rafmacalaba/datause-encoder-data huggingface dataset:
the cost elasticity. This mechanical negative correlation between the revenue and cost elasticities leaves the profit elasticity unchanged.

## **3.5 Heterogeneity and Robustness of Profit Discontinuity**

Ideally we would repeat the previous analysis for each economic sector; however the estimation relies on large sample size, such that the revenue distribution and average costs by revenue are smooth and the counterfactual extrapolation credible. To study heterogeneity by sector, we simply estimate the “donut-hole” discontinuity in profit margin for each sector (following equation 14), without applying the revenue adjustment. Table 3, Column 1 shows the sector level profit margin discontinuities _δs_ , separating the economy in fifteen sectors. All sectors, except “NGO and public administration”, display a sharp drop in profits past the threshold. The proportional change in profit margin is remarkably similar across sectors (Column 3): infra-marginal firms above the threshold always report margins 40 to 50% lower than same sector firms below the threshold. Figure A5 illustrates this result by plotting the firm distributions and average profit margin for each sector.

Given the homogeneity of profit responses sorting of firms from different sectors is unlikely to provide a key explanation. However, we directly test if firms sort on either sides of the thresholds by estimating a donut-hole discontinuity (equation 14) for the few fixed characteristics in the tax data: province, economic sectors and number of years filing taxes. Table A4 shows the resulting coefficients: overall there appears to be very limited sorting based on these fixed characteristics. Among the 46 regressions, only two coefficients are significant, corresponding to the share of firms<sup>28</sup> Finally, Figure A4 shows that the drop in profit margin past the threshold is not driven by a few outliers but by an entire downward shift in the profit distribution. It plots the quartiles of profit margin against revenue: the median profit margin starts at 6% below the threshold and drops to 3% above it. We observe a similar proportional decrease at the 25th and 75th percentiles.<sup>29</sup>

# **4 Estimating Elasticities with Selection into Bunching as a Function of Cost**

## **4.1 Rationale for Added Structure**

While the profit elasticity estimated in section 3 appears robust to assumptions, the separation into revenue and cost responses is more fragile. The bunching method abstracts from selection into bunching as a function of costs and only estimates the revenue response of the marginal bunching firm. Under heterogeneity in revenue elasticities, this corresponds to the highest elasticity firm

> 28One possibility to deal with the possibility of sorting in these two sectors is to reweigh the discontinuity in cost holding constant the sector composition. This explains less under 3% of the drop in profit margin. 29We also confirm that bunching and the profit discontinuity occur in every year (Figures A6)

17
to trade costs if the exporting country provides a larger share of the destination country’s imports, and the trading of specialized inputs which are less elastic. The inherent market power of the EU as it pertains to the UK’s products may also contribute to the low bilateral demand elasticity, as shown in Juarez (2023), based on evidence from Colombia. Soderbery (2021) takes a similar general approach as this paper, except his focus is on heterogeneous pass-through rates across countries and how trade impacts are shaped by importer market power. The result of negative correlation between tariffs and elasticities speak to Caliendo and Feenstra (2022) and the classic optimal tariff models showing that the relationship between tariffs and elasticities could affect the welfare gains from trade.<sup>5</sup> In addition, our results are validated by several robustness checks. First, we find larger trade and welfare losses when we replace the heterogeneous elasticities with the homogeneous elasticities. This is because the estimated EU-UK trade elasticities are about 30 percent lower than the homogeneous EU elasticities across all trading partners. Ex ante, smaller elasticities imply smaller losses, and the homogeneous elasticities remove the negative correlation between tariffs and trade elasticities, which pushes up losses. Next, we find that using different elasticity estimates from the existing literature does not qualitatively change our results. Then, we reshuffle the heterogeneous elasticities in such a way that there is no correlation between tariffs and elasticities. In this case, the welfare and trade impacts are much larger, demonstrating the importance of considering the possibility of correlation between tariffs and elasticities. We also further break down the trade and losses to the industry level. The results suggest lower losses for the machinery and transport equipment sectors, which are more integrated between the EU and the UK.<sup>6</sup>

> 5Empirical evidence supporting the optimal tariff theory can be found in Broda, Limao and Weinstein (2008), who estimate export supply elasticities at a disaggregated level and find strong evidence that importing countries set higher tariffs on products that face inelastic export supply. Here in this paper we focus on estimating import demand elasticities and studying how the correlation between these elasticities and tariffs may affect the calculations of the gains from trade.

> 6In the Appendix, we further extend our analysis to include some general equilibrium forces such as the

3
_“Large, creditworthy buyers force longer payment terms on less creditworthy suppliers. Large creditworthy suppliers incent less credit worthy SME buyers to pay more quickly”_ _CFO Magazine, April 2007_

# 1. **Introduction**

Trade credit is an important source of funds for both small and large firms around the world (Petersen and Rajan, 1997, Demirguc-Kunt and Maksimovic, 2002). Many firms use trade credit both to finance their input purchases (accounts payable) and offer financing to their customers (accounts receivable), even in the case of small firms with credit constraints (McMillan and Woodruff, 1999; Marotta, 2005; van Horen, 2005). In this sense, trade credit is an important capital structure decision linking the liability side to the asset side of the firm’s balance sheet.

A number of reasons could induce firms to extend and use trade credit simultaneously. For example, firms that need to extend credit to their customers – possibly as a competitive gesture – might be more likely to demand trade credit themselves. In other words, firms might leverage accounts payable received from suppliers in order to finance accounts receivable extended to customers. At the same time, the increased focus of firms and analysts on cash holdings might offer incentives to firms to extend payment terms in order to maintain higher cash balances (Bates, Kahle, and Stulz, 2008). Furthermore, the risk management practice of firms to match the maturity between their assets and liabilities suggests that firms might use trade finance to match their short-term payables and receivables.<sup>1</sup> This might be true for both small firms

> 1 For instance, changes in inventory practices have led to firms holding more inventory (short-term assets), which would increase the demand for payables (short-term liabilities).  Additional information on supply chain management operations is available from the “The Accounts Payable Network” (theaccountspayablenetwork.com).

1
Figure 3. Skills Measures by Gender and Educational Level of Workers

# Panel A: 2005-2010

<!-- Start of picture text --> Female workers<br>0.6 -0.4<br>0.4 -0.5<br>0.2 -0.6<br>0.0 -0.7<br>2005 2006 2007 2008 2009 2010<br>R manual R cognitive<br>NR manual NR analytical<br>NR interpersonal<br>NR skills<br>R skills and NR manual skills<br><!-- End of picture text --> <!-- Start of picture text --> Male workers<br><!-- End of picture text --> <!-- Start of picture text --> 0.8 -0.5<br>0.6<br>0.4 -0.6<br>0.2<br>0.0 -0.7<br>2005 2006 2007 2008 2009 2010<br>R manual R cognitive<br>NR manual NR analytical<br>NR interpersonal<br>NR skills<br>R skills and NR manual skills<br><!-- End of picture text --> <!-- Start of picture text --> High-educated workers<br>0.90 -0.10<br>0.70<br>-0.30<br>0.50<br>-0.50<br>0.30<br>-0.70<br>0.10<br>-0.10 2005 2006 2007 2008 2009 2010 -0.90<br>R manual R cognitive<br>NR manual NR analytical<br>NR interpersonal<br>NR skills<br>R skills and NR manual skills<br><!-- End of picture text -->

## Low-educated workers

<!-- Start of picture text --> 0.80 -0.10<br>0.60 -0.30<br>0.40 -0.50<br>0.20 -0.70<br>0.00 -0.90<br>2005 2006 2007 2008 2009 2010<br>R manual R cognitive<br>NR manual NR analytical<br>NR interpersonal<br>NR skills<br>R skills and NR manual skills<br><!-- End of picture text --> 51
21 Beck, Demirguc-Kunt, Laeven and Levine (2006) show that financial development boosts the growth of small firms disproportionately more. Using data from enterprise surveys for 54 countries, and direct measures of credit constraints reported by firms, Beck, Demirguc-Kunt and Maksimovic (2005) show that the growth of smaller firms is significantly more constrained by financing obstacles, particularly in countries with less developed financial systems. Furthermore, they also show that financial development is associated with a greater reduction in the financing obstacles of small firms, hence disproportionately benefiting smaller firms. Research on microenterprises also yields consistent results. For example, Del Mel, McKenzie and Woodruff (2006) find that for a randomly chosen sample of entrepreneurs in Sri Lanka, credit constraints are the main reason for entrepreneurs’ inability to expand the business.

What about the impact of access to finance by households? There is an extensive literature on microfinance which addresses this issue (see, Cull, Demirguc-Kunt and Morduch, 2008). Delivering financial services to poor households can be quite challenging since issues of risk management, monitoring and transaction costs tend to make it very costly. Innovative techniques and products developed by microfinance institutions have helped overcome some of these issues and attracted significant attention of the development community. Nevertheless, despite these innovations, microfinance services are costly to deliver and they typically require extensive subsidies (Cull, Demirguc-Kunt and Morduch, 2007). Reviewing the evidence suggests that benefits of microcredit is not overwhelming, and that there is considerable skepticism about the ability of microfinance programs to lead to wide- scale reduction in poverty, inequality or promote higher growth (World Bank, 2007). Hence, further research – ideally using more real experimentsis needed to convince the skeptics who continue to question whether subsidizing microfinance programs is the best use of scarce development assistance.
Our dataset differs from ones used in the earlier survey based work by Ng et al. (1999), Giannetti et al. (2011) and others in several important ways. First, we use data from actual trade credit contracts rather than data based on survey responses, thereby mitigating the usual misreporting concerns associated with survey based data. Importantly, our dataset consists of trade credit contracts signed, while papers using the National Survey of Small Business Finance (NSSBF) database use trade credit contracts offered. Our dataset also covers a broader set of industries that includes technology firms, allowing for an analysis of trade credit contract terms across a broader range of industries, and includes trade credit terms not only for U.S. firms but also for international firms, allowing for a comparison of trade credit terms across different jurisdictions. Moreover, our dataset covers suppliers of all sizes, and not just small firms as in the SSBF. This is an important difference because the credit terms offered by large firms could be very different than those by small firms.

We begin by summarizing the main characteristics of buyers, suppliers, and contracts. Panels A and B of Table 1 show summary statistics of buyer and supplier characteristics. First, the buyers in our sample are very large – 33 out of 56 (or 59%) of buyers have over US$ 10 billion in sales and only one buyer has less than US$ 2 billion in sales.

The buyers are also creditworthy as measured by whether or not they are investment grade – about 75% of buyers in the dataset are investment grade. That we have mostly large, investment-grade buyers will make it harder to find evidence in this dataset of a financing motive for trade credit. We should, therefore, treat our results with appropriate caution.

Buyers are active in a range of industries, with the majority in retail industries. The sectoral distribution of buyers is: 16% in auto manufacturing, 13% in diversified retail, 29% in diversified manufacturing, 7% in retail groceries, 16% in retail hard goods, 11% in retail soft goods, 5% in technology, 4% in food and beverages, and 2% in the utility sector. The data encompasses only one firm in the utility sector and two firms in the food and beverages sector. Approximately 77% of buyers are from North America (the U.S. or Canada) and 23% of buyers are from Europe.

In comparison to the buyers, our suppliers are relatively small: 56% of suppliers have less than US$ 100 million in sales and only 11% of suppliers have more than US$ 2 billion in sales.

8
**Table 14. Employment outcomes (ITT), disaggregated for women, 2010, 2011 and 2012 cohorts**

||Baseline mean for|Poole<br>|d 2010‐2012 Cohorts<br>IPSW model|||
|---|---|---|---|---|---|
||young women<br>[Std Dev]|Baseline mean for women<br>[Std Dev]|Young women<br>(1)|Older women<br>(2)|Difference<br>(3)|
|Any IGA (1=Yes)|0.5|0.543|0.140***|0.127***|0.013|
||[0.500]|[0.498]|(0.040)|‐0.041|(0.058)|
|Any non‐farm IGA (1=Yes)|0.168|0.225|0.196***|0.187***|0.009|
||[0.374]|[0.418]|(0.041)|‐0.042|(0.062)|
|Trade‐specific IGA (1=Yes)|0.096|0.131|0.213***|0.204***|0.010|
||[0.295]|[0.338]|(0.036)|‐0.039|(0.048)|
|Hours worked in past month|39.569|55.560|25.348***|27.881***|‐2.533|
||[62.475]|[78.058]|(5.746)|‐6.83|(9.259)|
|Total monthly earnings (NRs)|560.537|1026.533|834.168***|1283.426***|‐449.259|
||[1438.980]|[2113.341]|(183.918)|‐283.993|(320.888)|
|Logged earnings|2.063|2.857|1.633***|1.791***|‐0.158|
||[3.264]|[3.665]|(0.329)|‐0.366|(0.505)|
|Earnings > 3000 NRs. (1=Yes)|0.071|0.131|0.144***|0.192***|‐0.048|
||[0.256]|[0.337]|(0.030)|‐0.043|(0.051)|
|Self‐Employed (1=Yes), if any|0.323|0.303|0.071|0.101*|‐0.030|
|IGA|[0.468]|[0.460]|(0.043)|‐0.054|(0.067)|
|Works outside of home|0.434|0.424|0.193*|‐0.121|0.314**|
|(1=Yes), if any IGA|[0.496]|[0.495]|(0.107)|‐0.093|(0.140)|
|Any Savings (1=Yes)|0.558|0.791|0.016|0.058|‐0.041|
||[0.497]|[0.407]|(0.045)|‐0.039|(0.062)|
|Total Savings (NRs).|1991.965|5577.033|‐153.140|2026.673**|‐2179.813|
||[6304.061]|[10185.000]|(809.785)|‐968.191|(1326.634)|
|Logged Savings|3.953|6.264|0.316|0.596*|‐0.280|
||[3.685]|[3.483]|(0.332)|‐0.322|(0.489)|
|Taken out loan (1=Yes)|0.189|0.478|0.011|0.015|‐0.004|
||[0.392]|[0.500]|(0.032)|‐0.039|(0.050)|
|Clustered Standard Errors|||Yes|Yes|Yes|

Notes: Standard errors (reported in brackets) clustered at the event level where possible. *,**, and *** denote significance at the 10% level, 5% level, and 1% level. Younger women (aged 16 to 24) compared to older women (age 25 to 35).

37
imposes equity volatility to be constant.<sup>Such an assumption is inconsistent with the</sup> assumption of Merton's (1977) theoretical<sup>model that equity volatility is stochastic.</sup> Duan (1994) has developed a maximum<sup>likelihood framework to estimate the value</sup> of the deposit insurance which<sup>is consistent with the assumption of Merton's (1977)</sup> theoretical model that equity volatility<sup>is stochastic. By the process in (1), the one-period</sup> transition density of the unobserved<sup>values of the bank's assets can be characterized by</sup> ln(V,+, / _V )_ N(p,o-<sup>2</sup> _)_ . Therefore,<sup>the log-likelihood function for a sample of unobserved</sup> V, can be expressed as: Since the call-option formula (3) is<sup>an element-by-element transformation from an</sup> unobserved sample of asset values<sup>to an observed time series of equity values, we can</sup> write the log-likelihood function for<sup>the observed sample of equity values as:</sup> with t _= 1,_ ...,n, and where V,(cr)is<sup>the unique solution to (3) for any c, and</sup> _d,_ corresponds to d, with V, _(a)_ in place<sup>of</sup><sup>_V,._In the above expression, we have used the</sup> fact that _aE, I a Vt = CD(d, )._ With the log-likelihood function in<sup>(6), an iterative optimization routine can be</sup> used to compute the maximum likelihood<sup>estimates. Given starting values for</sup><sup>_,u_anda</sup> and data on equity values _E,_ and<sup>debt D,, equation (3) can be solved to yield a series of</sup> bank asset values V,. Equation (6)<sup>is then used to solve for</sup><sup>_Ai_and v. This process is</sup> iterated to find the maximum likelihood<sup>estimates of</sup><sup>_,A_and</sup><sup>_6,_and their standard errors.</sup> Using the put option formula for<sup>deposit insurance (2), one can solve for the value of the</sup> guarantee per dollar of deposits and<sup>its standard error. Although from a theoretical point</sup> of view, the maximum likelihood method<sup>as developed in Duan (1994) and in Duan and</sup> 8
_Africa’s Macroeconomic Story_ panel). Deficits have also widened substantially in fragile states, notably Sierra Leone, Guinea, Zimbabwe, the Gambia and Burundi. In some cases, however, large deficits are mainly due to imported capital goods used in the construction phase of major mining projects (see Appendix). Many of the projects are financed by foreign direct investment (and project grants to a lesser degree) and hence have little impact on external debt. If such investments turn out as planned, the growth benefits and significant expansion in exports could improve the debt sustainability outlook considerably.

**Figure 20: Current Account Balances, 2010-12 Average** _Percent of GDP_

<!-- Start of picture text --> 20<br>0<br>-20<br>-40<br>Source: World Bank Global Economic Prospects January 2013.<br>Figure 21: Current Account Balances in the Africa Region, 1985-2013<br>Percent of GDP<br>0<br>0<br>-4<br>-5<br>-8<br>-12 -10<br>-16 -15<br>1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010<br>Median Lower quartile Fragile states Oil importers<br>Sierra Leone Guinea Mozambique Lesotho Niger Seychelles Zimbabwe Gambia, The Burundi Tanzania Swaziland Malawi Uganda Congo, Dem. Rep. Mauritius Ghana Cape Verde Benin Kenya Rwanda Comoros Madagascar Mauritania Senegal Togo Guinea-Bissau Cent. Afr. Rep. Cameroon South Africa Mali Burkina Faso Ethiopia Zambia Cote d'Ivoire Eritrea Namibia Congo, Rep. Sudan Nigeria Botswana Angola South Sudan Gabon<br><!-- End of picture text --> _Source:_ World Bank _Global Economic Prospects_ January 2013.

**Figure 21: Current Account Balances in the Africa Region, 1985-2013** _Percent of GDP_

_Source:_ World Bank _Global Economic Prospects_ January 2013. _Note:_ Estimates for 2012; projections for 2013.

30
5) <!-- Start of picture text --> Madagascar 1<br>PFP - Projections and Actuals<br>.t~~so Is so to<br>Year<br>-Pjine +AsI.I<br><!-- End of picture text --> Madagascar 3 PFP - Projections and Actualb <!-- Start of picture text --> Ymr<br>Gi. ImNVO<br>=lbnodw +~t<br><!-- End of picture text -->

**c** -, -

<!-- Start of picture text --> Madagascar S<br>PFP Projectlons and Actualb Year<br>Pr.jseUatl@ +MIum<br>PFr*WoP P Projti 04 071 an. Actw<br>MoRmftw" f J<br><!-- End of picture text --> <!-- Start of picture text --> Madagascar 2<br>PFP - Projectlons and Actuals<br>W0er<br>-tPmiblw +AWMI '*AeWTOT vebd<br>TOT * Trms d Trade<br><!-- End of picture text --> <!-- Start of picture text --> Madagascar 4<br>PFP Projections and Actual.<br>Year<br>*it e vfgGO<br>-Proetbn= -4 AOId<br>_ _ _ _ _ | _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 1 _<br>Madagascar 6<br>PFP -Projections Year and Actuals<br>Prje.Ihae tuaule 4<br>P<br>C - +<br><!-- End of picture text -->
WORLD

# **1.** Estimates and Projections

|**Towr**|**IpuLti**|DP.<br>**ratio**|**Period**|Growth<br>**rate**|cm|CD|TFt|*o<br>**Itais**|*0<br>**F_mLm**|I|qs<br>x1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1965|4842303|65.3|1985-89|1.74|27.1|9.7|3.41|63.0|66.7|70|97|
|1990|5281929|62.9|1990-94|1.65|25.5|9.0|3.21|64.2|68.1|63|86|
|1995|5735527|62.4|1W5-99|1.53|23.8|8.5|3.02|65.5|69.6|56|76|
|2000|6191710|60.8|2000-04|1.43|22.3|8.0|2.89|66.8|71.1|49|66|
|2005|6651503|58.2|2005-09|1.33|21.2|7.8|2.77|67.7|72.1|44|60|
|2010|71091S6|5S.6|2010-14|1.25|20.2|7.7|2.65|68.7|73.2|39|53|
|2015|7567055|54.3|2014-19|1.18|19.4|7.6|2.56|69.7|74.4|34|46|
|2020|8026087|54.5|2020-24|1.10|18.5|7.5|2.48|70.8|75.7|29|38|
|2025|8479143|54.9|2025-49|0.79|15.9|8.1|2.24|73.6|78.9|18|23|
|2050|10318509|56.7|2050-74|0.41|13.5|9.5|2.J8|77.0|82.7|8|10|
|2075|11433912|62.1|2075-99|0.21|12.5|10.5|2.06|79.2|85.3|5|6|
|2100|12036362|_(A.3_|2100-24|0.10|12.0|11.1|2.06|81.1|87.5|3|4|
|212S<br>2150|12336567<br>12484984|<br>71.4<br>72.9|2125-49|0.05|11.8|11.4|2.06|81.8|88.5|2|3|

## 2. Age structure (percent): 1990 and 2025

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10 8 6 4 2 0 2 4 6 8 10 3. Distribution of population (percent): 1990 and 2025 <!-- Start of picture text --> AST AFICA (S *1 EAST ACQ'CA '5 3%)<br>45T APRICA (4 SW)<br>9uIA < \ IE5T | \ 00 IA CA C7 34)<br>N 'C1 ICA CS ) \ \ 5 I A a) 051<br>/ \ \\ | \ ~~~~ESE ASIA t33 7%) SVEST ASIA C3 \<br>moprm AfllCA C2 7) O OCEANIA COD ) \<br>') kAA , CA Co 1%)<br>I.AC (S 4*) j . AS:,CA C3 M)<br>\ \ ,/ LLAC Cs'm /<br>US4 1. \ /<br>RA 10 M JSP * O AS IA C2 55)<br>XSJTw *$ A,22 55)<br><!-- End of picture text -->

2