rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The issue of the increase in food prices has received renewed attention in recent months as the increase in prices worldwide has had large negative impacts on households (e.g., Ivanic and Martin, 2007; World Bank, 2008a and 2008b; IMF, 2008; Wodon and Zaman, 2008; Wodon et al, 2008). In Mali, prices for several commodities such as rice, millet and sorghum are today about 25 percent higher than they were a year ago. This has led the authorities as well as development partners to consider a range of compensatory measures that could help offset part of the negative impact on the poor of this increase in prices. However, at least from a conceptual point of view, the net impact of an increase in food prices on the poor is not obvious. Indeed, when discussing the link between rice and other cereal prices and poverty, a key issue is to assess the double and opposite impact that a change in prices can have through producers (who benefit from an increase in prices) and consumers (who lose out when the price increases).

The techniques for the analysis of the short term producer and consumer impacts of food commodity price changes are well developed in the literature. Early work in this area was conducted by Deaton (1989) using data from Thailand (see also Singh et al., 1986). Similar methods have been used in sub-Saharan Africa among others by Barrett and Dorosh (1996) for Madagascar and Budd (1993) for Cote d’Ivoire, among others. These are also the methods that we use in this paper. Most of these studies have found that food price increases tend to lead to an increase in poverty because the consumption effects dominate the production effects as many countries are net importers of food, at least in sub-Saharan Africa.

There has also been a literature on assessing whether in the medium to long term, the increase in prices is compensated by an increase in wages, among others for those workers who contribute to the production of food crops (see for example Ravallion, 1990; Boyce and Ravallion, 1991, Rashid, 2002; Christaensen and Demery 2006; and Ivanic and Martin, 2007). The findings from these studies suggest that wage offset compensate only in a limited way for the initial increase in food prices. Finally, there has also been a substantial amount of work looking at the impact of various policies to deal with food production and prices. This can be illustrated with the case of rice. Indonesia is a country that used to import substantial amounts of rice, but where restrictions were progressively placed on imports in order to help local producers, with imports of rice actually banned after 2004. Using a general equilibrium model, Warr (2005) find that the ban on rice imports raised the price of domestically produced rice, and that this led to an increase in poverty by almost one percentage point (on the Indonesia story as well as for a
# 1. **Introduction**

This paper offers some reflections on the pros and cons of using integrated household survey data in empirical analysis aimed at providing a quantitative basis for policy decisions concerning welfare, poverty and the fulfillment of basic needs. The experience reflected here is that of using four years of data from the C6te d'Ivoire Living Standards Survey (1985-88) in a research effort attempting to link changes in poverty and welfare to macro-economic trends. The design of this research is written-up in Grootaert and Kanbur (1990) and the main findings are discussed in Grootaert (1992).

The lessons we learned from this work have been grouped according to four themes: survey content, sample size and design, frequency of data collection, and the role of panel data. One section of this paper is devoted to each theme. A conciuding comment offers some recommendLtions for future surveys.

**1**
20 Hence, in spite of many improvements also reported in the FIAS study, the state administration has not as yet fully transitioned from its previous role of a micromanager to that of a facilitator required in a modern market economy.

Figure 6: **_Firms’ responses to questions concerning burdens associated with authorities’ implementation of tax regulations in 2004 (percent of firms)_** <!-- Start of picture text --> Frequency of changes in tax rates rules<br>Effectiveness of appeals mechanism<br>Severity of penalties<br>Availability of information on requirements<br>Completing tax forms/filing<br>Treatment by tax authorities<br>Burden of Audits<br>0 10 20 30 40 50 60 70<br>percent of firms<br><!-- End of picture text --> Source: FIAS 2004, p. 22.

This analysis provides evidence supporting the view that despite the fact that Bulgaria has made significant progress in simplifying regulations, the business regulatory environment remains an obstacle to the private sector development and economic growth. Business regulations hinder competitiveness and productivity of Bulgarian firms. Although the hassle cost of doing business is now overall lower than in Croatia or Romania, it remains in some areas considerably higher than in CEEC-8, particularly in Baltic states.

# _B. Business environment, infrastructure and external interaction_

While the above section has focused on the domestic dimension of business climate, i.e., regulatory regimes and measures directly affecting entry, conduct and exit for firms, this section will focus on policies and institutions affecting external interaction of domestic firms. These include both procedures for simplification and harmonization of international trade, state of infrastructure and its management together with provision of such backbone services as telecommunications, banking, insurance, transportation, business services, etc. Together with customs, related border clearance regulatory procedures, technical standards regulations and port efficiency, they shape the ease and speed with which goods and services move across national borders and, therefore, are crucial to trade in goods.

Weaknesses in provision of these services make impossible for domestic firms to participate in the emerging division of labor based on international outsourcing, just-in-time production and supply-chain management. Increasingly sliced value-chains, with the individual production stages being moved to countries with corresponding comparative advantages have become trademarks of a current global economic landscape. Interaction among “production blocs” of border-spanning production networks is particularly vulnerable to delays and disruptions between individual stages of the supply chain due to weaknesses in service links. Hence, poor quality of backbone services and trade facilitation acts deters foreign firms from incorporating domestic firms into their supply chains. It also acts as a barrier to other types of trade.

Liberalization of trade in services may improve the quality and availability of services through competition, economies of scale and, last but not least, incentives to policy makers to improve regulatory
Policy Research Working Paper 5677

# **Abstract**

As free trade areas have proliferated and statutory tariffs have been dramatically reduced in recent decades, nontariff barriers (NTBs) to international trade have risen in importance. Destination-specific product standards are one of the major types of NTBs as they impose additional costs on exporters and increase the time required to bring a product to market. This paper examines the response of U.S. manufacturing firms to a reduction of this NTB by looking at the harmonization of European product standards to international norms in the electronics sector. Using a highly detailed dataset that links U.S. international trade transactions to U.S. firms and a new industry-level database of EU product standards, the author finds that harmonization increases U.S. exports to the EU and that this increase is due to more U.S. firms entering the EU market—the extensive margin of trade. New entrants to the EU region are drawn mainly from the most productive set of firms already exporting to developing markets before harmonization -the extensive margin of trade composition. These firms are characterized by being smaller and less productive than the firms that were already exporting to the EU before harmonization. Furthermore, harmonization decreases export sales at existing exporters -the intensive margin of trade. These findings are consistent with a model featuring the role of product standards heterogeneity across market destinations and productivity heterogeneity across firms. These results suggest that working toward a harmonization of product rules across markets could be a supportive policy to encourage small and medium size firms’ ability to enter new export markets.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at jreyes2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> From market income to |From market income to fina<br>consumable income income<br>redistributivepolicy |<br>es<br>direct taxes: a<br>direct transfers: es<br>indirect taxes: a<br>indirect subsidies: a<br>in-kind transfers: es<br>leducation|<br>| preschool<br>primary&secondary|<br>| |S<br>Pp reaith tertiary! | —“—TMts—s—sE |<br><!-- End of picture text -->

|||From market income to<br>consumable income||From market income to fina<br>income|
|---|---|---|---|
|redistributivepolicy|**|**|||
|directtaxes:|a|||
|directtransfers:|e||s|
|indirecttaxes:|a|||
|indirect subsidies:|a|||
|in-kind transfers:|e<br>||s<br>|
||leducation<br><br>|||
|||<br>preschool<br><br><br>|||
||primary<br><br>|&seco<br><br>|ndary|<br><br>|
||tertiary<br><br>||<br>|
||~~reaith~~<br>~~CT~~|<sup>—“—TMt</sup>|<sup>s—s—sE</sup><br>~~8S~~|
The first source of follow-up data collected is a project assessment conducted between December 2015 and February 2016. The project quality assessment includes observations of community projects by a team of enumerators. For projects with a single output (e.g., a staff house or a borehole), enumerators directly observed characteristics of the output. For livelihood support projects where outputs were distributed to beneficiaries, a sample of beneficiaries was drawn and beneficiary-level outputs were observed. For example, for livestock projects, a sample of beneficiaries was selected, and enumerators visited the sampled beneficiaries to observe the animals provided by the project. The project assessment data allow for the measurement of a set of core outcomes for the impact evaluation, but also of intermediary outcomes (or main underlying mechanisms) that can lead to changes in final outcomes. For each domain, the project assessment allows us to capture a range of variables, which can later be aggregated into indices. The next sub-sections provide additional information on the main outcomes and intermediary outcomes to be tested and the indicators that were collected to measure them. The appendix provides tables with the full list of variables composing the indices (Tables A1 and A2).

The primary outcome is a measure of a project overall score, which is composed of indices that measure the quality of the project and the quantity of outputs delivered. The project overall score is the main outcome for the analysis. It is built as an interaction of a quality measure and a quantity measure. This allows us to account for situations in which a community received more output from a project but at lesser quality, and vice versa. The quality and quantity indices are also analyzed separately. As the quality and quantity indicators are created across different project types, the indices constructed are normalized within each project type to ensure comparability.

Project quality is measured within each project type through direct observation of a range of attributes of the project output. For livestock, the project quality score is an additive index of whether the animal received was of the appropriate age, whether it was a local or improved breed of animal, whether the animal was productive when visited by the survey team, and whether the animal displayed any signs of illness. For staff houses, the quality is measured in terms of how well the walls, roof, windows, doors, ceilings, and floors meet quality standards. For enterprise projects, quality is determined by whether individuals have access to materials, transportation, credit, labor, and markets. Road quality is measured by the material used in the construction. The 15
and an ownership module, which can be matched through a BvD firm identifier (ID).<sup>7</sup> The raw database comprises firm-level information from 24 European countries during the period 2010-2016.<sup>8</sup> We work with unconsolidated financial balance sheets, which better capture the activity of a firm in the country where it operates, to create the financial module. Building on the work of Kalemli-Ozcan et al. (2015) and Cusolito and Didier (2020), we clean the financial raw files. Appendix A describes the procedure implemented to do so. Appendix B presents the representativeness analysis for the final sample.

We build on the work by Cusolito and Vranic (2020) to create the ownership module.<sup>9</sup> Our main explanatory variable, SOE, measures the percentage of direct shares owned by all the public authorities (PAs) that govern the country where a firm operates. To create this variable, we work with ORBIS Links historical files (often known as Vintage disks) and the Entities file. A Links file is a matrix. Each row of the matrix contains information about the ownership structure of a firm at the first layer of the firm’s ownership tree. This includes the firm identifier, shareholders’ names, each shareholder’s ownership stake, source of information, and date the ownership information was validated. The Entities file contains additional information for each firm, such as the firm identifier, entity type, entity name, and whether the firm is a subsidiary or a parent company.

To build the SOE variable, we first clean the raw Links files and eliminate branches (See Appendix C for details). Then, we merge the Links and Entities files to identify the shareholders that are public authorities. We do so using the entity category S, which corresponds to governmental agencies, public departments, and local authorities. We exclude sovereign wealth funds and institutional investors from this set, as

> 7Each BvD firm ID has two components: a country code that refers to the country where the firm operates and a number.

> 8Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Estonia, Finland, France, Germany, Hungary, Italy, Luxembourg, Montenegro, North Macedonia, Norway, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Serbia and Ukraine.

> 9The authors develop a methodology and SQL code to recreate the ownership trees for all the firms in the ORBIS database.

15
20 a higher-than-average duty."<sup>7</sup> However, aside from these special situations Africa receives considerably more favorable treatment, on average, than other exporters. In other words, these is no evidence from Figure I or Table 5 that OECD tariffs caused the general loss of competitive position reflected in Africa's declining market shares. Rather, the evidence suggests that the tariff treatment which was provided enhanced Africa's position _vis-a-vis_ other exporters.'8

# B. Pre-Uruguay Round NTBs Facing Africa

If tariffs were not a factor in Africa's diminishing role in global trade, could OECD countries' nontariff barriers have played a role? Utilizing the World Bank-UNCTAD records, Table 6 shows the share of OECD imports from: (i) other OECD members, (ii) developing countries, and (iii) all subSaharan African countries that encounter NTBs.<sup>9</sup> As indicated, industrial countries' nontariff measures affect a notably higher share of imports from non-OECD countries than they do for OECD intra-trade. Approximately 17 percent of developing counties' exports (excluding petroleum) encounter NTBs, while the corresponding share for OECD intra-trade is under 10 percent. An even greater difference in NTB coverage ratios exists for several product groups. Approximately 53 percent of all developing countries' '<sup>7</sup> Adverse African tariff differentials are largest for Gabon and Nigeria because US customs regulations preclude the extension of GSP treatment to OPEC members. In addition, the United States does not provide GSP treatment for textile and clothing products. As a result, textile and clothing exports from developing countries face a U.S. MFN duty of over 20 percent. Ethiopia previously had GSP treatment withdrawn due to US opposition to the government's national policies.

'<sup>8</sup> 1n a related study, Amjadi and Yeats (1995) show that higher than average nominal freight costs on Africa's exports often more than offset the positive impact of these preferential tariffs. The authors indicate that anticompetitive effects the cargo reservation policies which have been adopted by most African governments probably played a major role in elevating freight costs. Under these reservation schemes a certain share of national trade (often 40 percent) is reserved for the domestic fleet which, in Africa, is often far less efficient than non-flag carriers. The reservation policies shield national carriers from foreign competition so excess prices can be charged.

"<sup>9</sup> Laird and Yeats (1990, Chapter 4) describe how this inventory of nontariff measures was constructed and discuss its utility for research and policy studies. In particular, they note that trade coverage ratios are a rough approximation of the importance of NTBs in that they provide no indication of the restrictiveness of the measures. Low coverage ratios, for example, could be associated with highly restrictive NTBs. Laird and Yeats also provide extensive empirical information on the results of NTB inventory studies for industrial countries.
# **Table 7: The Determinants of Greenfield FDI in the MENA region, Q1 2003 – Q4 2012**

# **Alternative Definitions of Political Instability**

**The Determinants of Greenfield FDI in the MENA region, Q1 2003 – Q4 2012** _Dependent Variable: Log GFI inflows (in millions of USD)  by broad sector and source_

LSDVC Estimates

|_Sector_|Resources|and Energy|Non-resource|Manufacturing|
|---|---|---|---|---|
||1-Year Moving<br>Average Political<br>Instability|2-Year Moving<br>Average Political<br>Instability|1-Year Moving<br>Average Political<br>Instability|2-Year Moving<br>Average Political<br>Instability|
||(1)|(2)|(3)|(4)|
|Political Instability|0.15|0.69|-1.37***|-1.34***|
||(0.53)|(0.57)|(0.40)|(0.43)|
|Economic Controls|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Observations|604|562|604|562|
|Number of Countries|17|17|17|17|
|_Sector_|Tradable|Services|Non-tr|adables|
||1-Year Moving<br>Average Political<br>Instability|2-Year Moving<br>Average Political<br>Instability|1-Year Moving<br>Average Political<br>Instability|2-Year Moving<br>Average Political<br>Instability|
||(5)|(6)|(7)|(8)|
|Political Instability|-0.98***|-0.97***|-0.29|-0.06|
||(0.35)|(0.37)|(0.44)|(0.46)|
|Economic Controls|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Observations|604|562|604|562|
|Number of Countries|17|17|17|17|

Bootstrapped standard errors in parentheses ***p<0.01, ** p<0.05,* p<0.10.

32
Heterogeneity is pervasive. For Bengal grams, at the wholesale level, the law of one price holds. This implies that a shock originating, for example, in Delhi, is fully transmitted to other markets within India in the long run, with the speed of adjustment to short‐term disequilibria being relatively high, in the range of 31‐35 percent (see Appendix). In retail, also for pulses (but slightly different products: grams whole and split, masur, moong, tur arhar and urad), while the pass‐through coefficient is significantly different from one, it is well above 80 percent, suggesting almost complete transmission of shocks in the long run, but at relatively slower speed, in the range of 10‐20 percent of adjustment per period ‐ more in line with what is found for other products (see Appendix to this section).

Prices of onions and potatoes ‐ both for retail and wholesale ‐ show a fast speed of adjustment to long‐run equilibrium levels (tomato wholesale prices show high speed of adjustment well above 50 percent per period). Conversely, rice and paddy show slow adjustments to shocks, at 10 percent per period both for retail and wholesale (Figure 12, Figure 13).

Also, for a given commodity, heterogeneity is high across states. For example, in Uttar Pradesh banana prices adjust at a rate of 32 percent per period after a price shock originating in Delhi, while in Meghalaya prices adjust at a rate of only 8 percent per period. For onion prices, the variation is lower, and the adjustment is faster, with Bihar’s prices adjusting at 78 percent per period, while in Punjab prices adjust at a rate of 53 percent per period, after a shock is observed in Delhi’s prices (see patterns of speed of adjustment by state for retail and wholesale prices in Figure 14).

Figure 12: Market Integration within India – Speed of Adjustment, Retail by Commodity Figure 13: Market Integration within India – Speed of Adjustment, Wholesale by Commodity <!-- Start of picture text --> 0%<br>0%<br>‐10%<br>‐10%<br>‐20%<br>‐20%<br>‐30%<br>‐30%<br>‐40%<br>‐40%<br>‐50% ‐50%<br>‐60% ‐60%<br>Source: Authors’ calculations. Source: Authors’ calculations.<br>Onion Potato Mustard Oil Banana Masur: Split Gram: Split Tur Arhar: Split Urad: Split Gram: Whole Moong: Split Rice:... Groundnut Oil Coconut Oil<br><!-- End of picture text --> Figure 14: Market Integration within India – Speed of Adjustment, Wholesale and Retail by State <!-- Start of picture text --> 0%<br>‐10%<br>‐20%<br>‐30%<br>‐40%<br>‐50%<br>‐60%<br><!-- End of picture text --> <!-- Start of picture text --> Wholesale Retail<br><!-- End of picture text --> Notes: Spearman’s rank correlation coefficient between retail and wholesale speed of adjustment equals 0.1429 Source: Authors’ calculations.

16