rafmacalaba/data-use-mentions-extended huggingface dataset:
for the analysis. The database covers retail and wholesale prices utilized in the previous two sections. Following the methodology outlined in the next subsection, we construct a database that measures deviations from the law of one price. The variable of interest for analyzing deviations from the law of one price – the main dependent variable ‐ is the average absolute price difference, in logs, between commodity prices in a given state and Delhi.

We compute the average of each variable for all commodities and states between 2005 and 2014. Through averaging, we distinguish signal from noise.<sup>17</sup> For retail, the sample is a cross section consisting of 23 states and thirteen of commodities. Table 6 describes the variables in our database. For wholesale, it covers 5 commodities in 21 states, averaged over the period 2008‐2015. The smaller set of commodities for the analysis of drivers of departures from the law of one price in wholesale markets is due to the lower availability of data points in the city of reference. The dependent variable is constructed as the log‐difference between prices in a district and prices in Delhi.

Table 6: Sample Overview

|Variable Name|Variable Description|
|---|---|
|Commodities in Retail|Includes: Bananas, Coconut Oil,|
|Database|Gram, Groundnut Oil, Masur,<br>Moong, Mustard Oil, Onion,<br>Potatoes, Rice, Tur Arhar, Urad,<br>Wheat.|
|Commodities in|Includes: Mustard, Onion, Paddy,|
|Wholesale Database|Potatoes and Wheat|

> 16 These potential drivers are aligned with those identified in the literature.

> 17 Averaging also implies that the data set is transformed from a panel into a cross‐section. Given that market integration is a long‐run concept, and that many of the determinants are slow to adjust (in some cases time‐invariant), analyzing this in cross‐section is reasonable.

27
any sector (only organized support for protecting a lobby’s own sector). If everyone were organized, α =1, then organized lobbying for protection in any sector would be balanced out by organized opposition to that protection, and tariffs would be zero.

Goldberg and Maggi (1999) and Gawande and Bandyopadhyay (2000) have empirically investigated these predictions using U.S. manufacturing industry data, Mitra, Thomakos and Ulubasolglu (2002) with Turkish data and McCalman (2002) with Australian data. They affirm the predictions qualitatively. Gawande and Bandyopadhyay also examine the lobbying side of the Grossman-Helpman model. They affirm the fundamental prediction 7 The Ramsey pricing logic applies here. The Ramsey tax formula implies that if the demand for a good is uniformly less elastic than that for another good, the optimal tax rate is higher for the first good due to the lower deadweight loss from taxing it rather than the second good. If the first good is totally inelastic there is no deadweight loss from taxing it, and the first best can be reached by taxing just this good.

14
only taken into account by sophisticated consumers. More formally, the following testable implications can be derived:

1. The staff of financial institutions are knowledgeable about the financial product but will only provide customers with information to contract it, without disclosing its cost voluntarily. As a result, both naïve and sophisticated customers are aware of all requirements to open a savings account or contract a loan. In addition, sophisticated customers ask about fees such as the minimum balance fee on deposit accounts or the late payment fee in credit products and thus learn about such costs while naïve customers do not ask about them and thus remain uninformed.

2. Naïve and sophisticated customers are offered the same product, but naïve customers will earn less net interest on savings and pay more in total for credit as they also pay fees.

In Section 6 we test these predictions more formally.

# **4. Experimental Design**

The audit study was conducted in collaboration with the Banco de la República (Central Bank of Colombia) and the Superintendencia Financiera de Colombia (Financial Superintendency). Bogotá (capital) and two other cities (Tunja and Pereira, with populations of 190,000 and 470,000 respectively, were selected for the study. A radius of 1.5 km was drawn from the business center, and a census of every financial institution inside the circle was conducted. We found a total of 64 distinct institutions across these 3 cities. For the purposes of the analysis we differentiate between audit studies conducted in the capital Bogota and other areas.

Credit auditors requested a 12-month loan for a household expenditure, preferably with monthly installments. The expenditures included house repairs, medical expenses and children’s school supplies, among others. Savings auditors were assigned an amount of 1,500,000 pesos (515 USD) that they wanted to deposit.<sup>14</sup> Since we wanted to capture all the information and materials that staff would provide to prospective clients up until the signing of the credit contract or opening
# **Trade Credit Contracts**

Leora Klapper, Luc Laeven, and Raghuram Rajan* _JEL Classification_ : G32 _Keywords_ : Trade Credit; Capital Structure; Contract Theory; Risk Management

* Klapper is Lead Economist at the World Bank. Laeven is Deputy Division Chief at the International Monetary Fund and Research Fellow at CEPR. Rajan is the Eric J. Gleacher Distinguished Service Professor of Finance at the University of Chicago and Research Associate at NBER. We thank PrimeRevenue for generously sharing the trade credit contract data, Alexander Ljungqvist (the Editor), two anonymous referees, Stijn Claessens, Shane Maine, John Sculley, and Chris Woodruff for useful comments or suggestions, and Teresa Molina and Douglas Randall for excellent research assistance. This paper‘s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, the IMF, their Executive Directors, or the countries they represent.
data transparency are positively correlated with forecast accuracy (absolute forecast errors) and reduce forecast optimism (forecast errors). However, the findings for forecast optimism are statistically significant, while the findings for forecast accuracy are not. Global sample coefficients are greater than those for just developing economies, suggesting that the effects of good data systems are much stronger in a sample that compares the performance of data systems in both advanced and developing economies. The results indicate that the positive effects of good data ecosystems are robust to the choice of the data transparency indicator.

## VI.2 Other Control Variables: Institutions, Polity, Informality, and Natural Disasters

This robustness check includes other potential factors that may affect forecast errors. They are institutions (proxied by rule of law), polity, informality, and natural disasters. The first three belong to the information set of forecasters at the time of forecasts at the beginning of the year (January), while natural disasters are considered as shocks that occurred during the year of the forecast.

The quality of political institutions may affect forecast errors. Merola and Pérez (2013) observe fiscal forecast over-optimism in election years not just in forecasts made by European governments but also, albeit to a lesser extent, in forecasts made for European countries by international organizations like the European Commission (EC) and the OECD. A system of checks and balances that restricts the power of the executive is a feature of a strong democracy (De Montesquieu, 1989). Therefore, we account for the quality of political institutions using the Polity Index and the Rule of Law Indicator. The Polity Index is defined as the lag of the combined polity score that is computed by subtracting the autocracy score from 20
- 14 -

Figure 6: Contractual savings portfolio distribution (%, common law countries) <!-- Start of picture text --> 100% _<br>0 40 4<br>40#'<br>30%_ 0<br>1982 1984 1906 1988 1990 1992 1994 1996<br>l 1C3h.anddeposits * Bill. and bonds 3Lo..s Shars- *Other<br><!-- End of picture text --> Source: 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Figure 7: Contractual savings portfolio distribution (%, non-common law countries) <!-- Start of picture text --> 100% _ _ 4 ,_ 1 1<br>90% 5 6 8 2 12 1> l<br>60%<br>60% 48 42 38 30 26<br>62 60 57<br>300__<br>40% 31<br>30%<br>0% 2=M774= =4 4<br>1962 1984 1986 1988 1990 1992 1994 1996<br>Cashhand deposits *BilsifandP onds OLoans OShare- *Oth. |<br><!-- End of picture text -->

Source: 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.
**Figure 3: Bangladesh: Bilateral Trade Growth Rates (%)**

<!-- Start of picture text --> 50<br>44.33<br>45 42.65<br>40<br>35<br>30<br>25<br>18.47<br>20 16.24<br>13.32<br>15<br>10<br>5<br>0.79<br>0<br>1981-1990 1991-2000 2001-2009<br>Export to India Import from India<br>(%)<br><!-- End of picture text --> _Source_ : Calculated based on IMF DOTS.

# **3.2. Trade Complementarity and Intra-Industry Trade**

The trade complementarity index (TCI) indicates whether trade is likely to grow between countries. Research indicates that trade complementarity between India and Bangladesh is relatively limited, although it has grown.<sup>18</sup> The TCI at the disaggregated level (6-digit HS) for 2002 and 2007 offers mixed results for the two countries (Table 3). Bangladesh had a higher trade complementarity than India in both years,<sup>19</sup> though Bangladesh‘s decreased and India‘s increased. The scale of trade is driving the complementarity. Hence, although India‘s TCI score is lower, the number of commonly traded products is far higher, so India fulfills a larger proportion of Bangladesh‘s import demands; similarly, Bangladesh‘s higher TCI score for fewer traded products indicates a smaller trade creation potential with the existing basket of goods. The TCI scores do indicate, however, that bilateral trade between India and Bangladesh has the potential to grow, since trade in similar product lines has grown. This will deepen production networks between the two countries.

The scope for production networks and vertical trade between the two countries can be judged by the intensity of intra-industry trade
clients or suppliers or within the labor market. In addition, HGFs' high demand for inputs may generate opportunities for suppliers to exploit economies of scale (Javorcik, 2004), and/or facilitate technology upgrading. Because of their advanced technology, HGFs may sell high‐quality or innovative inputs, benefitting downstream firms using that input. Assessing whether positive or negative spillovers prevail is the empirical question we explore in this paper.

We rely on detailed firm‐level administrative data from Hungary and exploit regional and time variation in the share of HGFs to infer their connection with the performance of other related firms. We focus on vertical spillovers and use the 2‐digit industry input‐output matrix to determine the share of HGFs in upstream and downstream industries. This focus is motivated by the fact that the related literature on MNE spillovers documents stronger evidence for vertical or intra‐industry spillovers rather than horizontal ones. We also consider a more refined measure of horizontal spillovers, or intra‐industry spillovers, where we measure the share of HGFs in the firm’s 2‐digit industry and county, omitting the firms' own 4‐digit industry.

In our empirical strategy, we carefully adapt the methods in the FDI spillover literature to the HGF environment. From a technical point of view, a crucial difference is that the HGF phase is more transient than the FDI one, which, in turn, limits the application of fixed effects methodologies employed in the latter literature. We handle this issue by regressing log differences on the level of spillover measures and firm controls. Relatedly, in contrast with the FDI case, the share of HGFs is mechanistically linked to the growth distribution, generating challenges discussed in the peer effect literature (see, for example, 2
, fully compatible with the classification utilized in the OECD Services Trade Restrictiveness Regulatory Database and the previous classification of measures developed by the World Bank 10 years ago.

# _Data collection methodology_

For the purposes of data collection, economies and information sources were divided into two groups. For the 43 economies that had been already included (in 2016) in the OECD STRI Database, the data were sourced from that database, thanks to the cooperation of the OECD Secretariat, which is gratefully acknowledged.<sup>10</sup> In a few cases, where information on certain measures in our database was not available from the OECD, other secondary sources were used. For the remaining 25 economies included in this analysis, data were collected through a survey jointly conducted by the World Bank and the WTO – the WB/WTO Survey on Impediments to Services Integration. In what follows, we will concentrate on the data-collection methodology used for the latter group.

For each of the 25 economies covered in the WB/WTO survey, we prepared 24 questionnaires: 23 (one per subsector) containing the <u>common</u> and <u>sector-specific questions</u> plus one containing <u>general questions. Each subsector questionnaire consisted of around 160 questions. The</u> questionnaires were completed by local law firms that were familiar with the policy regime in the respective economies and sectors and had experience in working on similar projects. The complete list of law firms that contributed to the questionnaires is contained in Annex 7.

The <u>general questions focus on</u> _economy-wide measures_ that are described in a single law or regulation applicable to all services sectors in the same way. Such measures include, among others, those pertaining to acquisition and rental of land and real estate by foreigners, notification and screening of foreign investment, restrictions on payments and transfers, conditions on visas, restrictions on cross-border data flows, etc. The <u>common questions cover measures that are<
rules and procedures, the issuance of new documentation, the adoption of new software as well as the training of officials in the new procedures and documentation requirements. Many least-developed countries have requested technical assistance for this purpose in the context of the needs assessments they have prepared. While it is clear that LDCs have significant needs in this area, it is also possible that other developing countries also have needs which have not been assessed, and would face difficulties in meeting their commitments without technical assistance.

The TPRs provide substantial informnation on problems developing countries have faced in under-valuation of products more broadly which give rise to requirements for preshipment inspection. According to the most recent information provided by the WTO, sixteen of the countries investigated had a PSI program (WTO, 1999b; see also Low, 1995). And there were an additional 18, usually small low income countries or LDCs using such services for which TPRs had not been prepared.

# 2. Technical Barriers to Trade, Sanitary and Phytosanitary Measures

The reviews show that practically all developing countries had established.institutions to maintain technical standards as well as enforce health, sanitary and phytosanitary regulations. The information available for most countries however, was not sufficiently detailed to permit judgements about the capacity of the institutions to implement regulations. In some cases, for example, some of the countries in Latin America and East Asia, there was little doubt about the adequacy of the institution; In others, especially in Africa, it was difficult to tell. For example, most developing countries have legislation defining national standards which are based on those of the International Standards Organization (ISO), of which many are also members. But in some cases, e.g. Zambia, there appear to be no government testing facilities. Most countries report that they have concluded many formal bilateral mutual recognition agreements on technical standards. But it is not clear whether these agreements provide adequate information to their producers about appropriate standards in countries where they may consider marketing their products.

41
Table 2.1. Base Year SAM for the Two-Sector Model

||Export D|omestic|Final|Intermediate Labor|Capital|Rural Urban|World|Total|
|---|---|---|---|---|---|---|---|---|
|Export|||||||30.00|30.00|
|Domestic|||73.00|2.00||||75.00|
|Final||||||40.00 60.00||100.00|
|Intermediate|5.00|||||||5.00|
|Labor|20.00|30.00||||||50.00|
|Capital|5.00|45.00||||||50.00|
|Rural Household||||35.00|5.00|||40.00|
|Urban Household||||15.00|45.00|||60.00|
|World|||27.00|3.00||||30.00|
|Total|30.00|75.00|100.00|5.00 50.00|50.00|40.00 60.00|30.00||

Source: Adapted from Devarajan, Lewis and Robinson (1990) To make the general equilibrium model fully computable, we must find numerical estimates for the structural parameters characterizing technology, the distribution of income among households and import aggregation functions. When these parameters cannot be estimated econometrically, one can resort to model _calibration_ . This process solves relevant equations for values of the parameters that are consistent with base year data. For the parameters of the Cobb-Douglas production functions we have the following expressions: The parameters of the Armington function are given by
constraint is that the variable must be numeric and should match across datasets, although it is recommended it follows a hierarchical structure similar to the one proposed by Zhao (2006).

- The hierarchical id should be of the same length for all observations. For example: AAMMEEE.<sup>30</sup> This structure facilitiates getting final estimates at different aggregation levels.

- The `dataout()` option indicates the path and filename of the Mata format dataset that will be used when running the Monte Carlo simulations.

## **6.1.1 Example: Preparing the Target Data**

The `sae` command requires users to import the target data into a Mata data format file. This is done to facilitate the process of simulations in the second stage due to the potential large size of the target data.

> 30In the case this were done for a specific country, AA stands for the highest aggregation level, MM stands for the second highest aggregation level, and EEE, stands for the lowest aggregation level.

14
to correspond to a 0.5-point increase in the Gini index. Cevik and Jalles (2023) focus more on the difference in the climate change effects across countries and find the impacts to be seven times greater in developing countries. Gilli _et al._ (2023) analyze country-level income deciles data and find rising temperature to have most negative impact on the poorest deciles within countries. Analyzing more disaggregated subnational inequality data across 134 countries, Dang _et al._ (2023a) similarly find that a 1<sup>◦</sup> C rise in temperature leads to 0.8 and 1.4 percent increases in the Gini and Theil indices, respectively.

Yet, single-country studies often find mixed evidence of climate change impacts on income inequality. For instance, Bui _et al._ (2014) demonstrate that natural disasters can have a significant impact on income distribution in Viet Nam, particularly in regions with larger rural populations and agricultural workforces. Other within-country studies, however, present a more complex picture, highlighting uneven climate change impacts across different income levels within the same country. Pleninger (2022) finds that in the United States, the short-term effects of natural disasters primarily affect middle income population groups, leaving overall income inequality unchanged. This finding aligns with Otrachshenko and Popova’s (2021) study for the Russian Federation showing that extreme hot temperatures negatively impact regional GDP per capita but do not significantly affect income inequality. However, both studies confirm the variation in vulnerability among income levels and regions, with the poor regions being more susceptible to disasters.

Some studies even highlight potential reductions in inequality following climate-related events. For example, Keerthiratne and Tol (2018) document a decrease in the Gini coefficient by 0.01 points in the aftermath of natural disasters in Sri Lanka. This reduction appears to be driven not by an increase in the income of poorer households, but rather by a decrease in the income of richer households. This finding adds another layer of complexity to our 23
data from the entire experiment.

# **Reduced-form evidence on demand persistence**

In Table 2, we extend the specifications reported in Table 1 by including information on past discounts. Corresponding to the current discount dummies, we construct variables representing the number of months that the household faced the same type of discount in the previous 2, 3, or 4 months. In columns 1, 4, and 7 of Table 2, for instance, along with dummies for whether the household is currently receiving the 50 or 75 percent discount, we have the number of months out of the last 2, 3, and 4, respectively, that they received either of these discounts. In the water price specifications (columns 3, 6, and 9), we simply include the mean price that the household faced over the previous 2, 3, or 4 months, which incorporates any discounts. Note that, while specifications that include separate dummies for each type of discount are more flexible in terms of functional form, they are also likely to be the least precisely estimated given the rather high monthly serial correlation across discounts induced by the experiment. At any rate, the upshot of Table 2 is that greater discounts (or a lower average price) in the recent past leads to higher consumption in the current month conditional on the current discount. The evidence is strongest in the specifications with the effective water price ( _p_ -value=0.014 in column 3) and weakest, as expected, when we separate out the 50 and 75 percent discounts, in which case only the effect of past exposure to the 75 percent discount approaches significance.

# **Habit formation/learning through experience vs. alternative mechanisms**

Both provincial water authorities and the director of An Thinh utility told us they expected households to increase piped water consumption over time. Indeed, in its first 3 years of operation (preceding our experiment), piped water use among An Thinh’s customers was growing by an average of 0.