rafmacalaba/datause-encoder-data huggingface dataset:
suppliers that do not engage in exports and thus are absent from the customs transaction data. Second, it miss intermediate that are but not this may products produced domestically imported. Lastly, approach assumes that products classified under the same HS 6-digit code are substitutes, implying that imported goods can be replaced with their domestically produced counterparts, which may not always hold true in practice.

Based on the definition of potential domestic suppliers, the CI for a single imported product ( _j_ ) of each exporting firm ( _i_ ) in each year ( _t_ ) is calculated as follows: where _Njt_ is the total number of potential suppliers of product _j_ in year _t_ , which equals the total number of exporters of product _j_ in year _t_ . The indicator variable, _Mjit_ , takes on the value 1 if firm _i_ imports product _j_ in year _t_ , and 0 otherwise; Likewise, the indicator variable, _dnit_ , takes on the value 1 if the potential supplier _n_ is connected to firm _i_ via the HSR network in year _t_ . So the denominator is the total number of potential suppliers of a given imported product in a given year, and the numerator is the total number of potential suppliers that are connected via HSR to the exporting firm in a given year. In short, for each imported product _j_ , the variable, _CIjit_ , is the share of potential suppliers that are connected to firm _i_ via HSR. The variable _CIjit_ takes a value of 0 if firm _i_ does not import product _j_ in year _t_ or if none of the potential suppliers are connected to firm _i_ through the HSR network. Conversely, if all potential suppliers of product _j_ are connected via HSR, _CIjit_ equals 1. A higher value of _CIjit_ indicates a greater likelihood that firm _i_ can source material _j_ domestically through HSR-enabled connections.

For firms importing multiple products, the variable _CIit_ represents the weighted average share of potential suppliers connected to the firm via HSR. The weights correspond to the share of each product _j_ in firm _i_ ’s total imports for year _t_ , capturing the importance of HSR connections across the firm’s product portfolio.

where _sjit_ is the share of product _j_ in the total import of firm _i_ in year _t_ . If a larger share of products firm _i_ imported has potential local suppliers that are connected via HSR, the likelihood that firm _i_ may source locally will be higher and may result in a higher DVAR. In addition, we refine the CI by incorporating the distance between potential suppliers and the exporting firm. Specifically, if the distance between firm _i_ and potential supplier _n_ falls within a certain range, _dint_ is assigned a value of 1; otherwise, it takes the value of 0. This adjustment allows us to capture more granular variations in how distance influences the likelihood of sourcing materials locally.

Figure 3 presents the evolution of CI from 2007 to 2016. The black line is the unrestricted CI which represents the share of potential suppliers that are connected to exporting firms via HSR, both 18
30 TABLE 4

# "Causality" Tests on the Effects of Exports on Growth

Study Saple/Period

# Test

Jung & 37 LDCs for Granger "causality" tests and F-tests for the Marshall the period signs of coefficients in: (1985) 1950-1981 Rt Wiai Qt-i + b j t-j +t it-Eici It -i + Ed1X t-j + vt (Box-Pierce Q to test for autocorrelation) -Lag length generally r3stricted to 2.

Hutchinson 34 LDCs for Granger "causality" tests and F-tests to & Singh the period capture export externality effects in: (1987) 1950-1985 nt -Zai 8- A~~~~~~~~ + Ebjfit- + Zckt- + P It i Zdi at-i + Ze nt j + Zf a t-s + vt where: n - (t-k) non-export sector growth St - AI/Y, change on Investment/GDP ratio.

(continued)
sources, we make use of questions related to activities of daily living to construct two indices: one is based on the number of activities that the respondent may perform with difficulty (Difficulty), and the second is based on the number of activities that a respondent cannot perform (Unable). Because each survey has some differences in activities and asks a different number of activities, we use these responses to construct within country z-scores (respondent count – average count)/(standard deviation of count).<sup>27</sup> Increases in each of these two z-scores reflect declining physical ability and worsening general health status. A particular value of the UnableADL z-score will be associated with more severe limitations than the same value of the Difficulty-ADL z-score.

We expect that declining health will have a negative impact on work activity, particularly for those workers in occupations in which physical strength and mobility are important (Bound, 1999). As the education of a spouse or adult children will be related to additional sources of income (from spouse income or transfers from children), we include average education of these family members, _E_  _i_ . Finally, we control for a vector of individual and household characteristics, **X** _i_ , which include age and age-squared and are associated with own productivity, numbers of grandchildren and living parents of the head and spouse, which are associated with preferences for employment, and the ln(1+per capita value of the household dwelling), as a proxy for household wealth.

Within the retirement literature in the US, recent research has focused on the important roles of spouse employment and spouse health status in labor supply and retirement decisions. First, the retirement decisions of husbands and wives may be interdependent. Structural models (Blau, 1996; Gustman and Steinmeier, 2004) suggest that labor supply decisions of older couples reflect preferences for shared retirement. Second, work decisions may be affected by the health status of a spouse. One may plausibly observe an added worker effect, in which a spouse’s health shock leads to increased labor supply so as to insure income against the earnings-loss associated with the health shock (e.g., Coile, 2004a), or alternatively find that spouse care requirements will

> not yet have appropriate panel data for the three countries in this study, we are not able to control for dynamic relationships between health and wealth.

> 27CHARLS asks 19, IFLS asks 18 and KLoSa asks 17. Each of the surveys asks questions related to functional abilities such as: ability to walk for a kilometer, ability to go up and down stairs, difficulties lifting 10 or 20 lbs, draw water from a well, ability to perform household chores, bath oneself or take medication.

17
**Figure 5: Mean reported life of knowledge capital assets**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>-5<br>Training Software Branding R&D Design BP<br>Improvements<br><!-- End of picture text --> _Note: Error bars represent 95% confidence intervals._

**III. LINKAGES BETWEEN KNOWLEDGE CAPITAL INVESTMENTS AND RISK MANAGEMENT**

In addition to being an important source of total investment and growth, different types of enterprise investments in intangibles also can play a critical role as investments in enterprises’ pillars of risk management, providing enterprises with essential capabilities to anticipate, absorb and adapt to exogenous risks, and undertake risky activities in pursuit of larger expected profits with higher probabilities of success – empowering firms to learn and execute as enabled information platforms.<sup>16</sup> The main types of intangible investments can be mapped into the four pillars of risk managed initially proposed by Ehrlich and Becker (1972) (see Figure 6):<sup>17</sup>

- Investments in **_knowledge_** of supply and demand trends and the likelihood of shocks, including changes in existing and emerging global technologies and changes in consumer preferences. This includes expenditures on R&D and global connectivity (including investments in knowledge diffusion networks and various search and match mechanisms to learn from and co-create with other local firms, global corporate partners, suppliers and buyers, universities and the diaspora).

- Investments in **_protection/enabling_** to reduce the probability of losses and increase the likelihood of successful input reallocation and innovation. This includes expenditures on market research, branding and advertising to expand product varieties and market reach and thereby diversify location-specific product risks both on production and demand sides. The optimal reallocation of resources under uncertainty may not be to invest all into a high-risk new technology, but to invest some resources in the existing technology and benefit from the option value of waiting until some additional

> 16 See Hulten (2013) on some policy implications of conceptualizing the firm as an information platform.

> 17 Sheffi (2005) surveys a wide range of intangible investments that firms have made to increase knowledge, selfprotect, insure against, and cope with low-probability high-impact disruptions, broken down into “reducing vulnerability” (early detection and security investments in databases and software to reduce the likelihood of intentional disruptions from industrial actions, sabotage or terrorism), “building resilience through redundancy” (investments in slack, non-used inventory, capacity and IT systems, and increased holdings of retained earnings) and “building resilience through flexibility” (investments in new business models to allow interchangeability of plants, parts and people, realign supplier relations in supply chains, and modify internal culture towards greater safety, quality, continuous communications, and conditioning for disruptions).

15
<!-- Start of picture text --> Figure 4: Government education spending has grown rapidly in East Asia and the Pacific but has<br>not kept pace with economic growth in the Middle East and North Africa<br>a. Public education spending b. Public education spending as a percentage of<br>(Billions, constant 2015 PPP $) GDP<br>8<br>60 EAP<br>1998–2001 2002–05 2006–09<br>2010–13 2014–17<br>6<br>40<br>SAR<br>ECA<br>4<br>20 LCR<br>MNA<br>2<br>AFR<br>0<br>0<br>AFR ECA LCR SAR EAP MNA<br>1998– 2001 2002–05 2006–09 2010–13 2014–17<br><!-- End of picture text --> _Source:_ World Bank calculations using UIS and IMF online databases. _Note:_ Statistics on public education spending are averages for countries in each region that have data available for at least one year in the four-year period. World Bank regional classifications are used to group countries as follows: AFR = Africa, ECA = Europe and Central Asia, LCR = Latin America and Caribbean, EAP = East Asia and the Pacific, MNA = Middle East and North Africa, and SAR = South Asia.

Even as all regions have registered growth in government spending on education, the share of GDP they **–** have allocated to education has varied widely. In 1998 2001, Africa and South Asia devoted a similar share **–** of GDP to public education spending. However, by 2014 17, the percentage for Africa exceeded that for South Asia by 0.6 percentage point. Latin America and the Caribbean has also increased considerably the share of GDP used for public education spending since the late 1990s (Figure 4). In contrast, in the Middle East and North Africa, the share of government education spending declined from 5.4 to 4.5 percent of GDP over the same period, which explains the relatively slow absolute growth in such spending for the region since 2000. The available data show that patterns and trends in public education spending in countries that are classified as Fragile and Conflicted-Affected Situations (FCS) are similar to those in other countries at a similar stage of development (Box 3).

9
horizon allows us to uncover more nuanced differences in the dynamics in these different decades. Indeed, to our knowledge, we are the first to attempt such a long-term analysis of mobility for India.

Regarding depth, while Dang and Lanjouw (2018) mostly focus on poverty mobility between the poor and the non-poor, we offer a richer analysis in this paper of mobility patterns for three different income groups consisting of the poor, the vulnerable, and the secure (whom we might also denote as the ‘middle class’). Dividing the population into these groups, rather than the more conventional breakdown into deciles, quintiles, or terciles, offers an additional level of insight into the underlying patterns observed, and allows us to relate our discussion to the large literature on poverty in India. We distinguish the vulnerable from the secure via a method introduced in Dang and Lanjouw (2017), whereby we identify a subset of the non-poor that has a consumption level above the poverty line but faces a heightened risk of falling into poverty in the next period. But for comparison purposes, and to gauge the overall robustness of our findings, we also report findings on mobility based on quintile-based transition matrices. These more refined disaggregations of the population groups are more relevant to policy advice than one might think, especially since attention has been increasingly placed on vulnerable population groups for more sustainable poverty reduction.<sup>2</sup> Moreover, we take advantage of the large sample size of the NSS surveys not just to explore patterns of mobility at the all-India level, but also to consider patterns of welfare dynamics at the state level—recognizing that the forces and policies at work in the Indian economy have often been different and uneven across states. We also examine differences between urban and rural areas and among population groups defined in terms of education levels, occupation sectors, and caste. The combination of a longer time horizon and more disaggregated state-

> 2 For example, the United Nations has called for more rights to economic resources and access to basic services both for the poor and the vulnerable in the Sustainable Development Goals (SDGs).

4
# Trade and Production Fragmentation: Central European Economies in EU Networks of Production and Marketing

by Bartlomiej Kaminski* and Francis Ng** ' The authors are grateful to Carlos Silva and Anthony Venables for very useful comments on an earlier version of this paper.

- ' University of Maryland, College Park and DECRG-Trade, World Bank, Washington D.C.

- / DECRG-Trade, World Bank, Washington D.C.
Appendix Table A3: Individual Applied Financial Knowledge Indicators

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Can Open<br>Account With<br>Rp. 10|Using Loan to<br>Buy TV is<br>Unproductive|<br>Suggests Health<br>Insurance|Suggests<br>MakingBudget|Suggests<br>Applying for<br>Loan|
|Migrant-Only Training (A)|0.000|-0.012|0.014|0.020<br>|0.023|
||(0.032)|(0.034)|(0.017)|(0.036)<br>|(0.043)|
|Family-Only Training (B)|0.028|0.010|0.015|0.036<br>|0.044|
||(0.036)|(0.033)|(0.017)|(0.036)<br>|(0.041)|
|Migrant and Family Training (C)|0.052|0.008|0.081***|0.030<br>|0.075*|
||(0.034)|(0.033)|(0.026)|(0.038)<br>|(0.042)|
|R-squared|0.034|0.016|0.021|0.228<br>|0.142|
|Sample Size|1133|1133|1133|1133<br>|1129|
|Number of Clusters|394|394|394|394<br>|394|
|Mean of Dependent Variable in Control Group|0.132|0.854|0.035|0.261<br>|0.308|
|Test: A-B = 0 (p-value)|0.411|0.474|0.945|0.660<br>|0.611|
|Test: A-C = 0 (p-value)|0.107|0.518|0.012|0.792<br>|0.213|
|Test: B-C = 0 (p-value)|0.498|0.948|0.015|0.883<br>|0.436|
|Test: A = B = C = 0(p-value)|0.340|0.889|0.025|0.769<br>|0.325|

This table presents pooled regression analysis across three follow-up survey rounds for Individual components of the aggregate measure "Applied Financial Knowledge", presented in Table 3. All regressions include survey round dummies. Standard errors in parentheses, clustered at the individual level. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
Policy Research Working Paper 7466

# **Abstract**

Almost a decade after large land-based investment for agriculture increased sharply, opinions on its impact continue to diverge, partly because (positive or negative) spillovers on neighboring smallholders have never been rigorously assessed. Applying methods from the urban literature on Mozambican data suggests that changes in the number and area of large farms within 25 or 50 kilometers of these investments raised use of improved practices, animal traction, and inputs by small farmers without increasing cultivated area or participation in output, credit, and nonfarm labor markets; or, once these factors are controlled for, yields. The limited scope and modest size of the estimated benefits point toward considerable unrealized potential. The paper discusses ways to systematically explore the size of such potential and the extent to which it is realized.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. 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 kdeininger@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
The dataset comprises a variety of different cross-sectional and time series information. Inflation data (CPI) and import and export figures for both the CFA zone and the trading partners from 1999 to 2004 were obtained from the International Monetary Fund’s _International Financial Statistics_ . Monthly bilateral exchange rates of the CFA vis a vis its trading partners were obtained from FX history, the largest foreign exchange database on the Internet. The site gives monthly exchange rates of any two currencies in the world as an average of all daily rates from 1997 to 2005. Data on terms of trade, defined as export prices divided by import prices, were obtained from the World Bank’s _World Development Indicators_ . Trade weights for the two zones were calculated based on aggregating individual country export and import data from the IMF’s _Direction of Trade Statistics_ and were determined by taking the share of the zone’s trade with each trading partner. Information on GDP and other relevant macro variables is from the World Bank’s _World Development Indicators_ . Data on aid flows were obtained from the World Bank’s _Global Development Finance_ database.

## **C. PPP APPROACH**

### **1. BILATERAL AND MULTILATERAL EXCHANGE RATES**

The first step of the analysis is the computation of the bilateral exchange rate comparing the price of a representative production or consumption basket in UEMOA and CEMAC with the price of a representative basket in a foreign country measured in the same currency.

### (1) BRERdc   = Edc * PGf / PGd

where Edc is the index of the nominal exchange rate, defined as the units of domestic currency per unit of foreign currency, PGf is the foreign price index, and PGd is the domestic price index.

The second step of the analysis is to calculate the real effective exchange rate as a geometric weighted average of the bilateral real exchange rates with each of its main trading partners or competitors.

# (2) REERdc = Π (Edci *PGf / PGd )<sup>_wid_</sup>

where m is the number of trading partners or competitors of the home country and Π denotes the product of the bracketed terms over the m countries. The geometric averaging method is used where _wid_ is the appropriate weight for each foreign country _i_ ( _i_ = 1, ... _m_ ) and the sum of the weights must equal one. Following the Hinkle-Nsengiyumva methodology, the external RER indexes can be measured either in domestic currency or foreign currency terms, which are the inverse of one another. Thus, an appreciation corresponds to an increase in the real effective exchange rate index in foreign currency terms but to a decrease in the index in domestic currency terms. The domestic currency measures were used to obtain the foreign currency equivalents.

### **2. CHOICE OF BASE YEAR**

In order to calculate a real effective exchange rate series and assess the degree of misalignment, a base year has to be chosen in which the key objectives of macroeconomic policy are fulfilled- internal balance and external balance. The long-run equilibrium real exchange rate is the rate at which the economy attains both external and internal balance. By definition, internal 8
**Table 4. Descriptive Statistics**

||Mean 1997<br>(Std. Dev.)|Mean 2000<br>(Std. Dev.)|
|---|---|---|
|**_Household_**|||
|Mother's education|6.32|7.14|
||(4.01)|(3.94)|
|Father’s education|6.87|7.55|
||(4.13)|(3.98)|
|Mother's height|150.14|150.38|
||(5.22)|(5.05)|
|Father’s height|161.03|161.61|
||(5.13)|(5.37)|
|Gender of HH head (male=1)|0.88|0.88|
||(0.31)|(0.32)|
|Main activity of HH (farm=1)|0.37|0.42|
||(0.48)|(0.49)|
|HH access to private toilet (Yes=1)|0.55|0.59|
||(0.50)|(0.49)|
|HH access to sanitation (Yes=1)|0.19|0.19|
||(0.39)|(0.40)|
|**_Community_**|||
|Type of community (urban=1)|0.43|0.44|
||(0.49)|(0.50)|
|Distance of comm. to bus station (km)|4.81|3.75|
||(7.14)|(7.01)|
|Prop. of land with technical irrigation|0.07|0.08|
||(0.17)|(0.18)|
|Asphalt road in the comm (Yes=1)|0.74|0.78|
||(0.44)|(0.41)|
|Village has sewerage (Yes=1)|0.55|0.52|
||(0.50)|(0.50)|
|Village has piped water (Yes=1)|0.56|0.55|
||(0.50)|(0.50)|

Notes: Calculated from IFLS2 and IFLS3 data 30
In this section, we evaluate this hypothesis by exploring the impact of the Adwdrs program on real outcomes. We are interested in estimating the elasticity of non-financial outcomes with respect to program exposure (the share of credit forgiven as a result of the program). To do so, we use district-level data on agricultural productivity, real wages and household consumption and estimate equations of the form where _Rit_ is a real outcome of interest for district _i_ observed at time _t_ , _αi_ are district fixed effects, and _βt_ are time fixed effects, which we again allow to vary across India’s four central bank regions _dr_ , _i ∈ r_ to account for local variation in business cycles. The variable _Zit_ measures the instrumented district’s exposure to the bailout, and _Xit_ is a vector of time varying controls. In this equation, the coefficient of interest _ω_ measures the elasticity of real outcomes with respect to program exposure. We estimate this equation using data at the district level, the most granular level at which information on real outcomes is available. Hence, we cannot use district-year fixed effects to separately identify exposure to the bailout from time-varying demand shocks, as in equation 4.5 above. As in equation 4.4 before, bailout exposure is instrumented using the interaction between the number of drought years prior to the bailout and the share of households below the Adwdrs collateral threshold.

We first explore the effect of debt relief on agricultural productivity. We focus on agricultural productivity as the outcome that we would expect to be most directly affected if the bailout alleviated problems of debt overhang, as claimed by its proponents. Indeed, household indebtedness is high in India’s large agricultural sector, and producers rely heavily on external financing, usually provided by commercial banks, to undertake productive investments. Proponents of the Adwdrs bailout generally argued that the debt relief initiative would create incentives for productive investment and free collateral so that rural house23