rafmacalaba/datause-encoder-data huggingface dataset:
state governments.

The ‘Unlock’ phase of containment policies commenced from June 1, 2020. Several restrictions were eased, and primary rule-making authority devolved to state governments. Thus, our analysis compares the severe restrictions which were uniform across the country in March and April 2020, to the time after May 4 when restrictions varied by district, until July 2020.

# **3 Data**

We combine multiple sources of district-level information on nighttime light intensity, household consumption and income, mobility, and district-specific characteristics. This information is merged with the government’s district level zonal classification and COVID-19 infection data. We use 2020 district boundary classifications to match how zonal containment policies and infections are reported.

We extract district level nighttime light data from the VIIRS-DNB Cloud Free Monthly Composites (version 1) provided by the Earth Observation Group at Colorado School of Mines. Due to a wider radiometric detection range and onboard calibration correcting for saturation and blooming effects, these data are more comparable over time than previous nighttime light products. However, the monthly composite still includes some temporary lights like fires and gas flaring and filtering out background noise strengthens the relationship of nighttime lights and economic activity. Following Beyer et al. (2020), we hence consider only lights outside a background noise mask. For the latter, we identify different clusters by removing outlier observations, averaging cells over time, and clustering areas based on their nighttime light intensity.<sup>4</sup> In practice, this approach amounts to setting to zero cells that are distant from homogeneous bright cores. The adjusted monthly data are aggregated to the district level and standardized by area.

We access mobility information from Facebook Data for Good,<sup>5</sup> which is based on individuals who use Facebook on a mobile device, provide their precise location, and are observed for a meaningful period of the day. Facebook quantifies how much people move around by counting the number of level-16 Bing tiles (approximately 600 meters by 600 meters area) they are seen in within a day, with the idea that people seen in fewer tiles are less mobile. The specific metric calculates the percentage of eligible people who are only observed in a single tile during the course of a day, and hence represents small distance mobility.<sup>6</sup> We aggregate Facebook’s tile-level information to match contemporary Indian districts, then invert and standardize the metric.

> 4 The same procedure has been used to aggregate the monthly composite to a clean annual composite (Elvidge et al. 2017). 5 https://dataforgood.fb.com. 6 Maas et al. (2019) provide more details about these data.

6
# D. Capacity of SOEs to adapt to structural changes

**Can Belarus’ SOEs adapt to the challenges of facing more competition in export markets as well as in the domestic economy?** Two facts shed doubts about SOEs capacity to adapt: (i) the response of production to fall in demand during 2008-2009, and (ii) slow technological innovation. Neither of these facts is proof of the impossibility of SOEs to adapt, but each illustrates the difficulties to do so in the face of critical shifts in market conditions.

**The response of private firm production to the fall in demand in 2008-2009 was markedly different than that of SOEs** . Figure 1.10 documents this fact.  For private firms there is a negative correlation between profits and productivity, for SOEs this correlation is positive.  While a fall in demand results in lower sales and a fall in profits for either of the two groups, private firms shed labor and capital resources and cut production therefore rising productivity (hence the positive correlation);<sup>19</sup> at the same time, SOEs do not shed labor and hence productivity falls at the same time that profits fall (hence the negative correlation).

Figure 1.10 Correlation between changes in productivity and profitability <!-- Start of picture text --> 0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>-<br>(0.10) 2006 2007 2008 2009 2010<br>(0.20)<br>(0.30)<br>State enterprises Private entrprises Mixed with state control<br><!-- End of picture text --> Source: Authors estimates based on the NBRB dataset.

**Why did SOEs respond so slowly to the fall in demand of 2008?  The short answer is that management of SOEs is required to meet a variety of quantitative targets rather than only a financial bottom line** .<sup>20</sup> As a result, SOEs did not cut supply sufficiently, accumulated inventories (which increased about 50% between 2008 and 2009, Figure 1.11), and increased their debt to banks (see the evolution of SOEs debts in the third line of Table 1.3).  This was especially the case for those SOEs where exports are a high share of sales. Government policy was aimed to mitigate the negative consequences of enterprise adjustment during the crisis, but such policy may have merely postponed the adjustment of production.

> 19 The reason this is true is that there are decreasing returns to scale in production.  This implies that shedding labor results in an increase in productivity of those employed.

> 20 While it is admittedly simplistic to assume that managers of private firms only pay attention to the bottom line or financial result before they adopt employment decisions it is clear that the room for maneuvering they have is way smaller than that of a firm less constrained by their financiers.

14
Annex - 1 Bangladesh: Major Water Sector Organizations and Related Agencies - Current Functions and Deficiencies

|Organization|Current Function|Major Deficiencies/Problems|
|---|---|---|
|Planning Commission|.<br>Establish multi-sector<br>investment priorities.<br>* Recommend allocation of<br>resources.|.<br>Inadequate personnel<br>resources.|
|National Water Council|*<br>Approval of national<br>water policies.|.<br>Low frequency of<br>meeting and inadequate<br>service support from the<br>Water Ministry.|
|WARPO|.<br>Collection and supply of<br>hydrology, and other<br>water data.|.<br>Water policy formulation.<br>.<br>National water planning.<br>.<br>Monitoring|
||* Preparation of reports on<br>major water programs.|.<br>Formulation of water<br>legislation and<br>regulations.<br>.<br>Intersectoral coordination<br>of water plans.|
|||.<br>Central data system.|
|RRI|*<br>Surface water modeling.<br>.<br>River training studies|<br>.<br>Mathematical modeling<br>including environment<br>and surface/ground<br>water.<br>* Inadequate Funding|
|SWMC|.<br>Mathematical river<br>modeling<br>* Flood management<br>modeling<br>* Irrigation system<br>modeling<br>.<br>National and regional<br>modeling<br>* Environmental modeling<br>.<br>Survey and data<br>collection<br>.<br>Development of a<br>national hydrological data<br>base.|.<br>Continued existence after<br>FAP.|

38
_Source: World Bank, World Development Report 2005._

# **3. The Challenge: Reducing Current High Logistics Costs in Latin America**

A complex logistics system, composed of transport infrastructure and services, business logistics practices and trade facilitation procedures, is responsible for the physical flows **.** Several studies analyzed the link between competitiveness and the physical flow of goods [World Bank 2005, 2006, 2007], concluding that three major areas have to be dealt with in order to optimize the flow of goods throughout the logistics chains: (a) transportation, (b) business logistics, and (c) trade facilitation. This conceptualization of the factors involved in the flow of goods makes clear that the analysis and policy options should not be limited exclusively to infrastructure bottlenecks (infrastructure being considered the hard component of logistics) but should also consider the rules and procedures regulating the services (soft component). Thus, the performance of a country’s logistics system depends on the activities of both the public and the private sectors. Figure 2 shows the impact that the supply of infrastructure, rules and regulations, and the performance of the private sector have on each of the activities defined. It also makes it possible to appreciate the diversity of instruments, both public and private, that converge to define the efficiency of the logistics system.

6
|Sub-Saharan Africa||8.71|7.63|6.61|7.30|
|---|---|---|---|---|---|
|Upper middle income||||||
|East Asia & Pacific|5.99|4.35|3.56|4.36|4.90|
|Europe & Central Asia||||8.62|9.52|
|Latin America & Caribbean|6.93|12.75|11.80|10.20|10.25|
|Middle East & North Africa|2.34|4.01|9.33|13.20|12.90|
|Sub-Saharan Africa|3.67|5.08|5.83|4.80|6.64|
|High income||||||
|OECD|11.29|9.87|8.06|6.03|5.71|

# **2.2.1 Who invests the most in housing?**

Using the full sample in our data set, we provide country rankings of housing investment (as percent of GDP in 2001-2011) in table 3 below. Not surprisingly, we find that small low and lower middle economies are among the top investors. Topping the charts is Kiribati (26 percent), followed by Panama (15.6 percent). Small economies such as St. Vincent and the Grenadines (14.3 percent), St. Lucia (13.1 percent) and Grenada (12.4 percent) are also among the top ten (Table 3). It is interesting to note that in SSA, low income countries such as Lesotho, Rwanda, Ethiopia, and Eretria, and middle income countries such as Mauritius and Namibia ranked among the top 30 for housing investments between 2001 and 2011.

**Table 3  Ranking of countries based on share of housing supply in GDP**

|||||Decades<br>||||
|---|---|---|---|---|---|---|---|
|Country|Incomegroup|1960-70|1971-80|1981-90|1991-00|2001-11|Rank|
|Kiribati|LIC and LMIC||||21.7|26.4|1|
|Panama|UMIC||||15.5|15.6|2|
|Iran, Islamic Rep.|UMIC|2.3|4.0|9.3|13.1|14.9|3|
|St. Vincent and the||||||||
|Grenadines|UMIC||26.3|20.7|17.6|14.3|4|
|Lesotho|LIC and LMIC||14.4|14.0|13.5|14.0|5|
|Argentina|UMIC||||14.5|13.9|6|
|Uruguay|UMIC|||13.1|13.8|13.3|7|
|St. Lucia|UMIC||7.5|7.0|7.6|13.1|8|
|Grenada|UMIC||25.0|20.5|16.0|12.1|9|
|Spain|HIC||||13.3|11.7|10|
|Jordan|UMIC||||13.3|11.3|11|
|Greece|HIC|32.8|35.7|29.1|14.0|11.1|12|
|Tonga|LIC and LMIC||||10.9|11.0|13|
|Montenegro|UMIC||||11.6|10.8|14|
|Brazil|UMIC|||9.6|9.5|10.2|15|
|Venezuela, RB|UMIC|||10.6|10.1|10.0|16|
|Dominica|UMIC||10.8|10.1|9.1|9.8|17|
|Norway|HIC|14.6|13.4|12.4|10.1|9.7|18|
|Philippines|LIC and LMIC|15.0|11.4|9.9|9.9|9.5|19|
|Mauritius|UMIC||8.8|8.8|7.5|9.2|20|
|Namibia|UMIC||5.3|5.6|5.4|8.9|21|
|Antigua and Barbuda|UMIC||12.6|10.6|9.1|8.9|22|
|Jamaica|UMIC|||5.4|5.8|8.7|23|
|Rwanda|LIC and LMIC||||8.9|8.7|24|
|Mozambique|LIC and LMIC||||12.9|8.5|25|
|Turkey|UMIC||||7.6|8.2|26|
|Ireland|HIC||||7.1|8.2|27|

7
for the Red River Delta region. Moreover, being initially a food crop producer in urban does not have a significant effect on change in household income. Changes in the structure of the income sources, namely changes in the share of food crop income, cash crop income and business do not provide a clear interpretation, potentially due to the correlation between these variables. Most of these results are intuitive and reinforce our earlier findings. However, as mentioned earlier, due to potential simultaneity with respect to change variables, we focus on the associations without pressing for causality.

# **5. CONCLUSION**

In this paper, we analyze the importance of different channels that link trade reforms and household welfare. Most studies capture only one, at most two, of these channels and hence the results are incomplete. Thus, analysis of the role of each link (even though indirectly) by looking at the data was the principal purpose. In addition, we investigate how different groups of households are affected from trade liberalization, using Vietnam as a case study for both. Using the panel component of 1993 & 1998 household surveys, we study the changes in welfare for different categories of households, and provide a decomposition of the growth by income sources.

Our results indicate that the growth has been broad-based and pro-poor. Poorer households experienced higher income growth for each and every category of households analyzed. In addition, net buyer households have gained more than net seller households, indicating that the first-order effects prevalent in the literature are not good representations of the overall effect. Furthermore, significant amount of switch between the two groups (net-seller and net buyer) indicates that net seller-buyer categories may not be appropriate to study the effect on households.

The decomposition of the growth in income indicates that the two most important links for rural households are the agricultural income (direct-effect) and non-farm employment (business income and wages). The direct effect is most important for netseller households and households that lived in South. The development effect, the spillover effect of changes in incomes through increased demand for local goods and services, proves to be a significant part of the growth. Business income and non-agricultural wages along with remittances were the driving forces for the growth experienced by households 28
socio-demographic characteristics to followers. Yet the study by Gibbons et al. (1980) documents the effective role of prominent and wealthier leaders as sources of information about innovations in rural settings. Sen (1969, p. 26) notes that “the direction of influence from leaders to followers is then vertical, from high to low strata of the village society.” Similarly, Bose and Saxena (1966) found that rural opinion leaders, who were identified by other farmers as the most important source for agricultural information, were more literate, more competent, and superior in socio-economic standing.

For the purposes of framing the analysis in this paper, we formulate, based on the discussion in the literature, three alternative propositions that can be tested empirically: _(a) People are more likely to seek and acquire information from those who are similar to them in various socio-economic respects._ _(b) People are more likely to seek and acquire information from those who have a higher status as opinion leaders, and who are thus superior to them in socioeconomic attributes._ A third proposition reflects the “optimal heterophily” concept and its implication that effective opinion leaders will tend to be superior to others, but within bounds.

_(c) People are more likely to learn from opinion leaders who are superior to them in socio-economic aspects, but who are not too differen_ t _._ These propositions will be tested using data pertaining to the introduction of integrated pest management (IPM) knowledge in Indonesia through an intensive training program to selected farmers. As will be argued, these farmers were typically selected for their opinion leadership status or potential. Analyzing the extent to which other farmers’ knowledge of IPM relates to the opinion leaders (who received intensive IPM training) in their communities will allow insights on the veracity of the three propositions.

7
_8_ _perhaps positively through generating the demand whlch can provide the burst of acceleration in growth rates?_ _Complex of_ _Complex of MR. OHLIN: I would like to organize my comments under Investment three headings to make it simpler rather than to follow the [Boltho] Technology and paper as a wnole. Those would be, first of all, the complex of Structural investment and technology and structural change; secondly, the role Change of policies; and thirdly, the international system and its imbalances._ _Let us start then with growth and our understanding of it. There is, in the paper, a devastating survey of growth theory and attempts to understand growth in the past. It makes it perfectly clear how little consensus there really is on the interpretation of historical growth, but also, for that matter, on the post-war Golden Age. The author cites a great many divergent views._ _He may have gone a bit too far in the assumption that all these views are borne equal, and there might be a bit more consensus about what the important ideas are and what th'e less important ideas are. Nonetheless, the basic impression of great intellectual confusion prevails._ _This must be of particular importance for those of us in international organizations who are supposed to know and are expected to tell countries what they can do to get more growth. The fact is that no one knows very much about it, and thi: ought to make for a great deal of humility._ _Let me return to the assertive rather than to the destructive aspects of the paper. If I understand Boltho correctly, he says that in generally stable conditions, with an ample supply of labor and other requirements met -- including the supply of entrepreneurs -- growth should be pretty automatic if investment is incited by expectations. And as far as the irregularities that we have observed in the past go, they should be understood to have resulted from disruptions, some of which might stem from economic policy._ _This is pretty close to a neo-classical approach. The difficulty I have with this is over the approach to the nature of growth and the sources of growth. One should not, it seems to me, dismiss technology as assiduously as Boltho tries to do. He raised the question of technology, but he nonetheless concludes that one can afford to disregard it._ _I think he is rightly ridiculing the fatalistic approach to Kondratieff, but I think that is flogging a dead horse. Nonetheless, when Schumpeter named the Kondratieff cycle, there was more to it than just reference to Kondratieff._

**WP/PROCISF:Jsq**
-2 -

# Alternative Approaches

There is not a great deal of theoretical literature that can guide the analysis of schemes such as mass privatization. Conventional auction theory, while somewhat relevant, is based on situations where the demand curve, often for a homogeneous product, is unknown and the auctioneer is concerned with revealing the demand curve to maximize revenue. The focus of much of the literature has been on the trade-offs associated with open versus sealed bid procedures, uniform versus discriminatory price rules, sequential versus simultaneous auctions, and the risk of collusive behavior under different schemes.<sup>2</sup> In a very interesting study of the determinants of privatization prices for enterprises auctioned by the Mexican Government, Lopez-de-Silanes analyzes the impact of company performance and industry parameters, the auction process, and the prior restructuring actions taken by the state on net governrment revenues from privatization.<sup>3</sup> He finds that higher slaes prices tend to be associated with the speed of the process, labor shedding, firing of the chief executive officer of the enterprise and deinvestment, while negative premiums result from large obligations to the labor force and minority control packages. Prior restructuring by the government, debt absorption, new investment or performance improvement programs have no beneficial impact on ultimate government revenues from privatization auctions. In the case of mass privatization considered here, the nature of the auction is rather special. The aggregate demand (coupons) and supply (shares)

- 2/ For surveys of this literature, see 3.L. Guasch and T. Glaessner (1993), 'Using Auctions to Allocate and Price Long-Term Credit," The World Bank Research Observer volume 8, number 2, July. R. Fedman and R. Mehra (1993), 'Auctions: Theory and Possible Applications to Economies in Transition," IMF Working paper 93/12, February. Feinstein, J. and M. Block (1985), 'Asymmetric Information and Collusive Behavior in Auction Markets.' American Economic Review. volume 75, June. R. Hansen (1985), 'Empirical Testing of Auction Theory." Amerizan Economic Review. volume 75, May. E. Maskin and J. Riley (1985), "Auction Theory with Private Values," Ainerican Economic Review, volume 75, May.

- 3/ Lopez-de-Silanes, Florencio (1994), "Determinants of Privatization Prices, rmimeograph, Harvard University, Cambridge, MA, January.
cases, not compatible with the nine-to-six workdays. In fact, most of the school systems in developing countries work on a half-day basis.<sup>2</sup> Female labor participation has experienced significant growth in Chile. The rate of female participation increased from 35 percent in 1990 to 49 percent in 2011 (in women from 15 to 65 years old). However, the situation of women in the Chilean labor market is still poor, particularly for women with lower income levels. Women who belong to the poorest 20 percent of the country’s population present a participation rate of 32 percent , while those from 20 percent of the wealthiest households achieve rates of 67 percent.<sup>3</sup> Female labor participation in Chile has been relatively late and is one of the lowest in Latin America in spite of the comparatively high levels of education that Chilean women achieve.<sup>4</sup> There are important reasons to study determinants of female labor supply in a developing country. First, the access of women into the labor market should have positive effects for the most vulnerable families as it represents a new source of income for these households and thus could lead to a reduction in poverty (Ganuza et al. 2001). Second, despite the fact that greater opportunities have emerged for women, there are still significant gaps between the participation of men and women in all areas and discriminatory cultural patterns persist (Antecol 2003; Araújo and Scalon 2005; Contreras and Plaza 2010).

> 2. However, there are various initiatives that promote the implementation of the full school day. In Latin America, Chile, Argentina, Uruguay, and Venezuela has promoted different policies to expand the school day. 3. Chilean Socio-Economic Characterization Survey (CASEN) 1990–2011.

> 4. According to ECLAC statistics on gender, education, and training for women (2007), economically active women (15 and over) in urban areas in Chile have an average of 11.6 years of schooling, placing them second in South America behind Argentina, which presents an average of 11.8 years. The average years of schooling for women living in rural areas of Chile is 9.3, placing the country first in South America for education levels of rural women.

3