rafmacalaba/datause-encoder-data huggingface dataset:
includes equation (1) in levels,<sup>butwiththelagged differencesoftheendogenous</sup> variables as instruments. Bond<sup>etal.(2001)showthatthe"weakinstruments"problem</sup> can be severe in cross-country<sup>growthregressionswith panel data.Thereforewefollow</sup> them, as well as Levine et al.<sup>(2000)inapplyingtheGMM</sup><sup>_system_estimatortoour</sup> growth models.

We follow Griliches (1995) in estimating<sup>the growthregressionasin equation (2),</sup> rather than estimating the TFP<sup>residual firstandthenusingthat residual asthe dependent</sup> variable of R&D. The single regression<sup>approachissuperiorinthis case,becausewe are</sup> interested in the returns to R&D<sup>investmentrelativetophysicalcapitalinvestmentsand</sup> thus we want to retrieve mutually<sup>consistentestimates of the returns for bothvariables.</sup>

# _C. Results_

The core data combines the R&D<sup>datawiththat of SummersandHeston(1991)</sup> panel updated to 2000 and the<sup>Leamermeasureof resource endowments. We estimatethe</sup> corresponding growth regressions<sup>usingpaneldataof five-yearaveragesbetween1975-</sup> 2000. Table 2 presents estimated<sup>returnsto R&Dforthe panelofcountriesthathad</sup> sufficient consecutive observations<sup>(atleastthree)requiredfortheGMM system</sup> estimator. All the regressions pass<sup>theSargantestfor thevalidityof theinstrumentsand</sup> there is no evidence that they<sup>sufferfrom residual secondorderserialcorrelation.8</sup> Column 1 indicates that, for the<sup>largestpossiblesample,returns to R&Dasa whole are</sup> around 78%. This estimate falls<sup>inthemiddleof those previouslycitedestimatesforthe</sup> U.S. and the OECD estimates from<sup>Coe and Helpman(1995).</sup> To see if this aggregate number<sup>ishiding variation acrossdifferentlevelsof</sup> development either across countries<sup>orwithincountries,weinteracttheR&Dtermand</sup> the physical investment term with<sup>per capita GDP.The negative R&Dinteractionterm in</sup> column 2 suggests a _decreasing_<sup>returnto R&D withdevelopment.Thisisconsistentwith</sup> the intuition of numerous conditional<sup>convergenceregressionsintheBarro(1991)</sup>

> 8 The null of the Sargan test is that<sup>thereis no correlation betweentheerrors andtheinstruments.Thusa</sup> high p-value indicates that the<sup>Instruments arenot correlatedwiththe errors.</sup>

12
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From space it is possible to observe the surface of the earth and measure the light that is emitted at different wavelengths. Vegetation indexes such as the Normalized Difference Vegetation Index translate visible red and near infrared radiation into a decimal number between -1 and 1 which describes the greenness of a specified geographical area. In order to use NDVI as a proxy for drought, it is common to calculate the anomaly, i.e. the deviation from a long-run average for a specific time of the year.

Predicted NDVI is a composite drought index that is based on greenness anomalies estimated by accumulated rainfall and temperature variations. The measure, explained in more detail in Fisker (2014), uses monthly information on rainfall, temperatures at night and temperatures at daytime to predict NDVI before aggregating to 11 yearly averages. This leads to a satellite based drought-indicator with a spatial resolution of 0.25*0.25 degrees that takes greenness into consideration, but importantly leaves out all anthropogenic causes of change in greenness.

Data on greenness and temperature are obtained from the MODIS Terra satellite. It has been orbiting Earth daily since 2000, and here we employ a pre-processed product made publicly available by NASA that has a temporal resolution of one month and a spatial resolution of 0.05 degrees (3 arc minutes or around 5.8 km at the equator). It is later aggregated to 0.25 degrees in order to match the resolution of the rainfall data and reduce the number of observations. In the end we have a data frame with 1440x720 observations over 180 months.

For each Enumeration area/cluster monthly NDVI is estimated using the four nearest weather data observations with the inverse distances as weights. This way, more weight is put on weather observations, the closer they are to the survey point, and every observation is unique and very precisely estimated.

# **6 Estimation Results**

This section presents the results from our empirical analysis on resilience using consumption and malnutrition measures. Estimated coefficients and residuals will then be used to construct counterfactual outcomes. These outcomes will be compared to (estimated) permanent welfare results, also discussed in this section. Using the taxonomy discussed in section 3, we calculate by country the shares of resilient, non-resilient and chronically poor population. This section concludes by comparing characteristics of these three groups. If the groups are properly defined, we expect the resilient group to show superior characteristics to the other two groups and the non-resilient group to be better off than the chronically poor group.

Consumption models were estimated separately for each country and by pooling different years together. All models use OLS and errors are clustered by country specific macro regions. Tables 2, 3, 4 15
free in public health facilities (including CSComs) to ensure that cost is not a barrier to accurate diagnosis.<sup>9</sup> Artemisinin combination therapies (ACTs) are recommended for simple malaria, while severe malaria cases should be treated with injectable artesunate followed by a dose of ACTs once the patient is stable.<sup>10</sup> These policies are in line with WHO recommendations (WHO, 2014) and are designed to limit the spread of drug resistance, which is hastened by overtreatment. Limiting overuse of antimalarials is particularly important, as drug resistance has rendered past generations of antimalarials ineffective across much of Sub-Saharan Africa and Asia (Arrow, Panosian and Gelband, 2004). This externality is one of the largest (potential) costs to overtreatment, though overtreatment also strains government budgets for drug subsidies, wastes resources of poor families, causes side effects, and can slow diagnosis and treatment of non-malarial illnesses.

In public facilities, ACTs are meant to be free for children under five and subsidized for older individuals, but there are no subsidies for other components of severe malaria treatment (PMI, 2016). In our study area, the ACT subsidy policy is only partially enforced, as evidenced by the fact that 37 percent of children under five receiving ACTs in our control group paid a positive price.

# **1.2 Status Quo Malaria Treatment in the Study Sample**

We use data from our Control group to quantify overuse of malaria treatment at the 60 clinics that participated in our study. Here we give a brief overview of the data; we provide additional detail on the sampling frame and data collection protocols in section 3. We stationed surveyors at each clinic for Previously, presumptive treatment of any fever cases was the main approach to malaria control (Kon ́e et al., 2015).

> 9In our data, RDTs were free of charge 70 percent of the time, while microscopy tests were free less than 3 percent of the time.

> 10Artesunate is a derivative of artemisinin. Artemisinin-based antimalarials are the most effective treatments for malaria in Sub-Saharan Africa, where the emergence of drugresistant parasites has rendered earlier generations of antimalarials ineffective. Quinine can also effectively treat both simple and severe malaria infections in this region, but the drug is less effective than artemisinin and has more side effects (Achan et al., 2011). Malian policy generally reserves quinine for pregnant mothers, though national policy allows for an initial dose of quinine to treat severe malaria if an injectable artemisinin-based therapy is not available.

9
6

||Sugar|Milk|Rice|Beef|Cotton|
|---|---|---|---|---|---|
|TRI Global Average|**54.8**|**44.5**|**42.9**|**32.0**|**-4.1**|
|US|7.3|11.9|5.5|-3.2|769.3|
|Vietnam|1.5||7.6|||
|Zambia|||0.0||-8.3|
|Zimbabwe|||||-26.0|
|Sum|100.0|100.0|100.0|100.0|100.0|

Source: Derived from Anderson and Croser (2009), based on CTE estimates reported in national studies covering 75 focus countries.

Note: the decomposition over the 5-year period can be greater than or less than 100, even though the decomposition sums to 100 in any one year. We have scaled the 5-year averages, so that the decompositions sum to 100.
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 8: Regional log Formal Employment - Tradable and Nontradable Sectors- 1992-2010 <!-- Start of picture text --> 14<br>12<br>Tradable<br>10<br>8<br>6<br>4<br>2  Nontradable<br>0<br>1992  1993  1994  1995  1996  1997  1998  1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010<br>-­‐2<br>-­‐4<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the change in regional log formal employment in the tradable or nontradable sector from 1991 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with state fixed effects and pre-trend control. Positive estimates imply larger formal employment declines in the relevant sector in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

47
|**Table A1. House rental regression**<br>||
|---|---|
|**House characteristics**<br>_Coef._|_t-stat._|
|Square footage of dwelling (log)<br>0.267|7.44 ***|
|Square footage of plot (log)<br>-0.047<br> <br>|-0.29<br>|
|Number of rooms (log)<br>0.454|8.69 ***|
|Share of kitchen in number of rooms<br>1.821|0.47|
|Share of bathroom in number of rooms<br>2.111<br> <br>|0.55<br>|
|Share of bedrooms in number of rooms<br>2.122<br> <br>|0.55<br>|
|Share of living room in number of rooms<br>2.195|0.57|
|Share of business room in number of rooms<br>2.713|0.71|
|Share of mixed use room in number of rooms<br>1.585<br> <br>|0.41<br>|
|Share of other room in number of rooms<br>1.878|0.49|
|Dummy if kitchen garden<br>0.053|1.27|
|**Construction material:**||
|Outside walls (omitted variable = cement or concrete)||
|Mud cemented bricks or stones<br>-0.423|-6.06 ***|
|Wood or branches<br>-0.368|-4.23 ***|
|Other permanent material<br>-0.536<br>|-5.73 ***|
|Flooring (omitted variable = earth)||
|Flooring material<br>0.323|4.94 ***|
|Roof (omitted variable = thatch, wood or mud)||
|Galvanized iron<br>0.467|7.49 ***|
|Concrete or cement<br>0.498|5.48 ***|
|Tiles or slates<br>0.418|7.45 ***|
|Windows (omitted variable = no window coverings)<br>||
|Shutters<br>0.278|5.44 ***|
|Screen or glass<br>0.549|5.97 ***|
|Other<br>0.255|1.28|
|**Amenities:**||
|Dummy if electricity<br>0.297|5.66 ***|
|Dummy if some sewerage system<br>0.230<br> <br>|3.09 ***<br>|
|Dummy if some garbage collection<br>0.408|4.97 ***|
|Water (omitted variable = piped water)||
|Covered well/hand pump<br>-0.065|-1.10|
|Open well<br>0.015|0.15|
|Other water source<br>-0.196|-2.53 **|
|Toilet (omitted variable = household flush)||
|Household or communal latrine<br>-0.338|-4.78 ***|
|No toilet facilities<br>-0.523|-7.47 ***|
|District dummies<br>included but|not shown|
|R-squared<br>0.9607||

The dependent variable is the log of imputed rental value per month, in Nepalese rupees.
Again, by specifying the logarithm of the variance ht as a function of ηt, Nelson ensured that the conditional variance stays positive even if some of the coefficients in (8) are

> negative. Introducing  _t_  1 /<sup>_h_</sup> _t_  1 permits us to analyze whether positive or negative values

of ηt will increase or decrease the conditional variance.

# **4. Estimation results**

# _5.1 The intervention equations_

We estimate the parameters of the intervention criteria, equations (4.1) and (4.2), with observations ItB and ItS as a standard probit model. These estimates are presented in Table 2.

# **Table 2. Probit estimates of the criteria functions for intervening. (Standard errors are in parentheses.)**

|**_Dependent Variab_**|**_le M_**<br>**_t_**<br>**_B_**|**_Dependent Va_**|**_riable M_**<br>**_t_**<br>**_S_**|
|---|---|---|---|
|_Coefficients_|_Estimates_|_Coefficients_|<br>_Estimates_|
|δ11|0.3810|δ21|-1.1235|
||(0.0633)||(0.0821)|
|δ12|-0.3855|δ22|1.0463|
||(0.0769)||(0.0817)|
|δ13|-8.0346|δ23|-4.8294|
||(0.5882)||(0.3789)|
|Observations|1469||1469|
|Log Likelihood|-879.26||-647.79|

The results indicate that the central bank seems to be consequent in its intervention decisions. That is, the probability of intervening buying (selling) foreign currency increases when the exchange rate observed at 11.00 am (ask price) at time t appreciates (depreciates) 20
-6-

# Figure 2: PRODUCTION CONSUMPTION AND EXPORTS OF BRAZILIAN SUGAR

mt <!-- Start of picture text --> 12 -<br>10 production<br>consumptlon,<br>6 -<br>4<br>2<br>80/81 81/82 82/83 3/84 84/85 85/86 86/87 87/88 88/89 89/90<br><!-- End of picture text --> Source: F.O. Licht's.
Table 7: Effects of forecast errors and forecast growth on different wage components, high vs low earners, according to managerial skill (defined by occupational category)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:|log bas|e wage|log overt|ime pay|log ot|her pay|
|High versus low earners<br>Definition|high<br>15%|low<br>85%|high<br>15%|low<br>85%|high<br>15%|low<br>85%|
|A. Firms with high-skilled|managers||||||
|Weighted forecast error|0.0087<br>(0.0120)|-0.0026<br>(0.0051)|0.0292<br>(0.0397)|-0.0093<br>(0.0456)|0.0927<br>(0.0638)|0.0820**<br>(0.0386)|
|Weighted forecast growth|-0.0035<br>(0.0058)|-0.0059*<br>(0.0036)|0.0198<br>(0.0270)|0.0096<br>(0.0301)|-0.0220<br>(0.0514)|0.0257<br>(0.0224)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|6226|6226|6226|6226|6226|6226|
|N (firms)|2739|2739|2739|2739|2739|2739|
|Adj. R<sup>2</sup>|0.0629|0.0886|0.0590|0.0684|0.0271|0.0229|
|RSS|148|38|6480|4630|6604|3038|
|B. Firms with low-skilled m|anagers||||||
|Weighted forecast error|-0.0072|-0.0020|0.1208***|0.0357|-0.0407|0.0263|
||(0.0083)|(0.0051)|(0.0389)|(0.0360)|(0.0579)|(0.0450)|
|Weighted forecast growth|-0.0089<br>(0.0060)|-0.0026<br>(0.0030)|0.1016***<br>(0.0297)|0.0362<br>(0.0262)|-0.0006<br>(0.0476)|-0.0101<br>(0.0282)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|6478|6478|6478|6478|6478|6478|
|N (firms)|3015|3015|3015|3015|3015|3015|
|Adj. R<sup>2</sup>|0.0572|0.0894|0.0419|0.0357|0.0294|0.0298|
|RSS<br>|191<br>|35<br>|5114<br>|4063<br>|7719<br>|3410<br>|

<u>Notes: In each column, the dependent variable is the change between the averages of each 3-year period.</u> Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
# Inappropriate use of Soil Resources

Agricultural productivity in the future can be adversely affected if soil resources are not being used appropriately. Soil resources have been damaged by the standard fertilizer packages recommended by research institutes packages that do not take account of local conditions. Infrastructure development has also played a role -- the location of sugar mills has led to large scale cultivation of sugar on sandy soils, thus aggravating water logging problems. Much land on steep slopes has been tilled, when it is really only suitable for forest. At the same time, arable land in the Indus plains remains under irrigated forests. Efforts are made to bring marginal land under arable use, at the expense of large tracts of arable land in the Indus basin that remain under-utilized. Current incentives may not reflect long-run sustainability issues. An example is the general cultivation of Basmati rice on the well-drained loamy soils of the northern Punjab, which from a sustainability point of view are ideal for the cultivation of maize, sunflowers, groundnuts, and pulses. Basmati is grown because the climate of the region is uniquely well suited to Basmati rice, and more importantly, Basmati offers high returns. Water-intensive crops such as rice can contribute to rising water tables.

# Issues ofLand Holding and Land Market

The previous section described Pakistan's agricultural resource base. It is also important to analyze how resources are managed, and the resulting impact on productivity and sustainability. Of particular importance is the nature and distribution of owner and operator rights to land. (Table 6).

Over one-quarter of farns are below 1 hectare in size, which represents a huge number of marginal or near-marginal farms. These farms are unlikely to be a source of significant productivity growth. Nearly half of all farms are below _5_ hectares in size, but occupy just 12 percent of total land. At the other end of the spectrum, note that 7 percent of farms account for over 40 percent of total farm area. The largest category of farms (above 60 hectares) accounts for 0.3 percent of farms but occupies over 10 percent of farm area. While we do not know the distribution of farms above 60 hectares, the average farm size within this class is 125 hectares, suggesting that this category contains some extremely large fanns.

Is the distribution of rights to land affecting productivity and sustainability and if so, how? Quantifying the size-productivity relationship is difficult. A stylized fact from the empirical literature is that there is an inverse relationship between farm size and productivity. A recent survey<sup>4</sup> cited a typical finding from Berry and Cline (1979). A comparison of value added per cultivated area between small (between 5 and 10 ha) and large (over 20 ha) farmers in Punjab found that small farmers were over 2.5 times as productive as large farmers. However, this was 4Binswanger and Feder (1993).

13
<!-- Start of picture text --> Carbon price by fuel type (2011 USD)<br><!-- End of picture text --> <!-- Start of picture text --> 14,000<br>12,000<br>10,000 —— Carbon price for coal (2011 USD)<br>—— Carbon price for oi! (2011 USD)<br>8,000 —— Carbon price for gas (2011 USD)<br>6,000<br>4,000<br>2,000<br>0 =<br>-2,000<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br>Total emissions/Total emissions in 2017<br>3.0 — Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Carbon tax and reduction in direct taxes<br>2.5 —— Scenario 3: Carbon tax and an increase in hh. transfers<br>—— Baseline<br>2.0<br>15<br>1.0<br>0.5E —<br>0.0<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br><!-- End of picture text --> <!-- Start of picture text --> Carbon price by fuel type (2011 PKR)<br><!-- End of picture text --> <!-- Start of picture text --> 1,200,000<br>1,000,000 —— Carbon price for coal (2011 PKR)<br>—— Carbon price for oi! (2011 PKR)<br>800,000 — Carbon price for gas (2011 PKR)<br>600,000<br>400,000<br>200,000<br>0 =<br>-200,000<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br>Emissions/GDP<br>10<br>8<br>6<br>4<br>—— Scenario 1: Carbon tax and it repayment<br>—— Scenario 2: Carbon tax and redu jn direct taxes<br>2 = —— Scenario 3: Carbontax and an increasei insfers<br>—— Baseline<br>0<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br><!-- End of picture text -->
54

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