rafmacalaba/datause-encoder-data huggingface dataset:
translation/version of the landing page of NSOs website; (2) a micro data dashboard/library and (3) a survey calendar <mark>. These features are chosen for several reasons. First, English is the most widely spoken language globally a</mark> nd has a dominant presence in the media. Hence a <mark>larger group of international audience including content creators and researchers will be able to better use an English version of NSOs’ websites. When an English version is unavailable, it is possible for international users to leverage other translation resources such as Google translate. However, it is still vital for NSOs to maintain an outstanding English translation of its website so that the information disseminated in the local language is translated appropriately from the original material and retain its intended meaning. S</mark> imilarly, organizing available microdata in a microdata dashboard/library/tab on NSOs website makes it easy to find. A survey calendar also helps microdata users know and track available and forthcoming surveys by the NSO or other agencies. Combined, a microdata library and survey calendar on an NSO’s website helps microdata users navigate NSOs website in search of microdata without wasting unnecessary time.

<mark>Following the implementation procedure outlined in section 3.3, countries are assigned scores for each of the 3 key features u</mark> sing the scoring criteria outlined in table 6.

**Table 6: Scoring criteria for the user experience element**

|Element|Representative indicators|Scoringcriteria|
|---|---|---|
|User<br>experience|English translation|0 if no English translation/version is available on NSO’s website<br>landing page.|
|||50 if an English translation/version for NSO’s website is available but<br>does not reasonably reflect the local language(s)and/ or not available<br>for other pages besides the landing page.<br>100 if English translation/version for NSO’s website is available,<br>reasonably reflects the local language and available for other pages<br>besides the landing page.|
||Micro data tab/dashboard/library|0 if no micro data tab/dashboard/library is available on NSO’s website<br>100 if micro data tab/dashboard/libraryis available on NSO’s website|
||Survey calendar|0 if no survey calendar is available on NSO’s website<br>50 if survey calendar is available on NSO’s website but not up to date.|
|||100 if surveycalendar is available on NSO’s website and upto date.|

# 5.2 Data openness element

In section 3, we describe and present results of an exercise that examines both the website of NSOs and international repositories such as web-portals of the MICS and DHS surveys and microdata libraries maintained by the World Bank (WB), International Household Survey Network (IHSN), IPUMS and Eurostat to determine the degree of accessibility of microdata. For each data 23
**Table C2. Probability of a Sudden Stop: Alternative Regression Models**

||Logit re|gressions|Prob<br>Rando|it with<br>m Effects|Probit wi<br>Fixed|th country<br>Effects|
|---|---|---|---|---|---|---|
||1991-2002|2003-2014|1991-2002|2003-2014|1991-2002|2003-2014|
|VIX, log|0.841*|1.362***|0.332|0.605***|0.596***|0.779***|
||[1.88]|[7.47]|[1.46]|[5.86]|[2.73]|[7.29]|
|US Policy Rate|0.905***|0.695**|0.375***|0.274|0.317|0.308**|
||[4.43]|[2.08]|[4.04]|[1.47]|[1.56]|[2.12]|
|Capital Flows/GDP|1.049***|0.146|0.493***|0.075|1.021***|0.032|
||[6.06]|[1.17]|[4.54]|[1.04]|[4.26]|[0.29]|
|Domestic Credit/GDP|-0.128|0.448***|-0.09|0.179***|0.196|0.410|
||[0.68]|[3.63]|[0.75]|[2.66]|[0.79]|[1.47]|
|Observations|862|1316|862|1316|515|914|
|Pseudo R-squared|0.116|0.285|.|.|0.237|0.348|

Dependent variable is a binary variable which is equal to 1 if a sudden stop occurs and 0 otherwise. The first quarter of sudden stop is included in the regressions, and all subsequent quarters dropped. Domestic variables are averages of previous eight quarters. All variables have been standardized around zero mean and standard deviation equal to 1. ***,** and * indicate significance at 1, 5, and 10% levels, respectively.

34
Figure A5: Export Share of the Two Types Processing (2000-2007) <!-- Start of picture text --> 2000 2002 2004 2006<br>year<br>Import and Assembly Pure Assembly<br>.8<br>.6<br>exp_shr<br>.4<br>.2<br><!-- End of picture text --> Figure A6: DVAR of Processing Exports (Multi-industry Firms, 2000-2007) <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>Measured DVAR 95 c.i. (upper bound)<br>95 c.i. (lower bound)<br>.6<br>.55<br>DVAR<br>.5<br>.45<br>.4<br><!-- End of picture text --> 15
a representative rural feeder road.

The discount rate usually plays a decisive role in determining the profitability of long-lived projects. In order to estimate the social discount rate, _ρ_ , I appeal to the (approximate) rule that, in a state of steady growth, _ρ_ = _|η| · gc_ + _δ_ , where 1 _/η_ is the inter-temporal elasticity of substitution in consumption (in other contexts, _η_ is the elasticity of the marginal utility of consumption), _gc_ is the rate of growth of per capita consumption and _δ_ is the rate of pure impatience. It is common to assume logarithmic inter-temporal utility, which implies _η_ = _−_ 1. As for _gc_ , India’s GDP per head has been growing rapidly for more than two decades, with the initial acceleration occurring in the early 1980s. Private per capita consumption has grown almost as fast, and the gap in their future rates is likely to be small. The current pace of about 4.5 percent annually will probably continue for at least another three decades. On ethical grounds, _δ_ should be no more than 1 percent p.a. These assumptions yield a social discount rate of 5.5 percent. It might be argued that, especially in poor economies, consumption in one period is a poorer substitute for consumption in another than _η_ = _−_ 1 implies. Consider, therefore, the rather extreme value _η_ = _−_ 2, which implies _ρ_ = 10 percent. It is rather hard to justify a test discount rate any higher than this.<sup>1</sup> The real market rate in India, _r_ , is much lower, and this will matter, even though the gestation period of feeder-road projects is unlikely to exceed one year. Indeed, the government always has the option of investing in high-quality bonds in international capital markets, wherein real rates are likely to remain at historically low levels for some time to come. The proposed rule _ρ_ = _|η| · gc_ + _δ_ is, therefore, a conservative choice.

Valuing the benefits, which will accrue overwhelmingly to households, requires much more than simple tinkering with shadow prices. It is not enough to value the underlying inputs and outputs at shadow prices; one must also value the (money-metric) benefits accruing to households relative to public income, which is the source of finance for the road and on which there should be a premium – unless the beneficiaries are sufficiently poor. The calculations are long and involved, quite apart from the necessity of estimating the SPs themselves. For present purposes, what matters most is the premium on public income. This is arguably close to zero where poor communities are concerned, for the government should then be indifferent between making a transfer to them and keeping the money for other purposes. In the participating states, the great majority of the communities that are scheduled to get a road surely fall into this category, so that the monetary value of the benefits their members receive will be accorded the

> 1An independent study by Murty and Goldar (2007) also yields this upper estimate.

7
**Table 5**

# **Managerial information and investment sensitivity to price**

Definitions of all variables are listed in Table 1. The dependent variable is investment. Managers’ uncertainty is proxied by the dispersion of analysts’ earnings forecasts ( _DIS_ ) and insider trading activity 1 _− INSIDER_ . T-statistics are in parentheses. ***/**/* indicate that the coefficient estimates are significantly different from zero at the 1%/5%/10% level.

|||_DIS_|1_−IN_|_SIDER_|
|---|---|---|---|---|
|Dependent variable: _I_|(1)|(2)|(3)|(4)|
|_Q_|0.215***|0.225***|0.212***|0.224***|
||(8.11)|(8.45)|(10.04)|(10.51)|
|_Q × PI_|0.038***||0.041***||
||(2.88)||(3.24)||
|_Q × UNCER_|0.012|0.009|0.011|0.006|
||(0.74)|(0.58)|(0.82)|(0.45)|
|_PI_|-0.002||-0.001||
||(0.24)||(0.05)||
|_UNCER_|-0.023*|-0.025**|0.009|0.007|
||(1.84)|(2.03)|(1.11)|(0.84)|
|Controls|Yes|Yes|Yes|Yes|
|Firm fxed efects|Yes|Yes|Yes|Yes|
|Year fxed efects|Yes|Yes|Yes|Yes|
|_R_<sup>2</sup>|0.39|0.38|0.38|0.37|
|Number of Observations|19009|19009|19009|19009|

28
Where the _ds_ are dummies for the 27 sub-district*firm density strata used in the block level randomization, and the standard errors are clustered at the block level. The first five rows show variables obtained from the listing survey, while the remainder show variables from the baseline survey. Out of 48 coefficients shown, 5 are significantly different from zero at the 10 percent level, and only one at the 5 percent level, which is in line with what would be expected by pure chance.

In contrast, since the inspector block firms were not interviewed at baseline, randomization for this group required randomizing among all firms originally listed. Our original plan called for assigning half the firms listed in these blocks to receive the inspection treatment, and half to be indirect inspector firms. However, this was not feasible with the existing number of inspectors, and so we instead had to randomly choose only one-quarter to be inspector firms. This resulted in 577 firms getting assigned to the inspector treatment. The remaining firms in this block are then all indirectly inspected, but budget constraints required us to randomly choose 593 of the indirectly inspected firms as the sample we would work with for follow-up surveys. The right control group for these firms consists of listed firms in the control blocks. Recall we already have a sample of 689 firms in the control blocks which had answered the baseline survey. We therefore randomly choose an additional sample of 709 control block firms which had not answered the baseline survey, and assigned them also for follow-up. We then reweight the control group sample to take account of the fact that we have all firms which answered the baseline, and only a sample of those which did not, in this group.

The first three columns of Table 3 then compare the inspector and indirectly inspected firms to this weighted sample of control firms, in terms of the characteristics available from the listing survey. Although this offers far fewer variables for checking balance than is possible for the communication and free cost treatments, the results are still reassuring, with only one out of ten coefficients being significant at the 10 percent level.

# **4.5. Follow-up Surveys**

A very short phone survey of firms selected for the communication and free cost treatments was carried out between April 10 and 18, 2012 by Sensus. The purpose of this survey was to follow-up with the firms that had received the communication or free cost content and see whether they had started the process of formalization, their intent to formalize, and whether the information in the brochure had been useful. It was also intended to serve as a last prompt to use the information and free cost offer. This survey was not given to firms that Sensus had not made the treatment offers to, and so was fielded to 464 firms, of which 367 responded (79 percent). In this survey 86 percent of the firms said 14
<!-- Start of picture text -->
1.00<br>0.89<br> 0.90<br> 0.80 0.75<br> 0.70<br>0.60<br>0.57<br> 0.60 0.53<br>0.50<br> 0.50<br> 0.40<br> 0.30<br>0.20<br> 0.20 0.13<br> 0.10 0.04<br>0.00 0.01<br> -<br> (0.10) (0.02)<br>Rank correlations AUC R-squared<br>Partial registry (10% of the census sample) PMT scores IHS training sample RWI<br><!-- End of picture text -->

The ROC curves of the four methods are presented in Figure 3. This is the graphical representation of the AUC results described in Table 6. The partial registry method leads to far superior targeting outcomes, especially at low poverty rates. The RWI is the second-best method but is still far from the partial registry results. The curves for PMT scores and IHS are very close to each other and to the 45-degree line that corresponds to randomly selected villages.

**Figure 3.** ROC curves of the targeting methods

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>False Positive Rates (FPR)<br>45 degree line Partial registry: census sample 10%<br>PMT scores RWI<br>IHS training sample<br>True Positive Rates (TPR)<br><!-- End of picture text --> 16
To do so, we analyze GAEZ grids that are (partially or fully) rain-fed croplands. Our objective is to assess whether groundwater irrigation can compensate for crop water deficits experienced within these GAEZ grids for rain-fed cultivation. Thus, note that in this paper, we do not shift the cropping landscape, but just change the irrigation landscape of SSA. We simulate the production gains by assuming that with adequate water supply, the yield of rain-fed croplands would match that of irrigated croplands either within the same grid, or of irrigated croplands from grids that are either neighbors or have similar geographic and climatic conditions. In other words, we assume that holding geographic and demographic factors constant, the gap in yields of rainfed and irrigated croplands is explained by water use.

We restrict our sample to the year 2010 because the data from that year are the latest made available to us.

# **5.1 Yield Difference**

We start by estimating the yield difference (YLD) for crop _c_ in grid _g_ between irrigated and rain-fed croplands. In other words, this estimation represents the change in productivity that is attributable to irrigating the land. We follow two methods to arrive at our yield-difference measure: (1) using a simple difference between crop yields of irrigated croplands and crop yields of rain-fed croplands, and (2) employing a Structural Ricardian model using Heckman selection (SRHS) to obtain predicted differences in yields by allowing irrigation choice to be endogenous.

## **5.1.1 Simple Difference (SD)**

In this approach, we consider the difference in crop yields of irrigated and rain-fed croplands. This approach is valid under the assumption that irrigated and rain-fed croplands are comparable within the same GAEZ grid cell given that the same controls apply, enabling a ceteris paribus evaluation of productivity gains.<sup>6</sup> Two cases are possible under the SD approach.

1. The GAEZ grid _g_ has both rain-fed and irrigated croplands for crop _c_ . In this case, the yield difference is calculated following equation (1).

Here, _Ycg_<sup>_D_and</sup><sup>_Ycg_are the crop yields of the grids that only consist of rain-fed crop-</sup> lands and the grids that adopted some level of irrigation practices, respectively.

2. The grid cell exclusively comprises rain-fed croplands for crop _c_ . In this case, the 6Note we cannot spatially identify which portion of the grid is rain-fed versus irrigated; this means all the information that we have on controls, such as soil conditions, other inputs in agricultural production, and so on, is at the GAEZ grid level.

9
Why was there such a high savings rate from the initial income gains? A number of explanations can be suggested, carrying rather different implications for the long-term impact of the SWP. Possibly households saved more to assure they could repay the loans. That depends on the extent to which repayment was enforced. While the World Bank’s loan is made to the (central) Government of China, and repayment is virtually certain, that is not the case for the loans made at local level, where enforcement problems are common. Indeed, local repayment rates on loans for poverty reduction under the government’s own program were less than 25% in the three provinces covered under SWP.<sup>6</sup> However, it may be that the necessity of the center repaying the World Bank “trickled down” in the form of greater local enforcement of SWP repayments than for the loans made under the government’s own poor-area programs.

Another possibility is that the high initial savings rate reflected a perception on the part of participants that the longer-term income gains from SWP would be modest or uncertain at best — raising concerns about the sustainability of the program’s impacts.<sup>7</sup> When interpreted in terms of the Permanent Income Hypothesis, the Ravallion-Chen findings imply that participants felt that a large share of the income gain was transient, and (hence) it was saved. While this would happen even without uncertainty about the future income gains, such uncertainty is likely, and would probably lead to precautionary saving in response to the project.<sup>8</sup> In this regard, it is instructive that Ravallion and Chen found large year-to-year differences in impact, which were primarily due to variability in the annual returns to the program’s investments rather than the level of investment. This variability in the returns suggests that participants would have had a hard time assessing the program’s impact on permanent income.

The transient-income explanation suggests that the income impacts of SWP would diminish appreciably after disbursement. Precautionary saving would also start to fall as

> 6 The repayment rates on loans for poverty reduction in 1997 ranged from 8% in Yunnan to 23% in Guizhou. Repayment rates were somewhat higher for other types of loans but the overall average was still only 30% (Government of China, 1998).

> 7 The ICR rated “sustainability” as “highly likely.” The Bank’s internal evaluation of SWP by its Operations Evaluation Department pointed to the need for further evidence on the longerterm sustainability and impact of SWP.

> 8 There is evidence of precautionary savings in response to uninsured risk in the same region of rural China; see Jalan and Ravallion (2001).

7
**<u>Table 4: Regression Results: IV Estimation (1)</u>**

||**(1)**<br>**Rem/**|**(2)**<br>**GDP**|**(1)**<br>**Log o**|**(2)**<br>**f Rem**|**(1)**<br>**Log of Re**|**(2)**<br>**m per capita**|**(1)**<br>**Rem/**|**(2)**<br>**GDP**|**(1)**<br>**Log o**|**(2)**<br>**f Rem**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Log of migrants abroad**|2.663***|2.689***|1.046***|1.070***|0.619**|0.650**|||||
||[0.787]|[0.778]|[0.317]|[0.300]|[0.312]|[0.272]|||||
|**Migrants/Population**|||||||0.716***|0.620***|0.269**|0.232***|
||||||||[0.245]|[0.210]|[0.111]|[0.087]|
|**Bank deposit/GDP**|-0.168||0.121||0.060||-3.275||-1.010||
||[1.274]||[0.721]||[1.212]||[2.351]||[1.331]||
|**Bank credit/GDP**||-0.003||0.285||-0.062||-1.492||-0.253|
|||[1.128]||[0.561]||[0.942]||[1.518]||[0.825]|
|**Log of GDP**|-2.165***|-2.182***|0.193|0.176|||1.217**|0.956**|1.490***|1.387***|
||[0.657]|[0.659]|[0.213]|[0.202]|||[0.536]|[0.433]|[0.254]|[0.204]|
|**Log of GDP per capita**|0.152|0.139|-0.139|-0.171|0.196|0.211|-2.316***|-2.181***|-1.086***|-1.064***|
||[0.504]|[0.505]|[0.231]|[0.228]|[0.182]|[0.206]|[0.628]|[0.610]|[0.260]|[0.265]|
|**GDP growth**|-0.203|-0.205|-0.041|-0.044|-0.003|0.001|-0.041|-0.083|0.019|0.002|
||[0.286]|[0.293]|[0.076]|[0.079]|[0.087]|[0.090]|[0.348]|[0.328]|[0.114]|[0.105]|
|**Diff. in the ratio of tertiary edu.**|0.041|0.041|0.013|0.013|-0.015|-0.013|-0.040|-0.046|-0.019*|-0.022**|
||[0.049]|[0.047]|[0.017]|[0.016]|[0.014]|[0.013]|[0.040]|[0.035]|[0.012]|[0.011]|
|**Constant**|20.916***|21.023***|2.842|3.136|-5.567|-6.060|-9.135|-3.921|-8.319*|-6.045|
||[6.916]|[7.094]|[2.104]|[2.109]|[4.172]|[3.799]|[10.772]|[8.563]|[5.185]|[4.116]|
|**Observations**|85|85|85|85|85|85|85|85|85|85|
|**R2**|0.071|0.065|0.548|0.543|-0.009|-0.04|-0.198|-0.042|0.244|0.355|
|**F-statistic (p-value)**|4.06 (0.00)|4.10 (0.00)|18.93 (0.00)|19.01 (0.00)|7.74 (0.00)|6.97 (0.00)|3.24 (0.01)|3.56 (0.00)|12.01 (0.00)|13.12 (0.00)|
|**Overidentification χ2 (p-value)**|3.84 (0.28)|3.74 (0.29)|3.63 (0.30)|3.28 (0.35)|5.66 (0.13)|5.35 (0.15)|1.06 (0.79)|1.13 (0.77)|0.47 (0.92)|0.83 (0.84)|

Note: Figures in parentheses are robust standard errors. ***significant at 1% level, **significant at 5% level, and *significant at 10% level. Only the migration variables are instrumented. The ratio of bank deposit to GDP is used in (1) and the ratio of bank credit to GDP is used in (2).

21
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overwhelmingly explained by the accumulation of physical capital (which contributes with 0.8 percent) and followed by human capital (0.4 percent). The contribution of TFP growth to long-term growth in the region is negligible. Across the different country groups in the region, oil rich countries registered the largest rate of growth per worker (1.8 percent per year) and it was vastly attributed to the growth of the ratio of physical capital per worker (2.3 percent per year) and the contribution of TFP growth is negative. The latter result points to inefficiencies in the allocation of resources among oil rich countries. Countries in the region with abundant minerals and metals, in contrast, registered a contraction in output per worker during the period 1961-2014 (-0.5 percent per year). The contribution of physical capital accumulation is negligible for this group of countries while that of TFP growth is detrimental. Finally, non-resource rich countries in Sub-Saharan Africa registered a rate of per worker growth that is in line with the regional average (1.1 percent per year). However, the relative importance of the sources of growth is different from that of the region. Human capital and physical capital accumulation contribute to growth per capita with 0.5 and 0.4 percent per year, respectively. In contrast to the other group of countries in the region, the contribution of TFP growth among non-resource rich countries is positive (0.2 percent per year).

**Figure 2. Source of Growth in Sub-Saharan Africa, 1961-2014**

<!-- Start of picture text --> (average annual growth rate, population weighted averages)<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Sub-Saharan Africa SSA Oil Rich Countries SSA Metal Rich SSA Non-Resource<br>(SSA) Countries Rich Countries<br>Physical Capital Human Capital TFP Output<br>percent<br><!-- End of picture text --> _Notes: Regional and sub-regional averages presented in this figure are population weighted averages. The human capital index_ H�exp�φ�s���� _is characterized by the relationship between human capital (H) and the years of schooling (s) as_ φ�s����φ� ∙s�� _for each country i in period t — where the returns to education (_ φ� _) are heterogeneous across countries. The returns to education are measured from the estimation of Mincerian wage regressions on schooling. We use the Mincerian returns estimated by Montenegro and Patrinos (2014) and the total years of schooling for each country and year to construct the human capital index H. Source: Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._ Figure 2 displays the long-run average growth rate of output per capita as well as its sources (physical capital, human capita, and TFP) over the past half-century. However, it does not capture whether the contribution of the factors of production vis-à-vis TFP growth remained constant over time. The proneness to shocks affecting growth in the region over the medium-term justifies assessing the sources of growth in the region across different time periods; namely, 1961-1977, 1978 – 1995, and 1996-2014, respectively. Figure 3 depicts the dismal performance of the region _page 6_