rafmacalaba/datause-encoder-data huggingface dataset:
45

# Table IV: Ban Effects (Country-Level)

|**Variable**|**% Effect**|**Effect**|**Description**|
|---|---|---|---|
|_fLKA_<br>|-96.5%|-4,043 MM kg|Fertilizer use (kg)|
|_M _<sup>_f_</sup><br>_q,LKA_<br>|-100%|-4,043 MM kg|Fertilizer imports (kg)|
|_M _<sup>_f_</sup><br>_LKA_<br>|-100%|-1,253 MM USD|Fertilizer imports (USD)|
|_p_<sup>_f_</sup><br>_LKA_<br>|+2,068%|+6.41|Fertilizer price pre-subsidy (USD/kg)|
|_p_<sup>_f,sub_</sup><br>_LKA_<br>|+2,072%|+1.36|(Average) fertilizer price post-subsidy (USD/kg)|
|_EXP _<sup>_A_</sup><br>_LKA_<br>|-22.6%|-596 MM USD|Agricultural exports (USD)|
|_NEXP _<sup>_A_</sup><br>_LKA_|-27.9%|-601 MM USD|Net agricultural exports (USD)|
|_t_|-27.3%|-2.4p.p.|Tax rate(percentagepoints)|

_Notes_ : The table shows absolute and relative (%) changes in several variables between the baseline equilibrium with no QRs and the counterfactual equilibrium with QRs. Fertilizer use ( _fLKA_ ), in MM kg, is the total fertilizer demand by Sri Lanka’s agricultural sector. Imported fertilizer quantity ( _Mq,LKA_<sup>_f_), in MM kg, is the total quantity of fertilizer that Sri Lanka buys from the RoW, while imported fertilizer value (</sup><sup>_M_</sup> _LKA_<sup>_f_), in MM USD,</sup> is the total revenue that foreign sellers obtain from such sales. The fertilizer price in Sri Lanka ( _p_<sup>_f_</sup> _LKA_<sup>), in USD/kg, is the price earned by fertilizer</sup> sellers. The (average) post-subsidy fertilizer price ( _p_<sup>_f,sub_</sup> _LKA_<sup>)atwhichfertilizercanbeboughtbySriLankanfarmersistheratiooffarmers’total</sup> fertilizer expenditures to their total fertilizer use. Gross agricultural exports ( _EXPLKA_<sup>_A_), in MM USD, are the total agricultural sales of Sri Lanka</sup> to the RoW, while net agricultural exports ( _NEXPLKA_<sup>_A_) are gross agricultural exports minus gross agricultural imports.The tax rate (</sup><sup>_t_) is a flat</sup> income tax levied by Sri Lanka’s government on all domestic agents.
training extends from the standard two days (for U.S. high-inference tools) to four days, which allows for additional time to practice coding additional videos, and conduct a field visit where participants participate in live classroom observations.

Before observers can code using _Teach_ , they are required to pass a certification exam that involves scoring three videos within 1-point of the master codes at least 80% of the time. Analyses of the training data from Mozambique, Pakistan, the Philippines, and Uruguay indicate that of 145 participants, almost 90% (130), passed the exam. The lowest passage rate was in Mozambique, with 34 of 46 participants passing. The highest passage rate was in Uruguay, with 21 of 21 participants passing. In all four countries, local observers conducted the observations. These observers had a comparable level of education to the average citizen in their country and had no previous experience conducting classroom observations.

After the piloting, the tool underwent additional changes based on the feedback from training participants and data from the certification exam. For example, the critical thinking element was difficult for observers to understand and code consistently. To address this shortfall, the _Teach_ team created a comprehensive table with examples for each subject and quality range. This was designed to aid the observers in processing and interpreting the information from the observation and manual.

# 4. Coherence and Correlation

# 4.1 Methods

## 4.1.1. Participants and Setting

We use data from the SABER Service Delivery (SABER SD) survey — which collects information with the goal of standardizing measures of student learning, teacher and school management quality, infrastructure and learning material, and student preparedness. The SABER SD survey grew out of a concern for poor learning outcomes, as evidenced by student tests and service delivery shortfalls. The survey builds upon the SABER (Systems Approach for Better Education Results) and SDI (Service Delivery Indicators) surveys (Bold et al., 2017).

For this study, we use a representative sample of 845 primary schools, consisting of 3,600 teachers and 19,000 students in Punjab, Pakistan (2018).<sup>6</sup> In each school, one teacher from a randomly selected grade 4 classroom was observed using _Teach_ for one, 20-minute

> 6 See Geven (2018) for details of the sample.

13
is considerable variability in the death rates found. We explore determinants of this variability in the next section.

# **4.3 Firm Death Need Not Mean Permanent Exit From Self-Employment**

As noted, our focus is on the firm. However, using our panel surveys we can also track what happens to the owner once the firm has closed. Of all owners that we observe closing a firm within a year, 20 percent are already operating a new firm again within the same year. Figure A.2. looks at these re-opening rates for the five surveys for which we have the largest numbers of closed firms. We see re-opening rates increase with time, with approximately 40 percent of owners of closed firms opening a different firm within 3 years, although this rate is lower in the Townsend Thai data than in the specialized firm surveys. We will return to this issue when examining the causes of firm death, but note the importance of being able to track specific firms rather than just self-employment status in order to accurately measure firm lifespans.

# **5. Which firms are more likely to die?**

We next examine how firm death varies with several key firm and owner characteristics. The aim in this section is to provide evidence that helps answer descriptive questions about which types of firms are more likely to die. The next section then provides some theoretical structure to help better understand why certain firms are more likely to die.

We begin with examination of univariate associations, and then move to multivariate analysis. Since the previous section shows that unconditional death rates are approximately linear over a five-year period, we summarize some of the information graphically by calculating standardized annual death rates. Graphically, we focus on time horizons of up to 3 years, since this is the range over which we observe firm death rates in a wide range of surveys and countries. Standardized annual death rates are then calculated by dividing the death rate by the period of measurement, so that a death rate of 18 percent over two years will be shown graphically as an annualized rate of 9 percent.

We then use probit and proportional hazard models to complement this graphical analysis and to examine the multivariate associations of firm and owner characteristics with firm death. We estimate a probit regression specification of the following form: 12
|**Table 5 – Placebo Tests**|**HAZ score**|**Stunting**|
|---|---|---|
|**Test 1: Mothers not present in refugee areas**|||
|q=1,<br>∂=1|-0.406|0.0862|
||(0.249)|(0.117)|
|_N_|_2315_|_2315_|
|**Test 2: South-East Vs. North-West Regions**|||
|q=1,<br>∂=1**, **South-East|0.0221|-0.135|
||(0.945)|(0.299)|
|q=2,<br>∂=1**, **South-East|0.00214|-0.00235|
||(0.132)|(0.0512)|
|_N_|_429_|_429_|
|q=1,<br>∂=1**,**North-West<sup>22</sup>|-0.765***|0.173*|
||(0.259)|(0.0917)|
|q=2,<br>∂=1**,**North-West|-0.0875**|0.0229*|
||(0.0342)|(0.0120)|
|_N_|_782_|_782_|
|q=1,<br>∂=1**,**North-West**,**Low Treatment areas(East)<sup>23</sup>|1.031|-0.0242|
|_N_|(2.859)<br>_75_|(0.828)<br>_75_|
|**Test 3: Young Vs Old Mothers (Born before 1993)**|1.204|-0.143|
||(0.762)|(0.187)|
|**Test 4: Refugee hosting Vs. non-hosting areas ( children before the crisis)**|-0.0615|0.0311|
|**Source:**2015/2016 and the 1991/1992 Tanzania DHS. Standard errors in parentheses, Sta|ndard Errors clustered at the cluster level. In a<br>(0.0777)|ddition to cluster fixed effects, the regression controls for<br>(0.0241)|

**<mark>Source:</mark>** <mark>2015/2016 and the 1991/1992 Tanzania DHS. Standard errors in parentheses, Standard Errors clustered at the cluster level. In addition to cluster fixed effects, the regression controls for the gender of the head, the birth order and gender of the child but also mother and child year of birth fixed effects; * p < 0.10, ** p < 0.05, *** p < 0.01</mark>

22 These are Kagera, Kigoma, Geita, Mara, Mwanza and Shinyanga.

23 These are are Biharamulo, Muleba, Bukoka Rural and Bukoka Urban districts, which have been defined as high treatment districts by Baez (2011) using retrospective information from the 5th round of 2004 Kagera Health and Development Survey (KHDS).

32
universities reflect a generalized problem of ageing throughout the science and technology system. The majority of full and associate professors are above 55 years old, and there are insufficient replacements in the pipeline. This is likely to lead to a generational gap in human resources of the university system in the near future. Notwithstanding isolated exceptions, there has been no entrepreneurial spirit among academics working in universities. The most entrepreneurial character so far is reflected in the desire of teachers to do “outside the class” teaching to earn extra money. This is explainable by the low base salaries of academic staff and their need to earn second or third incomes through various teaching and consulting assignments.

As seen in this section, the linkages between universities, research institutes and enterprises in general, and between universities and firms in particular, still face many hurdles that undermine university attempts to better serve the needs of the local economies where universities are located. The view from firms, reflected in various surveys, confirms that there is a demand for technology and training services provided by R&D institutes and universities. However, the demand has been hardly met, and the extent of relationship of firms with universities is less than satisfactory. In this context, the contribution of R&D and training institutions in general and of universities in particular is below the desirable level.

# **CASE STUDIES OF FOUR UNIVERSITIES**

# **HANOI UNIVERSITY OF TECHNOLOGY (HUT)**

Hanoi University of Technology (HUT) was established by government decree on March 6, 1956 as the first university of the Democratic Republic of Vietnam. Today 14
over 80 percent of the total arable land. Of this maize alone is grown on over 70% of the arable land. With most of the arable land in the smallholder subsistence sector, Malawi does not have a lot of room for export diversification, at least compared to the neighboring countries. The livestock sub-sector contributes about 7 percent of agricultural GDP, and in 2007, the cattle population was estimated at about 800,000 while that of shoats (goats, pigs and sheep) was estimated at 2.3 million, 636,000 and 175,394, respectively. Fishing and forestry sub-sectors each account for less than 6 percent of agricultural GDP.

9. **Food security has improved since 2006 due to the recorded surpluses in maize and other food crops** . The food balances sheets have shown a positive trend since 2006 season (see Table 1). However, in spite of the national food surpluses, the market signals indicate some increases in the producer price of maize which is inconsistent with the declared surpluses. Many attribute the market tightening to speculation among the private traders, and an anticipation of over-estimation of the food production level.

**<u>Table 1: Trends in domestic food gap (energy foods)</u>**

|**Year**|**Total Food Requirement (MT)**|**Domestic Availability**<br>**(MT)**|**Gross maize Gap/**<br>**Surplus (MT)**|
|---|---|---|---|
|1999/00|2,023,625|2,023,625|98,870|
|2000/01|1,643,274|2,432,334|789,060|
|2001/02|1,825,449|1,495,104|-195,229|
|2002/03|2,035,643|1,351,549|-684,094|
|2003/04|2,016,052|1,966,024|-50,028|
|2004/05|2,039,291|1,502,259|-537,032|
|2005/06|2,183,506|2,620,513|487,007|
|2006/07|2,255,049|3,444,655|1,189,606|
|2007/08|2,352,668|2,790,546|437,878|

Source: Ministry of Agriculture and Food Security.

10. **Although total agricultural output has been increasing over the past decade, Malawi’s agricultural productivity, particularly among the majority of the smallholder farmers, has shown signs of stagnation or decline.** For example, as shown in Figure 3, there has been no long-term improvement in average maize yields. Maize yields remain highly dependent on weather patterns and the implementation of input support programs, such as for example, the starter pack in the late 1990s, the drought recovery and targeted inputs program in early to mid 2000, and the input subsidy program thereafter. In the case of tobacco, the substantial yield gains attained in the early 1990s more especially after the repeal of the Special Crops Act have been reversed as average tobacco yield has been almost stagnant since the mid1990s<sup>3</sup> . Most of the yield stagnation and fluctuations experienced in the first half of the last decade can be attributed to low adoption and less intensive use of productive agricultural technologies, unreliable rainfall pattern and also production inefficiencies.

> 3 The repeal of the Special Crops Act provided the smallholder farmers the right to grow and sell burley tobacco. Before 1991, burley tobacco was exclusively and legally an estate crop (Ng’ong’ola et al. 1997).

5
firm-level datasets, this data spans the manufacturing and service sectors. Approximately 61 percent of the observations reflect establishments in manufacturing, while the rest are in the service sector.

The World Bank enterprise survey data was developed to provide information on firms’ performance as well as on aspects of the investment climate faced by firms. Regarding firms’ performance, the dataset contains information on a number of variables such as current employment, investment, or sales. It also contains retrospective information for these three variables, in most cases for up to three periods before the year of reference of the survey, but in some few cases only for two or one period before. We focus on the employment level or growth of _permanent_ workers as our outcome variables of interest. We do not include other forms of employment, such as contract labor or temporary workers because there are differences in the consistency of reporting of total employment (i.e. permanent plus temporary) across some countries. Other important, additional, reasons to focus on the level and growth of permanent employment are that these variables are more likely to reflect the long-run performance of the firm, and their evolution is of higher concern for policymakers, as non permanent forms of employment tend to be considered as substandard or less preferred by workers.

Following Davis and Haltiwanger (1992, 1999), our measure of employment growth refers to the change of permanent employment during the period _t_ and three years before, divided by the firm’s simple average of permanent employment during the same period.<sup>11</sup> The measure is symmetric around zero and bounded by values -2, and +2 and while monotonically related to the conventional growth rate and a second order approximation of the logarithmic first difference, it allows computing meaningful growth rates for firms suffering sharp expansions or contractions, avoiding any arbitrary treatment of outliers. This is quite useful, because even when by construction our sample does not register firm exit, some firms experience sharp changes in employment due to its early/late stage of

> 11 For firms that have been in business less than four years, we compute employment growth based on a two year difference, or if in business for only two years, one year difference.  We then define controls EG _(s_ ) as one if we have computed employment growth _s_ years apart, and zero otherwise and include these variables in our growth estimates to account for such differences in our estimations.

9
To perform statistical inference, empirical standard errors for (7) and (8) are obtained using the standard i.i.d. nonparametric bootstrap with paired sampling  _y_ , _i_<sup>_z_</sup> _i_  , resampling from the original data 500 times.

_Choice of kernel and bandwidth_ : Implementation of local linear estimation requires the specification of the kernel _k_ , the weighting function, and bandwidth _h_  0 , the window width in which the kernel function is applied. We opt for the triangular kernel given that it is boundary optimal and thus well suited to RD problems (Cheng et al. 1997).<sup>18</sup> The choice of bandwidth is a relatively more important decision given the trade-off between estimation bias and variance. Consensus in the literature on valid and robust methods for bandwidth selection for RD problems is lacking.<sup>19</sup> Given this, we discount the specific method used to select the optimal bandwidth and instead emphasize checking the robustness of our findings to reasonable deviations in bandwidth from the optimal bandwidth choice.

To select the optimal bandwidth, we rely on the default bandwidth choice in Nichols’ (2007) `rd` program for `Stata` which assigns positive weight to at least 30 observations on each side of the cutoff, and applies the same bandwidth for the local linear estimators of the conditional mean outcomes (and probabilities of treatment) above and below the cutoff (i.e.,   _h_  _h_  _h_ ). In our case, the default choice translates to a bandwidth of three or four percentage points for our RD impact estimations. We believe that this is a conservative choice, and

> 18 As Imbens and Lemieux (2008) note, while more sophisticated kernels are available, they do not provide any significant gain in asymptotic bias reduction. Furthermore, they note that, in general, parameter estimates appear to be robust to the choice of kernel.

> 19 This is, in large part, due to two reasons. First, standard automated procedures for bandwidth selection for nonparametric regression have been developed with the estimation of functions at interior points in mind; the nature of the trade-off between bias and variance at interior points may differ from the nature of the trade-off at boundary points (Ludwig and Miller 2005). Second, the literature on the development of bandwidth selectors customized to the RD context is an emergent one (e.g., McCrary and Royer 2006; Imbens and Lemieux 2008; Imbens and Kalyanaraman 2009). In addition, the relative finite sample performances of the proposed bandwidth selection methods for RD designs have yet to be rigorously assessed.

22
Table 3: Net gains from investing along the different corridors (in %)

||Dakar|-Abidjan(∼300|0 kms)||
|---|---|---|---|---|
|C|Total real inc|omegains|Net total|gains|
|osts|Countries along<br>the corridor|All countries|Countries along<br>the corridor|All countries|
|Low bound|3.85|0.46|3.53|0.42|
|High bound|385|046|299|035|
||.<br>Abidj|.<br>an-Lagos(∼100|.<br>0 kms)|.|
|Costs|Total real inc|omegains|Net total|gains|
||Countries along<br>the corridor|All countries|Countries along<br>the corridor|All countries|
|Low bound|0.13|0.10|0.12|0.09|
|High bound|0.13|0.10|0.09|0.07|
||Bamak<br>|o Conakry (∼9<br>|80 kms)<br>||
|Costs|Total real inc|omegains|Net total|gains|
||Countries along<br>h i|All countries|Countries along<br>h i|All countries|
||te corrdor||te corrdor||
|Low bound|0.47|0.12|0.10|0.11|
|Hih bound|047|012|-049|009|
|g|.<br>Bamako<br>|.<br>Monrovia(∼1<br>|.<br>150 kms)<br>|.<br>|
|Costs|Total real inc|omegains|Net total|gains|
||Countries along<br>|All countries|Countries along<br>|All countries|
||the corridor||the corridor||
|Low bound|0.77|0.14|0.38|0.12|
|High bound|0.77|0.14|-0.24|0.10|
||Conakr<br>|yAbidjan(∼22<br>|10 kms)<br>||
|Costs|Total real inc|omegains|Net total|gains|
||Countries along<br>the corridor|All countries|Countries along<br>the corridor|All countries|
|Low bound|1.75|0.23|1.36|0.20|
|High bound|1.75|0.23|0.72|0.15|
||Dak<br>|ar-Labe(∼1140<br>|kms)<br>||
|Costs|Total real inc|omegains|Net total|gains|
||Countries along<br>the corridor|All countries|Countries along<br>the corridor|All countries|
|Low bound|1.78|0.19|1.48|0.18|
|High bound|1.78|0.19|0.98|0.15|

Note: Authors’ calculations.

25
**Table A.5. Unrestricted irrigation: median norovirus and rotavirus infection risks per person per year from the consumption of 100 g of wastewater-irrigated lettuce every two days estimated by 10,000 KaravarsamisHamilton Monte Carlo simulations**<sup>_a_</sup>

|**Wastewater quality**<br>**(****_E. coli_per 100 mL)**|**Median norovirus**<br>**infection risk pppy**|**Median rotavirus**<br>**infection risk pppy**|
|---|---|---|
|10<sup>7</sup>−10<sup>8</sup>|1|1|
|...<br>10<sup>3</sup>−10<sup>4</sup>|0.25|0.36|
|100−1000|2.9 × 10<sup>−2</sup>|4.5 × 10<sup>−2</sup>|
|10−100|2.9 × 10<sup>−3</sup>|4.6 × 10<sup>−3</sup>|

> _a_ Assumptions: 10−15 mL wastewater remaining on 100 g lettuce after irrigation; 0.1−1 norovirus per 105 _E. coli_ ; no pathogen die-off; _N_ 50 = 6.7 ± 25% and α = 0.253 ± 25% for rotavirus; and dose-response data (Teunis _et al_ ., 2008) ± 25% for norovirus.

_Source:_ Mara and Sleigh, 2010a.

**Table A.6. Unrestricted irrigation: median norovirus infection risks per person per year from the consumption of 10−12 g of wastewater-irrigated lettuce on four occasions per week estimated by 10,000 Karavarsamis-Hamilton Monte Carlo simulations**<sup>_a_</sup>

|**Wastewater quality**<br>**(****_E. coli_per 100 mL)**|**Median norovirus infection**<br>**risk pppy**|
|---|---|
|10<sup>7</sup>−10<sup>8</sup>|1|
|...<br>10<sup>4</sup>−10<sup>5</sup>|0.31|
|10<sup>3</sup>−10<sup>4</sup>|3.6 × 10<sup>−2</sup>|

> _a_ Assumptions: 10−15 mL wastewater remaining on 100 g lettuce after irrigation; 0.1−1 norovirus per 105 _E. coli_ ; no pathogen die-off; dose-response data (Teunis _et al_ ., 2008) ± 25%. _Source:_ Mara and Sleigh, 2010a.

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