster financial globalization. One is the net financing from rich to poor countries, because the latter have higher returns to capital. Another one is risk diversification, because financially globalized capital markets would allow investors to diversify their assets worldwide, reducing risk and consumption volatility. Whereas initially these two channels were expected to be comparably important, new evidence suggests that financial globalization is (in relative terms) less about capital accumulation in developing countries and more about risk diversification. Developed and developing countries have simultaneously experienced a sharp increase in both inflows and outflows, whereas net capital flows are small and do not exhibit a clear trend. Therefore, movements of gross capital flows have outweighed 3