rafmacalaba/datause-encoder-data huggingface dataset:
be equal to the difference between the opportunity cost per unit of land used and the opportunity cost of water used in the second stage on that land. As clearly seen, the higher the higher and the lower are constants.

Assuming that the information related to all parameters of the model is available we can use (probability of the water flow) to calculate the optimal solution. The probability distribution of the water flow is provided in Figures 1.6 and 1.8 for the Zambezi and 2.6 and 2.8 for the Mekong. The results suggest that the mean flow (range) is 750 100 and 1900 200 m<sup>3</sup> /second/month for the Zambezi and Mekong, respectively. Therefore, the probability of having below mean, mean, and above mean flows respectively is .65, .15, and .20 for the Zambezi, and .30, .20, and .50 for the Mekong. These values can be used in an empirical analysis to evaluate the impact of stochastic flow on the economy of each of the basin states.

Flow probability information provides valuable information to policy makers. Nonetheless, the nature of a treaty and its institutional content are important to support the resilience of the riparian countries dealing with extreme flow events. Extreme flow events produce economic losses, hardships, and damages, which are reflected in grievances and complaints by or cooperation among the basin riparians. How can the basin riparian states prepare better treaties to address such possible deviation from the mean flow? What kind of institutional arrangements could be put in place to address such events? Are treaties ̳equipped‘ with such regulations and are institutions more resilient? In an effort to answer at least one of the above questions with implications for the other queries posed we review historical political events related to river flows (droughts and floods) identified by the BWI. In this context, we also highlight BWI‘s utility for research on conflict and cooperation over water.

# **BWI’S VALUE FOR RESEARCH ON CONFLICT AND COOPERATION OVER WATER**

The river flow data provided in the BWI provide hydrological and physical data required to investigate the links between water scarcity/variability and conflict and cooperation over water. To date, scholars have used national measures of water availability per capita as well as precipitation and drought to explore how physical water scarcity affects inter-state conflict (Toset et al. 2000; Hensel et al. 2006; Gleditsch et al. 2006). A similar methodology has been 20
reforms. However, the policies do explain the gains in average productivity, which was the more important driver of aggregate productivity growth during this period.

# **4.5 Firm-Level Regressions**

We now use the constructed panel to examine the results on average productivity in more detail. As discussed above, the average productivity measure we use can increase either because existing firms increase their productivity, or because less productive firms exit, or more productive firms enter.

The panel allows us to explore this issue by isolating within-firm productivity improvements. We estimate the following equation at the firm level: We use a fixed-effects estimator, which allows us to identify within-firm changes in productivity.

Table 9 presents results. In Column (1), we include all firms that were used in the state-industry level analysis (the estimated population). This specification includes industry and year dummy variables. As we would expect, the coefficients on the policy variables are similar to the average productivity results at the state-industry level.

Column (2) presents results for firms that appear in the panel for at least two years, without sampling multipliers. This specification includes industry and year dummy variables, but not firm fixed effects, and indicates that the results for the sample of firms that are in the panel are similar to the overall results. Column (3) controls for a number of firm characteristics, and shows that public firms and young firms (less than 3 years old) are relatively unproductive. We also created dummy variables for firms in three size categories: <20 employees (small), 20-99 employees (medium), and 100+ employees (large, the omitted category). The results indicate a positive correlation between size and productivity.

In Column (4), we present results with firm fixed effects, thus isolating within-firm changes in 23
that influence the effect of household tobacco adoption during the shock period should also be present in the period of price stability. The difference in coefficients produces a “difference in difference” type identification. Hence, the fortified approach of comparing TSLS estimates obtained from by two unique samples of children that differ in terms of exposure to the shock of interest should help remove any remaining within-district unobserved heterogeneity that might otherwise plague our estimates. Further examining household summary statistics from an exposure to shock perspective, table 2 hints at the influence of cash crop production during times of food price shocks. Tobacco-producing households are more dependent on the price of nutrition than maize-producing households. Periods of short supply are likely to increase the price of nutrition significantly.

# **5 Results**

Table 3 presents the overall OLS and TSLS estimates of the effect of household tobacco adoption in the year of or the year after child birth.<sup>24</sup> The OLS estimate confirms the descriptive relationship previously documented by World Bank (2007). The TSLS estimate of the overall effect indicates the existence of positive correlation between _D_ and _ε_ in equation 1 (i.e. positive omitted variable bias) that would have otherwise plagued the main coefficient of interest. The negative, TSLS-informed overall relationship between the endogenous variable and the outcome is seen in a different light in table 4 when the sample is split in accordance with the shock within the early development window and the regression is estimated separately for each sub-sample.

The results in table 4 are central to our fortified identification strategy that relies on TSLS in the context of child exposure to an exogenous staple food price shock. We find that the tobacco coefficient of interest, while negative, is no longer statistically significant among children that did not experience the shock. Conversely, children who experienced the shock within their early development window and originated from households producing tobacco in the year of or the year after child birth were significantly more likely to be stunted. Given our theoretical arguments in section 4.2, and the differences in the tobacco coefficients reported in table 4, the average causal effect of household tobacco adoption on child height-for-age z-score during staple food price spikes is estimated at -1.27, which is statistically significant at the 1 percent level and implies a 70 percent 24In tables 3 and 4, the standard errors are clustered at the EA-level and the regressions are weighted using the IHS2 household sampling weights. The results are consistent with or without the use of the sampling weights.

17
Page 46

# **Table IV.6 Cost Comparison with marginal flexibility in the GCDR**

|**DESCRIPTION **|**AS PERGDCR **|**AS PER REVISEDGDCR (G+4)**|
|---|---|---|
|PLOT AREAsqft|81681|81681|
|NO OFUNITS|330|710|
|CARPET AREAsqft|250|250|
|BUILT UP AREA sq ft|336|305|
|LAND COST in Rs.M. (plot 11km from<br>CBD in undeveloped area)|40,000,000|40,000,000|
|NO OF FLOORS|G+2|G+4|
|TOTAL BUILTUP AREA(SQ.FT.)|110250|223440|
|LANDCOST PERSQFTCARPET AREA|485|225|
|CONSTCOSTpersqft.|800|800|
|LAND COST PER UNIT|121212|56338|
|CONSTRUCTIONCOST PERUNIT|268800|244000|
|**TOTAL COST PER UNIT Rs.**|**390012**|**300338**|
|**SAVINGS W/ REVISED MODEL PER**<br>**UNIT**||**23%**|
|**LAND PRICE 5 X HIGHER**||**40%**|
|**LAND PRICE 10 X HIGHER**||**45%**|

To address the affordability problem of the majority of lower income households in Ahmedabad, it would make sense to study the land use standards that have been used by various developers in the past and select and adapt the types of layout that are more attractive from environmental and economic criteria. These models could become allowable under an expanded GDCR that offers different models of building by- laws in different parts of the city. Obviously, the type of layout to be recommended for legalization in a central location would differ from layouts adapted to suburban locations. The current legal land use standards are very rigid, and are ill adapted to high land prices. By contrast, many vernacular subdivisions built under more flexible regulations have been refined over time as developers respond to consumer tastes and preferences. By surveying the land use of these older subdivisions and the environmental satisfaction of its inhabitants, it would be possible to develop standards that are more anchored in the urban culture of Ahmedabad and less prisoner of abstract international norms of good housing standards. **A policy of land use regulations that make legal formal housing unaffordable to about 40% of the population of Ahmedabad must be reconsidered. Our review of the housing stock in Ahmedabad suggests that good home grown alternatives exist and deserve a place amongst models allowed in the GDCR.**
Table 1 presents the Trade Intensity Index (TII) (Drysdale and Garnaut 1982), which indicates whether the value of trade between two countries is greater or smaller than would be expected given their relative importance in world trade by the sectors used in the analysis.<sup>3</sup> Despite the crudity of this measure—it does not take into consideration factors such as transport cost and other characteristics—the index gives some preliminary indication as to how trade (both imports and exports) of both partners may be reduced by the presence of trade barriers. A value of TII greater than one suggests that country _i_ exports to county _j_ more than would be expected and vice versa.

Specifically, TIIij = [Xij/Xi]/[Mj/(Mw – Mi)] Where Xij is country _i_ ’s exports to country _j_ Xi is _i’s_ total exports Mj is _j’s_ total imports Mi is _i’s_ total imports, and Mw is _total world imports_ Mi is subtracted from Mw because a country cannot export goods to itself.

> 3 Throughout the paper, the 57 GTAP sectors are aggregated into 13 more manageable categories. Agriculture (“paddy rice”, “wheat”, “other grains”, “veg & fruit”, “oil seeds”, “cane & beet”, “plant fibres”, “other crops”, “cattle”, “other animal products”, “raw milk”, “wool”, “forestry” and “fishing”); Processed agriculture (“cattle meat”, “other meat”, “vegetable oils”, “milk”, “processed rice”, “sugar”, “other food” and “beverages & tobacco”); Coal, oil, gas and other mining (“coal”, “oil”, “gas” and “other mining”); Textiles and apparel (“textiles” and “wearing apparel”); Other light manufacturing (“leather” and “lumber”); Paper and mineral products (“paper & paper products” and “non-metallic minerals”); Chemical, rubber and plastic products (“chemical & rubber products”); Petroleum and coke products (“petroleum & coke”); Iron, steel and metal products (“iron & steel”, “nonferrous metals” and “fabricated metal products”); Transport Equipment (“motor vehicles” and “other transport equipment”); Electronics and machinery (“electronic equipment” and “other machinery & equipment”); Other manufacturing (“other manufacturing”); and Services (“electricity”, “gas distribution”, “water”, “construction”, “trade”, “water transport”, “air transport”, “other transport”, “communications”, “other financial intermediation”, “insurance”, “other business services”, “recreation & other services”, “other services (government)” and “dwellings”). Between parentheses are the original GTAP sectors available in the GTAP 8 database.

9
Table 10: IV regression IT use on firm-level productivity considering China’s competition

|Dependent variable: log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Computers-per-worker|0.736|0.672|0.460|0.449|-0.091|-0.423|
||(0.926)|(0.991)|(1.043)|(1.039)|(1.106)|(1.384)|
|Chinese competition*IT|8.722**|8.448**|8.186**|7.974*|8.095**|9.492*|
||(4.030)|(4.104)|(4.086)|(4.075)|(4.057)|(5.397)|
|Chinese competition|-2.605*|-2.591|-2.451|-2.463|-2.334|-3.027|
||(1.578)|(1.681)|(1.648)|(1.635)|(1.620)|(2.316)|
|F-frst stage IT|9.0|9.7|11.8|11.7|10.1|9.8|
|F-frst stage IT*competition|10.6|12.9|15.2|15.1|14.7|9.8|
|Sargan|0.62|0.60|0.84|0.93|0.93|0.91|
|Share of labor with internet|-1.657*|-1.814|-1.302|-0.902|-0.922|-1.453|
||(0.967)|(1.121)|(1.232)|(1.158)|(1.146)|(1.914)|
|Chinese competition*IT|18.483***|18.647***|17.006***|15.374**|14.151**|15.598*|
||(6.543)|(6.520)|(6.428)|(6.286)|(6.179)|(9.011)|
|Chinese competition|-2.966*|-3.153**|-2.915*|-2.658*|-2.292|-2.793|
||(1.529)|(1.566)|(1.538)|(1.486)|(1.465)|(2.277)|
|F-frst stage IT|15.2|13.5|12.5|14.0|13.1|10.2|
|F-frst stage IT*competition|8.7|10.6|11.3|11.6|11.0|11.3|
|Sargan|0.62|0.60|0.84|0.93|0.93|0.91|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects. Source: Authors’ calculations using data from ENTIC, INEGI 25
38

|Box 3.1 Bangladesh's Grameen Bank: A Model Program to Serve the Rural Poor<br>The success of Bangladesh's Grameen Bank in meeting the credit needs of the rural poor is due in large<br>part to the bank's ability to control the often high transaction costs of lending to this segment of the market<br>Grameen Bank overcame this problem through a group-based lending program that uses peer pressure to monitor<br>and enforce contracts and to screen good borrowers from bad.|
|---|
|The bank targets poor landholders with less than 0.50 acres. It lends for natural disasters and provides<br>loans for food storage and capital recoveryinaddition to providing loans. Borrowers are required to attend weekly<br>meedings and participate in other welfare activities. Because the opportunity cost of the meetings is high and loan<br>amounts are small (US$65 for the first loan rising to a maximum of US$250 for subsequent loans), large farmers<br>and other rural rich are excluded from the program. The Bank's principal target group is women (who constitute<br>94 percent of total bank membership and receive 80 percent of total lending). Along with loans, they receive<br>training in maternal health, nutrition, and child care as part of the Bank's comprehensive social development<br>prograra|
|Started in 1983, Grameen Bank has grown in terms of both members and outstanding loans in a short|
|time. In 1994 there were 1,045 Grameen Bank branches covering 2 million members in 35,000 villages (more<br>than half the villages in Bangladesh). E'^tween 1985-1993, its membership increased 960 percent to 1.8 million<br>members, 98 percent of whom are actually borrowers. Total loans outstanding increased from taka 244 million in|
|1985 to taka 11 billion in 1994. The bank also has mobilized rural savings, which increased from taka ilS<br>million in 1985 to taka 8.97 billion in mid-1993. The bank has a loan recovery rate exceeding 95 percent. The<br>program drop out rate is low, and borrowers, on average, save 19 percent of the loan amount they receive.<br>Leadership was vital in the bank's early stages. But what has mattered in the longer run are features that<br>have ensured the institution's financial viability such as group-based lending and decentralization, which facilitate<br>close contact with borrowers and ensures their financial viability. These institutional characteristics allow a quick<br>asessnent of loan recovery performance and branch profitability. These features, combined with greater on-|
|lending and an increase in the lending rate from 16 to 20 percent, have reduced the bank's subsidy dependency<br>index from 1.66 percent in 1987 to 0.73 percent in 1993. To eliminate the bank's subsidy dependency, on-lending<br>**interest rates**would have to increase from 16 to 28 percent, given 1993 figures.|

**Sore:** Based on S. Khandker, B. Khalily, and Z. Khan. (1994). Is Grameen Bank Sustainable? HRO Working Paper No. 23, World Bank, Washington, D.C.
- 1.29 -

and the other is to assume that some share of the Increment<sup>goes to labor, say 50</sup> percent. Both alternatives were simulated. The macroeconomic projections<sup>used In</sup> calibrating the multiperiod model are given In Tables l.C.1 and<sup>analogous projections for</sup> an associated annual model are given In Table I.C.2.

As already noted, the macroeconomic projections<sup>have assumed an elastic supply</sup> of arable land and a constant ratio of land to agricultural<sup>worker. in addition, the yields</sup> for annual and perennial crops are assumed to remain<sup>constant.</sup> In effect, these assumptions kIply the projection over 50 years<sup>of the static technology of traditional</sup> agricultural practice using bush fallow. As section 1.8<sup>has shown, this Is the current</sup> situatlon In Ghanaian agriculture. With the exc0ptlon of hybrid cocoa, high yielding varietles are not In use on a significant scale. Nor are<sup>the fertilizer and pesticide</sup> packages used In Intensifying agriculture elsewhere In the world<sup>In common use In Ghana</sup> or other countries In West Africa. In consequence, even the<sup>hybrld cocoa yields are 50</sup> percent lower In Ghana and Cote d'lvoire than In Brazil<sup>or Malaysia. Nonetheless,</sup> projecting the absence of significant technical change In agriculture<sup>over a half century</sup> Is a very strong assumption, and most agronomists and plant<sup>geneticists would be appalled</sup> at such a suggestion. The problem Is not that we expect<sup>technical change to remain</sup> static, but rather that meaningful crop specific estimates<sup>of the course of technical</sup> change In Ghanaian agriculture are simply not available.<sup>At least by projecting the</sup> absence of significant technical change against a backdrop<sup>of rapid population Increase,</sup> we highilght the resource imbalances that will occur unless agricultural<sup>productivity does</sup> start to Increase.
**Table 1. Urban employment by Employment Types Over time**

|||Unpaid<br>workers|Informal<br>wage<br>employees|LE-SE|HE-SE|Formal wage<br>employees|Total<br>informality|
|---|---|---|---|---|---|---|---|
|Cameroon|2007|19.5|15.9|40.4|3.6|20.6|75.8|
||2014|10.0|17.6|45.6|3.1|23.7|73.3|
|Annualize|d change|-9.1%|1.5%|1.8%|-2.2%|2.0%|-0.5%|
|Ghana|2012|18.4|12.7|43.8|1.8|23.4|74.8|
||2016|15.1|15.2|41.5|7.0|21.1|71.8|
|Annualize|d change|-4.7%|4.8%|-1.3%|40.4%|-2.6%|-1.0%|
|Liberia|2014|22.4|17.5|37.9|10.6|11.6|77.8|
||2016|14.6|14.1|41.5|12.1|17.8|70.1|
|Annualize|d change|-19.3%|-10.4%|4.7%|6.6%|23.6%|-5.0%|
|Uganda|2013|7.8|27.9|34.7|6.7|22.9|70.4|
||2016|16.0|30.0|33.5|4.5|16.0|79.5|
|Annualize|d change|26.9%|2.5%|-1.2%|-12.7%|-11.1%|4.1%|
|Zambia|2010|4.5|27.0|27.6|13.6|27.4|59.1|
||2015|4.2|28.3|29.2|13.1|25.2|61.7|
|Annualize|d change|-1.5%|1.0%|1.1%|-0.6%|-1.6%|0.9%|

Source: Own elaboration based on GMD data.

The mixed pattern of informality fluctuations across countries is not related to differential rates of economic growth in the short-run. If we correlate the annualized rates of informality increase with the annualized rate of GDP growth over the same period, we find a positive association (Figure 6, Panel A). This is in direct contrast to Kiaga and Leung (2020) and Mehrotra (2020), who found that formality increased in periods of positive economic growth in Africa and Asia, respectively. But our results are consistent with Merotto, Weber and Aterido (2018) who found that informal employment persists even in relatively richer countries, and even when labor is no longer mostly in agriculture or self-employment, and with Fiess et al (2010) and Bosch and Maloney (2008) for Latin America.<sup>47</sup> Disaggregating by informal job type, we find that informal wage employment is positively correlated with GDP growth (Figure 6, Panel C) and LE-SE is negatively correlated with GDP growth (Figure 6, Panel B), similar to findings by Merotto et al (2018). Due to our small sample size, our results are highly unstable.

> poor countries and 55 percent in middle-income countries. The share of informal wage employment remained around 20 percent, while the shares of other types of employment—mostly self-employment, either formal or informal—decreased. (Merotto et al. 2018)

> 47 <mark>Fiess et al (2010) argues that economic growth in a closed economy leads to greater consumption of nontradables, a high share of which are provided by informal enterprises.</mark>

18
_c_ = (5547 _/_ 87) _/_ (5899 _/_ 632) _≈_ 7.

The numbers obtained above are calculated from a highly aggregated budgetary figures. Therefore, we consider the generic, all-inclusive budget for a one year, 3,200-household living standards survey presented in Grosh and Mu ̃noz (1996, Table 8.2). While the numbers are hypothetical, they are meant to be used to create a prototype budget and may well serve our purpose to create a ballpark figure. We assume that each cluster has 16 households as with a majority of Living Standards Measurement Study (LSMS) surveys reviewed in Table 4.1 of Grosh and Mu ̃noz (1996), which implies that there are 200 clusters in this hypothetical survey.

There remain a challenge in deriving an estimate of _c_ because we need to assign each cost components to (i) variable costs proportionate to the number of clusters, (ii) variable costs proportionate to the number of households, and (iii) the fixed costs. We decided to assign all costs relating to “travel allowance” and vehicles, fuel, and car maintenance to (i) variable costs proportionate to the number of clusters, which amounts to $345,880 for 200 clusters. For (ii) variable costs proportionate to the number of households, we include all the costs relating to “base salaries” and printing of questionnaire as well as “materials” other than vehicles, fuel, and car maintenance, which in total amount to $475,150 for 3,200 households. The remaining costs including “consultancy and travel” and “other” are taken as (iii) fixed costs. Based on these classifications, an estimate of _c_ can be obtained as follows: _c_ = (345880 _/_ 200) _/_ (475150 _/_ 3200) _≈_ 12.

We have also collected similar budgetary information for several household surveys through personal communications. We then computed _c_ in a similar manner. Table 3 summarizes all the estimates of _c_ we have obtained using the budgetary information we obtained from personal communications. Based on this table, we choose to use the following values of the cost ratio: _c ∈{_ 4 _,_ 16 _,_ 64 _}_ .

Note that the cost calculations are based on average cost. Because the actual cost function for a survey depends on the logistical arrangement and is in general not exactly equal to eq. (16), the marginal and average costs are likely to differ. For example, we would not need to incur additional training cost to just add one more household to the sample. However, if we scale up the survey significantly, it is likely that we need to increase most of the cost components, such as the costs of personnel, travel, and materials. Therefore, the calculations of _c_ provided in Table 3 should be taken as approximations.

40