rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9436

# **Abstract**

Restricting cross-border trade through export bans in an attempt to stabilize domestic prices has been a particularly popular policy tool used by many sub-Saharan countries in recent years. However, little is known about how the variability in harvests and seasonality—two critical dimensions of smallholder agriculture in Africa—mediate the effects of export bans on household welfare. This study assesses the short-term impact of export bans on prices and welfare of households in Malawi, accounting for these heterogeneities. It uses monthly panel data on maize prices from 152 markets in Malawi and neighboring countries. To identify the impacts of the bans, the study compares the change in price dispersion between a domestic market in Malawi and another market in a neighboring country, relative to the price dispersion between the domestic market and other markets within Malawi that are at a similar distance as the domestic-foreign market pair. The findings show that export bans, in the short run, are associated with lower domestic prices, lower relative prices, and less seasonality in prices in Malawi. This is after accounting for harvest levels and the existence of trade restrictions in neighboring countries. The short-run effects of the export bans help explain why policymakers are likely to engage in the use of such policies. However, the welfare analysis shows that the welfare gains and poverty reduction effects are small in magnitude and likely to be offset by the long-run distortionary effects of restrictive trade policies.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at HFuje@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
are politically connected, we use data from Mobarak and Purbasari (2006a). They identify firms with family connections to Suharto, i.e. firms that are directly owned or managed by a Suharto family member (either directly or being owned by firms held by a family member). They also identify firms with other, i.e. cultivated connections to him. We will primarily focus on family connections, as this is the most conservative and strongest measure of connectedness.

The online appendix B provides supporting evidence that these variables capture connections that were specific to Suharto. It extends Fisman’s (2001) findings by showing that Suharto connections neither 4 We obtain this number by dividing total output produced by all firms in our sample by total manufacturing output reported by the World Development Indicators from the World Bank.

> 5 We use the terms _plant_ and _firm_ interchangeably.

6 Note that because we do not observe firms with fewer than 20 workers entry and exit rates may in part reflect movements in economic activity rather than actual firm creation or foreclosure.

7 These indicators are important markers of competition and allocative efficiency as well as determinants of productivity growth (Aghion et al., 2005; Jerbashian and Kochanova, 2017).

9
**_<u>________________________________________________________</u>_**

country and sector.

# **_2.5.1 Information and Communications Technologies (ICT)_**

# _<u>ICT at a Glance Tables</u>_

ICT at a Glance tables provide key data on:

- Country background information includes population, GNI per capita, and adult literacy rates

- ICT infrastructure & access covers access to fixed line and mobile telephones, cost of phone calls, and TVs sets available

- Computers and the Internet presents data on access to personal computers and the internet

- • ICT expenditures shows ICT spending in per capita and absolute terms and as a percentage of GDP

- ICT business & government environment includes survey data on the effect of the Internet in business and government

-

ICT at a Glance tables compile data from several sources, including the UNESCO Institute for Statistics, the International Telecommunication Union (ITU), World Information Technology and Services Alliance (WITSA), World Economic Forum (WEF), Netcraft and the World Bank.

# _<u>OECD Key ICT Indicators</u>_

The 15 ICT indicators below are drawn from various publications and databases produced by the OECD’s Directorate for Science Technology and Industry (DSTI). They will be updated annually on a rolling basis, as data become available.

1. Access lines and channels in total for OECD

2. Mobile subscribers in total for OECD Data compiled from original and/or official 3. Internet subscribers in total for OECD Data compiled from original and/or official

- 4a. Broadband subscribers per 100 inhabitants in OECD countries

- 4b. Availability of Digital Subscriber Lines (DSL) in OECD countries

5. Cable TV subscribers in total for OECD Data compiled from original and official

- 6a.Households with access to the Internet in selected OECD countries

- 6b.Households with access to a home computer

7a. Internet penetration by size class. Percentage of businesses with ten or more employees using the Internet

-
WPS3832

# **Entering the Union:**

# **European Accession and Capacity-Building Priorities**<sup>**+**</sup>

## John S. Wilson<sup>*</sup> , Xubei Luo<sup>**</sup> , and Harry G. Broadman<sup>***</sup>

### **Abstract**

This paper examines the impact of trade facilitation on bilateral trade flows. We examine trade facilitation and capacity building priorities in 12 countries in the Europe and Central Asia region —eight of the current members of the European Union: Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia and three candidate members: Bulgaria, Romania, and Turkey. Our results suggest that behind-the-border factors play an important role in determining bilateral trade flows (controlling for the effects of tariffs, development levels, distance, and regional characteristics of exporters and importers, among other factors).

The development of new data sets to expand work related to trade facilitation, including strengthening the empirical work explored here is a key priority without which intelligent policy and priorities cannot be made. Our analysis is based on data from the World Economic Forum, Global Competitiveness Report 20012002, World Competitiveness Yearbook 2000, and Kaufmann, Kraay and Zoido-Lobaton (2002). The results outlined here indicate that more gains in exports than in imports are expected should the values of three out of the four indicators (port efficiency, regulatory regimes and IT infrastructure)<sup>1</sup> of the new and candidate member countries improve halfway to the EU15 average. These countries would expect large trade gains as well as improvements in trade balances as their integration into the EU continues. For example, the greatest absolute trade gains – $49 billion and $62 billion respectively – could be expected if their port efficiency and IT infrastructure reach half the average level of the EU, and 70% of trade gains are associated
average intensity level for 2000. The effect is even greater in storm surge-prone, low-elevation coastal zones, where Category 3 hurricane winds are associated with a 16.1 percent decrease in nightlight. But where mangrove forests are a natural defense, a mangrove width of just 0.25 kilometer reduces nightlight intensity to 4.7–5.1 percent above and below the median. Where the mangrove width is greater than 1.26 kilometers and has an average width of 2.3 kilometers, nightlight reduction falls to 1–6.5 percent.

The remainder of the paper proceeds as follows. Section 2 describes the nightlight data used in this study, and the frequency of hurricane damages in the Caribbean. It also presents detailed mangrove forest data 3
households during the PPAs could be addressed at either a policy level or during the development of the Province's Hunger Eradication and Poverty Reduction plans.

# **The Origins of the PPAs in Vietnam**

## **_World Bank plans a collaborative, participatory poverty assessment_**

Planning for the PPAs began well in advance of their implementation. The need for a new poverty assessment had been identified by the World Bank in its Country Assistance Strategy (CAS) in early 1998. The first poverty assessment, published by the World Bank in 1995, was based on household survey data collected in 1992/3. There was very limited use of qualitative infornation and little ownership of the analysis by either Govemment or the wider donor community. The CAS identified a need to do the second poverty assessment differently. Importantly, the CAS specified:

- That the work would be done in partnership with central and local Government, with NGOs and with other donors and that there would be _one_ poverty assessment which could serve the needs of all these groups;

- That the poverty assessment would incorporate qualitative information in a way that would build upon what was already becoming standard practice within the World Bank.

It was hoped that PPAs could be implemented in each of the seven regions of Vietnam, though this turned out to be overly ambitious given the timeframe and range of possible PPA partner agencies. UNDP and SIDA had provided support for the first Vietnam Living Standards Survey (VLSS1) and had agreed to support the second round of household survey data collection (VLSS2). These two donors agreed at this early stage that they would collaborate in producing _one single, shared_ poverty assessment, which would reinforce the policy messages emerging from the analysis.

## **_DFID initiates discussions and offers support to the World Bank_**

At the same time as the World Bank was drafting its CAS, DFID was seeking to strengthen their own understanding of poverty issues in Vietnam and, following the spirit of their recent White Paper on development assistance, was
Policy Research Working Paper 10286

# **Abstract**

Investments in transport infrastructure lower trade costs and lead to integration of villages with urban markets. Does spatial market integration increase land inequality in rural areas? Theoretical analysis by Braverman and Stiglitz (1989) suggests that the interactions of lower trade costs with credit market imperfections can increase land inequality. The primary mechanism is the adoption of increasing returns technology by large landowners facing lower trade costs which makes it more profitable to expand their scale by buying land from small, credit-constrained farmers. Using high- quality household survey data (the India Human Development Survey) on land ownership in rural districts of India, this paper provides the first evidence on the effects of market integration on land ownership inequality. It develops an instrumental variables approach exploiting two sources of exogenous variation: the location of a rural district relative to the Golden Quadrilateral network (an inconsequential place design) and the length of colonial railroad in the 1880s in a district (a historical infrastructure design). This paper discusses and deals with potential objections to the exclusion restrictions. The evidence suggests that a 10 percent increase in a gravity measure of market access increases the land Gini coefficient by 2.5 percent and the share of landless households by 6.8 percent. This paper finds evidence consistent with the Braverman and Stiglitz (1989) hypothesis that the interaction of credit market imperfections with lower trade costs increases land inequality: a 10 percent increase in market access increases the adoption of increasing returns farming technology by 3.5 percent. There is a positive effect on land sales, but the instrumental variables estimates are imprecise. The robustness of the conclusions is checked by relaxing the exclusion restrictions using the Conley et al. (2012) approach, and the bias-adjusted ordinary least squares estimator of Oster (2019) that does not impose any exclusion restrictions. The estimated effects of market access cannot be accounted for by the colonial land revenue system, demographic pressure on land, and differences in inheritance law between the Hindu and Muslim population in
the LGA level indicating the presence of IDPs.

Since July 2014, the IOM has been collecting displacement data from key informants (such as village chiefs and religious leaders) at IDP-hosting locations. These data include the count of IDP households, the estimated number of IDP people, and key information on the host location such as the presence, type, and conditions of essential infrastructure such as health facilities, water sources, and hand-washing stations. The data collection is conducted quarterly in all locations known to host IDPs in the six northeastern states in Nigeria. According to conversations and email exchanges with IOM experts, the focus 16
Other limitations of the aggregate estimates are related to the partial equilibrium approach we take in aggregation. For instance, our regression estimates only measure the direct impact on connected firms and do not include any potential spillover effects from connected to unconnected firms or the general equilibrium effects on prices and wages.<sup>36</sup> Without a structural model incorporating these channels, predicting whether the general equilibrium effects will dampen or amplify the firm-level responses is challenging. Nevertheless, our simple and transparent approach provides a useful first step toward understanding the potential aggregate impact of China’s internationalization events.

# **4.5 Investor Behavior**

We exploit additional data sources to explore the behavior of investors around the internationalization events. We retrieve (1) aggregate data on foreign equity inflows from the IMF’s balance of payments statistics; (2) aggregate data on foreign equity holdings via the QFII/RQFII programs and Stock Connect program from Wind; (3) firm-level data on the share of foreign ownership from Refinitiv; (4) country-level bilateral data on foreign equity holdings from the IMF’s Coordinated Portfolio Investment Survey (CPIS).

Foreign investors entered relatively late in the internationalization process. Although foreign equity inflows increased in 2014, they experienced their fastest growth during 2018-20. Foreign equity inflows were about 15 billion U.S. dollars in 2015 and more than 80 billion in 2020 (Figure 8, Panel A). In addition, aggregate foreign equity holdings through the Stock Connect program, which channeled most of the 2018-20 expansion in foreign participation, rose from 15 billion U.S. dollars in 2015 to 151 billion in 2020 (Figure 9). Nonetheless, foreign equity holdings

> 36 Spillover effects to the unconnected firms could occur if more funds were available to them when connected firms tapped into the international markets for funding and became less reliant on domestic finance. In principle, these spillover effects could bias our difference-in-difference estimates downward, even after addressing selection issues between connected and unconnected firms. Nevertheless,
Foreign direct investment (FDI) worldwide has grown markedly since the 1970s, reaching $1.3 trillion in 2018 based on data from governments’ balance of payments statistics (BoP) which is the standard source of recording financial flows. Asia has been one of the main destinations for FDI, receiving almost 43 percent of the global total in 2018, up from 17 percent ($132 billion) in 2000. Despite global FDI steadily decreasing since it peaked in 2015 at $1.8 trillion – due to lower rates of FDI return, a slowdown in the expansion of international production, and the uncertainty in the global trade and investment policy environment – inflows to Asia remained relatively stable as shown in figure 2. This illustrates the region’s increasingly important role in international production networks as well as strengthening intraregional trade and investment linkages. Data since the COVID-19 pandemic are not available yet, but initial signs indicate that inward FDI to Asia remains comparatively resilient compared to global trends, though mostly due to flows to India and China (UNCTAD 2020). East Asia consistently attracts just over 50 percent of the inflows largely due to investments in China followed by Southeast Asia (around 25 percent).

**Figure 2: Global inward FDI, global and Asia, 2001-2018**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>$trillion<br>1.0<br>0.5<br>0.0<br> Inward FDI to Asia Inward FDI to non-Asia<br><!-- End of picture text -->

**Note:** Asia here includes Australia and New Zealand

**Sources:** ADB calculations using data from Association of Southeast Asian Nations Secretariat. ASEANstats Database. https://www.aseanstats.org/ (accessed July 2019); and United Nations Conference on Trade and Development. World Investment Report 2018 Statistical Annex Tables
14 Further assumptions are necessary when interpreting the income decomposition data in Table 1. In particular, I shall assume that the 'rural poor' are not only net labour suppliers, but also net demanders of food, i.e., they do not produce a positive marketable surplus. This is reasonable for Bangladesh. Thus the category "business income" should not include any income from the poor household's own-farm production, which will be found entirely under "imputed income".7 Furthermore, I shall assume that all imputed income is the value of foodgrain production.

Assumptions such as tihese are generally unavoidable when using the aggregated summary data typically available from income-expenditure surveys.<sup>8</sup> The categories used in the published results need not correspond exactly to the desired theoretical categories. However, the above assumptions appear to be reasonable. Casual observations suggest that business income for the rural poor in Bangladesh is largely obtained from non-agricultural cottage industries and services. And the implied estimate of 0.28 for mean farm income of the poor accords well with at least one independent survey: at a similar time, Osmani and Chowdhury (1983) obtained a mean of 0.27 for the proportion of income from agriculture in a sample of about 500 poor rural households in Bangladesh. Probably a small proportion of the imputed income from ownproduction is not from foodgrains; most peasants in Bangladesh typically devote a small amount of land to vegetables to be consumed at home. Thus, the assumption that all imputed income is from foodgrain production probably leads to underestimation of V*.
for corruption. Businesses in South Asia spend more time complying with tax regulations than in most other regions and corruption is common, particularly in Bangladesh and Pakistan, where over a quarter of firms are expected to give bribes when dealing with tax officials.<sup>1</sup> Tax revenues are low in South Asia, which contributes to macroeconomic instability and constrains government spending on essential public goods. Although corporate tax rates in the region are above global average and VAT/sales tax rates are comparable to the global average, tax revenue is lower than in countries at similar levels of economic development.<sup>2</sup> Low revenue contributes to macroeconomic instability and limits government spending on infrastructure, education and other essential public services, which has a negative impact on the investment climate and socio‐economic growth more broadly.

Reform of tax legislation in South Asia should aim at elimination of most special schemes and creation of an incentive neutral tax policy. This will broaden the tax base, level the playing field for all types of investments and improve revenue collection. Reform of tax administration should make paying taxes less costly and prevent non‐compliance. Better revenue collection will allow reducing corporate tax rates without sacrificing essential public spending. Lower tax rates and a more predictable tax policy and administration will help improve investment attractiveness of South Asian countries.

> 1 World Bank. Enterprise Surveys

> 2 World Bank. World Development Indicators

3
However, even though this form of data collection solves the problem of underreporting or misreporting, it raises a problem for recovering the full wage (income) distribution, which is key to understanding and analyzing inequality.

To better use the information from these types of surveys, we propose an imputation approach to simulate the distribution of the data that is only available in brackets. The method is an extension on the imputation approach described in Royston (2007), which considers heteroskedastic errors to model the conditional distribution of the censored data. The estimated conditional distribution is then used impute the data using draws from the estimated conditional distribution. Once the imputed data is obtained, standard aggregation methods (Rubin, 1987) can be used to analyze the censored data as if it were fully observed. For example, it can be used calculate poverty or inequality measures, as well as perform regression analysis. To demonstrate the flexibility of this approach, we use a Monte Carlo simulation to analyze the sensitivity of our method. As an empirical example, we use the approach to analyze wage inequality in Grenada utilizing the country’s Labor Force Survey.

Other approaches exist in the literature, and have been used for analyzing this kind of data. To measure income inequality with right-censored (top-coded) data, Jenkins et al. (2011) propose multiple-imputation methods for estimation and inference where censored observations are imputed using draws from a flexible parametric model fitted to the censored distribution, such as Generalized Beta of the second kind (GB2), Sigh-Maddala or Dagum distributions. Chen (2017) provides a generalized approach for the estimation of parametric income distributions using grouped data, showing its consistency through complementary simulation results. More recently, Walter and Weimer (2018) propose an iterative kernel density algorithm that generates pseudo samples from the interval-censored income variable to estimate poverty and inequality indicators. While the interval regression approach we propose fits with the models described in Chen (2017), Jenkins et al (2011), and Walter and Weimer (2018), these