rafmacalaba/datause-encoder-data huggingface dataset:
Further, most existing studies focus on investment in constant USD but do not take into account the differences across countries in the relative prices of ICT and NICT goods (see Jorgenson and Vu, 2005; Vu, 2005, and references therein). We devote Section 2.3 to explore the role of cross country variation in this relative price by constructing a measure of ICT units per NICT units that is comparable across countries.

Finally, we keep our methodology conceptually close to that of the PWT (Feenstra et al., 2013; Inklaar and Timmer, 2013). Note that, starting with version 8.0, the PWT constructs aggregate capital stocks by adding estimated capital stocks of six different asset types, among them computers, communication equipment and software. These are also based on the PIM, run separately for these categories, with depreciation rates that are similar for computers and software (31.5%) but substantially lower for communications (11.5%). As mentioned in the previous section, we also adopt these assumptions.

# 2.2. ICT Abundance Around the World

Using the procedure described above, we estimate the stocks of ICT and NICT for a balanced sample of 70 countries over the period 1993-2011.<sup>10</sup> Table 1 summarizes our estimates for 1995 and 2011, which suggest a number of interesting conclusions. First, in most countries, ICT capital constitutes a small fraction of the aggregate value of the capital stock. In our sample of countries, the value of ICT capital out of total reproducible capital is on average around 3.88% in 2011 (see panel A of Table 1).

Second, the summary statistics reported in panel A of Table 1 show that this range is relatively stable throughout our sample, with a mean of 3.95% in 1995, only marginally higher than in 2011. Panel B of Table 1 illustrates a similar picture for the value of ICT as a fraction of NICT, a measure we will use to proxy the relative abundance of ICT for the remainder of this paper.

Third, in relatively poorer countries, ICT constitutes a smaller fraction of the total value of capital. Panel A of Figure 1 summarizes this finding, which relates our measure of “ICT abundance” to log real GDP per capita in 1995 and 2011. To assess this observation more formally we run

> 10See Appendix A for details on the construction of this dataset.

9
consumption or expenditure is measured in per-capita terms and has implications for other indicators of well-being such as housing, rents, or crowding. The Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and Living Standards Measurement Surveys (LSMS) are the most common household surveys used to produce comparative statistics on well-being across time and countries. The DHS and MICSs define household members as (i) usual residents or people who slept in the dwelling the previous night and who (ii) share living arrangements and (iii) share food (ICF International, 2012; UNICEF, 2013). The LSMSs define household members as (i) people who slept in the dwelling three or more months of the last 12 months and (ii) share food (Grosh and Munoz, 1996).<sup>7</sup> While the definitions differ, they are defined on similar concepts and are unlikely to lead to major differences in key household characteristics.

The UNHCR has definitions for household that resemble the definitions used by MICSs and LSMSs but uses the concept of “case” as unit of observation. The UNHCR defines a case as: “A processing unit similar to a family headed by a Principal Applicant. It comprises (biological and non-biological) sons and daughters up to the age 18 (or 21) years, but also includes first degree family members emotionally and/or economically dependent and for whom a living on their own and whose ability to function independently in society/in the community and/or to pursue an occupation is not granted, and/or who require assistance from a caregiver.” (Verme et al., 2016; see also https://www.unhcr.org/5ea81c114). This definition is different from the DHS, MICS and LSMS, may pose challenges when one compares cases with households in surveys, and lends itself to exploitation on the part of users, for example by spreading different household members across different cases to maximize benefits.

Some socio-economic surveys for FDPs do not rely on the UNHCR family definition as the unit of socioeconomic analysis – in addition to the individual. Instead, they employ a household definition either from DHS, MICS or LSMS, or from the established national household survey implemented by the national statistical agency, which often is similar to the traditional definitions. When using a list-based sampling approach with UNHCR’s registration data, this creates the challenge of translating the family (case) definition used as a sampling unit to a traditional household definition for the interviews and the analysis. In most cases, not all members of a proGres family are members of the household, while the household usually also has members from other proGres families. Different approaches can be used to overcome this challenge.<sup>8</sup>

> 7 While the DHS and MICS define households as mutually exclusive, the LSMS definition suffers from individuals potentially belonging to multiple households leading to double-counting.

> 8 If not explicitly mentioned, we assume that the universe of FDPs is defined as FPDs registered in proGres.

10
Table A2: Heterogeneous effects of savings default on non-durable spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|Assets|Above median:<br>Patience|
|Savings Default|-307.617<br>(297.868)|-505.040<br>(426.939)|-177.042<br>(347.639)|-332.661<br>(345.476)|-165.889<br>(357.228)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Cash group|2768.27|2957.83|2606.91|2715.30|2385.11|
|R-squared|0.267|0.368|0.269|0.320|0.225|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|Assets|Below median:<br>Patience|
|Savings Default|-86.431<br>(314.279)|-81.618<br>(263.568)|-514.477*<br>(304.330)|-227.691<br>(315.036)|-229.478<br>(279.581)|
|Observations<br>Mean of dependent variable in Cash group<br>R-squared|162<br>1809.77<br>0.210|320<br>2151.46<br>0.167|239<br>2226.95<br>0.183|246<br>2111.08<br>0.153|282<br>2432.97<br>0.299|
|P-value: equal efect of savings default|0.594|0.374|0.451|0.815|0.884|
|The top panel includes respondents from fe<br>transactions at NBS in the 90 days precedin<br>those with above-median values of a princip<br>at baseline by amount required to prefer a t<br>Savings Default coefcients in Panels A and <br>value of the outcome measured at survey 1. <br>* p_<_0.10, ** p_<_0.05, *** p_<_0.01|male-headed <br>g the transf<br>le-index of a<br>ransfer in o<br> B. All spec<br> Exchange r|households, tho<br>er, those who liv<br>sset ownership, <br>ne month instead<br>ifcations include<br>ate: MK 420 per|se with abo<br>e above the <br>and those w<br> of immedi<br> village and<br> USD. Rob|ve-median va<br> median dist<br>ith above-m<br>ately. P-valu<br> week-of-frs<br>ust standard|lues for number of<br>ance from the bank branch,<br>edian patience, as measured<br>es are from tests of equality of<br>t-survey fxed efects, and the<br> errors in parentheses.|

44
non-linearities in the Engel curve for calories and this causes the elasticity to be higher for the poor than for the rich.

Though not directly comparable to our work which is based on cross-country data, the results of these micro studies are important references as we specify the empirical model we want to estimate. Since one of our objectives in this paper is to estimate the number of people that are undernourished due to the sharp fall in growth in 2009, these results help underscore two points. One that growth in poorer countries will yield a larger reduction in the population of undernourished compared to similar growth in a more affluent country. Second even in poor countries the distributional aspects of growth – particularly the growth experienced by the lowest deciles of the income distribution – will be just as important (if not more) as the absolute level of growth.

We use data on per capita income in purchasing power parity adjusted dollars available from the World Development Indicators for all low and middle-income countries from 19952003. Per capita calorie consumption data was available for a subset of the countries in this sample from the FAOSTAT website for the same period. Keeping only countries that had data for both calorie consumption and per capita income we ended up with 83 countries in the cross section and an overall sample size (country-year pairs) of 742. For comparison, Senauer and Sur (2001) used data from 1992-1996 for 55 low and middle income countries and their effective sample size was 220.

The regression we estimate is the following: where _Cit_ is the per capita calorie consumption in country _i_ in year _t_ , _Yit_ is the per capita income for country _i_ in year _t_ , _μi_ is the time invariant country fixed-effect and _εit_ is the random error. We put in the country fixed effect to control for time invariant country specific characteristics such as dietary preferences that determine caloric intake and may bias the coefficients if omitted from the regression.
Finally, we explore whether distance and wealth have different effects depending on whether the potential destination is urban or rural (column 6). To do so, we include a dummy variable indicating the urban status of the destination as a regressor, as well as its interactions with distance and wealth. Urban destinations are substantially less likely to be chosen, but controlling for whether the destination is urban or rural, we do not see any differential effect in the pull effect of the destination’s wealth. The deterring effect of distance also remains, but is twice as strong for rural than for urban destinations.

We explore these differences between urban and rural migrants further by altering how we form the dyads. In Table 5 we link all urban migrants to all 47 urban locations. For urban migrants selecting urban locations distance and wealth are both significantly associated with destination choice, with coefficients roughly of equal absolute magnitude. At a per standard deviation change the absolute magnitude for distance (sd=0.91 in this dataset pairing urban migrants with urban destinations) is 1.6 times greater than that for wealth (sd=0.50). In Table 6 we link all rural migrants to all 31 rural locations. Again, both distance and wealth matter, but expressed in standard deviation change (sd=1.03 for distance and a much lower sd=0.20 for wealth in the rural-rural dyadic dataset) distance outweighs wealth by a factor of 7.5.

For urban migrants, interactions with poverty and educational status at the outset further show that the pulling power of urban destination wealth is reduced for poorer households. Better educated individuals, on the other hand, tend to find wealthier urban destinations more attractive than lesser educated individuals and also tend to be less deterred by distance. Nonetheless, the deterring effect of distance remains, even for highly educated individuals (Table 5). It is only by around three years of postsecondary education that distance loses its effect completely. Urban migrants with such high levels of education would not be deterred by distance and, furthermore, the pull effect of destination wealth would be 50% higher for them, compared to those who only completed primary education (the modal level of education in our sample).

Poor rural migrants, on the other hand, are more deterred by distance than poor urban migrants, and as their urban counterparts, they are also less inclined to respond to larger wealth at destination. As with urban migrants, more education reduces the deterring effect 13
Cambridge University Press.

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Vega-Redondo, F. 2006. _Complex Social Networks_ . Cambridge: Cambridge University Press. Young, H.P., and M.A. Burke. 2001. "Competition and Custom in Economic Contracts: A Case Study of Illinois Agriculture." _American Economic Review_ 91 (3): 559–73 34
18

**Regression of ggg te r vote nvaestment rate on eal, growth of** output, **real** tLerest **rate and** credit supply (Annual **data** 1965-1985) _PIDPi_ **' 01 * *** **_RGDPOIC_ * %J** _EDCGDP_ **_1_**<sup>**_V_***</sup> * **_ERlzmJC_** _._ **_Ule_**

|Parameter|Brazil|Colombia|<br>**India**|**Korsa**|Turkey|
|---|---|---|---|---|---|
|||1mL<br>a||||
|ea|0.144|0.142|0.088|0.165|0.018|
||(3.90)|(7.00)|(25.3)|(9.22)|(1.24)|
|**_a1_**|**0.249**|0.159|0.064|0.029|0.135|
||(3.44)|(1.59)|**(1.16)**|(0.42)|(4.53)|
|**Ga**|0.264|-0.091|0.013|0.140|0.142|
||(2.56)|(-1.04)|(1.21)|(5.60)|(4.13)|
|**'as**|-0.210|0.112|-0.013|-0.001|-0.056|
||(-2.13)|<br>(2.61)|(-0.36)|<br>(-0.02)|<br>(-4.30)|
|||**Panel B**||||
|**a***|0.080|0.061|0.085|0.122|0.040|
||(3.48)|(2.60)|(23.4)|(7.52)|(3.12)|
|**a,**|0.054|0.075|0.026|0.089|0.006|
||(1.41)|(0.61)|(1.89)|(1.31)|(0.22)|
|**62**|0.409|0.299|0.026|0.220|0.110|
||(4.42)|(3.18)|(2.81)|(8.8)|(3.33)|
|**a3**|-0.155|0.093|0.029|-0.129|-0.061|
||(-2.35)|(2.15)|(3.04)|(-2.41)|(-5.22)|
|||Panel<br>C||||
|**sO**|0.70|0.063|0.083|0.130|0.026|
||(2.80)|(2.76)|(23.0)|(7.63)|(1.63)|
|**a1**|0.169|0.144|0.040|0.067|0.098|
||(3.34)|(1.37)|(1.24)|(0.98)|(2.91)|
|**_L2_**|0.406|0.2.8|0.30|0.207|0.126|
||(4.02)|(2.69)|(3.47)|(8.21)|(3.08)|
|**43**|-0.121|0.059|0.006|**-0.005**|-0.062|
||(-2.10)|(1.28)|(0.27)|**(-0.09)**|**(-3.65)**|
|Heteroe0dastL|city-consistent<br>**t-**|<br>**statistics**<br>in|<br>parenten<br>**al**|<br>**s**||
innovation, the two survey instruments are fully comparable as the same questions are asked in both surveys. The long questionnaire, however, includes some additional follow‐up questions designed with the purpose of collecting additional evidence on reported innovation while systematically probing the answers to the standard yes/no questions. Follow‐up questions include, a question on the year in which the innovation was introduced as well as open questions where respondents are asked to explain the innovation introduced and how this is different from previous products or processes in the firm. The description variables are critical in our analysis since they allows us to verify the innovation implemented. As a result, for product and process innovation our dataset contains three measurements: response in short questionnaire, response in the long questionnaire and response in the long questionnaire verified based on the description provided.

For organization and marketing innovation, the short questionnaire includes the standard questions based on the Oslo manual,<sup>8</sup> while the in‐depth innovation survey disentangles different aspects of organizational and management innovation and different marketing strategies in which firms may innovate. Answers to these more detailed questions are mapped to the more general yes/no question included in the short questionnaire. Finally, four questions about R&D are asked in the long questionnaire, covering firms’ engagement in internal and external R&D and the associated costs. Those questions are mapped to the yes/no question on R&D included in the short questionnaire. The reference period is the same in both survey instruments.

The following analysis focuses mainly on product and process innovations as these can be verified using the additional information in the long questionnaire to corroborate the reliability of the answers provided. We also provide some information on marketing innovation and R&D.<sup>9</sup> Data cover about 22,000 firms operating in the manufacturing and services sectors in the short section in the ES and 11,150 firms in the innovation follow‐up survey (see Table A2 in the Appendix for more details on the sample by country).

> 8 “During the last three years, has this establishment introduced any new or significantly improved organizational structures or management practices?” for management and “During the last three years, has this establishment introduced new or significantly improved marketing methods?” for marketing.

> 9 Organizational innovation is not included in the analysis that follows. The section of questionnaire dealing with organizational innovation is very different compared to the other section and the analysis of the results would require a separate approach.

11
Policy Research Working Paper 9790

# **Abstract**

This paper uses merged customs and administrative data from Serbian firms to quantify the impact of neighboring firms’ importing experience on the decision to start sourcing inputs from new markets. The analysis finds that firms are more likely to start importing from a new market if neighboring firms in the same industry and location have experience importing from that market and if those firms are increasing their imports over time. Further, the results support a distinction between imports and exports for the decision to enter foreign markets; unlike exports, import sourcing choices are not independent across countries. The analysis finds that imports across origins are substitutes, not complements. The paper also investigates origin-country and firm heterogeneity. The results indicate that the impact of neighboring firms’ importing experience is greater for source countries in the European Union market and for firms that are high productivity, foreign owned, and previous importers. Together, these findings suggest that a firm’s spatial connections are an important contributor to its access to global markets as sources for inputs.

This paper is a product of the Finance, Competitiveness and Innovation 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 swtan@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
majority of which are srnall and medium scale. Private finns account for about 45 percent of the total number of registered businesses.

Table 1: **Uzbekistan Enterprises Registered by the Anti-Monopoly Committee as Monopolies** (Republic and Local Levels; as of October 1, 1999)

|**Sectors**|**Number of Enterprise**|**s**<br>**Number**of<br>Products|
|---|---|---|
|Bakery, grain and flour|18|40|
|Coal mining|5|6|
|Natural Gas|4|5|
|Food|97|551|
|Light industry|30|62|
|Local industry|11|22|
|Petrochemicals|8|33|
|Machinery|30|63|
|Construction<br>materials|71|199|
|Furniture|16|48|
|Metallurgy|5|9|
|Services|323|690|
|Others|80|157|
|**Total**|716|1924|

_Source:_ Uzbekistan Anti-Monopoly Committee _Price Control vs. Monitoring._ Senior management of the AMC recognizes that reliance on price and profit control is neitlher an efficient or desirable approach to advance competition in the commercial sectors, and that the process is creating misallocation of resources in the economy. Accordingly, as set out in _Ma4jor Strategic Measures to Improve the Operations of the AMC,_ the Committee has set a goal to switch to a system of monitoring by end-2000. However, neither the AMC nor the business community has adequate capacity to appreciate fully how to make the transition to a system of monitoring and draw lessons from international experience in this regard.

_Defining Economic Market Boundaries._ More fundarnentally, there appears to be poor understanding of and confusion about the basic concepts of competition needed to implement such a system, such as: definition of product and geographic market boundaries (allowing for economically meaningful measures of market share and dominance ralher than application of statutorily defined quantitative formulae that artificially classify firms as monopolies); price and cross-elasticites to gauge market demand and product substitutability; marginal and fixed costs and their relationship to pricesetting and profit maximization; seller and buyer concentration; determinants of barriers to entry and exit; 3 -
# **Figure 10 Debt Markets**

# **Private Bond Market Capitalization to GDP**

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>China India East Asia Eastern Europe Latin America G7<br>Public Bond Market Capitalization to GDP<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>China India East Asia Eastern Europe Latin America G7<br>Central Government Debt to GDP<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>China India East Asia Eastern Europe Latin America G7<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br><!-- End of picture text --> East Asia is the average of Indonesia, Korea, Malaysia, and Thailand. G7 is the average of Canada, France, Germany, Italy, Japan, United Kingdom, and United States. Latin America is the average of Argentina, Brazil, Chile, and Mexico. Eastern Europe is the average of Czech Republic, Hungary, and Poland. Source: Beck, Demirgüç-Kunt and Levine (2006), and Jaimovich and Panizza (2006).