rafmacalaba/datause-encoder-data huggingface dataset:
# Figure V: Bunching, Information Reporting and Withholding

<!-- Start of picture text --> A: Self-Employed<br>A1: No Information Reporting A2: Reporting by Firms A3: Reporting by State A4: Reporting by Credit/Debit Card<br>Excess mass b=4.5(.3) Missing mass m=-.14(.07) Excess mass b=2.08(.18) Missing mass m=.75(.09) Excess mass b=2.17(.3) Missing mass m=.7(.16) Excess mass b=.52(.24) Missing mass m=.07(.07)<br>50 100 150 50 100 150 50 100 150 50 100 150<br>Taxable Income/Kink (%) Taxable Income/Kink (%) Taxable Income/Kink (%) Taxable Income/Kink (%)<br> Observed distribution Counterfactual Observed distribution Counterfactual Observed distribution Counterfactual Observed distribution Counterfactual<br>B: Corporations<br>B1: No Information Reporting B2: Reporting by Firms B3: Reporting by State B4: Reporting by Credit/Debit Card<br>Excess mass b=4.49(.48) Missing mass m=3.65(.97) Excess mass b=3.17(.22) Missing mass m=1.07(.21) Excess mass b=1.44(.39) Missing mass m=1.29(.44) Excess mass b=1.35(.33) Missing mass m=.37(.25)<br>50 100 150 50 100 150 50 100 150 50 100 150<br>Total Revenue/Notch (%) Total Revenue/Notch (%) Total Revenue/Notch (%) Total Revenue/Notch (%)<br> Observed distribution Counterfactual Observed distribution Counterfactual Observed distribution Counterfactual Observed distribution Counterfactual<br>.008 .008 .008 .008<br>.006 .006 .006 .006<br>Density .004 Density .004 Density .004 Density .004<br>.002 .002 .002 .002<br>0 0 0 0<br>.0008 .002 .002 .002<br>.0006 .0015 .0015 .0015<br>Density .0004 Density .001 Density .001 Density .001<br>.0002 .0005 .0005 .0005<br>0 0 0 0<br><!-- End of picture text --> Notes: The gure shows the density distribution of taxable income for the self-employed around the rst kink in the income tax schedule (top row, A), and the density distribution of total revenue for corporations around the rst notch in the corporation tax schedule (bottom row, B). The data is pooled for years 2006-2015, represented as percentage distance from the kink, and aggregated in taxable income bins of 1%. The panels show the distribution for dierent subsamples, as per the panel titles that indicate whether and by what type of information reporting rms in the subsample are covered.. The blue dotted line marks the empirical distribution, the red solid line marks the counterfactual, tted as a exible polynomial to the observed distribution outside the excluded range. We use an tenth-degree polynomial for self-employed and a sixth-degree polynomial for corporations. The excluded range above the threshold covers four and seven bins respectively in the two groups of rms. The excess mass _b_ and missing mass _m_ are estimated as the dierence between the observed and estimated density, weighted by the height of the counterfactual density. The standard errors are bootstrapped.
institutions are less developed. This is also an especially relevant question for micro, small and medium enterprises (MSMEs), since small firms have been shown to be the most vital job creators in developing countries,<sup>4</sup> as well as the most likely to be credit constrained.<sup>5</sup> In this paper, we use comprehensive firm-level data across a large set of developing countries to analyze the impact of access to finance on job growth and the heterogeneity in this relationship across firm size. In particular, we study the differential impact of access to finance on MSMEs’ ability to create jobs relative to that of larger firms.

Establishing a causal effect from access to finance to employment growth is complicated, since the variables measuring a firm’s access to finance may also reflect its demand for labor. Moreover, there are potentially many factors that could drive both firm employment growth and access to finance. We alleviate these identification concerns using a number of empirical strategies. First, we consider an exogenous shock to the supply of credit in the form of the introduction of a credit bureau.<sup>6</sup> We use a difference-in-difference approach in estimating the impact of the introduction of credit bureaus on employment growth, by comparing countries which underwent reforms and countries without reform, and years pre- and post-reform. Second, we use propensity score matching (PSM) to more closely match the treatment and control group of countries and re-estimate the difference-in-difference specification. Finally, following Rajan and Zingales (1998), we identify credit supply effects using industry measures of external finance dependence interacted with the credit bureau reform variable. If increased access to

> 4 Ayyagari, Demirguc-Kunt, and Maksimovic (2011) show SMEs to be important job creators. Haltiwanger, Jarmin and Miranda (2013) also find a strong role for SMEs in job creation in the US.

> 5 See Love and Mylenko (2003), Beck et al. (2005), Galindo and Micco (2005), and IADB (2007).

> 6 A credit bureau is defined in the World Bank Doing Business database as a private firm or nonprofit organization that maintains a database on the creditworthiness of borrowers (individuals or firms) in the financial system and facilitates the exchange of credit information among creditors. Credit investigative bureaus that do not directly facilitate information exchange among banks and other financial institutions are not considered.

3
Figure 2: Labor Force Participation Rates <!-- Start of picture text --> 1995 2000 2005 2010<br>Year 1995 2000 2005 2010<br>Year<br>ECA WE<br>Peers_ECA Peers_WE EU10 Balkan/Turkey CIS<br>Source: KILM, 7th Edition Source: KILM, 7th Edition<br>70 65<br>65 60<br>60<br>55<br>Labor Force Participation<br>Labor Force Participation<br>55<br>50<br><!-- End of picture text --> ECA is also the worst performing region with regard to employment ratios (Figure 3). Matching the pattern in Western Europe and in contrast to the pattern elsewhere, employment ratios in ECA declined noticeably as a result of the global financial crisis. In 2010, just over one in two working age persons were employed. Within ECA, the picture is again worst in the Balkan and Turkey and best in the CIS.

Figure 3: Employment Ratios <!-- Start of picture text --> 1995 2000 2005 2010<br>Year 1995 2000 2005 2010<br>Year<br>ECA WE<br>Peers_ECA Peers_WE EU10 Balkan/Turkey CIS<br>Source: KILM, 7th Edition Source: KILM, 7th Edition<br>65 60<br>55<br>60<br>50<br>55<br>Employment Ratio<br>Employment Ratio<br>45<br>50<br>40<br><!-- End of picture text --> Even through ECA's labor force participation rates are low, its unemployment rates are the highest across the four regions (Figure 4). After converging to ECA peers in the years leading up to the global financial crisis, unemployment rates increased above 10 percent in recent years. Unemployment rates are especially high in Balkan/Turkey. In the CIS, unemployment rates were noticeably lower in 2010 than in the late 1990s, while in the EU10, they increased to the levels of the mid-1990s with the global financial crisis.

5
We emphasize that the results presented in this paper are targeted at stimulating discussion and helping policymakers and stakeholders arrive at a tentative prioritization of their efforts in this area. In the future, more detailed analysis is required in relation to particular reform programs, covering both benefits and costs. This will require collection of new datasets covering all ASEAN member countries. As noted at the outset, the sources we have used here— _Doing Business_ , the _Global Competitiveness Yearbook_ , and WITS—are sometimes missing data for ASEAN. It would be desirable to correct this in the future. More generally, consistent and reliable data on non-tariff barriers are currently scarce. Given trade facilitation’s ability to act on such barriers, it will be increasingly important in the future to ensure that data collection in this area is adequately and sustainably resourced.

Another way in which policy-relevant research can add value is by complementing existing data with information on the ways in which trade facilitation reforms impact different sections of the supply chain, given particular industry and firm characteristics. One starting point for this line of policy research might be the World Bank’s Logistics Performance Index (e.g., Hausman et al., 2005). Alternatively, one could imagine expanding the _Doing Business_ data on “trading across borders” in order to assemble more detailed data on these questions. Such data would be an important input into the policymaking process, since they would help stakeholders target reforms—and resources—where they are most needed in particular countries.

In addition, our results have only addressed the static impacts of trade facilitation reform. We do not assess the possible effects on productivity, growth, or economic development as such.

However, as we have discussed in this paper, there are good reasons to believe that better trade facilitation can impact each of these positively. Although the net balance of costs and benefits cannot yet be stated with certainty—since we have not estimated the costs of improving trade

- 24 -
# **1. Introduction**

Rwanda was among the first countries in Sub-Saharan Africa to start the demographic transition, with total fertility rates dropping from 8.25 in the early 1980s to 6.3 in the early 1990s (Cohen, 1998; United Nations, 2011). Rwanda was however also among the first countries where the transition stalled (Bongaerts, 2008; Shapiro and Gebreselassie, 2008; Machiyama, 2010). After dropping substantially in the 1980s, the total fertility rate remained virtually stable between the 1992 Demographic and Health Survey (TFR of 6.2) and the 2005 DHS (TFR of 6.1).<sup>2</sup> Since 2005 however fertility rates have fallen rapidly, from 6.1 in 2005 to 4.6 in 2010, a 25 percent decline.<sup>3</sup> This drop is one of the fastest in the history of the Demographic and Health Surveys and comes during a period of rapid social and economic transitions.<sup>4</sup> Between 2005 and 2010, Rwanda’s economy grew at more than 8 percent per annum, poverty headcount fell by 12 percentage points, net enrollment in primary school attained near universal status (99 percent in 2010) and gross secondary school enrollment doubled from 15.9 percent in 2005 to over 32 percent in 2010.<sup>5</sup> Health indicators also improved, witnessed by a drop in the under-five mortality rate from 108 (per 1,000 live births) in 2005 to 60.4 in 2010. At the same time significant shifts were observed in the labor market: Labor force participation increased, particularly among women, and there was a marked increase in wage employment and employment in the nonfarm sector as opposed to traditional farming (World Bank, 2013).

Sharp drops in fertility are important as they usually bode well for economic development. During the transition from a high fertility high mortality society to a low fertility low mortality one, there is a particular period where there is an exceptional bulge of working-age adults. Because fertility is

> 2 Studying the fertility impacts of the genocide, Bruck and Schindler (2011) find a strong positive impact on fertility of having lost children during the genocide. This replacement effect may help explain why fertility remained virtually unchanged between the early 1990s and 2005 despite the dramatic increase in the number of widowed women, which in itself would predict a fall in fertility.

> 3 When a more current estimate of fertility is used, based on the last 12 months instead of the last 36 months for the standard TFR, fertility dropped even more – 27 percent (Westoff, 2012).

> 4 Using the STATCOMPILER tool on the “measureDHS” website, we found that no other country with more than one DHS survey experienced such a large drop in the total fertility rate over a five-year period. 5 Data from World Development Indicators, 2012.

2
that responding is positively correlated with incomes in the US CPS and that correcting for this raises the mean income in the top percentile by around 40%. The authors note that only regional level response rates are required to implement the method.

The Korinek et al. (2006) method has subsequently been used in several papers to correct for selective compliance on income. One of these is Hlasny (2020), who used 66 Luxembourg Income Study surveys for 38 upper- and middle-income countries and collected regional unit non-response rates to implement the Korinek et al. (2006) corrections. He finds extremely large effects across the countries for some statistics- the mean top 1% share of income is 6.5% across the 66 surveys but the Korinek et al. (2006) non-response adjustment increases this to 17% (Hlasny (2020), appendix A6). There are also several extreme or even implausible cases- the Italian top 1% share rises from 6% to 44% in the 2008 survey after the unit non-response corrections but only from 5% to 16% in the 2010 survey.

# Imputation for item non-response

Like in the case for unit non-response, it is possible to ignore item non-response by using only the data from responders. To the extent that item non-response is ignorable this is reasonable. It would still mean the sample size is reduced, however, which matters for the variance of any estimates and can worsen sparsity problems.

But the missing data is very unlikely to be ignorable and so other solutions are required. The most common solution is imputation. This involves using responses to other questions in the survey and/or external data to predict earnings for those who did not respond to the question on income. A common method for incomes is hotdeck imputation, in which the income of a person or household with the same set of specified characteristics is “donated” to the individual with missing income (Lohr, 2009).

The problem with single imputation methods, including hotdeck imputation, is that they fail to capture the uncertainty about the true values for those whose values are imputed. Multiple imputation methods (Rubin, 1987) were developed to overcome this problem, in which missing data is imputed multiple times- for example 10 donors are chosen randomly instead of one for hotdeck multiple imputation. The variation across the estimates from the multiple samples captures the uncertainty.

One issue with any imputation method when measuring the top of the income distribution is that there may be a high fraction of individuals with missing incomes at the top of the income distribution. In this case, the few individuals with incomes will each donate their incomes to multiple other sample members. This is a version of the sparsity problem discussed above.

We noted that some surveys allow bracket responses, which is still partial item non-response. Wittenberg (2008) provides a solution when there are bracket responses. The idea is similar to 15
|P048204|2000|2008|10/20/2007|16 Closed|Cameroon|
|---|---|---|---|---|---|
|P048204|2000|2008|12/12/2007|17 Closed|Cameroon|
|P048314|2000|2000|6/28/2000|1 Closed|Morocco|
|P048314|2000|2001|3/9/2001|3 Closed|Morocco|
|P048314|2000|2002|6/28/2002|6 Closed|Morocco|
|P048314|2000|2003|6/26/2003|10 Closed|Morocco|
|P048314|2000|2004|3/22/2004|12 Closed|Morocco|
|P048314|2000|2005|2/4/2005|14 Closed|Morocco|
|P048314|2000|2006|12/6/2005|15 Closed|Morocco|
|P048314|2000|2007|8/3/2006|16 Closed|Morocco|
|P048314|2000|2007|12/28/2006|17 Closed|Morocco|
|P048314|2000|2007|6/28/2007|18 Closed|Morocco|
|P048314|2000|2008|12/5/2007|19 Closed|Morocco|
|P048314|2000|2009|7/30/2008|20 Closed|Morocco|
|P048315|2002|2003|5/7/2003|3 Closed|Tunisia|
|P048315|2002|2004|5/12/2004|5 Closed|Tunisia|
|P048315|2002|2005|4/27/2005|7 Closed|Tunisia|
|P048315|2002|2005|6/27/2005|8 Closed|Tunisia|
|P048315|2002|2006|3/2/2006|9 Closed|Tunisia|
|P048315|2002|2007|8/1/2006|10 Closed|Tunisia|
|P048315|2002|2007|3/20/2007|11 Closed|Tunisia|
|P048315|2002|2008|10/8/2007|12 Closed|Tunisia|
|P048315|2002|2008|5/19/2008|13 Closed|Tunisia|
|P048315|2002|2009|10/25/2008|14 Closed|Tunisia|
|P048315|2002|2009|5/8/2009|15 Closed|Tunisia|
|P048357|1998|1998||1 Closed|Brazil|
|P048357|1998|1999||3 Closed|Brazil|
|P048357|1998|2000|2/14/2000|5 Closed|Brazil|
|P048357|1998|2001|3/14/2001|7 Closed|Brazil|
|P048357|1998|2002|5/31/2002|9 Closed|Brazil|
|P048357|1998|2003|5/29/2003|11 Closed|Brazil|
|P048389|1997|1997||1 Closed|Rwanda|
|P048389|1997|1998||3 Closed|Rwanda|
|P048389|1997|1999||3 Closed|Rwanda|
|P048461|1999|1999||0 Closed|Bosnia and Herzegovin|
|P048461|1999|2000|2/24/2000|1 Closed|Bosnia and Herzegovin|
|P048461|1999|2001|6/25/2001|3 Closed|Bosnia and Herzegovin|
|P048461|1999|2002|6/25/2002|5 Closed|Bosnia and Herzegovin|
|P048462|1998|1998||1 Closed|Bosnia and Herzegovin|
|P048462|1998|1999||4 Closed|Bosnia and Herzegovin|
|P048462|1998|2000|6/29/2000|6 Closed|Bosnia and Herzegovin|
|P048462|1998|2001|12/22/2000|7 Closed|Bosnia and Herzegovin|
|P048505|1999|1999||2 Closed|Mexico|
|P048505|1999|2000|4/3/2000|4 Closed|Mexico|
|P048505|1999|2001|6/27/2001|6 Closed|Mexico|
|P048505|1999|2002|6/17/2002|8 Closed|Mexico|
|P048505|1999|2003|6/6/2003|10 Closed|Mexico|
**exceed cash for the median firm.** Exhibit 3 provides the main descriptive statistics of these financial vulnerability indicators and Exhibit 4 breaks them down by region and by sector.

- In 2019Q4, the median firm in our sample has an ICR of 2.57, and the 75<sup>th</sup> percentile of ICR is well above 1, suggesting that most firms in this sample are able to cover their interest expenses. At the same time, the 25<sup>th</sup> percentile is well below 1, suggesting that a sizeable portion of firms experience difficulties.

- In contrast, the median firm’s QR is only slightly above 1, and firms between the 25<sup>th</sup> and 75<sup>th</sup> percentile have similar quick ratios slightly below or above 1. This suggests that many firms may be vulnerable to rollover risks if a sudden drop in liquid assets were to occur.

- Finally, strong cash buffers are crucial for firms during the economic slowdown. We therefore calculate the difference between cash and cash equivalent assets and current liabilities, scaled by total assets. Similar to Banerjee et al. (2020), we find that this number is slightly negative for the corporate sector . Current liabilities exceed cash and cash equivalents by 0.23 percent of total assets for the median firm, suggesting the specter of financial stress if funding conditions were to tighten and inventories cannot be promptly liquidated.

**Our data on corporate insolvency are an index constructed as an average percentile rank of three indicators from Doing Business representing different aspects of the strength of corporate insolvency institutions of a country.** The three indicators are Strength of insolvency and Legal rights index, Recovery rate and Quality of judicial process index, representing legal framework, process efficiency and quality of civic and community justice, respectively. We calculate an average percentile rank of these three indicators, where a higher percentile rank indicates weaker corporate insolvency institutions/higher corporate insolvency vulnerabilities.

**Exhibit 2. Full sample coverage by region (2019Q4)**

||**Number of**<br>**Firms**|**Number of**<br>**Countries**|**Total**<br>**Corporate**<br>**Debt**<br>**(Billions**<br>**US**<br>**dollars)**|**Total**<br>**Corporate**<br>**Asset**<br>**(Billions**<br>**US**<br>**dollars)**|**Median**<br>**Country**<br>**Corporate**<br>**Debt to**<br>**GDP,**<br>**percent**|**Median**<br>**Country**<br>**Corporate**<br>**Asset to**<br>**GDP,**<br>**percent**|
|---|---|---|---|---|---|---|
|**EAP**|6,992|9|2,459.3|7,943.2|13.4|44.5|
|**ECA**|3,138|20|526.2|2,009.4|4.2|16.2|
|**LAC**|932|16|759.3|2,173.5|6.3|25.2|
|**MENA**|1,019|12|429.7|1,507.2|11.9|38.6|
|**SA**|4,343|4|1,802.3|8,146.8|7.4|25.9|
|**SSA**|510|12|76.5|354.1|1.6|13.9|
|**TOTAL**|**16,933**|**73**|**6,053.2**|**22,134.3**|**6.9**|**25.0**|

8
application threshold, relative to those that entered earlier. The high-frequency multi-year nature of the court data, together with the staggered reform application across chambers, allows us to attribute this change to the reform, net of seasonality and other structural changes (internal or external) to the court.

Appendix Figure A2 confirms our identification strategy: in each panel, we plot the raw (uncontrolled) average pre-trial duration around each individual chamber decree introduction cutoff; we display horizontal lines on either side of the cutoff to visualize and motivate the [-3;2] adjustment period later used to estimate the average treatment effect of the decree.<sup>12</sup> The raw data from each chamber display drops in duration at the chamber’s cutoff, and only around that cutoff (Figure A2). This set of figures suggests no selection on trends, as the pre-period trends appear parallel. In Section 5.2.3 and Appendix B.1, we also consider selection on shocks and trend shocks as potential sources of bias.

In line with this identification strategy, we estimate three main models to measure the impact of the decree on the speed and nature of court procedures. The first event study model verifies our main identifying assumption across all application cutoffs. Letting _τi,j_ be, for case _i_ in chamber _j_ , the number of hearing periods since chamber _j_ was treated when _i_ entered court (normalized to zero at -4, the last hearing before the adjustment period), we estimate a flexible functional form that estimates one treatment effect per case-entry period, as follows: We include cases starting their pre-trial between 22 months before and 12 months after the decree application date, and define the period before the decree is introduced as the before period, and the period of the event study coefficient as the after period (after decree introduction).<sup>13</sup> If the reform had an effect, we expect to see a significant jump or drop in these

> 12Results including the adjustment period are reported in Tables A1 and A5; this does not affect any of the conclusions of the paper.

> 13We construct the same time window around each of the chamber-level decree application dates: 38

11