rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 6330

# **Abstract**

A unilateral trade reform generates two opposite effects: market access expansion and strengthening of competitive pressures in the liberalized market. Using detailed trade and firm-level data from France, the authors investigate how French firms’ product scope and export sales changed after Chinese liberalization vis-àvis Asian liberalization. The findings suggest that lower Chinese import tariffs account on average for 7 percent of the new products exported by French firms, and for 18 percent of additional French export sales. These results are robust when accounting for foreign competition faced by French firms in the liberalized market.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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 authors may be contacted at maria.bas@cepii.fr and pamela.bombarda@u-cergy.fr.

_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
for the previous year and then subsequently disaggregated to the firm level using each firm’s share of fuel use in total industry-level fuel use. A detailed description of how this variable is created, including information on depreciation rates and assumed assets lives, is provided by Haller (2014) and Haller and Hyland (2014).

For our analysis, we are interested in the machinery and equipment component of capital stocks, disaggregated by type of capital - where type refers to the fuel used. To break down the machinery and equipment component by fuel used we follow Steinbuks (2012) and use data from the TIMES model for Ireland (Gallach ́oir et al., 2012). We calculate five components of equipment based on the TIMES data, they are: those that can only run on electricity (for example, electrical motors and refrigeration units); those that run on electricity, but where other fuels can be used (for example high- and low-temperature heating processes)<sup>6</sup> ; those that run on natural gas; those that run on oil; and those that run on coal.<sup>7</sup> Average sectoral-level capital stocks in 2004 (the first year of our data) for each of the five subcomponents are given in Table 1.

> 5This information is available for the sum of all fuels, but not disaggregated by fuel type.

> 6These are referred to as “Electricity (no substitution possible)” and “Electricity (substitution possible)” respectively in Table 2.

> 7For machinery that runs on natural gas, oil or coal, we assume other fuel options are always available for these processes.

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documented in the literature as a good predictor of longevity, education and earnings (Behrman and Rosenzweig 2004; Case and Paxton 2008; Strauss and Thomas 1998). The impact of pre-adulthood exposure to conflict on height identified in Akresh et al. (2012) can therefore be viewed as a permanent labor productivity loss for the exposed individuals.

The long-term effect working through human capital formation plays a key role for our study as it is consistent with the relationships between conflict and poverty in the country and regional data. This suggests that the latter are indeed driven by a causal relationship running from conflict to reduced economic capacity that is only slowly recovering. In this interpretation we can rely on a diverse body of evidence which finds large negative effects of armed conflict on education. Ichino and WinterEbmer (2004) for instance estimate the long-run educational cost of World War II. They find that individuals who were 10 years old during the conflict in the most affected countries received less education than individuals with comparable characteristics in less affected countries. Affected individuals also experienced a sizable earnings loss, some 40 years later. Blattman and Annan (2010) use the exogeneity in child military conscriptions into rebel groups in Uganda to provide evidence for the widespread and persistent economic and educational impact of conflicts. They find that schooling falls by nearly a year, that skilled employment halves, and that earnings drop by a third among child soldiers as compared to the control group. Akbulut-Yuksel (2014) exploits the plausibly exogenous region-by-cohort variation in the intensity of World War Two (WWII) destruction as a quasiexperiment and finds that exposure to destruction had long-lasting detrimental effects on the human capital formation, health, and labor market outcomes of Germans who were at school-age during WWII. An important channel for the effect of destruction on educational attainment is the destruction of schools whereas malnutrition is partly behind the estimated impact on health. Minoiua and Shemyakina (2014) use survey data and information on the dates and locations of conflict events to examine the causal impact of the 2002–2007 Ivorian
average weekly wage growth of 23 percent is slightly higher than the average hourly wage

> 40 For changes in ownership categories within the formal sectors, the changes may reflect either the change in nationality of the establishment for which an individual works or individual’s move from one sector to another. 41 The VHLSS is likely to exclude migrants who hold a temporary or no registration status since the sampling of the surveys is limited to the households who had permanent registration status. Based on the _Vietnam Migration Survey_ (VMS) data in 2004, Fukase (2013b) finds that only 10 percent of migrants who work for foreign firms possess permanent registration status.

> 42 The wages are converted to January 2002 constant prices using the deflators available in the VHLSS.

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WW2, financial crisis lending has become much more institutionalized and “serial” bailouts to highly indebted countries have become more common. This is partly the result of the rise of international and regional financial institutions, in particular the IMF and the World Bank. Lending by these multilateral creditors overtook total bilateral lending for the first time in the 1970s.

By comparing our novel data on official flows with previously assembled long-run data on private international capital flows by Reinhart, Reinhart and Trebesch (2019) we show that both series are negatively correlated. Official capital flows are highest when private capital flows dry out. During wartime, private investors usually face widespread capital controls, while governments lend at record pace, not just via domestic “buy war bonds” campaign, but also internationally. Furthermore, the data show that official creditors were the “only game in town” during the Bretton Woods era in the 1950s and 1960s, while private cross-border capital flows were scarce and heavily curtailed. An expanded official role is also evident during the height of the Great Depression in the 1930s and during the 2008 global financial crisis and its aftermath on periphery Europe, when central bank and sovereign lending spiked, while private capital outflows reversed.

It is not widely appreciated that we witnessed a comeback of official lending in the past two decades. China’s rise as an international creditor has been underestimated due to a lack of data and transparency. We document how China has become one of the most important official creditors worldwide, as almost all of its foreign lending is extended by the government and its state-owned banks (see also Horn, Reinhart and Trebesch 2019). China’s official lending boom is part of a more general rise of new creditor powers, especially emerging markets such as Russia, India, Brazil or the Arab oil states, who have all become active official lenders in varying degrees. In addition, dozens of new official lending institutions were founded since WW2, including a range of regional development banks and regional financial arrangements in Asia, Africa
# **Supplementary Information**

## **Supplementary Texts**

## **Arsenic concentrations in groundwater**

The first national-scale survey of groundwater in Bangladesh (National Hydrochemical Survey) was conducted jointly by the British Geological Survey (BGS) and the Department of Public Health Engineering (DPHE) of Bangladesh during a 1998–1999 campaign that sampled a total of 3,534 well points throughout Bangladesh except for some parts of Chittagong Hill districts and the Sundarbans.<sup>1</sup> According to the national hydrochemical survey, nearly 90% of these surveyed wells have an intake depth of <150 mbgl and are considered shallow wells. Approximately 25% of these shallow tubewells were drawing groundwater with arsenic (As) concentrations >50 μg/L (Bangladesh drinking water standard); nearly 42% of these wells were recorded to contain As concentrations >10 μg/L (WHO drinking water standard). The distribution of observed As concentrations in Bangladesh is highly (positively) skewed, with values ranging from <0.5 to 1660 μg/L, and the spatial distribution is also highly variable.<sup>2-5</sup> High As concentrations (>50 μg/L) are observed in most parts of southern Bangladesh at depths <100 mbgl<sup>1</sup> . There is no systematic, long-term monitoring of As concentrations in groundwater anywhere in Bangladesh. Patchy and short-term, time-series monitoring of groundwater As concentrations suggests that there is no discernible long-term trend in As in groundwater but seasonal variations in As concentrations are reported by some studies.<sup>6</sup> Our analysis applies interpolated As concentrations (Figure 2a) for developing groundwater risk maps at the national scale in Bangladesh. Arsenic concentrations at geographic points ( _n_ =3207) were interpolated using the Inverse Distance Weighting (IDW) algorithm and resampled at 2.5-km × 2.5-km grid resolution using the ‘raster’ package in the ‘R’ programming language.
of earnings in the adjustment process may not be precise. It is areassuring, however, that data on earnings from seven middle income countries also tend to show moderate to large earnings adjustments (Cho and Newhouse, 2011).

_The analysis compares growth rates during the crisis year with averages over the prior two years._ Focusing on the change in the growth rate, rather than post-crisis declines, is important to accurately measure the effect of the crisis relative to pre-crisis trends.<sup>8</sup> Extending back two years allows a comparison with changes immediately preceding the crisis, while mitigating the influence of the rapid price increases in food and fuel in 2008. GDP growth in crisis-affected countries fell sharply in the fourth quarter of 2008. Therefore, the post-crisis period includes the four quarters from Q4 2008 to Q3 2009, while the pre-crisis period includes the eight quarters from Q1 2006 to Q3 2008.<sup>9</sup>

# **The Extent and Nature of the Labor Market Adjustment**

_The_ **cost of the crisis to** _workers was considerable but delayed and gradual._ As shown in Figure 1 the average wage bill, which had been growing by nearly 10 percent over the pre-crisis years of 2007 and 2008, came to a near standstill in 2009, with the growth dropping to less that 1 percent.<sup>10</sup> The drop was only slightly less than the fall in GDP growth over this period (9.4 percentage points) but the decline in wage bill growth began later than that of GDP and continued longer. This is in line with findings from previous crisis that suggest long term impacts on workers may be deep and protracted.

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>-5<br>-10<br>GDP growth Employment growth<br>Real earnings growth Wagebill growth<br>Percent<br>
much bigger (around 12 percentage points per year) because it includes the bounce back in trade after the crises from the depressed growth rate during the crises period 1997-2001. In any case an export growth rate of 20% per year is unusually high both in terms of China’s own record, which registered real export growth of 16.3% per annum in the period 1978-1993, and in terms of the record of the newly industrializing countries in Asia, which achieved average annual growth rates of just 12.1% over the course of their development from 1965 to 1993 (Yang, 1996).

**Table 3 Comparison of actual and simulate real export growth rates**

||Average annual real export growth<br>rate|
|---|---|
|1990-1997|13.3|
|1997-2005|16.0|
|Implied WTO accession effect including<br>effect of crises|2.4|
|1997-2001|7.1|
|2001-2005|20.0|
|Implied WTO accession effect including crisis<br>recovery|12.0|
|Simulation effects||
|Wang (1997, 2003)|4.4-5.1|
|Ianchovichina and Martin (2004)|3.4|
|Walmsley and Hertel (2001)|6.2|
|Yang (1996)|1.4-4.7|

Source: Data on exports come from Amiti and Freund (2007) based on China Customs Statistics and export values deflated using 1992 US Consumer Price Index. Note: The accession-related effect is computed as the difference in growth rates calculated as (1+g1/100)/(1+g2/100)-1)*100. The annualized rates are obtained from the cumulative effects reported in some studies the formula ((1+gc/100)^(1/n)-1)*100, where n is the number of years associated with reform.

We next turn to the
--> Source: Author’s compilation based on Global BOS database and IADB (2019) data Furthermore, the database fills an important gap in the coverage SOEs in developing countries. The number of SOEs captured through this methodology is significantly larger than the number of SOEs captured by comparable exercises led by other international organizations as seen in Figure A.3. The BOS database even covers more companies and sectors than the approach proposed by the IMF (2019) using ORBIS.

_Figure A.3_ Number of countries captured by cross-country BOS databases <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>WBG Global OECD[1] EU[2] EDRB[3] IMF[4] WB IDB[6] ADB[7]<br>BOS Infrastructre<br>database SOEs<br>database [5]<br><!-- End of picture text --> <!-- Start of picture text --> WB IDB[6]<br>Infrastructre<br>SOEs<br>database [5]<br><!-- End of picture text --> Source: Global BOS database and [1] OECD Size and sector distribution of state-owned enterprises (2017); [2] State-owned Enterprises in the EU: Lessons Learnt and Ways forward in a Post-Crisis Context (2016); [3] Economic Performance of State-Owned Enterprises in Emerging Economies: A Cross-Country Study (2020); [4]
to assess valuation in housing markets. Further, it provides metrics such as price-to-rent and price-to-income ratios. Similarly, the Dallas Federal Reserve Bank’s International House Price Database publishes quarterly house prices for 25 mostly developed economies by drawing on national public sources primarily from central banks, statistical offices, or other non-government organizations (Mack & Martínez-García 2011).<sup>8</sup> The data collected by these institutions are mostly for developed economies, and these institutions collate secondary data from a plethora of different sources, and primarily make data available in an indexed format.

Fifth, in collecting and analyzing actual house prices _across_ emerging economies, the Center for Affordable Housing Finance in Africa (CAHF) is unique in its efforts. It systematically collects house prices for African countries by surveying local housing experts on the cost and size of the _cheapest_ house built by a private developer. Figures are published in CAHF’s annual housing finance yearbook, covering the last decade. CAHF’s approach also turns the conversation on house prices away from mean or aggregate measures and to the lower tail of the formal market. From the perspective of policy dialog on affordable housing, this approach may be more appropriate. However, the usability of the data for policy purposes suffers partly because i) the price point provided represents only the extremely lower end of the formally developed new housing units and ii) is not paired with information regarding the quantity supplied at or near this price range.

> 5 <u>https://www.bis.org/statistics/pp_detailed.htm</u>

> 6 - <u>https://data.oecd.org/price/housing prices.htm</u>

> 7 <u>https://www.imf.org/external/research/housing/index.htm</u>

> 8 <u>https://www.dallasfed.org/institute/houseprice</u>

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Zeller (2011) assessed and compared the target, impact and cost-effectiveness of proxy means tests (an indicator-based targeting system) of universal and community-based targeting systems using quintile regressions and nationally representative data from Malawi’s Second Integrated Household Survey (IHS2). The study finds that although the proxy means test is associated with relatively higher administrative costs, its overall benefits in targeting poor and smallholder farmers outweighs the cost involvements; and the proxy means test tends to be potentially more target, cost and impact efficient than the universal and community-based targeting systems.

Kilic et al. (2014) analyzed the overall performance of the decentralized targeting of Malawi’s farm input subsidy program using nationally representative data of the 2009-10 agricultural season by decomposing the national targeting performance into district and community level components: inter-district, intra-district inter-community, and intra district intracommunity components. The authors find that Malawi’s farm input subsidy program is not poverty targeted and that the national government, districts, and communities are nearly uniform in their failure to target the poor, with any minimal targeting (or mis-targeting) overwhelmingly materializing at the community level.

Classifying farmers into kins and non-kins of chiefs (traditional leaders), Basurto et al. (2016) used a five-wave panel from Malawi to explore the trade-off between the informational/accountability advantages of decentralized targeting systems and its associated elite capture in the context of large-scale subsidy programs in Malawi decentralized to chiefs. The authors find evidence of elite capture and poverty-mistargeting for the subsidy programs considered; and also find that the poverty-mistargeting by chiefs is partly due to productive efficiency considerations. This study contributes to the targeting discourse by seeking to empirically estimate the overall gain in yield for targeting non-poor farmers instead of poor 6
**<mark>Figure 1. Percentage of children attending child care</mark>**

<!-- Start of picture text --> 79.6<br>68.9<br>65.6<br>50.9<br>47.7<br>32.2<br>19.8<br>14.6<br>3 3.4<br>.2 .4<br>Age 0 Age 1 Age 2 Age 3 Age 4 Age 5<br>2010 2016<br>80<br>60<br>40<br>20<br>Percentage of child care attendance<br>0<br><!-- End of picture text --> Note: This figure presents the level of child care attendance by children age 1-5.

Source: Estimation from VHLSS 2010 and 2016.

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stands to reason that if firms in industries that are more dependent on external financing are able to secure such financing, they will be able to grow at a faster rate.

This follows the strategy proposed by Rajan and Zingales (1998). However, while they have used this strategy to assess how country level financial depth affects industry output growth, in this case we use it to study how the supply shock to credit from introductions of CBs affects firm employment growth. Essentially, this involves estimating equation (3) below: _Employment Growth_ i,j,k,t = α _CB reform_ j,t-2 + θ _CB reform_ j,t-2* _EFD Indexk_ + β _X_ i,j,k,t + γZ j,t-2 Where _EFD Index_ is the Rajan and Zingales (1998) external finance dependence index and the remaining variables have been described above. The identifying assumption in (3) is that external dependence represents a true technological measure of demand for external financing independent of supply. As discussed in the previous section, we follow Rajan and Zingales (1998) in constructing the Index, using data from US firms assuming that capital markets in this country are relatively frictionless and thus an industry reliance on external finance in that country is a reliable indication of true technological demand for financing.

While in principle we could undertake propensity matching in the ES sample, we do not show these as the main results since the one-to-one nearest neighbor matching with replacement results in a much larger reduction in the number of control countries in the ES data. We are unable to apply the Rajan and Zingales (1998) methodology to the ES sample since its industry classification of firms does not correspond to standardized classifications such as ISIC, SIC or NAICS. Our attempt to match industries by name relies on far too many simplifying 18
follow-up), gender attitudes, gender-based violence (collected only at follow-up), decision-making in the household, income-generating activities, and access to finance. Gender-based violence (experience of emotional, physical, and sexual violence) is measured using questions adapted from the DHS survey. Girls’ mental health is measured using the GHQ-12 (General Health Questionnaire, 12 items), a tool used to detect common mental disorders.<sup>8</sup> The measures of socio-emotional skills used were developed as part of the Effective Socio-emotional Skills to Gain Economic Empowerment (ESTEEM) initiative at the Africa Gender Innovation Lab.<sup>9</sup> At baseline, the total sample size was 3,792 adolescent girls and their respective households. At follow-up, despite an intensive tracking exercise, only a total of 3,007 adolescents girls were successfully interviewed along with 3,680 of their baseline households. This resulted in an average attrition rate of 20% of the total baseline sample of adolescent girls. Tests for differential attrition are presented in table A1 in the appendix. The attrition rate in

> 7In households with more than one eligible girl, only one beneficiary was selected by lottery, unless the number of eligible girls was less than ten in the locality.

> 8Statements used to construct the gender equality, violence, socio-emotional skills and mental health indices are listed in tables A5 and A6 in the appendix.

> 9For more details, see Marsh et al. (2025).

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infrastructure broadly defined, to research that examines specific types of energy, digital and transportation literature, again primarily during the last decade (Figure 1). From 2010 the literature became more oriented towards micro-economic approaches, exploiting increasingly granular data that is often spatially explicit. In particular, the availability of satellite images, maps, and georeferenced surveys allows for a much higher degree of resolution in examining infrastructure and development linkages. This has allowed the literature to examine a broader range of development impact variables, with household welfare captured through expenditure, employment status, wages, human capital formation, or poverty and inequality, and firm performance captured through output, exports, or productivity. Moreover, spatially disaggregated data allows local output to be captured at the municipality, district, or city level. Nevertheless, just under half of the studies identified are still concerned with investigating the impact of broad infrastructure aggregates on macroeconomic measures of output.

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