rafmacalaba/datause-encoder-data huggingface dataset:
exporting due to a supplier-relationship with a foreign customer ( _exp_start_ ) as the dependent variable.

**Table 14: The Effect of Assistance on the Probability of Starting to Export due to Relationship with Foreign Firm, Part 1, Probit**

|Dependent variabl|e:_exp_starti_|_sc_||||||||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|_auditisc_|0.9181*<br>(0.070)|0.9638*<br>(0.055)|0.9207*<br>(0.071)|0.9022*<br>(0.071)|0.8890*<br>(0.077)|1.0122*<br>(0.062)|0.9072*<br>(0.073)|0.9092*<br>(0.068)|0.9766*<br>(0.051)|
|_imprisc_|-0.2019<br>(0.712)|0.2380<br>(0.659)|-0.0289<br>(0.955)|0.0364<br>(0.945)|-0.1210<br>(0.817)|-0.1012<br>(0.845)|-0.1148<br>(0.824)|0.0158<br>(0.976)|-0.0980<br>(0.853)|
|_devisc_|<br>0.6726|<br>0.4277|<br>0.7549|<br>0.8038|<br>0.9490*|<br>0.8419|<br>0.8734*|<br>0.7910|<br>0.3870|
||(0.221)|(0.458)|(0.185)|(0.127)|(0.061)|(0.102)|(0.084)|(0.130)|(0.509)|
|_licenseisc_|0.8968*|0.5970|0.8004|0.7349|0.6149|0.8788*|0.5805|0.6898|0.7940|
|_assist_payisc_<br>_assist_imprisc_<br>_assist_fundsisc_|(0.097)<br>1.1684**<br>(0.024)|(0.324)<br>1.7908**<br>(0.026)|(0.159)<br>0.8546<br>(0.286)|(0.191)|(0.277)|(0.092)|(0.305)|(0.223)|(0.187)|
|_assist_planisc_<br>_assist_inpisc_||||0.9034<br>(0.210)|0.9644<br>(0.143)|||||
|_assist_sourcisc_||||||1.1450*<br>(0.083)||||
|_assist_trainisc_<br>_assist_equipisc_<br>_assist_techisc_|||||||1.2032*<br>(0.067)|0.9497<br>(0.160)|1.6031**<br>(0.020)|
|_constantisc_|-7.4756***<br>(0.000)|-7.7162***<br>(0.000)|-7.7334***<br>(0.000)|-7.8291**<br>(0.000)|* -7.8037***<br>(0.000)|-7.8395***<br>(0.000)|-7.7525***<br>(0.000)|-7.8026***<br>(0.000)|<br>-7.3524***<br>(0.000)|
|Country-sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Adj. R2<sup>1)</sup>|-0.159|-0.163|-0.205|-0.202|-0.197|-0.179|-0.184|-0.199|-0.163|
|Observations|55|55|55|55|55|55|55|55|55|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses).<sup>1)</sup> McFadden’s adjusted pseudo R2. Note: All regressions control for country-sector fixed effects. Standard errors are robust to heteroscedasticity.

Ten types of assistance significantly increase the likelihood to start exporting as a consequence of supplying to foreign firms, namely (i) advance payment ( _assist_pay_ ), (ii) provision of financing for improvements ( _assist_impr_ ), (iii) support for sourcing raw materials ( _assist_sourc_ ), (iv) training of workers ( _assist_train_ ), (v) product or process technologies ( _assist_tech_ ), (vi) licensing of patented technology ( _assist_license_ ), (vii) help with the organization of production lines ( _assist_orga_ ), (viii) help with quality assurance ( _assist_qual_ ), (ix) help with finding export opportunities ( _assist_exp_ ), and (x) help with implementing health, safety, environmental, and/or social conditions ( _assist_hse_ ). Overall, all types of assistance show a positive coefficient sign, and many miss the threshold level of 29
variables such that the total seasonal effect zero over a full day-of-week dummy equals cycle of 7 days.

For estimating the causal effect of the Covid-19 lockdowns in our sample of cities, the regression component is crucial. It allows us to construct counterfactual predictions of the daily traffic congestion index series from a synthetic control using traffic conditions from a city or a combination of cities that did not face Covid-19 lockdown measures ( _untreated cities_ ). Unlike the local trend and seasonal sub-models, the observed traffic congestion index series from the pool of _untreated_ cities allows us to capture the variance in traffic conditions in the treated cities. In this application, we primarily use Hong Kong SAR as a relevant untreated candidate city for two reasons other than data availability. First, Hong Kong SAR did not implement a Covid-19 lockdown over the entire period of the analysis. Second, and more importantly, it is implausible that traffic conditions in Hong Kong SAR are affected by the effects of lockdown-related changes in our sample of treated cities (Bogotá, New Delhi, New York City, and Paris), an important assumption for causal inference in the BSTS setting.

Using the natural logarithm of the traffic congestion index data from Paris before the lockdown—i.e. between March 20, 2019 and March 17, 2020—we show in figure 2 the contribution to the BSTS model of the trend, seasonal, and regression components, respectively. There is clearly a daily pattern in the congestion data characterized by significantly more variation in the seasonal component relative to both the trend and regression components. With substantially fewer variation than in the regression component, the local trend exhibits a few instances of major reduction in congestion, especially around summer and other national holidays (e.g., Armistice day), followed by relatively short-lived episodes of increased traffic congestion. Note that in addition to the traffic congestion index for Hong Kong SAR, the covariates in the regression component include precipitation and the natural logarithm of temperature. These two control time series are also unlikely to be themselves affected by the lockdown policy in the 18
of Nigerians are classified as severely food insecure. However, there is also variation between the different components of the FIES module: the most prevalent element of food insecurity is being unable to eat healthy or nutritious foods (FIES Component 2), affecting 72.4 percent of households, while the least prevalent element of food insecurity is going a whole day without eating (FIES Component 8), affecting 10.0 percent of households. Importantly, there is little the FIES can do to differentiate among the large share of the population that is categorized as severely food insecure since those individuals already affirm nearly all of the component questions.

And lastly, aside from differences between the FIES and the FCS in the share of the population with poor food access, there are significant differences in the identity of those having poor food access in the two measures. Table 2 reports that approximately 21.0 percent of Nigerians are severely food insecure according to the FIES but have an adequate FCS; and approximately 10.1 percent of individuals are either moderately food insecure or food secure according to the FIES and have either a poor or borderline FCS. Strikingly, these figures illustrate that approximately two-thirds of the population with poor food access according to the FCS, which is a critical threshold used to target emergency food assistance, would either be categorized as moderately food insecure or food secure according to the FIES.<sup>24</sup> As mentioned in the background section, these inconsistencies between the FIES and the FCS are difficult to interpret precisely. Misalignment between food access measures could be driven by differences in the severity of thresholds defining poor food access, differences in the exact dimensions captured, or poor alignment of one or more of the food access measures with the underlying food access dimensions that they are trying to capture.

Specifically, the higher share of poor food access when using the FIES could be consistent with the fact that the FIES has a lower severity threshold than the possibly overrestrictive threshold used to denote poor food access using the FCS (e.g., WFP 2012;

> 24The exact figure is the share of households that have a poor or borderline FCS and either are moderately food insecure or are food secure according to the FIES, divided by the total share of the population with a poor or borderline FCS ( (4 _._ 8+1010 _<u>.</u>_ <u>1</u> _._ 1)<sup>).</sup>

15
Table 5: Does Future Teacher Value-Added Predict Current Teacher Value-Added When Students Change Schools?

||(1)<br>Coeffcient (se)|(2)<br>N|
|---|---|---|
|Forward Lag of English|0.051<br>(0.049)|3,231|
|Forward Lag of English (Within School)|-0.023<br>(0.056)|1,976|
|Forward Lag of Math|-0.076<br>(0.061)|3,231|
|Forward Lag of Math (Within School)|-0.017<br>(0.036)|1,976|
|Forward Lag of Urdu|-0.081<br>(0.072)|3,231|
|Forward Lag of Urdu (Within School)|0.017<br>(0.040)|1,976|
|Forward Lag of Mean Score|-0.033<br>(0.067)|3,231|
|Forward Lag of Mean Score (Within School)|0.002<br>(0.046)|1,976|

This table tests for bias in the teacher value-added calculations. The current teacher value-added of students who change schools in the next period is regressed on the value-added of their future teacher. Observations are at the child level, and standard errors are clustered at the teacher level.

Table 6: Out-of-Sample Validation of TVAs

||(1)<br>Math Test<br>Score|(2)<br>English Test<br>Score|(3)<br>Urdu Test<br>Score|(4)<br>Mean Test<br>Score|
|---|---|---|---|---|
|_MathTVA_|0.781***<br>(0.065)||||
|_EnglishTVA_||0.857***<br>(0.068)|||
|_UrduTVA_|||0.845***<br>(0.077)||
|_MeanTVA_||||0.852***<br>(0.078)|
|Lagged Score Control|Y|Y|Y|Y|
|Number of Observations|3,822|3,822|3,822|3,822|
|Adjusted R Squared|0.557|0.542|0.590|0.636|
|Clusters|1,090|1,090|1,090|1,090|

This table tests if TVAs predict the test score gains of school changers who are allocated to the new teachers. If the TVA estimates perfectly predict the “true” teacher value-added, these coefficients should be 1. Standard errors are clustered at the teacher level.

38
**Table 2:** Baseline balance of information experiment

||Co|ntrol:|Trea|tment:|D|iference|
|---|---|---|---|---|---|---|
||No Grant|& No Info|No Gra|nt & Info|||
||Mean|(S.D.)|Mean|(S.D.)|T-C|(S.E.)|
|_Panel A: School characteristics_|||||||
|Type of school|||||||
|General school (1=Yes)|1.000|(0.000)|1.000|(0.000)|0.000|(0.000)|
|Parent association president|||||||
|Highest edu. is primary (1=Yes)|0.650|(0.479)|0.662|(0.476)|0.013|(0.072)|
|Years as president|1.590|(0.830)|1.688|(0.894)|0.098|(0.129)|
|Indigenous (1=Yes)|0.200|(0.402)|0.150|(0.359)|-0.050|(0.058)|
|Teachers|||||||
|Prop. with teaching college degree|0.207|(0.323)|0.246|(0.333)|0.038|(0.049)|
|Prop. with university degree|0.603|(0.424)|0.529|(0.419)|-0.073|(0.063)|
|Failure rate|0.080|(0.060)|0.068|(0.063)|-0.013|(0.009)|
|Repetition rate|0.055|(0.053)|0.047|(0.044)|-0.009|(0.007)|
|Dropout rate|0.025|(0.042)|0.026|(0.045)|0.001|(0.007)|
|Number of schools|1|00||80|||
|_p-value of joint F-test_||||||_0.477_|
|_Panel B: Student characteristics_|||||||
|Indigenous (1=Yes)|0.151|(0.358)|0.115|(0.319)|-0.036|(0.047)|
|Female (1=Yes)|0.499|(0.500)|0.492|(0.500)|-0.007|(0.011)|
|Household wealth index (S.D.)|0.000|(1.000)|-0.011|(0.916)|-0.011|(0.080)|
|Grade 3 (1=Yes)|0.314|(0.464)|0.329|(0.470)|0.015|*<br>(0.008)|
|Grade 4 (1=Yes)|0.349|(0.477)|0.332|(0.471)|-0.018|**<br>(0.007)|
|Grade 5 (1=Yes)|0.336|(0.473)|0.339|(0.473)|0.003|(0.005)|
|Language score|495.214|(107.029)|489.013|(102.780)|-6.201|(9.327)|
|Math score|513.129|(120.626)|505.739|(116.290)|-7.390|(10.694)|
|Number of students|4|576|3|602|||
|_p-value of joint F-test_||||||_0.284_|

Notes: Robust standard errors clustered at school level in Panel B. Reported p-value is from a joint F-test of the null hypothesis that there are no mean differences between treatment and control across all variables in the panel. All variables measured in 2009. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01
variation in the price of their product, relative to other suppliers, across destination markets. For example, Australian bilateral relative prices range from 0.98 in China to 1.19 in Argentina. Canadian export price ratios vary from 1.02 to 1.24. In some cases (see italicized entries) this ratio falls below the 0.85 trigger point specified in the SSM. Therefore, we have truncated these values at 85% of the average import price for use in the empirical model, since values of BIPR below 0.85 are not permitted. For such exporter/importer pairs, any further decline in price will immediately trigger the SSM. In the case of high unit value exporters, (e.g., Canada), export prices will have to fall by more than 15% in order to trigger the SSM.

The second key difference between the two SSM approaches has to do with the application of the price trigger on a shipment-by-shipment basis. This contrasts with the year-to-year price volatility reproduced by the model. The price of grain varies considerably both within a given year, and across suppliers, but much of this variability is averaged out in the annual statistics used in our modeling work. Thus, in the absence of any adjustments, our model would not invoke the bilateral, shipment-based safeguards with sufficient frequency.

In an effort to remedy this problem, while retaining the same basic model structure, and retaining the capability to compare results between the price- and quantitybased safeguards, we introduce a multiplicative factor: _kr_   _r_ which operates on the bilateral _cif_ prices in the model in order to compute the appropriate price trigger: _ptriggerrs_  _kr pcif rs_ . Setting the parameter  1.15 bridges the gap between annual price volatility and the monthly variations in price that we use as a proxy for the shipment-by-shipment volatility data that were not available. This factor was estimated using monthly price data for Canadian wheat over the period January, 1983 to June, 2008 as a proxy for the prices of individual shipments<sup>11</sup> . The second adjustment factor,  _r_ , is indexed by exporting region, and brings bilateral annual prices in line with those observed over the historical period. Together these ensure that the frequency with which

> 11 We believe that the variability of prices across shipments is largely captured by the variability across suppliers and the intertemporal variability across months included in our analysis. However, we recognize that there are other elements, such as variation across wheat varieties, which make the variance across prices of shipments even greater than is captured in our analysis. Given this, we would expect the analysis to provide a lower-bound estimate of the frequency with which the P-SSM is invoked.

21
**Figure 5: Risk comparison of contracting diarrhea for households consuming intermittent water (compared with reliable supply)**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2.4<br>2 1.8<br>1.58<br>1.49<br>1.35 1.33 1.31<br>1.08<br>1<br>0<br>Mexico, 2002 Gaza, 2004 Gaza, 2006 Norway, 2007 Gaza, 2008 India, 2015 Alabama, US, Taiwan, China,<br>2015 2004-2006<br><!-- End of picture text --> _Source: Bivins et al. (2017) and modified by authors. In Mexico, 2002 and Gaza 2004, risk ratios are derived from odd ratios and diarrhea prevalence in the population at risk as prevalence in the control group is not available. Note: This means that, for instance in Mexico, a household with intermittent water supply has 1.8 times the risk of diarrhea compared to a household with reliable supply._ Even though the impact of IWS on mortality seems limited, the effect on morbidity is large. The impact of IWS on health are documented by Bivins et al. (2017). They use quantitative microbial risk assessment to characterize the risk of infection for fecal-oral pathogens associated with IWS and the attributable burden of diarrheal disease. Their results are reproduced in Table 2 and Figure 6. Overall, the three considered pathogen agents – which are likely to be found in water when intermittently served – are not strong drivers of mortality but are significant factors of morbidity, causing several millions of infections and diarrhea cases every year in all parts of the world, especially in South and East Asia, and Western Pacific. These findings support the claim that improving health conditions go through the improvement of health care but also through actions on disease vectors, spread by ineffective water distribution systems.

12
Bevan, A. A. and Estrin, S. (2004), The Determinants of Froeign Directg Investment Into European Transition Economies. _Journal of Comparative Economics_ , 32, 775-787.

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Campbell, H. F. (1975), Deadweight Loss and Commodity Taxation in Canada. _Canadian Journal of Economics_ , 8, 441-447.

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Clemens, Michael, Radelet, Steven, and Bhavnani, Rikhil (2004), Counting Chickens When They Hatch: The Short-Term Effect of Aid on Growth. Center for Global Development Working Paper no 44. Washington DC: Center for Global Development.

Collier, P. and Dollar, D. (2001), Can the World Cut Poverty in Half? How Policy Reform and Effectived Aid Can Meet the Interatnioal Development Goals. _World Development_ , 29, 1787-1802.

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Dahlby, B. (1994), The Distortionary Effect of Rising Taxes. In R. Robson and R. Scarth (eds.): _Deficit Reduction: What Pain; What Gain?_ Toronto: C. D. Howe Institute, pp 43-75.

30
# **Table 1: Megacities**

||**2003**|||**2015**||
|---|---|---|---|---|---|
|**Rank**|**City**|**Population**<br>**(million)**|**Rank**|**City**|**Population**<br>**(million)**|
|1|Tokyo, Japan|35.0|1|Tokyo, Japan|36.2|
|2|Mexico City, Mexico|18.7|2|Mumbai (Bombay), India|22.6|
|3|New York, United States|18.3|3|Delhi, India|20.9|
|4|São Paulo, Brazil|17.9|4|Mexico City, Mexico|20.6|
|5|Mumbai (Bombay), India|17.4|5|São Paulo, Brazil|20.0|
|6|Delhi, India|14.1|6|New York, United States|19.7|
|7|Calcutta, India|13.8|7|Dhaka, Bangladesh|17.9|
|8|Buenos Aires, Argentina|13.0|8|Jakarta, Indonesia|17.5|
|9|Shanghai, China|12.8|9|Lagos, Nigeria|17.0|
|10|Jakarta, Indonesia|12.3|10|Calcutta, India|16.8|
|11|Los Angeles, United States|12.0|11|Karachi, Pakistan|16.2|
|12|Dhaka, Bangladesh|11.6|12|Buenos Aires, Argentina|14.6|
|13|Osaka-Kobe, Japan|11.2|13|Cairo, Egypt|13.1|
|14|Rio de Janeiro, Brazil|11.2|14|Los Angeles, United States|12.9|
|15|Karachi, Pakistan|11.1|15|Shanghai, China|12.7|
|16|Beijing, China|10.8|16|Metro Manila, Philippines|12.6|
|17|Cairo, Egypt|10.8|17|Rio de Janeiro, Brazil|12.4|
|18|Moscow, Russian Federation|10.5|18|Osaka-Kobe, Japan|11.4|
|19|Metro Manila, Philippines|10.4|19|Istanbul, Turkey|11.3|
|20|Lagos, Nigeria|10.1|20|Beijing, China|11.1|
||||21|Moscow, Russian Federation|10.9|
||||22|Paris,France|10.0|

_Source_ : United Nations, World Urbanization Prospects: The 2003 Revision database.

_Note:_ For the purposes of this table, megacities are those with a population of 10 million or more.

# **More of the Poor will be Living in Cities**

As people transfer from rural to urban areas and from agricultural to other occupations, poverty which hitherto has been concentrated in the primary sector could become much more pervasive in urban regions. In 2000, approximately 30 percent of the poor lived in cities—a number which could rise to one half by 2035 (Meeting the Urban Challenge 2002). Their absolute numbers are sure to rise as is their share of the urban population with the increase depending upon the economic performance of urban economies. Thus a second stylized fact is that, _Poverty is on a trend line to becoming a predominantly urban phenomenon, with its scale largely determined by how many and what kinds of jobs are created in cities_ .

4
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fuelwood collection, reducing time on productive activities, leisure or household care. This type of indirect but important relationship between resource collection efforts and welfare is identified in a recent dissertation by Nankuni (2004). She finds that stunting in children in Malawi is associated with the long periods of time spent by women in collecting water. Thus, under circumstances where markets are lean, increasing resource scarcity can force households to reallocate labor and thereby reduce welfare.

There are other ways in which fuelwood scarcity can affect household well-being. Households may reduce their use of energy and switch to lower quality wood (Brouwer _et al._ 1997). This may limit their ability to cook or light their homes. More generally, forest cover losses can also reduce the availability of other forest products such as fruit, mushroom, poles, bush meat etc, and can have long-run effects on local hydrological services and agricultural output.

In this paper, we test the hypothesis that biomass scarcity in Malawi has a ‘net’ negative effect on household welfare. We test this hypothesis for different regions in the country and for rich and poor households. In a recent paper, Monica Fisher (2004), studying three villages in southern Malawi, shows that forests can prevent poverty by supplementing income and may even help raise standards of living. Our work focuses on biomass availability and asks similar questions using country-wide data.

Households of course engage in efforts to ‘manage’ biomass scarcity. From a practical and policy perspective, it is important to understand how households respond to fuelwood depletion. Households cope with scarcity in many ways: some plant trees, others increase the time allocated to collection, and still others decrease consumption or change cooking practices. Strategies may include improvements in fire management practices, sharing fires for cooking, preparation of fewer meals or changes in diet favoring fast-cooking (Dewees 1989).

For households the issue of biomass scarcity is a dynamic optimization problem of the renewable natural resource. That is, in each period a household decides how much biomass stock to consume and how much to leave for the future. The revealed preference of the households indicates that households in Malawi have depleted their biomass stock over the years. This may be because of high discount rates of the poor households.

4