rafmacalaba/data-use-mentions-extended huggingface dataset:
international standards is larger than the impact of domestic mandatory standards. In other words, one additional international standard leads to more exports than one additional domestic standard. The different results for the impact of mandatory standards might be related to the ability of the standard to provide information on the product characteristic and to serve as a quality signal for potential consumers on the one hand and product adaption costs on the other hand. The ability to provide information on pesticide residues, for example, leads to reduced transaction costs and increases trade.

The impact of voluntary standards is less clear. Domestic voluntary standards are positively correlated with Chinese exports for most product groups but the impact of voluntary international standards is ambiguous. The impact of international standards is positive and highly significant for vegetables and sugar but negative and significant for tea and fish. A plausible explanation for the low impact of voluntary standards is the lack of implementation by Chinese farmers and food producers. Although there is no data on the implementation of voluntary standards, the China Statistical Yearbook of Certification and Accreditation (cited in Jin et al. 2008) shows that in 2005 the number of voluntary food safety standards for hazard analysis and critical control point (HACCP) was implemented by only 21.9 percent of the food producers Assuming that this figure is representative for implementation of voluntary 12
the pandemic created a large disruption to the way firms had to operate. Restrictions restrained workers’ mobility, introduced bottlenecks in supply chains and made it difficult for firms to reach consumers in person. These constraints, in turn, induced changes in production and demand patterns to which firms had to rapidly respond if they wanted to continue to operate. Indeed, Apedo-Amah et al. (2020) found evidence of accelerated digitalization among firms as an immediate response to the pandemic with BPS data collected between April and August 2020. Using a subsequent wave of these data, Cirera et al. (2021) document that this pattern of digitization persisted between September 2020 and February 2021 in medium-term patterns (for more details on digital adoption see Comin et al.

> 8In some countries, sampling weights are available in order to produce nationally representative results at the country level, but for cross-country comparison purposes, these weights are not included.

> 9This exercise is based on strong assumptions of no seasonality, which means that sales per month in 2019 were constant so total sales can be recovered using the percentage change in sales in each wave. Because there is no information on differences in sales across firms in levels, one way to think about this exercise is that we weight the changes in sales observed in the BPS by the participation in the total sales of the economy pre-COVID 19.

6
firms in response to the shock.<sup>2</sup> The paper aim to address three key questions: i) What is the share of firms that started or increased the use of digital technologies in response to COVID-19 and for what business purpose? ii) How has the digital response varied across sector, size, and digital readiness of the firms? and, iii) Has the inequality in the digital sales markets increased over this period? This analysis is complemented by testing if these patterns on digitization are associated with firm

> 2See Apedo-Amah et al. (2020) for more details on the BPS and www.enterprisesurveys.org for more details on the WBES.

3
|Three supercycles: 1861-1882, 1966-1996,<br>1996-|
|Buyuksahin, Mo, and<br>Zmitrowicz (2016)|Bank of Canada commodity index,<br>1899-2015, deflated by the U.S. PPI|Four supercycles: 1899-1932, 1933-1961,<br>1962-1995, 1996-|
|McGregor, Spinola,<br>and Verspagen (2018)|Five price indices, 1960-2016, de-<br>flated by the MUV|Two supercycles: 1960-1995, 1996-|
|Ojeda-Joya, Jaulin-<br>Mendez, and Bustos-<br>Pelaez (2019)|Index, 1865-2013 and 24 commodi-<br>ties, 1962-2010, deflated by the<br>MUV index|Supercycles are synchronized and de-<br>mand-driven:**_Oil_**: 1885-1950, 1965-1995,<br>1996-;**_Metals_**: 1877-1920, 1920-1945,<br>1945-1995, 1995-;**_Non-oil_**: 1895-1937,<br>1937-1996, 1996-|
|Jacks (2019)|Indices and 40 commodities, 1900-<br>2015, deflated by the U.S. CPI|Three or four medium-term cycles (de-<br>pending on the commodity) and mod-<br>estly increasing trend: 1903-1932, 1965-<br>1996, 1996-|
|Cordano and Zellou<br>(2020)|Natural gas prices, 1922-2015, de-<br>flated by the U.S. CPI|Three supercycles, strongly correlated<br>with oil supercycles: 1948-1970, 1970-<br>1994, 1994-2017|
|Erten and Ocampo<br>(2021)|Four indices as in Erten and<br>Ocampo (2013)|Four supercycles: 1890-1930, 1930-1965,<br>1970-1998, 1998-|
|This study|Six metal and nine agricultural<br>prices, 1900-2020, deflated by the
Our paper addresses these issues in evaluating a rural roads program in Bangladesh, applying a dynamic panel data approach to household data collected under the program (Arellano and Bond, 1991, Jalan and Ravallion, 2002, 1998). The dynamic model also allows us to compare short-term with longer-term outcomes from the program, accounting for time-varying unobserved heterogeneity. We use new data collected by the Bangladesh Institute of Development Studies (BIDS) of project and control households under the Rural Roads and Markets Improvement and Maintenance Project (RRMIMP), spanning three rounds (pre-program and post-program) between 1997 and 2005. The RRMIMP project was a part of an LGED project financed by the World Bank. We examine whether rural roads generate increasing returns over time in poor areas, accounting for factors as pre-program community characteristics affecting road placement, presence of such complementary investments as electricity, institutional and market development, and other factors associated with road construction (e.g., quality and access).<sup>1</sup> Completed in 2005–06, RRMIMP is a road-paving project that targeted several villages across various districts of Bangladesh; control villages were also selected in separate districts. BIDS conducted the panel survey of households in project and control villages in three rounds: (i) prior to start-up of the RRMIMP in 1996–97, (ii) immediately following project completion in 2000–01, and (iii) in 2005 via a project follow-up survey. Khandker, Bakht, and Koolwal (2009) used the first two survey rounds to study the short-term effects of rural road development, finding positive effects on a range of household outcomes including higher per capita expenditure and lower transport costs.<sup>2</sup> The study used household fixed-effects estimation, controlling for a range of pre-program village

> 1 Our study closely follows Jalan and Ravallion (1998), who examine the impact of a poor-area development program on growth in household consumption using panel data collected from targeted and non-targeted areas across four provinces in China.
detailed occupation and sector information for each worker. We construct i as the fraction of sector ’s employment in occupation . As the number of observations in the National Sample SShT TT Survey data is limited, we calculate this variable at 2-digit NIC 2008 level. The similarity of worker types between industries DD and are proxied as the correlation between oo i and across all occupations, denoted as L . The values of the three measures are summarized in Table 2. mmii DD jj SShT TT SShT TT iimm **Table 2. Summary statistics of the coagglomeration measures**

||Coagglomeration index|Input-Output linkages|Laborpoolinglinkages|
|---|---|---|---|
|Mean|0.0003|0.0618|0.0861|
|Median|-0.0004|0.0219|0.0161|
|Standard deviation|0.0124|0.1068|0.3678|
|Largest value|0.6122|0.9881|1|
|Smallest value|-0.0504|0|-1|
|Number of sectorpairs|23871|24310|6662|

In the analysis, we use the three measures to calculate the indirect exposure of a sector to central excise tax incentives as: I = ∑ I T T where I denotes the coagglomeration index ( E ), input-output linkages ( TT NN I ) or labor pooling linkages ( L )between mm kk=1 mmkk mmkk industry and , and DDEEIIT T is an indicator variable that is equal to 1 if sector DDEEII ∗II{kk: T tt} DDEEII is treated or receiving central excise tax incentives. Similarly, the indirect exposure of a sector to non-excise incentives as: I jj = ∑kk II{kk: TI tt} T T . To make sure the results are comparable across kk these indices, we also normalize all NNTT NN I and I to have a zero mean and a unit standard mm kk=1 mmkk deviation. DDEEII
while 15 countries appear for three benchmark years. We also include a robustness check for a balanced sample of countries. Specifically, we analyze changes between 1993 and 2008 for which we observe 19 countries (second to last column). On average, these countries represent 68% of regional GDP and 52% of regional population. For low income countries, this sample covers only 35% of the population, compared with 70% for the non-low income countries. For natural resource rich country it is nearly 60%. At 30% population coverage, the coverage of fragile states is low but comparable to that observed in the entire sample.

> 18 A country is defined to be resource-rich if the average natural resource rents from 2006 to 2011 are at least 10% of its GDP. The fragile state status is defined by the World Bank’s Harmonized List of Fragile Situation FY15. That is, a country is a fragile state, if the harmonized average CPIA country rating is less than or equal to 3.2, or if there is a presence of a UN and/or regional peace-keeping or peace-building mission during the past three years.

> 19 These two countries account for 26% and 15% of the total population of all fragile states respectively.

> 20 Of course, the lack of data on fragile countries also affects estimates of global poverty. In the estimates of global poverty by the World Bank, countries with missing surveys, many of which would be fragile, are assigned the regional headcount (Chen and Ravallion, 2010; World Bank 2014). It is probably reasonable to expect that poverty would be systematically higher in these countries.

> 21 Fragile, natural resource rich and income status are held constant at the end-year. Net aid received (in current USD) is obtained from OECD.Stat, accessed January 5 2016.

> 22 These four countries are Eritrea, Equatorial Guinea, Somalia, and Zimbabwe.

7
skewed, with a large majority of caregivers perceiving their children to be more intelligent compared to other children in the community (Figure 1).

<u>Table 1. Cohort Characteristics, Madagascar, N=3,361</u>

||Mean(SD;range)|%(N)|
|---|---|---|
|Child Characteristics|||
|Age (months)|29.38 (5.15; 16-42)||
|Female||50.28 (1,690)|
|Birth Order|2.64 (1.25; 1-4)<br>||
|HAZ|-2.34 (1.05; -5.99-3.53)||
|WAZ|-1.56 (0.93; -4.94-2.45)||
|Stunted (HAZ < -2)||63.37 (2,130)|
|<br>Wasted (WHZ < -2)||4.08 (137)|
|Underweight (WAZ < -2)||30.17 (1,014)|
|ASQ-I age-standardized score|0.00 (1.23; -4.93-4.16)||
|ASQ-I ranked scores [Whole sample]|||
|≤ -1.42||14.31 (481)|
|-1.41 to -0.62||14.28 (480)|
|-0.61 to -0.10||14.04 (472)|
|-0.11 to 0.34||14.31 (481)|
|0.35 to 0.75||14.25 (479)|
|0.76 to 1.27||14.25 (479)|
|≥ 1.28||14.55 (489)|
|Perceived
<!-- Start of picture text --> ( annual purchase GMV )<br>number of online buyers 2013<br>( annual purchase GMV )<br>residential population 2013<br>(rev annual purchase GMV —_) x 100<br>national online purchase GMV 2013<br>( number of online buyers ) x 100<br>total number of national online buyers /5913<br>z — score of measure x = ———————_(Xct—mean of x)<br>standard error ofx<br><!-- End of picture text -->
**

Firm-to-firm transaction-level data sourced from tax records have become a valuable tool for mapping domestic firm networks and trade flows. Aside from studying the micro dynamics of firm-to-firm relationships, they allow researchers to compute often hard-to-observe flows of goods and services across regions within national economies, which can be leveraged to estimate the welfare implications of policy interventions. This paper shows that applying such data in settings with high informality can pose important challenges. We find evidence that formal sector trade patterns provide a skewed representation of overall economic activity: they are more spatially concentrated and underestimate intra-regional trade in favor of trade with urban hubs.

We incorporate informal firms into a structural model of network formation leading to a revised 35
- Hummels, D. and V. Lugovsky (2005), ―Trade in Ideal Varieties: Theory and Evidence,‖ National Bureau of Economic Research Working Paper No. 11828.

- Ianchovichina, Elena and Will Martin (2004), ―Impacts of China‘s Accession to the World Trade Organization,‖ _World Bank Economic Review,_ Vol. 18, 3-27.

- Inklaar, Robert, Marcel P. Timmer and Bart van Ark (2008), ―Market Services Productivity Across Europe and the U.S.,‖ _Economic Policy_ , Vol. 53, 139-171, 178-194.

- Ivanic, M., 2004. Reconciliation of the GTAP and Household Survey Data. GTAP Resource Memorandum No. 1408.  Available at:

- http://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=1408

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2010) ―Modeling Services Liberalization: The Case of Tanzania,‖ _Journal of Economic Integration,_ Vol. 25(4), 644675.

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2007), ―The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization,‖ _Review of Development Economics_ , Vol. 11 (3), August, 482-506.

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2006) "Telecommunications Reform within Russia's Accession to the WTO," _Eastern European Economics_ , Vol. 44 (1), January-February,  25-58.

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2004) ―The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization,‖ World Bank Policy and Research Working Paper No. 3391, September. Available at:

- http://econ.worldbank.org/external/default
).|
|_Tropical country_|Dummy equal to one if part of the country's territory lies within 20<br>degrees of the equator.|
|_Access to safe_<br>_water_|Percent of population with access to safe water. From the World<br>Bank's Social Indicators of Development database.|
|_Oil exporter_|Dummy equal to one if the country primary export is fuels (mainly oil)<br>as classified by the World Bank's World Development Indicators<br>(1996)<br>plus Kuwait.|

36
ability to continue operations in a remote environment, the intensity and type of transport costs, the strength of contractual enforcement, credit risks and so on (Brenton et al., 2022). This pattern of heterogeneous impacts across firms in their ability to cope with the pandemic depending on attributes of the sector is explored with the BPS data. Irrespective of the sectoral attribute, exporters experienced a lower drop in total sales in the initial as well as the recovery phase.

Exporters in sectors with higher flexibility to function in a remote setting had, on average, a decline in sales of 4 percentage points less and a probability of falling into arrears of 2 percentage points less than firms in sectors with less flexibility for remote work (Figure 11, panel a). Exporters in sectors including more durable products were significantly more vulnerable in the early phase of the pandemic but the differences in their sales declines relative to other exporters disappeared as the pandemic evolved, lockdowns and related factory closings stabilized, and the demand for durable goods required for working remotely in the aftermath of the acute phase of the pandemic increased (Espitia et al., 2022) (Figure 11, panel b).

Higher GVC intensity of a sector did not play a role for exporters’ sales declines in the pandemic but it did for non-exporters, significantly attenuating their sales declines (Figure 11, panel c). This finding masks variation across sub-sectors though, with higher GVC intensity playing a role for agriculture and manufacturing sectors in the early phase of the pandemic when exporters in these sectors experienced a significantly larger decline in sales than exporters in lower GVC intensity sectors, possibly due to disruptions in supply chains (Appendix Figure C10). These findings are broadly consistent with the fact that trade in complex products organized in GVCs tends to be more sensitive to global downturns than other trade, particularly that of motor vehicles, as observed during the GFC (Ferrantino et al., 2014). The fact that the decline in sales of exporters in the recovery phase does
**Figure 5.** Levels of CAP funds received by different member states are drastically different.

<!-- Start of picture text --> Total CAP Payments (2008 - 2013)<br>8E+10<br>7E+10<br>6E+10<br>5E+10<br>4E+10<br>3E+10<br>2E+10<br>1E+10<br>0<br>Purchasing Power Standard<br><!-- End of picture text --> _Source: DG AGRI (2017) Clearance Audit Trail System (CATS) database provided by the European Commission_ Figure 5 above shows how the allocation of total CAP payments differs across the EU member states. The EU determines how much CAP funds each of the member states receives. Nevertheless, member states have certain flexibility in allocating the CAP funds between the program’s pillars. This creates heterogeneity as to how the funds are spent, between both pillars, within countries, as showed in Figure 6 below.

**Figure 6** . There is clear heterogeneity of CAP funds allocation choices made by different countries.

<!-- Start of picture text --> Composition share of CAP payment by country (2008-2013)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>LFA Agrienvironment Investment aid Other Pillar 2 payments Decoupled payments Coupled payments<br><!-- End of picture text --> _Source: DG AGRI (2017) Clearance Audit Trail System (CATS) database provided by the European Commission_ _Allocation of CAP funds based on regional characteristics_ To explore the characteristics of regions where the CAP funds tend to reach, we create four different clusters of regions at the
China’s quarterly growth rates fell from more than 10 percent to around 6 percent (Figure 1, Panel B). During this time period monthly export orders fell from more than 120 billion US dollars to less than 70 billion US dollars (NBSC, 2009). In response to faltering growth, China’s leaders responded with a bold stimulus package that expanded state bank loans, triggered massive waves of centrally-funded public investment projects and encouraged local governments to increase investments. It is likely that these moves contributed to the rise in China’s growth rates in Q2 in 2009 over Q1 (Figure 1, Panel B).

# **(a) Off-farm Employment Effects**

In order to compare the difference between what actually happened in 2008 and 2009, we developed a method of predicting the counterfactual levels of off-farm employment in these years under a BAU scenario.<sup>5</sup> To do so, we used our estimates of off-farm employment trends (based on data from 2005 to 2008) and extrapolated the time trends out to 2008 and 2009 under the assumption that the trends would have continued had there not been a global financial crisis. Although the trends appear to be linear, in fitting the trend we used the average annual growth rates in 2005-2007 (2006-2008) to estimate 2008 (2009).<sup>6</sup> Under the assumptions embodied in our simple forecast model of BAU Figure 2, Panel A shows the net impacts of the financial crisis on China’s off-farm employment in 2008 and 2009. If the 2005 to 2007 (2006 to 2008) trend in off-farm employment had continued as BAU, the share of the rural labor force that had off-farm employment would have risen to 63 percent in 2008 (69 percent in 2009).<sup>7</sup> Instead, in 2008 the share of rural labor force employed off-farm declined 62 percent, a drop of 1 percentage point 9
not undermine its implementation of the sector’s longer-term strategic objectives. ONEE’s 20 to 30-year master plans are used for annual reporting, rather than informing decisions on generation and transmission. These nominal plans are not very impactful since they are based on assumptions beyond the utility’s sphere of influence. Schedules for the installation of additional capacity can often be overoptimistic. The forecasting of demand, a key input during the planning process, is not always satisfactory because realistic estimates of disaggregated demand require high quality granular data that are not always available.<sup>44</sup> With respect to the RE strategy, it was the strong political commitment that placed it on the agenda, rather than an outcome of technical forecasts and planning by key government agencies.<sup>45</sup> Multiple goals are pursued in a seemingly conflicting manner. For instance, Laws N°13-09 and N° 2-15-772 allow private renewable energy producers access to electricity end-users while the largest share of projects developments is assigned to the state-owned ONEE and MASEN; new PPAs are announced in gas, coal and fossil-fuel plants while clean energy technologies are simultaneously promoted, and the tariff structure remains rigid.

Nevertheless, ambitious reforms are still pursued within this institutional configuration of powerful legacy stakeholders wedded to the status quo. There is a tendency to create new agencies entrusted with implementing specific projects, bypassing legacy state-owned companies, which are then allowed to retain their influence and market share.<sup>46</sup> Thus, the decision to assign the implementation of the solar plan to MASEN was largely to ensure

> 44 A recent World Bank investment project, the “Clean and Efficient Energy Project” aims to build capacity on demand planning.

> 45Interviews with Moroccan power sector policy makers, 8 February 2017.

> 46 Interviews with Moroccan policy makers, 2 February 2017.

22
1997).</mark> _<mark>Do the poor pay more for food? Item selection and price differences affect low-income household food costs</mark>_ <mark>(No. 1473-2016120710).</mark> <u><mark>https://www.ers.usda.gov/webdocs/publications/40816/32372_aer759.pdf?v=0</mark></u> <mark>Kirti, Divya et al. (2022). Tracking Economic and Financial Policies During COVID-19: An AnnouncementLevel Database. IMF Working Paper No. 2022/114. Available at:</mark> <u><mark>https://www.imf.org/en/Publications/WP/Issues/2022/06/03/Tracking-Economic-and-Financial-PoliciesDuring-COVID-19-An-Announcement-Level-Database-518896</mark></u> Lambert, Peter (2001). The Distribution and Redistribution of Income, 3rd ed. Manchester University Press.

<mark>Lara Ibarra, Gabriel, Nistha Sinha, Rana Fayez and Jon Jellema (2019). Impact of Fiscal Policy on Inequality and Poverty in the Arab Republic of Egypt. Policy Research Working Paper 8824, World Bank, Washington, DC. Available at:</mark> <u><mark>https://documents1.worldbank.org/curated/en/617221555617293604/pdf/Impact-of-Fiscal-Policy-onInequality-and-Poverty-in-the-Arab-Republic-of-Egypt.pdf</mark></u> <mark>Lara Ibarra, Gabriel, Rafael Macedo Rubião, and Eduardo Fleury (2021). Indirect Tax Incidence in Brazil: Assessing the Distributional Effects of Potential Tax Reforms.</mark> _<mark>Policy Research Working Paper 9891</mark>_ <mark>, World Bank, Washington, DC. Available at: https://openknowledge.worldbank.org/handle/10986/36783</mark> <mark>Lara Ibarra, Gabriel; Campante Cardoso Vale, Ricardo. 2022. Brazil 2020 Data Update: Methodological