rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 8. Difference-in-Differences Equity Issuance Estimates: Shanghai and Shenzhen Events**

This table shows difference-in-differences (DiD) regressions comparing equity issuances by connected and unconnected firms in the propensity score matched (PSM) samples of firms listed in Shanghai (left panel) and Shenzhen (right panel). For each comparison, the table shows regression results obtained by estimating Equation (1) using two different dependent variables: the amount of equity raised over 2012 assets and the cumulative amount of equity raised over 2012 assets. The treated variable equals one for connected firms and zero for unconnected firms. The table shows the DiD coefficients, which estimate, for each year, the average differences in equity issuances across groups of firms (relative to the 2012 difference). The 2012 coefficient shows the differences in 2012. The regressions include year fixed effects. Standard errors are clustered at the industry (two-digit SIC) level. ∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

|Sample:|PSM S<br>Shan|ample<br>ghai<br>Cm Eit|PSM S<br>Shen|ample<br>zhen<br>Cm Eit|
|---|---|---|---|---|
|Dependent<br>Variable:|Equity over<br>2012 Assets|u. quy<br>over 2012<br>Assets|Equity over<br>2012 Assets|u. quy<br>over 2012<br>Assets|
|Y_2005 x Treated|-0.002|0.010|0.002|0.003|
||[0.01]|[0.02]|[0.00]|[0.01]|
|Y_2006 x Treated|-0.002|0.010|0.002|0.003|
||[0.01]|[0.02]|[0.00]|[0.01]|
|Y_2007 x Treated|-0.001|0.011|0.002|0.003|
||[0.01]|[0.02]|[0.00]|[0.01]|
|Y_2008 x Treated|0.001|0.013|0.004|0.004|
||[0.01]|[0.02]|[0.00]|[0.01]|
|Y_2009 x Treated|-0.021|-0.006|0.002|0.004|
||[0.01]|[0.01]|[0.00]|[0.01]|
|Y_2010 x Treated|0.001|-0.003|-0.003|-0.001|
||[0.01]|[0.01]|[0.01]|[0.00]|
|Y_2011 x Treated|-0.001|-0.002|0.006|0.002|
||[0.01]|[0.01]|[0.01]|[0.00]|
|Treated (2012 Diff.)|0.007|0.022|-0.007|-0.024 *|
||[0.01]|[0.02]|[0.01]|[0.01]|
|Y_2013 x Treated|0.017|0.019|0.006|0.004|
||[0.02]|[0.02]|[0.01]|[0.01]|
|Y_2014 x Treated|-0.002|0.019|0.024|0.026|
||[0.02]|[0.03]|[0.02]|[0.02]|
|Y_2015 x Treated|0.074 ***|0.094 **|0.065 ***|0.088 ***|
||[0.02]|[0.04]|[0.02]|[0.03]|
|Y_2016 x Treated|0.042|0.138 **|0.224 ***|0.314 ***|
||[0.04]|[0.06]|[0.05]|[0.06]|
|Y_2017 x Treated|0.019|0.159 ***|0.055 **|0.356 ***|
||[0.03]|[0.05]|[0.02]|[0.07]|
|Y_2018 x Treated|-0.002|0.159 ***|0.038 ***|0.392 ***|
||[0.01]|[0.05]|[0.01]|[0.08]|
|Y_2019 x Treated|0.009|0.170 ***|0.008|0.397 ***|
||[0.01]|[0.06]|[0.01]|[0.08]|
|Y_2020 x Treated|-0.001|0.171 ***|0.026 ***|0.421 ***|
||[0.01]|[0.06]|[0.01]|[0.08]|
|No. of observations|2,736|2,736|11,344|11,344|
|No. of clusters|43|43|60|60|
# **1. Introduction**

Trade credit is an important source of external financing for firms of all sizes (Demirguc-Kunt and Maksimovic, 2005). For instance, suppliers often offer working capital financing to their buyers, reported as accounts receivables, some of whom may be small or credit constrained (McMillan and Woodruff, 1999; Marotta, 2005; and Van Horen 2005). Trade credit has also been shown to act as a substitute for bank credit during periods of monetary tightening or financial crisis (see, for example, Himmelberg et al., 1995; Choi and Kim, 2005; and Love et al., 2007).

Trade credit, however, is not used for financing purposes alone. Trade credit, it has been argued, is a way for a supplier to engage in price discrimination, giving favored or more powerful clients longer terms (see, for example, Wilner, 2000; Fisman and Raturi, 2004; Van Horen, 2005; and Giannetti, Burkart, and Ellingsen, 2011). Furthermore, trade credit may simply be customary in an industry, with customs driven by economic rationales such as allowing buyers time to assess the quality of the supplied goods (Lee and Stowe, 1993).

Studies have explored the supply and demand of trade credit around the world (for instance, Petersen and Rajan, 1997; Johnson, McMillan and Woodruff, 2002; Boissay and Gropp, 2007; and Fabbri and Klapper, 2009). Yet, in part due to the lack of detailed contract-level data on trade credit terms, little is known about how the contract terms of trade credit vary across buyer and supplier characteristics. For example, what is the typical contract period of trade credit? Which buyers receive longer net days (days before payment is due)? Which firms are offered early payment discounts?

This paper addresses these questions using a unique database that includes contract information for about 30,000 supplier transactions for 56 large buyers in the United States and Europe. The data includes detailed information on contract terms: the contract size (the amount in trade credit to spend in U.S. dollars), net days (days within which the buyer has to pay the amount owed), discount days (days within which the buyer has to pay to get the full discount), discount rate (the size of the discount if the amount is paid by the discount date), and the currency of the transaction.

2
# **Table 4: Structure of debt in MENA as of December 2008**

||**Egypt**|**Morocco**|**Tunisia**|**Jordan**|**Lebanon**|
|---|---|---|---|---|---|
|**Treasury bills**|64 %|24 %|2 %|39 %|6 %|
|**Treasury bonds**|36 %|76 %|98 %|61 %|94 %|
|**Total**|**100 %**|**100 %**|**100 %**|**100 %**|**100 %**|
|**Average**<br>**maturity in**<br>**years**|2.1|5.9|5.3|2|1.75|
|**Original**<br>**maturities**|n.a.|58 percent in 10<br>and 15-year bonds|67 percent in 10 and<br>12 years bonds|Bulk in 1 to 5<br>years|82 percent in<br>3-year bonds|

_Source: Ministries of Finance and Central Banks (webpages)_

**Table 5: Instruments issued by country with tenors and terminology as used locally**

||**Egypt**|**Morocco**|**Tunisia**|**Jordan**|**Lebanon**|
|---|---|---|---|---|---|
|**Short term**|**T-Bill -discount**<br>3 months<br>6 months<br>9 months<br>12 months|**T-Bill -discount**<br>13 weeks<br>26 weeks<br>52 weeks|**BTCT-discount**<br>13 weeks<br>26 weeks<br>52 weeks|**T-Bill discount**<br>6 months|**T-Bill discount**<br>3 months<br>6 months<br>12 months|
|**Medium**<br>**term**|**T-Bond-coupon**<br>3-year<br>5-year|**T-Bond-coupon**<br>2-year<br>5-year|**BTA-coupon**<br>5-year|**T-Bond-coupon**<br>3-year<br>5-year|**T-Bond coupon**<br>24 months<br>36 months<br>5-year|
|**Long term**|**T-Bond-coupon**<br>7-year<br>10-year|**T-Bond-coupon**<br>10-year<br>15-year<br>20-year<br>30-year|**BTA-coupon**<br>10-year<br>15-year<br>BTZc -zero<br>coupon<br>10-year|**T-Bond-coupon**<br>7-year (last in<br>2003)|n.a.|

_Source: Ministries of Finance and Central Banks (webpages)_ _Placement mechanisms_

**Placement mechanisms, their frequency and operational procedures are critical in determining the level of transparency, competition and cost-effectiveness of issuing debt.** Although different selling mechanisms exist (e.g. auctions, syndication, tap sales,

23
subject to the budget constraint where E( _pj_ )( _sz_ ) is the expected price paid for the good _j_ and _Nz_ is the total number of products consumed in the hierarchy. The expected price depends on the shopping intensity _sz_ in the following way. A household that chooses to spend time _sz_ in shopping samples one (two) price(s) in the retail market with a probability _α_ ( _sz_ ) (respectively 1 _− α_ ( _sz_ )). We assume that there is perfect recall and the household sampling two prices always chooses the minimum price when purchasing the good _j_ .<sup>2</sup> Thus, conditional on the shopping intensity _sz_ , the expected price paid for the product _j_ is In the above Equation (3), the first term captures the expected price paid by consumers if they meet only one retailer in the goods market, while the second term denotes the expected price paid by households who meet two retailers. Differentiating the above expression with respect to the shopping time _s_ yields where the second line directly obtains from integrating the term in brackets by parts. The observation that expected price paid for any product _j_ is decreasing in the shopping time follows immediately from the facts that _α_<sup>_′_</sup> ( _s_ ) _<_ 0, while the term inside the bracket is always positive. Thus, households that spend more time on shopping have a higher probability of finding a retailer who offers a product _j_ at a lower price compared to its competitors, and pay

> 2We make the assumption that there is no retailer-level dispersion in the variety of goods, and that each retailer can supply all categories _j_ demanded by the households. Thus, once a household-retailer link has been established through a matching process, quotes for all prices become available to the household. While this assumption may be theoretically restrictive, and indeed is adopted for expositional convenience, it is empirically supported by the data. In the IRI sample, each retailer, on average, sells products in almost all 31 categories sampled in the IRI data. On average, each store sells 5,456 UPCs every quarter across all categories.

10
T. BL!E '+ imp.ac, **S.)vt.et-ignlty anld Dett,u; s**

|Constant|-2_087_|-2.231|
|---|---|---|
||,,-12 801)|(-11.932)|
|Reserves/'GNP|-1.823<br>|-2.438<br>|
||(-6.064)|(-5.227)|
|*<br>Export Ratio|-0.111<br>|-0.385<br>|
||(-0.762)|(-2.275)|
|Agriculture/GDP|-0.006|0.002<br>|
||(-2.090)|(0.060)|
|Real GDP/Capita|-0.001<br>(-3.431)|-0.001<br>(-3.162)|
|Total Debt/GNP|0.191|**0.098**|
||(1.220)|(0.525)|
|IMF Standby|0.099|0.192<br>|
||(1.790)|(2.604)|
|Sovereign|-0.<br>161<br>(-2.390)||
|D30|-|0.101<br>(2.112)|
|AdjR<sup>2</sup>|.53|.68|
|Nobs|292|131|

# Notes:

Estimated equation is (?) and the<sup>numbers in parenthesis are</sup><sup>**'t'**values.</sup> The data are annual.
**Rong, Fang** . 2010.  ―Understanding developing country stances on post-2012 climate change negotiations: Comparative analysis of Brazil, China, India, Mexico, and South Africa.‖ _Energy Policy_ 38: 4582–4591.

**Sir Nicholas Stern** (2006), Stern Review on the Economics of Climate Change, UK Treasury. **Timilsina, Govinda R** . 2008. ―Atmospheric stabilization of CO2 emissions: Near-term reductions and absolute versus intensity-based targets.‖ _Energy Policy_ 36: 1927–1936.

**Timilsina, Govinda R., John C. Beghin, Dominique Van der Mensbrugghe and Simon Mevel. 2010. “** The Impacts of Biofuel Targets on Land-Use Change and Food Supply: A Global CGE Assessment‖. World Bank Policy Research Working Paper No. 5513. The World Bank, Washington, D.C.

**Timilsina, Govinda R., Stefan Csordás and Simon Mevel.** 2011. ―When does a carbon tax on fossil fuels stimulate biofuels?‖ Ecological Economics 70 (12):2400–2415.

**Tol, Richard S.J.** 2005. ―Emission abatement versus development as strategies to reduce vulnerability to climate change: an application of FUND, _Environment and Development Economics_ 10: 615–629.

21
in particular, transfers operations and maintenance responsibilities of secondary canals or laterals to IAs (World Bank 2001). This transfer in O&M responsibility is accompanied by changes in how water-user fees are obtained from farmers and used by associations. In most cases, the change marks a move to a simple 50-50 sharing of water user fees between IAs and NIA – this money is collected by IAs from members and sent to NIA, which then returns part of the fees.

The motivation behind IMT is that it will reduce government responsibilities for operation and maintenance and simultaneously increase farmer supervision over wateruse. This is in line with a broad government strategy to empower communities through decentralization, increase accountability and quality of public sector services, and, streamline the public sector. By lowering government expenditures and strengthening local governance, IMT is expected to have a long-term impact on the country’s agricultural and natural resource sectors. Figure 1 summarizes some of the mechanisms through which IMT can be expected to benefit farm households.

First, IMT is expected to increase the control local farmer associations have on irrigation infrastructure and water. For example in a recent survey of 63 IMT contracts in 19 systems across the Philippines provides, association leaders were asked what they liked most about IMT -- the top two reasons were the sense of ownership and control and access to revenues (Hassal and Associates International 2004). With an IMT contract, these associations can make better decisions regarding water delivery and timeliness and can organize themselves to resolve conflicts and maintain infrastructure. Without local 8
_Source:_ Authors’ analysis based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note:_ * Sum of legal and formal and mixed. ** Sum of legal and illegal; see table S1.1 in appendix S1.

Employment reflects these changes, and grows more in smaller than in larger ones (that is, in firms that are less intensive in more highly educated workers). These differences are magnified if we focus on firm types: employment in informal firms increases by 114.5 percent vs. 5.7 percent in formal ones. These trends are noteworthy given that, during this period, there were large improvements in the schooling composition of the labor force; so one cannot argue that the absence of a growing supply of educated workers is responsible for them.

# **Absent Increasing Misallocation, There Would Be Increasing Excess Demand for Skilled Workers**

The trends in the size and type distribution of firms can be contrasted with the trends in the supply of workers by schooling levels as shown in table 5. While the periods do not match exactly, the overlap is substantial. The key point is that, over practically the same period, the educational composition of the working-age population (potential supply) and the economically active population (observed supply) changed importantly as the number of educated persons and workers increased more rapidly than the average of all persons and workers (and, indeed, the number of those with the least education fell in absolute terms). In parallel, however, the degree of the misallocation of resources increased, and always in the direction of firms that are less intensive in educated workers. So, the broad picture that emerges is that of an economy within which, on one hand, the supply of educated workers is increasing relatively quickly but, on the other, given that misallocation is also increasing, firm and employment growth is faster in the informal sector.

To illustrate the effects of these trends, equation (3) is solved for each year between 1996 and 2015. Figure 6 plots the percentage excess supply of workers of educational categories with 40
characteristics, production, income sources, migration, and expenditures for a representative sample of rural households surveyed in January and February 2003. The sample includes 1,782 households in 14 states. INEGI, Mexico’s national information and census office, designed the sampling frame to provide a statistically reliable characterization of Mexico’s population living in rural areas, or communities with fewer than 2,500 inhabitants. For reasons of cost and tractability, individuals in hamlets or disperse populations with fewer than 500 inhabitants were not included in the survey.<sup>4</sup> The result is a sample that is representative of more than 80 percent of the population that the Mexican census office considers to be rural.

To implement the survey, Mexico was divided into five regions, reflecting INEGI’s standard regionalization of the country: Center, South-Southeast, West-Center, Northwest, and Northeast. The survey was designed to be representative both nationally and regionally. Data from this survey make it possible to quantify migration and remittances at the household level, as well as to test for influences of these variables on household consumption and investment expenditures.

Detailed data were gathered on migration in 2002 by the household head, the spouse of the household head, all other individuals living in the household, and all sons and daughters of either household head, regardless of where they resided at the time of the survey. Twenty-six percent of households in the sample had at least one internal migrant in 2002; they averaged 2.7 internal migrants each. Sixteen percent participated

> 4 The percentage of the population of Mexico that lives in hamlets of less than 500 people is no more than

18
Table 1: Summary Statistics: Sub-district level Employment, Yield and other indicators (Full Sample)

||Mean|SD|Mean|SD|Mean|SD|
|---|---|---|---|---|---|---|
|**Economic Census**|**20**|**00**|**200**|**6**|**20**|**09**|
|Employment (Firm size>=10)|3128|4161|4256|6537|5158|9487|
|Manufacturing|1419|2925|2117|5806|2225|6256|
|Services|1062|1142|1319|1044|1267|991|
|of which|||||||
|Trade|64|178|47|127|40|110|
|Education|692|672|978|684|951|658|
|Health|151|208|169|196|162|183|
|Other(construction,transport,utilityetc.)|1701|2052|2132|2010|2926|5343|
|**Population Census**|**20**|**00**|||**20**|**10**|
|Employment (Total)|14054|4161|||32214|31322|
|Manufacturing|1458|10871|||7985|12043|
|Services|12595|2559|||24229|22137|
|Proportion urban|0.13|0.13|||0.14|0.11|
|Population|213274|108269|||276587|153595|
|Proportion with Secondary or higher Education|0.11|0.04|||0.13|0.04|
|Proportion of households with electricity|0.25|0.23|0.36|0.25|0.46|0.27|
|**Household Income and Expenditure Survey**|**20**|**00**|**200**|**5**|**20**|**10**|
|Rice Yield (mt/acre)|0.94|0.11|1.01|0.13|1.36|0.48|
|% of land irrigated|61|30|60|33|69|26|
|Real Per Capita Expenditure|749|200|848|206|1098|266|
|Real DailyWage|74|29|76|39|94|31|
|Annual Rainfall(mm)|1444|487|1653|410|1472|379|
<!-- Start of picture text --> 50%<br>45%<br>40% @ Share of<br>exports<br>35% 2007<br>30%<br>25%<br>mw Share of<br>20% exports<br><!-- End of picture text --> <!-- Start of picture text --> 10%5%OO<br>0%<br>Capital goods|\Consumption| Passenger |Consumption| Fuelsand | Intermediate |Consumption Raw products<br>goods motor cars goods lubricants: goods goods<br>processed<br>Final Intermediate Raw<br><!-- End of picture text -->
# **Figure A6. VAR dynamic accounting: contribution to the IRFs of prices**

<!-- Start of picture text --> Benchmark BRA MEX IND<br>0.05 0.05 0.2<br>IP<br>EMEs 0 0.15 STIFX<br>0 0.1 S<br>-0.05 0.05<br>IP IP<br>-0.05 STI STI 0<br>FX FX<br>-0.1<br>S S<br>-0.05<br>-0.1 -0.15 -0.1<br>1year 2year 3year 1year 2year 3year 1year 2year 3year<br>Large 0.05 DEU 0.05 GBR 0.05 CAN<br>advanced<br>IP STI FX S Baseline IP STI FX S Baseline IP STI FX S Baseline<br>economies<br>0<br>0 0<br>-0.05<br>-0.05 -0.05 -0.1<br>1year 2year 3year 1year 2year 3year 1year 2year 3year<br>Other 0.05 KOR 0.05 CHN 0.05 ZAF<br>economies 0<br>0 -0.05<br>-0.1<br>-0.05<br>0 -0.15<br>-0.1 -0.2<br>-0.25<br>IP STI FX S Baseline -0.15 IP STI FX S Baseline -0.3 IP STI FX S Baseline<br>-0.35<br>-0.05<br>1year 2year 3year -0.2 -0.4<br>1year 2year 3year 1year 2year 3year<br><!-- End of picture text --> Note: Contributions of variables to Impulse response functions of prices following a one-standard-deviation monetary policy shock. The Y-axis indicates the percentage point, and the X-axis indicates years.

33
enrollment growth. This correlation holds even for the sample of older, pre-existing religious schools and hence corroborates the hypothesis that madrasas have encouraged greater female participation in secondary education rather than selectively emerging in regions where households have responded to the stipend scheme by sending daughters to schools. These findings highlight the previously undocumented role played by religious schools in removing gender disparity in rural Bangladesh.

Next, we explored to what extent feminization of secondary madasas is driven by the presence of BRAC schools in the union by using madrasa level enrollment data. We found that madrasas located in regions with larger number of BRAC schools saw a greater growth in female enrollment where presence of BRAC schools in the union is treated as endogenous. The effect of BRAC schools remains even when we use an alternative measure where we replace the number of BRAC schools by the number of female students enrolled in BRAC schools.

Such externality effect on madrasas is unusual but not surprising considering the fact that both BRAC schools and madrasas are inspired by non-profit motives and target relatively poorer regions<sup>15</sup> . Entry of NGO schools, which only operate at the primary level, therefore can put pressure on secondary madrasas in the community which usually cater to boys, compelling them to open up to girls -- greater NGO activities can have unintended consequences in the form of a positive, complementary spill-over effects. Our finding therefore not only provides a new explanation for the rise in female enrollment in religious schools in Bangladesh, it also highlights yet another policy rationale to support and promote the type of targeted services delivered by NGOs in the rural areas.

# **Reference**

Asadullah M, Chaudhury N, Dar A. (2007) “Student Achievement Conditioned Upon School Selection: Religious and Secular Secondary School Quality in Bangladesh", _Economics of Education Review_ , 26(6): 648-659.

- Chaudhury, Nazmul, Jeffrey S. Hammer, Michael Kremer, Karthik Muralidharan, and F. Halsey Rogers. 2006. "Missing in Action: Teacher and Health Worker Absence in Developing Countries." _Journal of Economic Perspectives_ 20(1): 91-116

- Chaudhury, Nazmul and Devarajan, Shantayanan (2006) “Human Development and Service Delivery in Asia”, Development Policy Review, 24(s1):s81-97

- Arif, G. M. and Saqib, N. (2003), “Production of Cognitive and Life Skills in Public, Private, and NGO Schools in Pakistan”, _Pakistan Development Review_ . 42: 1 (Spring 2003) pp. 1–28.

- Barr, Abigail and Fafchamps, Marcel (2006) “A client-community assessment of the NGO sector in Uganda”, _Journal of Development Studies_ , 42:4, 611 – 639.

> 15 This finding is similar to that of Khwaja et al (2007) who find an externality effect of public schools on the emergence of private schools in rural Pakistan.  The authors find that private schools were set up in Pakistani villages that have had government schools because ample local supply of public schools graduates made it feasible for private entrepreneurs to set up low cost private schools.

14