rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Population, Workforce, Occupation, Enterprise Head Offices and Students in Higher Education: Tokyo’s Central City, Tokyo Metropolis, Greater Tokyo, National Capital Region and Japan

||Central City<br>(23 wards)|(a) Tokyo<br>Metropolis|(b) Greater<br>Tokyo<br>(a)+3Pref.*|National Capital<br>Region<br>(b)+ 4 Pref.*|Japan|
|---|---|---|---|---|---|
|Population (millions)|8|12|33|42|127|
|(percent share)|(6.3%)|(9.5%)|(26.0%)|(33.1%)|(100%)|
|Workforce (millions)|7|8.5|24|28|62.9|
|(percent share)***||||||
|(11.1%)|(11.1%)|(13.5%)|(38.2%)|(44.5%)|(100%)|
|No. of Business||||||
|Establishments|587,000|725,000|1,508,000|1,191,000|6,350,000|
|(percent share)|(9.2%)|(11.4%)|(23.8%)|(30.1%)|(100%)|
|Enterprise Head Offices|35,032|39,572|64,988|75,140|217,047|
|(percent share) in 2001|(16.1%)|(21.0%)|(30.0%)|(34.6%)|(100%)|
|No. of corporations<br>with capital of more||||||
|than 100 billion yen in<br>1998||682|759||1,255|
|(percent share)||(54.3%)|(60.5%)||(100%)|
|Percent share of<br>students in higher<br>education||23.6%|39.2%|42.8%|(100%)|

*3 prefectures include Kanagawa, Saitama and Chiba around Tokyo.

**4 prefectures include Gunma, Tochigi, Ibaraki and Yamanashi surrounding greater Tokyo. ***At place of work

Sources: Statistics Bureau, _Population Census in 2000_ ; _Jigyosho.Kigyo tokei chosa hohkoku (Survey on Establishments and Enterprises in 2001)_ _; Kagaku gijutsu hakusho (White Paper on Science and Technology in 2002)_ , Tokyo, Japanese Government; TMG, _Urban White Paper on Tokyo in 2002_ , p. 14 76
- ill -

urban incomes. While per capita incomes are higher in towns than in the countryside, the absolute size of the market for manufactured goods is often larger in rural areas. Moreover, villagers spend much of any extra income on manufactures, and an increase in agricultural income generates substantial demand for industrial goods.

4.40 The pace of economic development is affected not only by the overail rate of growth in agriculture but also in composition. Reasonably broad-based growth with rising rural incomes of large numbers of producers, including small or marginal ones, contributes to growth in demand for rural goods and services that are labor intensive and that utilize a sizable share of local resources. In a study of Nueva Ecija province in the Philippines, an increase of 1 percent in agricultural output resulted in an increase of 1.5 percent in value added in most sectors of the nonfarm economy. In Malaysia's Muda district, its largest 'rice bowl," every $1.00 increase in agricultural output indirectly added 80 cents in value added to the rest of the village economy. Unfortunately, these multipliers also work in reverse. In the Philippines, a reduction of 1 percent in agricultural output resulted in a reduction of 1 to 1.5 percent in rural nonfarm employment, most of which is in local towns. In Malaysia, each dollar's decline in agricultural income contributed to a decline of 80 cents in nonfarm rural income.

4.41 While the agricultural sector contributes to overall economic growth, it is dependent on growth in the rest of the economy to generate jobs for workers who migrate from agriculture and to create demand for agricultural products. In countries of Southeast Asia, rising per capita incomes increase the demand for agricultural products with a high income elasticity of demand, particularly livestock products and high-value fruits and vegetables. Where domestic demand is limited by overall low average incomes and the internal distribution of income is significantly skewed, as in most part of South Asia, only high-income consumers can afford to have more varied diets.

4.42 These patterns of rural incomes and employment are an expected part of the structural transformation, as a country's economy becomes
# **1 Introduction**

In 1985, in the midst of the highest levels of hyperinflation ever recorded in the history of the Bolivian economy, President Victor Paz Estenssoro proclaimed that the country was in its death throes and that it could survive only by exporting more of its production. Thus, the phrase “export or die” was coined. As part of the reform package –whose cornerstone was to reduce inflation and introduce macroeconomic stability– an export promotion agency (EPA) was created (INPEX).

– Bolivia’s search for development through exports is not exceptional. The first EPA still existing– was created in 1919 in Finland, and in the mid-1960s they became a popular instrument to boost exports and reduce trade deficits, under the auspices of the International Trade Center (a joint UNCTAD-GATT multilateral organization). By the early 1990s their efficiency began to be questioned (Keesing and Singer, 1991 and 1991a). EPAs in developing countries were criticized for lacking strong leadership, being inadequately funded, hiring staff which was bureaucratic and not client oriented, and suffering from government involvement.<sup>1</sup> As a result, many development institutions withdrew their support to EPAs.<sup>2</sup> Part of the blame for the failure of the early EPAs was put on the import substituting trade regimes that prevailed at the time. Overcoming such a strong anti-trade bias was probably too much to ask of any specialized agency. However, more than a decade later, the trade environment has significantly changed in the developing world and some EPAs under the auspices of the ITC have evolved in the direction suggested by Hogan, Keesing and Singer (1991) in their influential piece.<sup>3</sup> The objective of this paper is to provide an assessment of the impact of today’s EPAs on national exports.

The objective of EPAs is to help (potential) exporters find markets for their products, as

> 1Similar critiques emerged for EPAs in developed countries; see for example Kotabe and Czinkota (1992) study of the United States sub-national EPAs.

> 2Of the 73 export promotion agencies in developing countries surveyed for this paper only 21 had some budgetary support from multilateral donors in 2005, and in only 11 agencies the budgetary support from multilateral donors represented more than 25 percent of the total budget. In the case of one Sub-Saharan Africa agency more than 75 percent of its budget in 2005 came from multilateral donors.

> 3That is there is more private sector involvement, larger funding, and a stronger organization and leadership.

1
-_—TM—— ——_

<!-- Start of picture text --> Calinski. Harabasz Davies.Bouldin Hartigan<br>60 -<br>1.15-<br>110- 50<br>1.10 - 40-<br>100 - 1.05 4 30-<br>1.00 - 20-<br>90 - 0.95 - 10-<br>Krzanowski.Lai MSE Silhouette<br>1600 - 0.45 -<br>2.5- 41200 - 0.40-<br>2.0- 800 - 0.355<br>0.30 -<br>15- 400 -<br>1 1 1 1 1 1 1 1 1 1 0.25-, 1 1 1 1<br>234562345623456<br>K<br><!-- End of picture text -->
Investment in Intervention Projects 1972-1981

# Figure 3a: Projects with Interventions

## 1972-1981

<!-- Start of picture text --> LAA<br>LAC 15% ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~5<br>7%<br>E. Asia 2%~~~~~~~~~~~~~~~~~E.Ai<br>~~~~~ 8%~~~~~~~~~~~2<br>EME N J e A5l8% EMENA 49% -eAl<br>EMENA17<br>31% ~~~~~S.Aia<br>15%<br><!-- End of picture text --> <!-- Start of picture text --> Figure 3b: Projects with Interventions<br>1982-1992<br><!-- End of picture text -->

# Investment in Intervention Projects **1982-192**

<!-- Start of picture text --> EMENA LAC<br>22% 8%<br>Arica<br>S. Asia .:Z.fja<br>17%<br>E. Asia<br>39%<br><!-- End of picture text --> iAffica 14% E <!-- Start of picture text --> S. Asia 19X<br>f M E N A~~~~~~~~~~~EMN 112;<br>22~~~~~~~~j222. ~~~~~LAC 1%<br>S *1.t.. E IAfrica a E 4%<br><!-- End of picture text --> Source: Caloulated from Table 1.
different combinations of spending/savings and spending allocations: BBR, BBRHR, SSR, and SSR-HR (see Table 1).<sup>3</sup>

**<u>Table 1: Taxonomy of fiscal rules for Government resource revenues</u>**

|||**Spending**<br>|**allocation**<br>|
|---|---|---|---|
|||**Mostly Govt. consumption**<br>(Historical budget shares)|**Public investment**<br>(Hartwick rule)|
|**ng of**<br>**ding**|**Spent on impact**<br>(Balanced budget rule)|BBR|BBR-HR<br>(“HR” in Default submodel)|
|**imi**<br>**pen**|**Save for the future**||SSR-HR|
|**T**<br>**s**|(Structural surplus rule)|SSR|(Not in Default submodel)|

BBR=Balanced Budget Rule; SSR=Structural Surplus Rule; HR=Hartwick Rule The LTGM-NR-External-Balance also analyzes how resource revenues and different fiscal rules interact with international capital flows. This mostly affects the SSR, which often leads to large movements in international borrowing/lending. Most important is the effect on the current account balance and private investment. If the current account and private savings are relatively fixed, the extra public savings through an SSR can crowd in private investment dollar-for-dollar, leading the SSR to have a similar path for investment and growth as a BBR-Hartwick rule. In contrast, without crowding-in of investment (full adjustment of the current account), the SSR in the External-Balance model performs similarly to that of the simple model (which abstracts from international flows). Of course, real-world countries are somewhere in between these two extremes, and so the strength of crowding in is chosen via a parameter that is calibrated to match the data. In addition, the reduced international borrowing generated by a fiscal surplus under an SSR can reduce the government’s interest bill–through reduced borrow and lower interest rates–which can free up funds for public investment in the long-run under the SSRs.

**Angola Case Study.** To illustrate how the LTGM-NR works, we discuss three simulations with the model calibrated to Angola. First, we simulate the baseline

> 3 The difference between the SSR and SSR-HR depends on the timing of the analysis. In the short term, revenues are mostly saved, and, consequentially, both rules yield a similar allocation of spending. However, they diverge substantially in the medium and long terms, as fiscal surpluses under the SSRs can improve the government net asset position, freeing up funds for extra spending. In this case, the SSR-HR would lead a higher path of public investment than the SSR.

4
He found that these indices moved simultaneously across countries in the same continent but were not closely related across continents. Levy and Samat (1990) also showed the lack of interdependence across equity market retunms and, therefore, supported the diversification benefits of international investment. Eun and Shim (1989) analyzed the interdependence structure of nine developed equity markets using daily data between 1980-1985. They find, using vector autoregression (VAR) analysis, that although developments in the U. S. exchanges are rapidly transmitted overseas, movements in other equity markets do not explain the behavior of U.S. stock market returns. Similar results have been obtained by Becker. Finnerty and Gupta (1990) who looked at the U.S. and Japanese equity markets over time, and Hamnao, Masulis and Ng (1990) who used the autoregressive conditional heteroskedastic (ARCH) model to examine short-run interdependence of stock prices across the U.K., U.S. and Japan.

The behavior of equity retums in industrial countries around the October-1 987 crash has been examined by Arshanapalhi and Doukas (1993), Lau and Mclnish (1993). Recently, Park and Fatemi (1993) have used the VAR approach to examine the linkages between the emerging markets in the Pacific-Basin (namely, Hong Kong, South Korea, Taiwan and Thailand, among others) and the equity markets in the U.S., U.K. and Japan. They find that despite their strong economic integration with industrial countries, these emerging markets exhibit a weak linkage to the equity markets of the U.S., U.K and Japan. More interestingly, they find that "as these markets have grown in size, both in absolute and in relative terms, and as government regulation has eased (in some cases relaxing rules on access by foreign investors), the linkages have strengthened." Availability of flow data of late has permitted researchers to address the issue of possible linkages between international equity markets from the point of view of _por(folio flows._ Tesar and Wemer (1993) have examined the behavior of U.S. equity investment 4
fixed costs of sourcing from another country. Thus, source countries can be complements or substitutes, depending on the firm’s demand elasticity. Using US data, Antras, Fort, and Tintelnot (2017) also present evidence that countries not only differ in the cost of inputs, which lowers production costs, but have different levels of fixed costs of importing.

Given the significance of the global sourcing decision for firms, it is noteworthy that there is substantial heterogeneity amongst otherwise similar firms in both the decision to source inputs internationally and the foreign markets that they choose to source from. One explanation for this variation is that the costs associated with initiating a new sourcing arrangement in a foreign market are not known with certainty by the firm and must be inferred from outside sources. In this context, one of the most valuable sources of information about foreign markets for a firm is neighboring firms’ experiences in those markets. The empirical literature has found that these informal barriers to trade help explain the observed firm-level heterogeneity in exporting and that firms learn about foreign markets from neighboring exporters. The earliest studies of this effect found mixed results, for example, neither Aitken, Hanson, and Harrison (1997) nor Bernard and Jensen (2004) find evidence for export spillovers specifically. However, later research refined the scope of potential export spillovers to be market-specific, which is more aligned with international trade theories that feature heterogeneous firms (e.g., Melitz (2003)). Koenig (2009), Koenig, Mayneris, and Poncet (2010), and Fernandes and Tang (2014) find a strong learning effect for exports by using data that allow for a firm’s neighbor set to be more specifically defined in terms of smaller geographic and industry groupings as well as more detailed customs data that allows for the measurement of market-specific spillovers. However, the focus of this literature evaluating how networks and information spillovers reduce informal trade barriers has been to explain heterogeneity in firms’ export decisions. Papers on import learning are limited. However, Bisztray, Koren, and Szeidl (2018) bridge the gap by extending a framework most similar to Koenig, Mayneris, and Poncet (2010) to a panel of Hungarian firms that allows for an evaluation of the impact of managerial networks in addition to spatial connections.

2
**More on the Energy/Non‐Energy Commodity Price Link**

# **John Baffes**

THE WORLD BANK 1818 H Street, NW Washington, D.C. 20433 tel: (202) 458‐1880 fax: (202) 522‐1151 email: jbaffes@worldbank.org

## **_Follow up to WPS 4333_**

### **JEL** : O13, Q33

**KEY WORDS** : energy prices, non‐energy prices, transmission elasticities, pass‐through
# 2. Conceptual framework and sources

## 2.1 Conceptual framework

To frame our review, we lean on the Total Survey Error (TSE) paradigm as articulated in the survey methodology literature (Biemer, 2010; Groves et al., 2011; Groves and Lyberg, 2010) and applied to agricultural data collection among others by Carletto et al. (2021). Total survey error refers to the sum of errors, that is, bias and variance, arising in the survey lifecycle. Each stage of the survey lifecycle and related survey design choices are associated with different sources of survey error. The objective for survey designers is to make survey design choices that reduce the total survey error, subject to a budget constraint.

In their useful systematization of the survey lifecycle, Groves et al. (2011) distinguish the dimensions of _measurement_ and of _representation,_ as depicted in Figure 1. The _measurement_ dimension broadly comprises what data a survey collects and how. The _representation_ dimension refers to the target population a survey describes.<sup>3</sup> Survey errors associated with the representation dimension include coverage, sampling, and non-response errors. Measurement error in its various forms as well as data processing errors are related to the measurement dimension.

In keeping with this framework, we analyze phone survey programs for agricultural data collection in terms of both the representation and the measurement dimensions, each with its associated sources of error. On the representation side, covered in Section 3, we tackle (i) sampling and representatives, (ii) respondent selection, and (iii) post-survey adjustments. On the measurement side, covered in Section 4, we tackle (iv) questionnaire design and look at the roles of (v) respondents and (vi) interviewers.

_Figure 1. Conceptual framework_ <!-- Start of picture text --> Representation Measurement<br>Sampling and<br>Questionnaire design<br>representativeness<br>Respondent behavior and<br>Respondent selection<br>effects<br>Post-survey adjustments Interviewer effects<br>Survey costs<br><!-- End of picture text --> _Source: Authors’ adaptation based on Groves et al. (2011)_

> 3 On the _measurement_ side, Groves et al. (2011) distinguish ‘construct’, ‘measurement’, ‘response’, and ‘edited response’ as the stages in the survey lifecycle at which survey design choices to reduce error are made. On the _representation_ side, Groves et al. (2011) distinguish ‘target population’, ‘sampling frame’, ‘sample’, ‘respondents’, and ‘post-survey adjustments’.

4
Differences in educational attainment predict large gaps in WFH measures: the jobs of college graduates are 0.70 standard deviation more amenable to WFH than those of their less educated counterparts in the PIAAC sample. That figure for the MNA region is 0.61. In all three samples, workers aged 25 and older have jobs less amenable to WFH than those 24 years or younger. In PIAAC countries, the relationship between amenability to WFH and age has an inverted U-shaped pattern, where those aged 25 to 34 years have the jobs most amenable to WFH, while those younger than 25 and older than 55 are at the opposite end. Among countries in the STEP and LMPS data sets, workers in the youngest age bracket have the jobs most amenable to WFH, but there are little differences by age among those aged 25 to 65.

**Table 3. OLS regression of the WFH index (adjusted for home internet access)**

||PIAAC|STEP|LMPS|
|---|---|---|---|
|Women|0.0611|0.267|0.38|
||[0.00613]***|[0.0108]***|[0.0167]***|
|College education|0.702|0.487|0.615|
||[0.00642]***|[0.0130]***|[0.0173]***|
|25-34|0.305|-0.138|-0.0788|
||[0.0141]***|[0.0181]***|[0.0213]***|
|35-44|0.302|-0.177|-0.144|
||[0.0121]***|[0.0186]***|[0.0215]***|
|45-54|0.224|-0.200|-0.162|
||[0.0119]***|[0.0191]***|[0.0229]***|
|55-65|0.0747|-0.193|-0.139|
||[0.0122]***|[0.0213]***|[0.0292]***|
|Wage employee|0.204|0.109|0.0471|
||[0.00870]***|[0.0127]***|[0.0155]***|
|Constant|-1.045|-0.205|-0.297|
||[0.0264]***|[0.0186]***|[0.0240]***|
|Observations|138,954|24,085|22,088|
|R-squared|0.21|0.319|0.148|

Notes: All models include country fixed effects. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1

# **4. Concluding Remarks**

This paper provides new evidence on which countries and types of workers have jobs that are less amenable to working from home. Using data from 53 countries on the types of tasks that each person does at work—as 18
set of empirical strategies to account for selection, she finds that 1990s immigration did not harm, and possibly benefited the student outcomes of U.S. citizens.

Despite the importance of this question for European countries, the literature on the effect of immigrant peers on natives’ achievement is still thin and reports mixed findings. This question was studied by Jensen and Rasmussen (2011), Brunello and Rocco (2013), Ohinata and van Ours (2013, 2016), Geay et al. (2013), Ballatore et al. (2015), Schneeweis (2015), Tonello (2016) and Branden et al. (2016).<sup>8</sup> While Ohinata and van Ours (2013, 2016), Geay et al. (2013) and Schneeweis (2015) report no effect on natives’ test scores, other studies find statistically significant negative impacts.

Jensen and Rasmussen (2011) examine this issue in the Danish context using test score data from the Project for International Student Assessment (PISA) at age 15, combined with Danish administrative data on neighborhood composition. To address the non-random selection of immigrants between schools, they instrument the share of immigrants in the school by immigrant concentration within a larger geographical area. They report a negative effect of immigrant concentration on the school performance of natives in both mathematics and reading, although estimated effects are small in magnitude.

Brunello and Rocco (2013) rely on cross-country differences in immigrant concentration among 27 European countries to estimate the effect of immigrant students on natives’ achievement. They use test scores at age 15 from the Program for International Student Assessment (PISA) from 2000 to 2009, and their identification strategy relies on variations in immigrant concentration over time within countries, by aggregating micro-level data to the country level. Their results show a small 8Outside Europe, Gould et al. (2009) have also investigated the long-term impact of immigrant concentration in the classroom on the matriculation rates of natives in Israel.

6
**<u>Table 3.</u>** <u>Descriptive statistics by business registration status</u>

||**Informal**||**Formal**|||
|---|---|---|---|---|---|
||**_Mean_**|**_SD_**|**_Mean_**|**_SD_**|**_T-tests_**|
|_Performance indicators_||||||
|Labor productivity|1530.44|2600.83|2448.54|3290.25|-1003.4***|
|<br>Profits, GHC|773.19|1041.59|1382.02|1367.58|-625.4***|
|_Main explanatory variables_||||||
|<br>Gender Norms index|40.82|10.47|41.87|11.36|0.0104|
|Personality Groups||||||
|Impulsiveness|2.41|0.54|2.41|0.54|0.0645|
|Work centrality|4.25|0.69|4.21|0.64|0.044|
|Tenacity|3.98|0.71|3.9|0.69|0.031|
|Polychronicity|2.23|0.69|2.17|0.64|0.0624|
|Locus of control|3.09|0.7|3.29|0.66|-0.199***|
|Achievement|3.77|0.59|3.67|0.6|0.0824*|
|Power motivation|3.39|0.82|3.37|0.78|0.0364|
|Organized|4.15|0.69|4.13|0.78|0.0197|
|Optimism|3.62|0.51|3.56|0.52|0.00776|
|_Owner characteristics_||||||
|Male|0.27|0.44|0.49|0.5|-0.224***|
|Age|40.5|10.27|41.18|10.43|-0.442|
|At least secondary education|0.85|0.36|0.88|0.33|-0.0106|
|<br>Married|0.69|0.46|0.76|0.43|-0.0427|
|Muslim|0.59|0.49|0.47|0.5|0.145***|
|_Household characteristics_||||||
|Under 5|0.53|0.77|0.56|0.8|-0.0258|
|Under 15|1.90|1.82|1.76|1.73|0.180|
|Poor|0.33|0.47|0.12|0.32|0.265***|
|_Business characteristics_||||||
|Registered||||||
|Business tenure|8.87|7.8|9.76|8.22|-1.183*|
|Businesses operated|1.35|0.59|1.34|0.55|-0.0258|
|<br>Home|0.46|0.5|0.3|0.46|0.150***|
|Traditional|0.07|0.26|0.1|0.31|-0.0245|
|Commercial|0.05|0.21|0.11|0.32|-0.0661***|
|Roadside|0.14|0.35|0.35|0.48|-0.214***|
|Mobile|0.28|0.45|0.13|0.34|0.154***|
|Separate expenses|0.35|0.48|0.44|0.5|-0.0974***|
|Apprenticeship|0.26|0.44|0.34|0.48|-0.0962***|
|Credit|0.03|0.17|0.09|0.29|-0.0458***|
|Business account|0.21|0.41|0.46|0.5|-0.258***|
|COVID support|0.02|0.15|0.04|0.2|-0.014|
|Literate employees|0.51|1.64|2.33|5.2|-1.595***|
|Social media|0.21|0.41|0.42|0.49|-0.192***|
|Association|0.03|0.18|0.17|0.37|-0.135***|
|Subsistence|0.32|0.47|0.29|0.45|0.0481|
|_Sector of business_||||||
|<br>Manufacturing|0.29|0.46|0.23|0.42|0.0688**|
|<br> Retail|0.53|0.5|0.46|0.5|0.0815**|
|_Geographic controls_||||||
|<br>Urban|0.88|0.33|0.94|0.23|-0.112***|
|Ashanti|0.52|0.5|0.65|0.48|-0.159***|
|Observations|632||229|||

t test if the mean difference between formal and informal; * p<0.10, ** p<0.05, *** p<0.010 28
Springer.

- Verbeek, M. and Nijman, T. (1993). Minimum mse estimation of a regression model with fixed effects from a series of cross-sections. _Journal of Econometrics_ , 59(ss):125–136.

- Verbeek, M. and Vella, F. (2005). Estimating dynamic models from repeated cross-sections. _Journal of Econometrics_ , 127:83–102.

- You, J.-S. and Khagram, S. (2004). Inequality and corruption. Harvard University, Kennedy School of Government, RWP04-001.

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