rafmacalaba/datause-encoder-data huggingface dataset:
We also analyze the impact of alternative regulatory regimes on efficiency. The ideal environment in which to measure the impact of different types of regulatory schemes is one in which all factors other than the regulatory regime itself are held constant. This was not the environment in which regulatory regimes were introduced in Latin America, since high-incentive regulation was introduced at the moment of privatization. Besides, the goals of regulators, the laws behind the reform processes, and relevant histories all vary across countries. Therefore, empirical findings regarding the impact of different regulatory regimes on efficiency must be interpreted with great care.

Taking the above caveat in mind, labor productivity analysis provides the following main results: (i) that private firms operating under price cap and hybrid schemes are more labor efficient than both public firms and private firms under rate of return regulation; (ii) that firms regulated under hybrid regimes have intermediate efficiency levels; and (iii) that private firms operating under rate of return regulation have, at most, similar labor efficiency as public firms. Result (i) and (ii) are consistent with the prediction from theory and with the findings of similar studies conducted for electricity distribution and other sectors.

Overall, the empirical evidence presented here does not provide definitive conclusions about the effects of privatization and incentive regulation on the efficiency of electricity distribution firms in Latin America in the period 1994-2001 since some important variables are missing from the analysis. For similar reasons, the evidence is also only partial on whether final customers have been benefited from the sector’s reforms but it does suggest that price changes from the viewpoint of users and efficiency gains are not as correlated as expected. The difference between users price and efficiency 24
Our estimates of the share of _useless water_ , _deeper water_ and _smoke_ in total _water_ are reported in Table 2. They suggest that around 28 percent of world’s tariff water is _smoke_ , and therefore does not represent truly available policy space. Around 75 percent of this is due to useless water (bounds above their prohibitive levels), and the remaining 25 percent due to _deeper water_ (the presence of preferential trade). The prominence of useless water is particularly noticeable among low income and high income countries where it represents close to 90 percent of _smoke_ . _Deeper water_ represents more than 60 percent of _smok_ e in middle income countries.

There are also some interesting differences across sectors. Around 67 percent of agriculture’s tariff water is _smoke_ , but only 18 percent in manufacturing. This suggests that there are some economic limits to the protectionist response in agriculture. There are also striking differences across countries. Smoke represents less than 5 percent of tariff water in Benin, Botswana, China, Ghana, Guinea-Bissau, Macedonia, Chad, Thailand and Uganda, but it is more than 50 percent of tariff water in Australia, Chile, the European Union, Cambodia, Mexico, Namibia, Norway, Venezuela and Zimbabwe.

In any case, the economic irrelevance of tariff water due to the presence of _smoke_ does not constraint in an economic significant way the protectionist response allowed by GATT’s bindings. On average the remaining policy space still represents 62 percent of tariff water. And given that tariff water is about twice the existing levels of MFN protection, there is enough policy space left to more than double MFN protection.

# **3. Assessing the protectionist response in previous crisis**

One important step on our way to assessing the protectionist response during the current crisis, is to look back and measure the protectionist response in previous crises (see also Eichengreen and Irwin, 2009). We concentrate ourselves on the recent past, with an _a priori_ that the creation of the WTO at the end of the Uruguay Round may have changed the scope for protectionist responses for member countries.<sup>8</sup> Thus, our sample spans from 1995 to 2008 and it includes all countries reported in Tables 1 and 2. We identify economic crisis as an annual decline in real GDP. During the

> 8 One could test this hypothesis using earlier data, but tariff line data is only available for a few countries before 1995.
last place of residence 12 months or 5 years ago, making it possible to look at the age of return for recent return migrants.

Figure 2 plots the age distribution of return migration in five countries, showing that migrants tend to return at fairly young ages. The median age of return migration is 24 in Mexico, 28 in South Africa, 30 in Pakistan, 31 in Albania and 34 in the Philippines. This includes the return migration of some individuals who migrated as young children, and does not tell us the duration of time spent abroad. Three of the household surveys also ask when migrants left their home countries, as well as when they returned, allowing calculation of durations of migration, and age of return conditional on leaving as a youth. The median (mean) time abroad for youth migrants from Mexico is 3 years (5 years); from Pakistan 4 years (6 years) and from Albania 7 months (19 months). The median age of return for youth migrating between age 12 and 24 in all three countries is 24 to 25 years. This shows that most youth who ever return, do so while still fairly young, meaning that they have many years of working life in their home countries.

# **9. Conclusions**

This paper shows that youth constitute a large share of the migrant flow, and that most return migrants are also relatively young. The rich descriptive detail provided in this paper offers a basis for understanding better the characteristics and experience of these youth migrants – which should be of interest to policymakers in both sending and receiving countries.

- 16 -
Table 11 presents the ATT estimates from comparison of matched Jalanidhi and KWA built and managed schemes for selected indicators.<sup>4</sup> The ATT estimates indicate that although for most of the constituent indicators of availability and reliability index Jalanidhi schemes perform better than the KWA schemes, the differences between them are not significant except for adequate water pressure in the network, no colored water, and no seasonal shortage. In the matched sample, 96 percent of the Jalanidhi schemes reported no colored water compared to 35 percent of the KWA schemes. Similarly, 49 percent of Jalanidhi schemes reported no seasonal shortage and 64 percent of Jalanidhi schemes reported adequate pressure in the network. The respective proportions for KWA schemes are only 4 percent and 15 percent. Jalanidhi schemes also have been rated consistently and significantly higher for all dimensions of household satisfaction. However, for satisfaction with water quality rating, the difference is only significant at the 10 percent level. There are no significant differences between Jalanidhi and KWA schemes in terms of household opinion regarding connection charges and water tariffs. Similarly, there are also no significant differences between these two types of schemes in terms of longest breakdown of the system or the percentages of schemes facing no water shortage anytime last year. However, Jalanidhi schemes experienced significantly lower number of breakdowns than the KWA schemes, whereas significantly higher proportion of KWA schemes carried out yearly testing of water quality compared to Jalanidhi schemes.

**Table 11: PSM Comparison of Jalanidhi with KWA Built & Managed Schemes: Selected** **<u>Indicators</u>**

|**Variable**|**Jalanidhi**|**KWA-**<br>**BM**|**ATT**|**SE**|**P-value**|
|---|---|---|---|---|---|
|**Availability & Reliability Index**||||||
|No Irregular Supply|0.62|0.41|0.20|0.23|0.379|
|No Low Pressure|0.64|0.15|0.49*|0.22|0.022|
|No Seasonal Shortage|0.49|0.04|0.45**|0.14|0.001|
|**Household Satisfaction Index**||||||
|Overall Satisfaction|3.5|2.9|0.64**|0.20|0.001|
|Regularity of Supply|3.4|2.5|0.97***|0.24|0.000|
|**Household Cost of Service Index**||||||
|HH Opinion on Contribution|2.1|2.4|-0.29|0.32|0.362|
|HH Opinion on Tariffs|2.1|2.0|0.05|0.19|0.813|
|**Operation & Maintenance Index**||||||
|Yearly Analysis of Water Quality|0.28|0.69|-0.41+|0.24|0.082|
|Number of Breakdowns Last Year|3.4|8.7|-5.3**|1.93|0.006|
|Scheme Facing No Water Shortage|0.60|0.39|0.20|0.23|0.367|
|� p�0.1, *p<0.05,**p<0.01, *** p<0.001||||||

> 4 ATT estimates for full set of indicators for both Jalanidhi and KWA-BM comparison and Jalanidhi-other community managed schemes comparison are presented in the Appendix (Table A3 and Table A4 respectively).

18
the average relative quality of entering goods generated by quantile regressions of entering goods on the OECD regional dummy, with the non-OECD countries captured together by the constant term. To the extent that we expect newly entering goods to be higher quality goods, the median values for both OECD and non-OECD groups are surprisingly low; the median value for the non-OECD countries is 0.35 and for OECD countries is 0.55. This is consistent with our earlier discussion concerning the high levels of heterogeneity in relative product quality in goods in both OECD and non-OECD baskets. However, it does not appear to be the case that the low quality goods in the OECD basket are merely reflecting inertial persistence of products fundamentally now out of their cone of specialization. Even at entry, there appears to be a very high level of heterogeneity. As Table 3 also indicates, the distribution of relative quality of entering goods in the OECD dominates that of the non-OECD countries at all levels, that is to say that at every quantile, the quality levels of entering goods in the OECD is higher than in the non-OECD countries.

This does not imply that the quality of incoming goods is not higher, on average than incumbent goods. Table 4 repeats the previous analysis, but using the ratio of the relative quality of entering goods to the median relative quality of the incumbent goods. The median ratio for the non-OECD regions is 1.03 and the median ratio for the OECD is equal to 1.05. This implies that new goods enter at only slightly higher level as existing goods for both the OECD and the non OECD regions. Given that OECD goods are of higher relative quality to begin with, this also tells us that new goods enter at higher quality levels in the OECD than in the poorer regions. However, at the upper end of the distribution, the quality ratio of entering to incumbent goods is larger for the non-OECD region suggesting a degree of convergence at the top end of the quality distribution between OECD and non-OECD countries.<sup>4</sup>

> 4The range of goods found at this end of the distribution is large and includes HS 7213990060 (Bars and Rods/non-alloy Steel Hot-Rolled 19mm) from Taiwan, HS 3808305000 (Herbicides) from China, HS

12
the informal enterprise surveys is that they employ the same methodology and the same survey instruments across countries. The resulting data set where informal firms across 12 economies over 3 continents can be compared due to similar methodology is a rarity and ideal for this study.

# **3. Empirical Specification**

Informal firms may lack resources to offset inadequate infrastructure, and thus we expect water shortages to affect their output. Informal firms also tend to be labor intensive and thus water shortages may have a direct effect on labor productivity. It should be noted that informal firms operate in a very different environment than formal firms. Informal firms typically do not maintain any records and may even operate on a day-to-day or seasonal basis. Therefore, most survey questions pertain to the last month of activities. Furthermore, the lack of records implies that several covariates that typically enter a productivity analysis estimation are impossible to be directly measured, but can be approximated using information about firm characteristics. We follow the literature and estimate the following equation using OLS (Amin and Islam, 2015).

��������� ����������� ���������� � ������ ������� ������� �������� � ���� �1� Where ������ is the log of sales per worker of firm _i_ in region (within-country) _r_ , and country _j_ over a typical month. Firm size is captured as the log of total number of employees (����) over a typical month. The details of how sales and employment data for a typical month are calculated are as follows. The survey questionnaire asks firms to report their sales and employment figures for the last month. If these figures for the last month were abnormal then the firms report the sales and employment figures for a typical month as well. Thus, to account for factors such as the seasonality of the business, for the variables ������ and ���� we use the values for the last month only for firms that indicate last month was a normal month. For firms that indicate that the last month was not normal we use the estimates for a regular month 8
# **Indicator of impact**

The cost of moving goods across borders is now as important as tariffs in determining their landed cost; an important element in such cost is the time goods spend in transit. The vertical division of production across countries and the introduction of just-in-time inventory systems have added to the importance of such improvements. For seasonal or fashion goods, cost of delay is more than the financing of inventories. Untimely delivery can significantly diminish the value of the products to the distributor hence improvements in trade facilitation can have a significant impact on trade. A recent study by Wilson, Mann and Otsuki (Wilson, Mann, Otsuki 2005b)quantified the conceptual experiment of improving trade facilitation processes in countries where they are least efficient. They estimated the impact of moving ‘below-average’ countries ‘halfway’ to the global average of four country-by-country indicators of (1) port efficiency, (2) customs environment, (3) own regulatory environment and (4) use of information technology.

Overall, they estimate that world trade would increase by about 10 percent. Their work also provides interesting details, e.g., Sub-Saharan Africa’s exports would be affected more by their own reforms than by those of their trading partners. Regarding domestic inefficiencies in trade facilitation processes, results show that they are a significant drag on realizing any countries' export potential.

# **How improvements are made: examples**

An important operational difference between the trade effects of tariffs and of the quality of trade facilitation is that the measures of trade facilitation such as Wilson, et all, used are not direct measures of the factors that affect trade. They are indicators of such measures. If the trade barrier in question is the tariff rate, change is accomplished by lowering the rate. Improving the “Customs environment” might take many forms – dealing with corruption, providing for electronic data input, improving physical security in customs houses, etc. The measure of “customs environment,” for example, is from a survey of customs users who ranked countries on a scale, taking into account and weighing in an unspecified way the various elements that influence the customs environment.

Because improvements in trade facilitation can take many forms, examples of actual improvements provide a better sense of what improvement involves than do generic statements. We will thus provide several such examples.

<u>Taiwan(China) express shipments</u><sup>27</sup> Taiwan(China) in a submission to the WTO Negotiating Group on trade facilitation provides useful information on its establishing a mechanism for the speedy clearance of express consignments – this because "just-in-time" and "zero inventory" management techniques have become a critical element in

> 27WTO Negotiating Group on Trade Facilitation, Communication From The Separate Customs Territory Of Taiwan, Penghu, Kinmen And Matsu, document /TF/W/44, 8 June 2005.

23
Table 9: Estimates by individual agricultural product

|Dep. variable|ln simpl|e tarifs|ln weigh|ted tarifs|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ln rain_jt_|0.034|0.015|0.082|0.062|
||[0.076]|[0.074]|[0.093]|[0.092]|
|ln rain_jt ∗_live animals|0.118|0.137|0.306|0.326*|
||[0.114]|[0.115]|[0.184]|[0.182]|
|ln rain_jt ∗_meat products|0.286***<br>[0.074]|0.305***<br>[0.075]|0.507**<br>[0.195]|0.526***<br>[0.194]|
|ln rain_jt ∗_dairy products and birds eggs|0.262**<br>[0.114]|0.281**<br>[0.118]|0.536***<br>[0.159]|0.555***<br>[0.161]|
|ln rain_jt ∗_cereals|0.320***<br>[0.062]|0.339***<br>[0.066]|0.348***<br>[0.088]|0.368***<br>[0.092]|
|ln rain_jt ∗_vegetables and fruit|0.296***<br>[0.059]|0.315***<br>[0.062]|0.336***<br>[0.090]|0.355***<br>[0.094]|
|ln rain_jt ∗_sugar and honey|0.301***|0.320***|0.354**|0.374**|
||[0.072]|[0.077]|[0.148]|[0.151]|
|ln rain_jt ∗_cofee_,_ tea, cocoa and spices|0.245***|0.264***|0.266***|0.286***|
||[0.062]|[0.063]|[0.092]|[0.094]|
|ln rain_jt ∗_oilseeds and oleaginious fruits|-0.015|0.004|0.045|0.064|
||[0.085]|[0.084]|[0.108]|[0.110]|
|ln rain_jt ∗_feeding stuf for animals||0.001||0.205|
|||[0.125]||[0.151]|
|ln rain_jt ∗_miscellaneous edible products||0.252***||0.256***|
|||[0.065]||[0.078]|
|ln rain_jt ∗_beverages||0.335***||0.126|
|||[0.075]||[0.150]|
|ln rain_jt ∗_tobacco||0.292*||0.379**|
|||[0.159]||[0.151]|
|ln rain_jt ∗_crude animal and vegetable materials||0.180*<br>[0.102]||0.127<br>[0.118]|
|Observations|41,043|41,043|40,606|40,606|
|Number of countries|70|70|70|70|
|Number of products|65|65|65|65|
|R-squared|0.574|0.576|0.404|0.405|

The estimation method is OLS. Each model includes product, country and year fixed-effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 6
# **The Business of Product Innovation: International Empirical Evidence**

Daniel Lederman<sup></sup> The World Bank Keywords: innovation, firm performance, multi-level analysis, contextualization

>  Lederman is Senior Economist with the Development Economics Research Group of the World Bank. Address correspondence to: <u>dlederman@worldbank.org, 1818 H Street NW, Washington, DC 20433; (t) 202-473-9015, (f) 202-522-1159. The author</u> gratefully acknowledges the impeccable research assistance provided by Daniel Chodos and Javier Cravino. Roberto Alvarez, Bruce Blonigen, Pablo Fajnzylber, Rodrigo Fuentes, Alvaro González, José L. Guasch, J. Humberto López, Marcelo Olarreaga, Arjen Van Witteloostuijn, and two anonymous referees provided insightful comments on earlier drafts. Financial support from the World Bank’s Latin America and Caribbean Regional Studies Program is gratefully acknowledged. The views expressed in this paper do not represent the views of the World Bank or of its member countries. All remaining errors are the responsibility of the author.
Figure 4: The impact of the decile of consumption on the economic standing <!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10<br>Decile of consumption<br>Responses 1/2 Responses 3/4<br>Responses 5/6 Responses 7/8<br>Responses 9/10<br>.6<br>.4<br>.2<br>Probability of responding<br>0<br><!-- End of picture text --> Note: LiTS data from 2005, 2010 and 2015. The y-axis represents the average adjusted predictions of economic standing for each decile of consumption. Responses on a 10-step scale are aggregated to a 5-step scale for presentational purposes having been employed at least one month in the previous year increases the probability of respondents to position themselves in the middle steps of the economic ladder and decreases the probability of positioning in the bottom, while no effect is visible for the probability of positioning in the top. A sense of the magnitude of this effect can be given by the fact that the probability of choosing the steps 1 and 2 of the ladder for someone in decile 7 with no employment is the same as for someone in decile 4 with full employment - that is, the effect of not having an employment on the probability of “feeling poor” is equivalent to a change in three deciles of objective consumption.

The effect is stronger when looking at the (self-rated) health status as shown in figure 6. For the same objective level of consumption, individuals who declare to be bad health have up to 10 percentage points more probability of ranking themselves in the bottom of the ladder than those who declare to be in good health. The effect is of the opposite sign and larger magnitude for the probability of ranking themselves in the middle of the ladder: respondents declaring to be in good health are 20 percentage points more probable to position in the middle than those declaring to be in bad health. Lastly, figure 7 look at the effect of having a university degree on subjective ranking. As for the case of having worked and feeling in good health, having a university degree increases the probability of choosing the middle steps of the economic ladder and decreases the probability of choosing 20
36

**Table 10  Investment Restrictions** **<u>on Pension Funds</u>**

||Quantitative Restrictions on single Asset<br>Classes<sup>33</sup>|Restrictions on Issuers|
|---|---|---|
|100%<br>75%<br>50%<br>30%<br>30%<br>20%<br>25%<br>5%|Accounts receivables related to Swiss<br>issuers<br>Real Securities<br>Real estate (Swiss) and investments in<br>real estate<br>companies (with at least 50% invested in<br>Switzerland)<br>Swiss shares<br>Accounts receivables related to foreign<br>issuers<br>Currencies and convertible currency<br>receivables<br>Foreign shares<br>Foreign real estate and investments in<br>real estate<br>companies (with at least 50% invested<br>abroad)|15% except for issuers as<br>The Government, Cantons,<br>Banks or Insurance Comp.<br>10% Per company<br>5% Per issuer<br>5% Per debtor<br>5% Per company|
||Quantitative Restrictions on the total of<br>invested capital<sup>34</sup>||
|100%<br>70%<br>50%<br>30%<br>30%|Cash and accounts receivables that are<br>related to cash<br>Real estate, Shares and other Securities<br>Swiss and foreign shares<br>Accounts receivables related to foreign<br>issuers and<br>currencies and convertible currency<br>receivables<br>Currencies and convertible<br>currencyreceivables and foreign shares||

Source: Bundesamt für Sozialversicherungen Autonomous pension funds are allowed to have a temporary under-funding (c.f., Section 5.4). The missing implementation regulations are thus not crucially important in the short run. On the other hand, life insurance companies are not allowed to have any underfunding. They are subject to more complex capital requirement rules.

The capital regulations of life insurance companies comprise general rules, which are legally binding for all life insurance companies, and specific rules for those which act in the realm of old–age provisions. The specific rules are listed in Section 3.5. The main feature of the so–called transparency standards is that life insurance companies must build up a separate _Security Fund_ for their liabilities related to old–age provisions (see below). Another important new feature is the asset segregation rule, which requests the separation of assets belonging to the pension funds from other assets.

> 33c.f. Art. 54 BVV

> 34c.f. Art. 55 BVV