rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5 Robustness Checks of Different Estimation Methods.**

||**1a**|**1b**|**2**|**3**|**4**|**5**|**6**|
|---|---|---|---|---|---|---|---|
|**_Estimation method_**|**Two-stage H**|**MRprocedure**|**OLS**|**OLS**|**Tobit**|**ET-tobit **|**PPML**|
|**_Dependent variable_**|**ln(**|**X)**|**ln(x)**|**ln(1+x)**|**ln(1+x) **|**ln(a+X)**|**(X)**|
|Distance (log)|-1.121|<br>-0.439|-1.125|-1.144|-1.153|-1.102|<br>-0.625|
||[0.024]***|[0.062]***|[0.024]***|[0.024]***|[0.026]***|[0.024]***|[0.040]***|
|GDP Importer(log)|0.883|0.253|0.886|0.906|0.913|0.87|<br>0.595|
||[0.027]***|[0.049]***|[0.027]***|[0.028]***|[0.027]***|[0.025]***|[0.073]***|
|GDP Exporter(log)|0.816|0.228|0.819|0.842|0.849|0.803|0.427|
||[0.029]***|[0.046]***|[0.029]***|[0.030]***|[0.028]***|[0.025]***|[0.057]***|
|Population Importer(log)|0.122|<br>0.034|0.122|0.125|0.126|0.124|<br>0.188|
||[0.023]***|[0.037]|[0.023]***|[0.024]***|[0.023]***|[0.021]***|[0.048]***|
|Population Exporter(log)|0.261|<br>0.016|0.261|0.255|0.255|0.254|<br>0.351|
||[0.024]***|[0.037]|[0.024]***|[0.024]***|[0.023]***|[0.021]***|[0.054]***|
|LandlockedImporter|-0.049|0.104|-0.047|-0.018|-0.019|-0.026|-0.075|
||[0.056]|[0.091]|[0.056]|[0.056]|[0.057]|[0.052]|[0.131]|
|LandlockedExporter|-0.202|<br>-0.093|-0.204|-0.224|-0.232|-0.205|-0.176|
||[0.057]***|[0.091]|[0.058]***|[0.058]***|[0.055]***|[0.050]***|[0.098]*|
|Commonborder|1.256|0.376|1.255|1.262|1.258|1.27|<br>0.85|
||[0.142]***|[0.448]|[0.142]***|[0.145]***|[0.137]***|[0.124]***|[0.178]***|
|Common language|1.319|0.865|1.328|1.411|1.429|1.332|<br>-0.039|
||[0.074]***|[0.250]***|[0.074]***|[0.074]***|[0.075]***|[0.068]***|[0.142]|
|TTRI|-1.319|-0.302|-1.331|-1.407|-1.437|-1.297|<br>-2.944|
||[0.368]***|[0.148]**|[0.372]***|[0.393]***|[0.185]***|[0.167]***|[0.960]***|
|NTB-RI|0.993|-0.932|0.977|0.615|0.588|0.582|<br>0.167|
||[0.312]***|[0.411]**|[0.312]***|[0.325]*|[0.305]*|[0.275]**|[0.812]|
|LPI Importer|0.367|<br>0.298|0.369|0.386|0.392|0.375|0.311|
||[0.071]***|[0.145]**|[0.071]***|[0.073]***|[0.073]***|[0.066]***|[0.151]**|
|LPI Exporter|1.177|<br>0.882|1.179|1.206|1.21|1.178|0.52|
||[0.073]***|[0.158]***|[0.073]***|[0.074]***|[0.071]***|[0.065]***|[0.142]***|
|DB Import Costs (log)|-0.271|<br>-0.213|-0.274|-0.302|-0.307|-0.282|<br>-0.507|
||[0.050]***|[0.091]**|[0.050]***|[0.051]***|[0.051]***|[0.046]***|[0.111]***|
|DB Export Costs (log)|-0.367|<br>-0.207|-0.373|-0.416|-0.43|-0.369|-0.432|
||[0.051]***|[0.090]**|[0.051]***|[0.051]***|[0.048]***|[0.044]***|[0.105]***|
|**Entry cost indicator **||-0.198||||||
|||[0.086]**||||||
|**Constant**|-29.803|-6.253|-29.901|-30.459|-30.656|-29.163|-13.548|
||[0.697]***|<br>[1.331]***|[0.698]***|[0.706]***|[0.722]***|[0.656]***|[1.485]***|
|**Observations**|10508|10508|10278|10508|10508|10508|10508|
|**R-squared**|||0.74|0.74||||

_Note_ : Robust standard errors are in brackets.

* significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

30
and the independent variables are in log form) or semi-elasticities (where the dependent variable is in log form, and the independent one of interest is a dummy variable) that can be readily converted to elasticities.<sup>4</sup> The starting point of the sample collection is Straub (2008), which covered 140 specifications from 64 empirical papers. We then collected additional references from other meta-analysis papers and literature reviews on transport, energy, and digital issues written since 2008.

These papers include Bom and Ligthart (2014) and García et al. (2017), which focus specifically on production function studies. For transport-related infrastructure improvements, we identified additional references from the surveys of Redding and Turner (2015), Redding and Rossi-Hansberg (2017), and Berg et al. (2017). For energy, we collected papers from Burgess et al. (2020) and Lee et al. (2020). Finally, for digital and ICT topics, we relied on Bertschek et al. (2015), Greenstein (2020), and Vergara Cobos and Malasquez (2023). We also added references through searches for infrastructure-related keywords in Google, the World Bank Working Paper Series, and the George Washington Universities’ digital libraries, as well as relevant recent working papers submitted to the World Bank conferences.

We include all the and that with the basic papers reports comply following requirements: they are written in correct, understandable language, mostly in English with a few exceptions of papers written either in Spanish or French, they state the research question clearly, contain basic information on the econometric specification allowing an assessment of its soundness, and report estimates together with either standard errors or t-values, as well as number of observations used in the estimations.

We report several estimates for each study. Although we aim to cover all relevant specifications, we apply the following criteria to limit their number. When covering a given econometric specification that reports several results successively including additional controls, we pick only the last one including all the relevant controls. So for example, for 4Elasticities could be computed even when the independent variable is not a dummy variable, but the information needed to do so, such as the sample average of the independent variable, is generally lacking.

7
regime for their industry. In contrast, firms in the KIBS sector appear to have the most ground to make up in this aspect of evolving from efficiency to innovation.

# **4.4 Innovation enablers**

Obtaining appropriate labor to facilitate their innovation efforts appears to be a common concern for firms in all the three innovative sectors. Most firms in our sample (87.5%) report that the supply of innovators – those who develop new ideas and transform them into intellectual property - is a real issue for them (see **Table 4.4.1** ). The need is especially acute in the high-tech and creative content sectors, whose survival is more directly linked to developing innovative products and services, than for the KIBS firms, who provide a supporting role for them. Nevertheless, over three-quarters of KIBS firms also report that obtaining innovators is an issue for them; not only to innovate their own companies, but because of their interdependence on the other two sectors. Investment firms, for example, need innovative people and companies to invest in.

**Table 4.4.1: Supply of enablers of innovation**

|Is the supply of innovators a real issue for your<br>company?|High-tech|KIBS|Media/<br>creative|Overall|
|---|---|---|---|---|
|No|9.4|21.4|7.1|12.5|
|Yes|90.6|78.6|92.9|87.5|
|Total|100|100|100|100|

_Note_ : One company answered ‘yes and no’. Its response is split half for each The main issue in the supply of innovators appears to have been a prevailing culture in Singapore that until recently may have discouraged creativity, risk-taking, and failure. Although a few companies report the need for people with further training and greater expertise, the majority point to the lack of innovative and entrepreneurial spirit in society as a whole. Based on the annual surveys of the Global Entrepreneurship Monitor (GEM), Singapore has ranked relatively low in the propensity of her adult population engaging in starting up new businesses; in 2003, only 5% of Singapore’s adult population engaged in new start-up activities, significantly below that in the US (11.9%) or China (11.7%), and ranking 21<sup>st</sup> out of 31 countries covered (Wong et. al. 2004). A major factor contributing to the relative lack of entrepreneurial participation appears to have been the high opportunity cost, given that Singapore had enjoyed full-employment for many years, and a generation of young educated people had experienced rapid career advancement and steady and secure income growth working in subsidiaries of global MNCs or the public sector. In this sense, Singapore is a paradox of her past success in creating rapid economic growth through leveraging foreign MNCs (Wong, 2002).

39
6 A major if not the most serious obstacle to FDI is the restriction on ownership,. German compan'es have a strong preference for 100 percent ownership. The reasoning for this is similar to the one for the preference for direct investment over licensing (see Buckley and Casson (1985)). It eases the decision process inside the company and allows for better control over intangible assets such as technology, product quality and credibility. It has to be mentioned, however, that joint ventures have their particular advantages too. Country specific aspects of production and distribution can be more easily handled with a local partner sharing the risk burden. Especially small and medium-size companies appreciate these advantages of joint ventures.

According to a study of the HWWA-Institute companies own on average 84 percent of their affiliates abroad; in Latin America the percentage share is even higher (87.3 percent), whereas in Asia, where ownership restrictions used to be particularly high in the past, it is significantly lower (56.3%, see Scharrer and Kragenau (1988)). Companies are generally more restricted with respect to their control of ownership in developing than in industrialized countries.

Some countries have codified limits varying from sector to sector with complete foreign ownership being the exception, often approved particularly to export-orientated companies (for example in Malaysia). Other countries decide on a discretionary basis (like Thailand), and many countries' regulation requires a gradual increase in national participation (for example Peru). An important qualification to the above described regional pattern of shares has however to be made: most developing countries, especially so in Asia, liberalized their investment policies substantially. In particular possibilities for fully foreign owned investment projects were extended. The most important holder of German FDI, Brazil, however, is following a more restrictive stance, at least in comparison
|- 67-|
|---|

Devarajan, Shantayanan and Jaime de<sup>Melo (May 1987). "Adjustment with a Fixed Exchange</sup> Rate: Cameroon, Cote d'Ivoire, and Senegal."<sup>The World Bank Economic Reiew, Vol. 1, No.</sup> 3: 447487.

Edwards, Sebastian (1989a). "Exchange<sup>Rate Misalignment in Developing Countries."</sup> 'eerch Qbsrver, Vol. 4, No. 1. World Bank. Edwards, Sebastdan (1989b). "Openness,<sup>Outward Orientation, Trade Liberalization, and</sup> Economic Performance in Developing<sup>Countries". PPR Working Paper No. 191. Washington</sup> D.C.: World Bank.

Eicher, Carl K. (1986). "Strategic Issues<sup>in Combating Hunger and Poverty in Africa" in Berg</sup> and whitaker.

Gitfinger, Price J., Joanne Leslie and<sup>Caroline Hoisington (1987). Food Policy: Integrat</sup> Supply. Distribution, and Consumption.<sup>Baltimore: The Johns Hopkins University Press.</sup> Haggblade,Steven, Peter B. Hazel and<sup>James Brown (1987). "Farm/Nonfarm Linkages in Rural</sup> Sub-Saharan Africa Empirical Evidence<sup>and Policy Implications.' BGRAP Economic Discussion</sup> Paper No. 2. Washington D.C.: World Bank.

Harvey, Charles ed. (1988). Agricultural<sup>Pricing Policy in Africa. Four Country Case Studies,</sup> Hayami, Yujiro and Vernon W. Ruttan<sup>(1971). Agricultural Davelopment. Baltimore: Ihe</sup> Johns Hopkins University Press. Imran, Mudassar and Duncan Ron (1988).<sup>"Opfimal Export Taxes for Exporters of Pernnial</sup> Crops." PPR Working Paper No.<sup>10. Washingon D.C.: World Bank.</sup> Jaeger, William and Charles Humphreys<sup>(1988). "The Effect of Policy Reforms on Agricultural</sup> Incentives in Sub-Saharan Africa."<sup>American Journal of Agriculural Economics Vol. 70 No.</sup><sup>_5,_</sup> December 1988.

Jansen, Doris (1988). Trade Exchange<sup>Rate. and Agrictltural Pricing Policies in Zambia.</sup> Washington D.C.: World Bank. Johnston, Bruce F. and William C.<sup>Clark (1982). Redesining Rural Development - A Strategic</sup> Perctive. Baltimore: The Johns Hopkdns University<sup>Press.</sup> Johnston, Bruce F. (1986). "Government<sup>Strategies for Agricultural Development' in Berg and</sup> Whitaker.

Kmenta, Jan (1971). Elements<sup>of Econometrics. New York: Macmillan Publishing Co., Inc.</sup> Krueger, Anne 0. (1974) "The Political<sup>Economy of Rent-Seeking" American Economic</sup> Review 64, No. 3: 291-303.
36 Table 3. Structural Equations for Current Contraceptive Choice for Tunisia

|Variables|I<br>Pills/Condoms/<br>Vaginals|IUD|Traditional<br>Methods|
|---|---|---|---|
|Age 15-25|0.58 (1.56)|1.50 (3.15)***|-0.50 (1.06)|
|Age 26-30|0.84 (2.71)***|1.51 (4.31)**t|-0.08 (0.21)|
|Age 31-35|0.71 (3.15)***|0.92 (3.61)***|0.08 (0.25)|
|Education spouse|0.01 (0.30)|0.04 (1.97)*|0.14 (4.57)***|
|Education 4 to 6|-0.16 (0.81)|0.1C (0.55)|0.03 (0.13)|
|Education 7+|0.41 (1.48)|0.02 (0.08)|-0.21 (0.56)|
|Rural age 12|.0.11 (0.64)|-0.20 (1.55)|.0.39 (1.99)**|
|Small city|0.14 (0.60)|-0.09 (0.58)|-0.16 (0.66)|
|Urban|-1.28 (1.32)|-5.26 (6.85)..|0.45 (0.70)|
|Remote|0.34 (1.26)|-3.78 (1.86)*|-0.13 (0.30)|
|Message|0.34 (2.49)***|0.61 (6.06)***|0.14 (0.82)|
|Sum assets|0.15 (2.93)***|0.06 (1.33)|0.13 (2.38)***|
|Doctor_< 5_km|0.12 (0.38)|0.93 (2.50)0**|0.56 (1.45)|
|Clinic 2**<**_5_km|-0.18 (0.74)|-0.21 (1.29)|0.37 (2.07)0*|
|Clinic 3**<**5 km|0.17 (0.62)|-0.09 (0.47)|-0.10 (0.52)|
|Hospital < 5 km|0.34 (1.82)*|0.15 (1.16)|0.39 (2.04)*|
|Pharmacy < 5 km|1.14 (1.88)*|0.16 (0.26)|0.07 (0.12)|
|Clinic 3**<**10 km #|-0.55 (0.75)|-5.28 (11.25)*0*|-0.11 (0.22)|
|Supplies #|0.13 (0.27)|-3.37 (6.18)0**|-0.13 (0.12)|
|Frequency supplied#|-0.06(0.09)|2.53 (3.89)*0*|0.85 (0.78)|
|Husband Absent|-0.50 (2.63)..*|-0.35 (2.32)0*|-0.72 (3.60)***|
|Fertility Intentions|.02 (2.14)0*|2.47 (3.46)00*|0.65 (1.18)|
|Constant|-4.75(2.99)*0*|-3.71 (1.69)00|4.40 (3.01)00*|
|Choice Based Coefficient|6.74 (9.26)00*|.||
|N Based|2840|||

# Rural areas

***** Significant at 10% level **00** Significant at 5% lcvel Significant at 1% level
targeted such a program is to poor people, who will be even poorer (in terms of welfare) than their consumption suggests. And some participants who are not considered poor when the disutility of the type of work they do is ignored will now be seen to be poor.

The upshot of these observations is that the implications for assessments of the performance of a workfare scheme in reducing poverty are theoretically ambiguous. While ignoring the disutility of work will tend to overstate the welfare gains to the given set of participants it will understate targeting performance. Which of these two effects dominates will determine how the gains from the program are distributed across the population and (in combination with how the chosen poverty measure weights gains at different levels of living) whether or not the poverty impacts are underestimated by standard methods that ignore the disutility of work.

This paper examines the sensitivity of poverty and inequality measures and assessments of the performance of NREGS to the fact that the implicit welfare indicator ignores the disutility of work. We only allow for the disutility of casual manual wage labor. There are, of course, other types of work in this setting. The main alternative to supplying casual manual labor (often for work on another farm) is working on one’s own farm. We treat this as a very different form of work that gives little disutility to the farmer, at least relative to casual wage work. Indeed, ownfarm work may well give personal satisfaction that is more than enough to outweigh any disutility of the physical labor involved. We also assume that permanent work in this setting (such as working for the government or in a formal-sector enterprise) has very different welfare consequences. Here too there is likely to be significant job satisfaction. Indeed, studies of subjective welfare typically find that a regular job is a direct source of personal satisfaction, with a positive effect on perceived happiness or satisfaction with life.<sup>6</sup> Although we do not know of any supportive evidence, it would seem a plausible assumption that casual manual wage labor in rural India provides little or no intrinsic job satisfaction; the overwhelming direct welfare effect is negative—to be balanced against the positive value of the gain in earnings, as in the classic formulation of the work-leisure choice problem. We comment on the likely direction of bias in our main results if other (non-casual non-manual) work gives disutility.

> 6 For an overview of the evidence on this point see World Bank (2012).

4
Chad, and Niger- have a per capita GDP of US $ 500, which is 21.5 percent of the per capita GDP in PNG (US $ 2,333).

The patterns are robust to concerns about the accuracy of reported GDP figures. For example, some have suggested that there is too much volatility in reported figures, even in years where there have been few observable changes (e.g., Howes et al. 2022). However, Figure 9a illustrates that the figures are qualitatively identical when using an additional source for per capita GDP aside from those reported in the World Development Indicators;<sup>29</sup> and Figure 9b illustrates that the figures are identical when using the five year average of GDPs in the underlying regression and the projection to reduce potential noise.

As described above, the inability of economic activity in the resource sector to spill over and benefit the majority of the population through public transfers, direct employment, or spillovers of increased economic activity in other sectors is possible in resourcedependent economies (e.g., IMF 2012). However, we illustrate that higher-than-expected deprivations in PNG are even extreme relative to other resource-dependent economies.

Figure 10 reports how much larger actual deprivations are than the level of deprivations projected by economic activity alone. The figure reports this difference separately for PNG and the average for all resource-dependent countries that are low- or middleincome, as defined by the IMF (e.g., IMF 2012).<sup>30</sup><sup>_,_31</sup> For the MPM itself and for each deprivation aside from the educational attainment of adults, the difference was larger in PNG than the average for all other resource-intensive low- and middle-income countries. The point estimate for the other resource-dependent economies was below the lowest projection in PNG for all indicators; and for four of the seven total indicators (6 deprivations and the MPM), the lowest projection in PNG is still higher than the highest average projection in other resource-dependent countries.

> Although it is difficult to precisely interpret the difference between GDP-based pro29 Figure 9a uses GDP per capita obtained from the PNG Economic Database, available at (accessed March 2023): ”http:/devpolicy.org/pngeconomic”.

> 30A list of resource-dependent countries that are low- or middle-income, as defined by IMF (2012) is available from the authors upon request.

> 31The estimates are qualitatively identical when including other comparator countries to PNG, such as Peru, Malaysia, and Solomon Islands (e.g., World Bank 2023).

18
real GDP per capita of the neighbor province _q_ in time _t_ , and _mi_ the number of the neighbor province of _i_ . In our case, _mi_ is an integer between 2 and 8.<sup>18</sup> The value of _GAWi_ , _t_ is conditioned by the growth rate of the neighbor provinces _q_ , _gaq_ , _t_ . The influence of the growth rate of each neighbor province _q_ is conditioned by its economic size relative to all the _mi_ provinces surrounding province _i_ .

In order to control for the effects of the labor supply, we introduce the variable _GPOPj_ , _t_ , which measures the population growth of the province _j_ in time _t_ .

Hence, for each industry _i_ , we estimate separately the following model in panel with fixed effects using the data of 1988-1997 at the provincial level:<sup>19</sup> We will study the geographic concentration of activities and the change in location of the non high-tech industries at the national level in section 3.3; and at the regional level in section 3.4.

# **3.3. Geographic concentration of activities at the national level**

Our econometric results are mixed. They suggest that the geographic concentration of activities differs across industries. For the sake of readability, we only present the determination of the concentration of some representative industries: two high-tech industries that are the most concentrated (“electronic and telecommunications equipment manufacturing” and “instruments, meters and other measuring equipment manufacturing”), one less concentrated labor intensive industry (“chemical material and products manufacturing”), and one relatively concentrated industry that is based on natural comparative advantages (“timber processing, bamboo, cane, palm bibber and straw products”)<sup>20.</sup> Table 4 shows that openess plays a positive role in the concentration of the labor intensive industry “chemical material and products manufacturing” and the primary material industry “timber processing, bamboo, cane, palm bibber and straw products”.<sup>21</sup> However, it plays a negative role in the high-tech industries “electronic and telecommunications equipment manufacturing” and “instruments, meters and other measuring equipment manufacturing”. In the case of China, FDI

> 18 For example, Shanghai has only 2 neighbors (Zhejiang and Jiangsu); while, Sichuan has 8 neighbors (Gansu, Qinghai, Xizang, Yunnan, Guizhou, Hunan, Hubei, and Shaanxi).

> 19 The results presented in the paper are based on fixed-effect estimations. The estimations of the two-step GMM with two year-lagged dependent variables as IV matrix do not suggest the superiority of the GMM over the fixed-effects estimations.

> 20 Not each independent variable plays a significant role in the determination of the concentration of all industries. In addition, some industries only concentrate in a very limited number of provinces, for example, tobacco in Yunnan. We exclude these from our analysis. The results of the estimations of the 30 industries at the national level and at the regional level are available upon request.

> 21 The role of openness is significant in the former but insignificant in the latter.

15