rafmacalaba/datause-encoder-data huggingface dataset:
their merchant business identification number (SIRET code). Using detailed card transactions<sup>8</sup> I calculate the number of cards and number of new cards for each merchant that I consider to be cards that have never made a transaction before 2019. Additionally, using merchant business identification number (SIRET code) and public data from the National Institute of Statistics (SIRENE) on French establishments, I obtain further information on each merchant: date of creation, sector activity (NAF code), number of employees, and geographical location (postal code or longitude and latitude).<sup>9</sup> The data set offers several key benefits for my study. First of all, one of the main advantages of my data is that I have card and mobile transactions information from all the banks in France, which allows me to fully capture all mobile and card sales for each merchant and then be able to generalize my findings. Second, digital payment is transforming the global payment system, household consumption behavior, and merchant selling behavior. Merchant card transactions give me the ability to directly verify the impact of a payment technology shock (such as an increase in the contactless payment limit) as well as a global shock (such as a lockdown due to the COVID19 epidemic) on the ability of merchants to mitigate them. Third, the high-frequency card-sales transactions data recorded with little measurement error, compared to the traditional survey-based data set can allow me to track the sales to each merchant through the transaction record, which is crucial for my study. Relative to very few studies utilizing merchant transaction sales, I am able to study a long time series (two-year period) of sales from a large scale of merchants in different categories.

In 2020, the world was affected by the COVID-19 pandemic. To stop the spread of the virus in France, the government imposed a first containment from mid-March to mid-May. In addition, in mid-May 2020, the French banks increased the limit for contactless payment in order to reduce physical contact and promote social distancing. Again, after a significant increase in the contamination of individuals by coronavirus in October 2020, the French government decided to impose a second containment. I use these three staggered shocks to study the resilience of merchants using contactless payment technology and their effects on merchant sales and substitutions between means of payment. For comparability purposes, I focus the analysis on offline merchants who accept contactless payments since 2019:01 (the treatment group) and those who still do not accept

> 8These datas set are a representative sample of CB transactions and were made available by CB. I exploit the card payments data in accordance with the EU General Data Protection Regulation, in application of Article 89. I use the abbreviation ‘CB’ to indicate the source of the card payments.

> 9The SIRENE database is available on the following link: https://www.data.gouv.fr/en/datasets/base-sirene-desentreprises-et-de-leurs-etablissements-siren-siret/

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|Fed.Micron.|0.0%|0.0%|
|---|---|---|
|Fiji|79.0%|0.4%|
|Gabon|78.9%|0.0%|
|Ghana|57.2%|0.2%|
|Grenada|79.0%|0.6%|
|Guyana|64.5%|0.0%|
|IvoryCoast|70.2%|0.3%|
|Jamaica|72.7%|0.1%|
|Kenya|55.6%|0.7%|
|Marshall Is.|0.0%|0.0%|
|Mauritius|85.0%|0.4%|
|Namibia|87.2%|2.7%|
|Nigeria|56.3%|0.5%|
|Palau|0.0%|0.0%|
|Papua N.G.|81.6%|0.5%|
|Senegal|88.6%|0.4%|
|Seychelles|88.6%|2.3%|
|South Africa|21.2%|14.4%|
|St Lucia|98.5%|0.0%|
|St Vincent|58.1%|0.0%|
|St.Ch.&Nevis|66.8%|0.3%|
|Surinam|64.9%|0.3%|
|Swaziland|53.6%|3.0%|
|Tonga|37.5%|0.8%|
|Trinidad.Tob|48.9%|0.8%|
|Zimbabwe|68.9%|0.6%|
|Total|49.4%|6.2%|

The significant difference between the utilization rate of GSP and Cotonou preferences may reflect that for most of the products exported by the ACP countries the Cotonou scheme offers better access. One important difference between the two schemes is in the rules of origin. Rules of origin define the conditions that a product must satisfy to be originating from the exporting country which asked for the preferential access. The main justification for rules of origin is to prevent trade deflection, whereby products from non-participating countries destined to the free trade area partner are redirected through the other free trade partners to avoid the payment of customs duties. When products are produced in a single stage then the origin of the products should be relatively easy to establish. For all other cases the rules of origin define the methods by which it can be determined that the product has been sufficiently processed in the free trade partner to qualify for preferential access. The specification of rules of origin has become especially important in recent years as technological progress and globalization have led to the increasing fragmentation of the production process into different stages or tasks which are 9
This period is one of substantial commodity price volatility. It included the plunge from mid-2014 to its trough in early 2016, the subsequent rebound, the collapse during the pandemic, in spring 2020, and again the subsequent rebound—for many commodities to historic highs. Since many of these swings were unusually strong by historical standards, model performance is likely to be generally poorer than in previous studies.<sup>10</sup>

# **3.1 Futures prices and consensus forecasts**

Consensus forecasts were drawn from Consensus Forecast Reports accessed through the IMFWorld Bank library for 2000-2022. Data is available monthly, with quarterly forecasts until the end of the subsequent year.

Generic futures prices were drawn from Bloomberg for all seven commodities for 2000-2022: Oil (tickers CL3-CL6-CL9-CL12); copper (tickers HG3-HG6-HG9-HG12); aluminum (tickers AA3AA6-AA9-AA12); lead (tickers LL3-LL6-LL9-LL12); nickel (tickers LN3-LN6-LN9-LN12); tin (tickers LT3-LT6-LT9-LT12); and zinc (tickers LX3-LX6-LX9-LX12). Monthly average data is used, with 3-, 6-, 9, and 12-month-ahead futures prices.

# **3.2 Bivariate correlations**

Alquist, Kilian, and Vigfusson (2013), in their review of oil price forecasting models, identify several variables that are strongly correlated with oil prices over the subsequent 6-12 months. These include the Commodities Research Bureau (CRB) Raw Industrial Commodity Index, U.S. M1 growth, futures prices, and consensus (or other professional) forecasts. These variables are augmented with indicators for China, which plays a large role in global metal markets, and purchasing managers indicators. These include China’s Manufacturing Purchasing Managers’ Index (PMI), the Global Manufacturing PMI, and the Global Composite PMI.

Based on Alquist, Kilian, and Vigfusson (2013), the forecast is obtained by regressing the future change of a commodity price against past independent variables: ∆ hh∆ tt tt tt,tt+hh YY = ββMM + εε where ∆∆ indicates the percent change of a commodity price within the next hh tt tt+hh tt _h_ months, and YY = YY −YY ∆ where ∆∆ _h_ months, and is one input. hh tt tt YY MM Six inputs are used separately: U.S. base money (M1 from the FRED database), 10-year Treasury bond yields (from the FRED database), and the CRB Raw Materials Index (from Bloomberg) in past percent differences equivalent to the horizon.<sup>11</sup> For example, the percent change over the last

> 10 Several studies employed different approaches to address the breaks introduced by the COVID-19 pandemic in forecasting (Schorfheide and Song 2021; Ng 2021; Lenza and Primiceri 2022). There was no need to make assumptions about the treatment of the pandemic in the econometric exercise here as the COVID-19 period formed part of the out-of-sample evaluation period in our comparison.

> 11 The CRB Raw Industrial Commodity Index includes 22 commodities. The 22 commodities are combined into an “All Commodities” grouping, with two major subdivisions: Raw Industrials, and Foodstuffs. Raw Industrials include burlap, copper scrap, cotton, hides, lead scrap, print cloth, rosin, rubber, steel scrap, tallow, tin, wool tops, and zinc.

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4 and re ilts are thien discussed, followed by the major conclusions. Our findings generally support the hypothesis that istortions in the formal labor market are a major factor explaining wage rigidity and the relatively small responsiveness of the real exchange rate to a nominal devaluation.

# 2. The Conceptual Model

The model considers two sectors: (1) the formal sector, which is subject to unionization and government regulations and uses both skilled and unskilled workers, and (2) the informal sector, which is not subject to unionization, is de facto free from government regulations (including the minimum w;ge) and uses only unskilled workers. Because of the distortions in the formal labor market, open unemployment prevails among skilled workers. The equilibrium wage in the informal sector is assumed to be below the minimum wage. It is assumed that skilled workers do not accept working in the informal sector for a lower wage but wait for an opening in the formal sector. In contrast, unskilled workers mc.ve between the sectors. No distortions exist in the informal sector so wages are fully flexible. Since unskilled workers unable to find a job in the formal sector at the ongoing minimum wage are willing to work in the informal sector, there is no unemployment among unskilled workers.3/ In the formal sector, the wage rate for unskilled workers is equal to the minimum wage rate while the actual wage for skilled labor is assumed to be determined by a premium over a notional or nmarket clearing wage. The model allows for change in this premium according to existing market conditions and government regulations. For unskilled labor, the relationship between formal

> 3/ Open unemployment in Latin America is largely a middle-class phenomenon. The typical unemployed person is relatively well educated, young and dependent, and waiting for a job opening in the formal sector (World Bank 1988).
# APPENDIX: UPDATING THE NEHRU AND DARESHWAR (1993) DATA BASE

The data base constructed by Nehru and Dareshwar (1993) for the period 1950-1990 was updated until 1995 for the 18 Latin American countries considered in this paper. To this end, we used information from the World Bank's "World Development Indicators Data Set" (WDI). A brief description of the procedures used in this updating are described in this appendix.

1) Capital Stocks This series was calculated using the perpetual inventory method, which is based on the following accumulation equation: Kt= (1- d)<sup>t</sup> K(0) + Z(1- d)' **It-i** (1) where Kt is the capital stock at time t (in 1987 prices), K(0) is the initial capital stock (in period 0), It-i is the Gross Domestic Fixed Investment in period t-i, and d is the depreciation rate. Nehru and Dareshwar (1993) estimate K(0) by a modification of a technique proposed by Harberger(1978). The procedure is based on the assumption that in steady state the rate of growth of output (g) is equal to the rate of growth of capital stock. By re-arranging (1), this rate can be written: (Kt-Kt_)/Kt_, = -d +(It/Kt-1) (2) which, by the above assumption, implies Kt-I = It/(g+d) (3) Thus, in period 0, the capital stock can be calculated as: K(0) = I/(g+d) Nehru and Dareshwar (1993) calculate I, as the fitted value of a log-linear trend of _I,,_ adjusting for trend-breaks when appropriate. The depreciation rate is assumed to be 4 percent, and g is derived from the series of real GDP at market prices. Equation (1) is then applied to calculate the rest of the values of _K&._ To continue this procedure for the post1989 values, we used data on Gross Domestic Fixed Investment (GDFI), available for all countries except for Argentina. For this country, only the data on Gross Domestic Investment (GDI) was available. To solve this problem, we regressed the log of the ratio of GDFI to GDI on a linear and a quadratic trend, using the years for which both variables were available (from 1970 to 1990). The estimates were then used to extrapolate the figures of GDFI from 1990 through 1995.

2) Gross Domestic Product While comparing the WDI data for this series with the data from Nehru and Dareshwar (1993), we found considerable discrepancies in the levels but not in growth rates of the series. Thus, we performed the updating by multiplying the 1988 levels from the original source by the subsequent years' rates of growth, as derived from the WDI data base.

- 3) Labor Force

Nehru and Dareshwar (1993) use the population aged 15-64 years as a proxy for the labor force. Their data covers the period from 1960 to 1988. We updated this series with WDI data for the period 1989-1995. For the period 1950-1959, however, we only had access to data on total population. Thus, we regressed the log of the ratio of the workingage population to the total population on a linear and a quadratic trend, with data on the period 1960-1995. We then used the fitted values of this regression to extrapolate the figures for the working-age population from 1950 through 1959.
processes under a unified framework. For instance, the sociological literature on intersectionality points out that the dynamics of gender-based exclusion can be different from that of class, race, ethnicity or caste, but empirical studies often neglect the distinct processes underlying each of these categories.<sup>9</sup> This is especially the case in quantitative studies, which rely on preexisting categorizations of individuals in available data sets (Artazcoz et al. 2001, Mohindra et al. 2006). Empirical work on intersectionality is also limited by the number of social divisions that can be reliably analyzed. Among many possible identities or social structures that may confer disadvantage, only a few have received attention in the literature—class, ethnicity, gender and race (Chow et al. 1996, Brewer et al. 2002).<sup>10</sup> It is difficult to find studies that focus on more than two (at most three) identities at a time, since each added layer increases the level of complexity, along with analytical and data requirements.<sup>11</sup> As a result, the empirical literature looking at intersectionality is sparse, mainly focused on economic outcomes rather than their underlying processes, and concentrated in developed countries that have robust data sources. An important part of this literature is driven by sociologists in the United States, who study labor market outcomes of racial and ethnic minorities (see Browne and Misra 2003 for an overview). These studies disaggregate census data to look at the intersections of race, ethnicity, gender and class in occupational segregation (e.g. Padavic and Reskin 2002), earnings (e.g. Bose 2001, Torres-Stone et al. 2006) and job authority (e.g. Browne, Tigges, and Press 2001), among other outcomes. For example, Torres-Stone et al. (2006), in their study of the 2000 U.S. Census in a multivariate earnings model, explore the earning differential between female workers from different ethnic and economic backgrounds.

> 9 Theorists of intersectionality have criticized the empirical literature that stratifies social groups by discrete categorizations for neglecting the unique processes that give rise to each of these divisions (McCall 2005, Hornschied 2009, Choe and Ferree 2010). However, quantitative studies find it difficult to measure mutually constitutive processes that underpin each social identity, and as a result, use the categorical distinctions in available data sets.

> 10 In contemporary development discourse, it is increasingly acknowledged that characteristics like age, religion, citizenship status, place of residence, disability, sexual orientation, to name a few, can also confer advantage or disadvantage. The intersectionality literature has so far focused on more traditional axes of exclusion, such as class, race, and ethnicity.

> 11 The research gap is larger in developing countries, where in many cases quantitative data sources did not allow disaggregation of wellbeing indicators by gender until recently, let alone disaggregation by a combination of identities.

5
# _(ii) Real-time macroeconomic uncertainty_

<mark>Real business cycle data is permanently revised as new information becomes available with some lag which undermines the ability to identify economic activity fluctuations and to provide timely countercyclical support. P</mark> apaioannou et al. (2013) find that elevated macroeconomic volatility and market-risk uncertainty can make it difficult for investors to distinguish temporary price fluctuations from more fundamental changes in risk. Macroeconomic monitoring informs risk assessments by measuring expected losses, and Eicher et al. (2019) find that the poor quality of high-frequency data in many low-income countries can diminish the ability to grasp the timing of downturn and upturn cycles. In addition, frequent statistical revisions negatively affect the ability of analysts to separate cyclical fluctuations from secular macroeconomic trends, complicating efforts to precisely date expansions and recessions. In turn, the absence of precise information on the state of the business cycle in client countries may undermine efforts to implement a countercyclical investment strategy.

# _(iii) Mechanics of co-financing, sponsor and staff incentives_

Disbursement may be delayed due to pending completion of the financing plan (that is, the cofinancing partners transmit their cyclicality to IFC disbursement due to covenants which require other financing to be in place). Debt is more likely to be delayed by inability of sponsors to put in equity (or raise it from others) in a downturn. Equity may be deferred in a downturn because it is harder to agree on a valuation – sponsors may not prefer to take equity at what they perceive as temporarily lowered equity prices.

As internal investment targets are set only for commitments not for disbursements, staff incentives can be another potential factor behind commitment countercyclicality and disbursement procyclicality. Indeed, targets on commitments tend to favor a countercyclical pattern for commitments as they push staff towards continuing to meet commitment targets during downturns, as has been the case in FY21. However, in the absence of targets for disbursements, incentives to materialize commitments and accelerate disbursements in downturns are weaker, therefore, the cyclical pattern of disbursements is predominantly driven by the countries’ economic cycles, i.e.

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# Direct and Indirect Effects of Malawi’s Public Works Program on Food Security

Kathleen Beegle,<sup>_∗_</sup> Emanuela Galasso,<sup>_†_</sup> and Jessica Goldberg<sup>_‡_</sup> JEL Codes: I31, J22, O1. Keywords: public works, food security, Malawi.

> _∗_ World Bank Ghana office. Email kbeegle@worldbank.org.

> _†_ Development Economics Research Group, World Bank, 1818 H St. NW, Washington DC 20433. Email egalasso@worldbank.org.

> _‡_ 3115 G Tydings Hall, Department of Economics, University of Maryland, College Park MD 20742. E-mail goldberg@econ.umd.edu.
# **1. Introduction**

Global Value Chains (GVCs) in the textiles and clothing (T&C) sector have emerged as vital elements of international trade and investment. GVCs have created new opportunities for firms in emerging and developing economies to participate in global markets and achieve economies of scale by specializing in specific activities involved in designing, producing and bringing to market complex goods.

The T&C sector is one of the pillars of GVCs, and has emerged as a robust driver of the trade-led industrialization in emerging and developing economies. The growth of global T&C trade was accelerated by the abolition of the Multi-Fibre Arrangement (MFA) in 2005, which had previously restricted market access of developing-country products to high-income countries, as well as by unilateral and multilateral trade liberalizations. This development has created new trade and investment opportunities in developing countries and has enabled their participation in global T&C value chains.

Global T&C value chains include a range of actors that include global lead firms, intermediate players, and suppliers that are positioned at different stages of T&C value chains. Global large firms (buyers) concentrate more on high value-added activities (research & development, design, distribution and branding) while intermediate players and suppliers focus more on manufacturing operations, such as production of yarn and made fabric.

In South Asia, T&C is one of the largest manufacturing sectors that play a vital role in employment generation and exports. It employs over 55 million people directly and nearly 90 million indirectly in this region. This sector plays an important role in cross-border trade flows in the South Asian region. Over the period 2007-14, intra-South Asia trade in the T&C sector has grown at a Compounded Annual Growth Rate (CAGR) of 15.91 percent, while its trade with the world has grown at a CAGR of 10.17 percent. In addition, the intra-South Asia textile trade has experienced much faster growth than the region’s trade with the world. The inter-industry trade in the T&C sector offers crucial insights on specialization in different product lines that could play a pivotal role in fostering backward and forward linkages. Such value chains could be powerful drivers for developing regional production networks.

This study analyzes the bilateral T&C value chains between India and Bangladesh with a focus on policy related issues. The issues include tariff, non-tariff barriers (NTBs), industrial policies, trade facilitation, and rules of origin (RoO). Based on the comprehensive analysis of both primary and secondary data, it provides a set of recommendations which include common inextricably linked areas of reforms where India and Bangladesh can work together to strengthen bilateral T&C value chains. It also delves into areas of domestic reforms to enhance the competitiveness of their domestic T&C value chains and their subsequent integration into Global T&C value chains.

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