rafmacalaba/datause-encoder-data huggingface dataset:
principal mechanism by which Japanese firms create new products and services. As overseas operations acquire information from host countries, they transmit it back to their head offices in Tokyo, where it is collated and interpreted, and then disseminated to local branches in various parts of Japan. [<sup>15</sup> ] Tokyo based agencies of the central state, like the Bank of Japan, and government linked third sector organizations, like JETRO, assist multinational companies in gathering information from overseas and transmitting it locally in Japan.

However, foreign direct investment flows to Japan are still a miniscule percent of Japanese GDP: 1.2% in 2001, as compared, for example, with 25.1% in the U.S., 38.6% in the UK and 14.2% in Germany (IMF 2003). World cities are sometimes depicted as global basing points for transnational corporations whose ties to any given nation-state are weak at best (Sassen 1991). Tokyo, by contrast, is mainly a national basing point for the global operations of Japanese TNCs (Fujita 1991; Hill and Fujita 2003). Tokyo's relationship to the world economy is not driven in the first instance by global market efficiency, but by a strategic concern to preserve national autonomy through global economic power. In Japan, economic power is indexed by the share of world markets held by the nation's industries, not by quarterly dividends and privately accumulated wealth (Matsumoto 1991; Dore 2000). Japanese learning from foreign firms based in Tokyo is limited in contrast to world cities like London where international investment is high (Simmie and Sennett 2001).

_Manufacturing innovation._ Manufacturing innovation in Tokyo is rooted in a product pull network of relationships among corporate headquarters, R&D labs, pilot production "mother" plants, and test markets. (See Figure 2). The process begins with market and customer analysis (Womack, et al 1990; Clark and Fujimoto 1991; Boulton 1995). Corporate headquarters plan and develop strategies for future growth based upon consumer research. Corporate R&D labs translate the plans into new research projects and product designs. Corporate divisions and smaller, specialized suppliers engineer and manufacture prototypes for new product models, parts and production technologies. Pilot production of new items is performed in corporate mother plants. The new products are tried out in test markets. When new products and technologies prove successful in Tokyo's demanding markets, higher volume production for domestic and global markets is turned over to plants located mostly elsewhere in Japan, and to factories abroad where necessitated by host country markets.

16
different types of losses, combining them does not seem to lead to an accurate estimate of crop losses.

# <u>What do we conclude and still need to know?</u>

We find that proxying for losses on a percent scale captures ordinal differences in losses fairly well, i.e. their rough magnitude, however, they are inherently hard to handle and lack a tangible benchmark for farmers. This makes them prone to measurement error where, in the eyes of a farmer, even seemingly small inaccuracies in their reports of percent area and productivity reductions create considerable error in absolute terms. Furthermore, there is a risk of overlap between the two measures that is particularly high if shocks were more severe. At the same time, small losses may go undetected or may be underreported in years with low shock intensity. As a result, we observe an overestimation of losses in years with severe shocks. The accuracy of our measurement may be further compromised by very heterogenous plant productivity reductions which would make it hard to report a single, averaged productivity reductions figure. The two measures thus serve as approximations of the magnitude of losses but giving them an intervalscale interpretation is less accurate.

Therefore, by measuring losses due to reductions in the planted area that was harvested and losses due to reductions in productivity, respectively, we gain complementary information on the size of crop losses. Together, the two measures also correctly identify years in which losses were conceivably particularly high. However, the questions are prone to satisficing by farmers and can cause large measurement error in absolute loss estimates. Consequently, the survey questions used may need improvements to be suitable for actually inferring the attainable harvest (and thus loss). Several (non-exclusive) options emerge:

- Improving the phrasing of both proxies, clearly differentiating planted area losses from productivity reductions

- Providing (visual) aid in handling percentages more accurately and consistently

- Directly asking for losses by merging both proxies into a single question eliciting the loss benchmarked against what farmers believe could have been their attainable harvest

<u>Question 2: How do farmer reports compare to crop losses based on expert assessments and can we establish an objective, reliable benchmark (‘gold standard’) for crop losses?</u> Eliciting losses directly from farmers may be unbureaucratic and easily scalable, however, the previous analysis has indicated that it comes with a number of potential drawbacks. In order to better assess the accuracy of farmers’ self-reports, there is a need to understand the way in which farmers form their estimates and compare them to a credible benchmark or ‘gold standard’. For example, methodological research comparing self-reports to an objective measure uncovered systematic measurement error in farmers’ reports of plot size and harvest quantity (Carletto, Savastano and Zezza, 2013; Carletto, Gourlay and Winters, 2015; Gourlay, Kilic and Lobell, 2019). Using data from the Uganda MAPS experiment, we can compare farmers’ loss reports to 19
# _Demand model sensitivity analyses_

We conducted two additional sensitivity analyses of the projections of physician demand resulting from the optimal demand model chosen to alternative input parameters.

First, we assess the stability of the predictions to alternative estimated future values of GDP per capita. We use alternative estimated real GDP per capita (US$2010) from the Economic Research Service (ERS) International Macroeconomic Data Set published by the United States ‐ Department of Agriculture (USDA), available at <u>http://www.ers.usda.gov/data products/international‐macroeconomic‐data‐set.aspx (Accessed on September 27, 2015). There</u> was a relatively small (9 percent) difference in the total estimated shortages in 2030 based on the two methods (15.6 million with the main method we used, and 17.0 million using USDA numbers) Second, we examine the possible upper and lower bounds of the predictions older than 65 resulting from high and low future population estimates. Because population is the largest driver of demand in our model, using the high and low variant estimates, we can obtain predicted total health worker deficits that may result from population growth among people older than 65 that is higher and lower than expected, compared to the median estimate that is presented in the main results. These alternative low and high estimates are shown in Figure A3 below, indicating a rather tight band for the resulting predicted values.

# **Figure A3. Projected total health worker deficit using low and high projections for the size of the population older than 65**

<!-- Start of picture text --> Deficit by population scenarios<br>2013 2030<br>year<br>Low scenario Median scenario<br>High scenario<br>22000000<br>Healthworkers<br>11000000<br><!-- End of picture text --> Note: where the predicted demand was less than the predicted supply, the estimated deficit was assumed to be zero.

30
reported having saved in some other way, at more than 60 percent. Given its history of bank failures and currency devaluations, it is not surprising that 17 percent of adults in the region reported lacking trust in financial institutions, similar to the share of adults who reported preferring to save outside the financial system.<sup>2</sup>

# How do account holders save?

GO TO BOX 3.1 Having an account does not necessarily imply formal saving; even among account holders there is great variation in the use of accounts to save. Globally, 42 percent of account holders in 2014 reported having saved formally in the past 12 months (figure 3.2). In high-income OECD economies, East Asia and the Pacific, and Sub-Saharan Africa about half of account holders reported doing so, but in Europe and Central Asia only 15 percent did (box 3.1). And in four regions—Europe and Central Asia, Latin America and the Caribbean, the Middle East, and South Asia—a larger share of account holders reported saving only semiformally or in some other way than reported saving at a financial institution.

> FIGURE 3.2

Savings behavior among account holders Adults with an account by savings behavior in the past year (%), 2014 <!-- Start of picture text --> Did not save<br>80 81<br>73 Saved using<br>other methods only<br>50 54 50 Saved<br>45 semiformally<br>Saved both formally<br>and semiformally<br>Saved formally<br>East Asia Europe High-income Latin Middle South Sub-Saharan<br>& Pacific & Central OECD America & East Asia Africa<br>Asia economies Caribbean<br>Source: Global Findex database.<br><!-- End of picture text --> Indeed, having an account does not necessarily imply that people save at all. In those same four regions about half of account holders reported not having saved or set aside any money in the past 12 months.

How has savings behavior changed over time?

The share of adults who reported having saved formally in the past 12 months increased in all regions between 2011 and 2014 (figure 3.3). In high-income OECD economies the share who reported saving at a financial institution increased by 7 percentage points to 52 percent, while in developing economies it increased by 4 percentage points to 22 percent. The increase in formal saving is in line with the increase in account ownership over the same period, though somewhat smaller.

## FIGURE 3.3

<!-- Start of picture text --> Formal saving, 2011 and 2014<br>Adults saving at a financial institution in the past year (%)<br>2011 2014<br>East Asia & Pacific 36<br>Europe & Central Asia 8<br>High-income OECD economies 52<br>Latin America & Caribbean 13<br>Middle East 4<br>South Asia 13<br>Sub-Saharan Africa 16<br>Source: Global Findex database.<br><!-- End of picture text --> In developing economies the share of adults who reported saving semiformally, by using an informal savings club or a person outside the family, increased by 4 percentage points to 9 percent (figure 3.4). The Middle East had the largest increase among regions, though from a very low base: the share of adults who reported saving semiformally quadrupled, rising from 3 percent in 2011 to 12 percent in 2014.

46 THE GLOBAL FINDEX DATABASE
**Table A26: Inspectors’ Survey Responses and Excess Interaction**

|**Dependent variable:**|**Overall**<br>**job satis-**<br>**faction**|**Pay satis-**<br>**faction**|**Esprit de**<br>**corps**|**Sufcient**<br>**discretion**|**Sufcient**<br>**training**|**Knowledge**<br>**about**<br>**risky**<br>**frms**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Average signifcant excess interaction|4.543**|-0.659|5.111*|-1.968|0.208|4.605**|
||(1.839)|(2.515)|(2.744)|(2.441)|(0.288)|(1.774)|
|Observations|20|20|20|20|29|20|
|R-squared|0.253|0.004|0.162|0.035|0.019|0.272|
||**Corruption**<br>**brokers**|**Corruption**<br>**col-**<br>**leagues**|**Corruption**<br>**supervi-**<br>**sors**|**Ethical**<br>**behavior**<br>**is sanc-**<br>**tioned**|**Receives**<br>**threats**|**Promotions**<br>**are fair**|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Average signifcant excess interaction|2.239|0.364|4.033|-0.053|3.267|1.606|
||(2.068)|(2.078)|(2.506)|(2.221)|(2.567)|(2.124)|
|Observations|20|20|20|20|20|20|
|R-squared|0.061|0.002|0.126|0.000|0.083|0.031|

_Notes:_ Standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. _Average significant excess interaction_ is measured as the share of declarations handled by the inspector that were subject to significant excess interaction calculated using calibration methods (as explained in Section 4). The dependent variables are taken from a nationwide survey of inspectors conducted in 2017, typically scored from 1 to 5 (see Table A1 part H for a detailed description of the variables).The sample covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

90
the extreme case in which external knowledge can be assimilated without any specialized expertise, a firm's internal R&D would have no effect on its absorptive capacity (Cohen and Levinthal 1990). Also, external innovation moderates the internal and especially longer-term incentives to innovate. Innovation outsourcing, such as R&D outsourcing, and innovation performance have an inverse U-shaped relationship, which is positively moderated by the extent to which firms engage in internal R&D and by the breadth of formal R&D collaborations. Both serve as instruments to increase the effectiveness of R&D outsourcing (Fu et al. 2010).

# **3. Data**

The data used in this study come from the annual enterprise survey, L'Enquête Nationale sur les Activités Économiques, which is conducted by the Tunisian Institute of National Statistics (L'Institut National de la Statistique, INS).

The data are collected for the period 1997 to 2009, with about 2,300 firms surveyed each year, including some 1,500 firms in the manufacturing sector. The survey coverage is extensive, as about 30 percent of firms with six or more employees in each sector, excluding agriculture, are included in the sample. In each year, a sampling method is applied which takes into account stratifications by industry and company size in terms of actual employees. I have confirmed that the sample includes representative Tunisian enterprises by comparing key firm characteristics of INS data with the aggregated information of firms listed by the Investment Promotion Agency (Agence de Promotion de l’Industrie, API).

The drawback of the INS data is that the survey provides a repeated cross-section, which accurately represents the Tunisian economy, but makes it difficult to apply econometric techniques for the panel data. However, approximately 300 (N) firms are repeatedly selected for 13 consecutive years (T), and most of the firms appear for at least 2 of the 13 years; therefore, the whole set of data is a rather weakly balanced panel, which allows me to use econometric techniques for the panel data.

The advantage of the INS data is that they provide rich information on firm characteristics (firm creation year, export status, ownership), balance sheets (revenue, labor, capital, 11
0.3 percent. (The trend rate of economic growth is defined here as the regression coefficient of the log mean on time.) However, as was seen in the NAS data, there was a marked break in the trajectory around the turn of the century. The trend growth rate prior to 1999 was 0.9 percent per annum (standard error = 0.1) while it was 4.3 percent (0.2 percent) from 1999 onward.<sup>5</sup> (The difference in trends is statistically significant: _t_ = 3.32, _p_ = 0.009.) It is especially notable that this break reflects the higher economic growth rate of the developing world outside China, which rose from 0.6 percent (0.1 percent) prior to 1999 to 3.8 percent (0.3 percent) from 1999 onward.

The key point to emerge from these observations—drawing on both national accounts and surveys—is that the output and living standards of the developing world as a whole have been growing at faster rates than for the rich world since the turn of the century, reversing a prior pattern of little or no progress toward economic convergence. How this happened is not yet clear. Possibly the (private and public) capital flows to developing countries since 2000 have been sufficient to get out of past poverty traps. Or possibly the conditions required for the existence of poverty traps did not in fact hold in practice (as argued by Kraay and Raddatz, 2007), or they only hold for certain countries, or for pockets of poverty within countries, but not in the aggregate. Then it was just a matter of time, aided by more supportive policy environments (including political stability), before the catching up process would be revealed. Less optimistically, given the uncertainties about the dynamics of the processes involved, one cannot yet rule out the possibility that the forces creating the earlier traps are still there, and will reemerge in due course in the form of set-backs to continuing economic growth.

Economic forecasters have never seemed particularly good at predicting reversals, or even significant slow-downs. It is possibly not surprising then that current expectations amongst mainstream forecasters are that this stronger new convergence process since the turn of the century will continue. At the time of this writing, the World Bank’s economic growth projections assume that a full-blown Euro crisis will be avoided and the annual growth rate of GDP for the developing world will continue to be 6 percent in the coming few years (having fallen slightly

> 5 Two further tests were used. First, the calculation was repeated dropping the 2010 observation because weaker survey coverage means that the estimate is more affected by NAS consumption growth rates used for interpolation. However, the growth rate for the recent period was almost identical (4.3 percent with a standard error of 0.4 percent). Second, these calculations do not constrain the predicted values of the log mean to be identical in 1999. If one prefers to impose that constraint, then the trend growth rates for the two subperiods are slightly lower, at 0.7 percent per annum (0.1 percent) and 4.1 percent (0.2 percent), respectively.

6
Table 6 presents the provincial distribution of Chinese FDI on an accumulated, or stock, basis. The FDI gap between the coastal and inland regions becomes even more apparent when accumulated FDI is adjusted for population or geographical size. While the coastal regions attracted over 9 times as much FDI as the inland regions in aggregate terms, they attracted over 12 times as much FDI on a per capita basis, and over 70 times as much FDI per 1000 square meters of land area.

**Table 6: Cumulative FDI in China by Region, Year-end 1992** $US **Millions**

||Total<br>l____________|Per<br>Capita|Per 1000<br>Sq Km|
|---|---|---|---|
|Coastal|27,455.99|54.35|25.01|
|Guangdong<br>|12,051.49<br>|184.70<br>|66.95<br>|
|Fujian|2,944.64|75.25|24.54|
|Jiangsu<br>|2,222.45|32.16|22.22|
|Beijing|2,041.41|185.25|121.51|
|Shanghai|1,938.55|144.13|323.09|
|Shandong|1,824.82|21.19|12.17|
|Liaoning|1,556.41|38.76|10.38|
|Hainan|944.70|137.71|27.79|
|Tianjin|610.24|66.33|55.48|
|Zhejiang|566.12|9.70|5.66|
|Guangxi|448.38|10.24|1.95|
|Hebei|306.78|4.89|1.61|
|Inland|2,838.51|4.30|0.35|
|Shaanxi|459.11|13.48|2.42|
|Hubei|378.85|6.79|2.10|
|Sichuan|355.60|3.23|0.64|
|Heilongjiang|290.57|8.05|0.63|
|Hunan|248.30|3.96|1.18|
|Henan|245.28|2.77|1.53|
|Jilin|180.66|7.14|1.00|
|Jiangxi|169.79|5.45|1.06|
|Anhui|156.94|3.70|1.21|
|Guizhou|89.08|2.65|0.52|
|Shanxi|82.94|2.78|_0.55_|
|Yunnan|67.53|1.76|0.18|
|Xinjiang|54.12|3.42|0.03|
|Inner Mongolia|42.75|1.94|0.04|
|Gansu|11.97|0.52|0.03|
|Qinghai|3.53|0.77||
|Ningxia|1.46|0.30|0.02|
|Tibet|0.03|0.01||
|Total|30,294.50|25.99|3.33|

_Source: Chzina Statistical Yearbook_

# **Hypothesis Development**

Observers have cited various factors to explain the skewed geographical pattern of FDI within China. Among them, the government's incrementalist approach in implementing the "open-door" policy is reflected in the impressive amount of FDI in the coastal areas as compared to other regions. The autonomy given to certain coastal areas with regard to investment, production and other economic policies is surely another reason why these regions have been particularly attractive to foreign investors. The close geographical proximity and tight cultural and linguistic links between southern China and the overseas Chinese communities in Taiwan, China, Hong Kong and Macao have also directly contributed 6
So far, the data I have used is for the manufacturing sector. Now I focus on the agriculture and service sectors. These two sectors are also particularly important for South Asia given the weight of the agriculture sector in the economy of the countries in the region and the potential of the service sector to generate economic growth. Table (4) evaluates the RTA effect of these two sectors in the same way done with the benchmark results for the manufacturing sector, showing the world average, intra-regional, and rest of the world effects. Manufacturing results are included again for comparison purposes. Starting with the world average effects, the results suggest that RTAs have a similar effect in agriculture and manufacturing, slightly smaller. On the other hand, the service sector sees its RTA effect cut in half with respect to manufacturing. This is not surprising given the nature of services and trade barriers it might face, in this case at the global level. The specific results for South Asia are again interesting. The South Asia RTAs have a strong positive trade creation effect, but only for intra-regional ones with a total effect of 57% = exp(0.09 + 0.36) _−_ 1, above the total effect for manufacturing within the region.<sup>4</sup> In the service sector, the effect is positive, both intra-regional and with the rest of the world, with a total effect of 31% = exp(0.05 + 0.22) _−_ 1 for RTAs with third countries and 51% = exp(0.05 + 0.22 + 0.14) _−_ 1 within the region.

||Manufa|cturing|Agric|ulture|Ser|vices|
|---|---|---|---|---|---|---|
||(1)<br>trade|(2)<br>trade|(3)<br>trade|(4)<br>trade|(5)<br>trade|(6)<br>trade|
|RTA|0.10<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.09<sup>_∗∗∗_</sup>|0.09<sup>_∗∗∗_</sup>|0.05<sup>_∗∗∗_</sup>|0.05<sup>_∗∗∗_</sup>|
||(11.97)|(11.97)|(6.13)|(6.12)|(7.80)|(7.80)|
|RTA_×_ SAR|0.11<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|0.01|0.23<sup>_∗∗∗_</sup>|0.22<sup>_∗∗∗_</sup>|
||(6.88)|(6.00)|(2.70)|(0.33)|(12.30)|(11.13)|
|RTA_×_ SAR-intra||0.20<sup>_∗∗∗_</sup>||0.36<sup>_∗∗∗_</sup>||0.14<sup>_∗∗∗_</sup>|
|||(5.70)||(7.10)||(4.09)|
|_N_|933660|933660|933660|933660|933660|933660|

**Note:** the variable RTA measures the world average effect of trade agreements. The SAR effect is measured in addition to the average, and SAR-intra is in addition to the SAR effect. _t_ statistics in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

Table 4: South Asia sectoral results Now that the results are available for different sectors, I look into the different uses

> 4Note that the RTA effect in the agriculture sector is the same as the world average.

11
-8-

explanation for utilization. Complete utilization of such information, however, may require specialized modellir,g techniques (Bollinger 1990). The study reported below circumvents these problems, to a degree, by modelling the effects of (largely unobserved) community factors and, subsequently, the role of such outcomes in the production of anthropometric measures of nutrition. The approach may oe suited for a number of the integrated household data sets based ;.n the methodologies of the Living Standards Measurement Study or the Social Dimensions of Adjustment in Sub-Saharan Africa project as well as a number of recent Demographic and Health Surveys. Each of these data sets, as well as our own, contain information from a number of clusters. One can utilize such information either using community fixed effects or community covariates (Strauss 1990; Thomas, Strauss, and Henriques 1991).

We begin by specifying the presumed error structure of the estimating equations for nutrition production and for input demand as follows: Here the v subscript denotes the village or community. The turn & indicates unobservdble community characteristics which influence health production and input demand. The terms X and Z denote vectors which