rafmacalaba/datause-encoder-data huggingface dataset:
In addition, the results are likely to be also valid for children who had an older sibling in school but whom the older sibling forgot to list at the time of the interview, and to children who had a sibling in school but whose household was omitted during the enumeration. Those children share the most important characteristic of baseline children, which is that they were either in school themselves or that they had an older sibling in school. Therefore it is reasonable to assume that their elasticity of preschool attendance with respect to preschool conditions was similar to the elasticity for children in the baseline. Children who were not in school themselves and did not have an older sibling in school might have a di¤erent elasticity of preschool attendance, so the e¤ects of the school meals program could have been di¤erent for them<sup>25</sup> .

# 4.2 Measuring school participation

The ...rst outcome of interest is school participation. In this paper, a child is de...ned to be participating in school on a given day if she is present in school on that day. School participation can quite di¤erent from o¢ cial enrollment. For example, children might be enrolled in school but miss class because they are ill. Since we are interested in the amount of time children spend in school, participation is the right outcome to look at. If the program in‡uences the probability that a child will show up in school given that she is enrolled, for example because it a¤ects the probability that a child is ill, the change in enrollment will be a biased estimator of the change in school participation. In the Kenyan context, there is an additional reason why one should not trust enrollment data as indicators of school participation. As described in section 3, even when a child is enrolled in school, the teacher has a lot of discretion in deciding whether to keep a child in class when the parents have not paid “enough.”This makes enrollment a very noisy measure of school participation.

To circumvent these problems, this evaluation uses direct observations of school participation rather than measures of o¢ cial enrollment. The NGO ...eld sta¤ visited the schools at least once per school term and observed and recorded pupil and teacher attendance, and conducted interviews with pupils and teachers. Attendance checks were done through name calls rather than headcounts, which allowed the ...eld sta¤ to match school participants to the baseline of the school. When the ...eld sta¤ found a child in class who was not yet on their list of existing participants for that school, they asked the child, the teacher and/or one of the child’s older siblings for the name of her parent, and tried to ...nd the parent in the baseline.

> 25 The 1999 Kenya Census data will, when available, enable us to estimate the probability that a child does not have an older sibling in school and is not enrolled in school herself. This will allow us to assess how representative the baseline is of the general population.

14
**Table 3. Dutiable LDC exports**

||**Non dutiable**<br>**– MFN**|**Non dutiable –**<br>**Preferential**|**Dutiable**|
|---|---|---|---|
|Canada|16.3|83.6|0.06|
|Japan|54.2|44.5|1.2|
|Norway|27.3|63.0|9.7|
|Switzerland|66.9|32.2|0.9|
|USA|6.1|48.8|45.0|
|Brazil|16.5|0.00|83.4|
|China|85.9|5.9|8.2|
|India|0.44|29.5|70.1|
|Korea|17.5|18.1|64.4|
|Mexico|6.9|0.00|93.1|
|_Total (with EU)_|30.4|51.5|18.1|
|_Total(without EU)_|_27.0_|_38.3_|_34.8_|

Source: Laborde (2008) using MAcMapHS6v2 1 data.

In most of the potential markets considered, the 3 percent of tariff lines with the highest burden of tariffs accounts for 90 percent of the tariffs paid by LDCs. Figure 6 shows the share of tariffs levied on exports from LDCs for three economies in which duty-free-quota-free access might be particularly important. The figure makes clear that duties paid are extremely concentrated in many markets. In China, the 97 percent of LDC exports which yield the lowest tariff revenues account for 12.8 percent of the total burden imposed by duties. In the United States, this 97 percent of imports accounts for 2.2 percent of duties paid. In Japan this 97 percent accounts for a minuscule fraction of imports. Clearly, this high concentration of duty payments means that a 97 percent DFQF might bring about a much smaller improvement in market access than its nominal coverage of 97 percent might suggest.

12
structures of the parent and subsidiary and the implications for the demand structure. Reforms in his model impact the global reallocation of capital. But there is no zero-profit constraint for firms and no endogenous productivity impacts from the actions of imperfectly competitive firms. Building on Petri, Dee _et al_ . (2003) and Brown and Stern (2001) employ multi-country, three-sector models with homogeneous imperfectly competitive firms. Despite including firms in their model, liberalization remains based on a global reallocation of capital. The welfare results for the countries in the model of Dee _et al_ ., depend heavily on rents gained or lost. In the Brown and Stern model, countries generally gain or lose from the liberalization depending on whether they are capital importing or exporting, respectively. However, there is no entry or exit of firms in national markets. So, profits or losses may occur on activities in individual markets without an entry or exit response from the firm or its competitors in that national market; and there is no DixitStiglitz productivity externality from additional providers of services.

Markusen, Rutherford and Tarr (2005) developed the first numerical model of FDI in an imperfectly competitive framework that incorporated the above stylized facts with an entry and exit decision by the foreign and host country firms based on zero profit constraint for the firm types. They show that the endogenous productivity effects from the Dixit-Stiglitz externality in their model results in important differences from the implications of a Heckscher-Ohlin model. Their model was applied to datasets and policy issues of real economies in small open economy models by Jensen, Rutherford and Tarr (2006; 2007; 2010), Rutherford and Tarr (2008; 2010) and Balistreri, Rutherford and Tarr (2009) in Russia, Kenya and Tanzania. But these models could not assess regional preferences in services. Konan and Maskus (2006) assessed FDI liberalization in Tunisian services with a small open economy, perfectly competitive model in which regulatory barriers imposed a wedge between lowest possible costs and prices due to both high-cost producers and cartel pricing (the latter of which was not endogenously modeled).

Recently, Arkolakis, Ramondo, Rodriguez-Clare and Yeaple (2018) produced an important numerical model of foreign direct investment. Their model is a heterogeneous firms, single sector, single factor, multi-country model, where firms can locate production in any location. They do not, however, compare their heterogeneous firms model to a Krugman or Armington style model.

The model described in this paper is closest to the small open economy models developed by Balistreri, Jensen and Tarr (2015) and especially Jensen and Tarr (2012). Balistreri, Jensen and Tarr (2015) have shown that there is an analogy to trade diversion in goods whereby preferential 7
Thus, the machinery/labour ratio is a function of past and present values of the machinery/labour price ratio and the land/labour price ratio, plus lagged values of public and private R&D, extension expenditures, farm size and the policy dummies for tax concessions and interest rates. Variable deletion tests reduced the list to the five variables reported in Table 2, meaning that extension, mechanical patents and farm size were not even jointly significant.

**Table 2: Explaining Factor Ratios:** OLS **Regressions in the Levels**

||**OLS: Depe**|**ndent variab**|**le is Log Rati**|**o Machinery/L**|**abour: AdjR2**|=**0.924, DW**|=**1.63**||
|---|---|---|---|---|---|---|---|---|
|**Variable**|**_PM/pL_**|**pA'/P**|**RD(-10)**|**TAXDUM**|**INTDUM**||**CRDW**<br>**Test**|**DF Test**|
|**Coerricient**<br>**t-statistic**|**-0.71**<br>**(-10.6)**|**0.12**<br>**(2.9)**|**0.10**<br>**(2.4)**|**0.09**<br>**(2.6)**|**0.006**<br>**(2.0)**||**1.63**<br>**(0.49)**|**-5.58**<br>**(-5.16)**|
||**OLS: De**|**pendent vari**|**able is Log Ra**|**tio Fertilizer/L**|**and, Adj RZ**|=**0.949, DW**=|**1.56**||
|**Variable**|**P,/P,**|**P,/PL**|**RD(-5)**|**CHP(-2)**|**SIZE**|**(PS/PA)MA**|**CRDW**<br>**Test**|**DF Test**|
|**Coefficient**<br>**t-statistic**|**-1.79**<br>**(-7.7)**<br>l|**-1.63**<br>**(-8.1)**|**0.42**<br>**(2.4)**|**0.18**<br>**(3.3)**|**-3.3**<br>**(-3.3)**|**-0.20**<br>**(-I.8)**|**1.56**<br>**(0.49)**|**-4.97**<br>**(-5.16)**|

Three tests for cointegration between combinations of variables were used; namely the DF test, the CRDW test and the Johansen procedure, which is further discussed below. Both the CRDW and DF tests suggest that the machinery/labour regression cointegrates and the more powerful Johansen procedure (reported in the next section) identified four cointegrating vectors. Thus, the relationship is not spurious, the fit is good and there is no serial correlation. The key variables are significant and the coefficients have the expected sign. The coefficient of the own price term (-0.71) is the direct partial elasticity of substitution. The positive sign on the land/labour price ratio conforms to Frisvold's (1991) expectation and suggests that machinery and land are substitutes. Public R&D has its greatest effect with a lag of ten years and both tax concessions on machinery purchases and negative interest rates increase the machinery/labour ratio.

The fertilizer/land equation also cointegrates according to the CRDW test and the Johansen method, which again identified four cointegrating vectors. The DF test indicates that the regression does not cointegrate, but the 14
# **<u>Appendix</u>**

# **_Appendix 1:_** _LPMT scoring mechanism and CIMS proxy LPMT scoring method_

|**Category**|**LPMT Question**|**LPMT Scoring Mechanism**|**Availability in CIMS survey and**<br>**Proxy variables**|
|---|---|---|---|
|**Household**<br>**characteristics**|What is the gender of the head of the<br>household?|1 if female, 0 if male|Available|
||How many children below 18 years of age<br>are in the household?|1 if above 3, 0 if below 3|Available|
||How many members above 18 years of age<br>are in the household?|1 if above 6, 0 if below 6|Available|
||Can the head of the household read or write<br>in English or Sierra Leonean language?|1 if illiterate, 0 if literate|Not available. Education level (i.e.,<br>below primary dummy) of<br>respondent is used as aproxy|
|**Household Assets**|How many rooms does your house have?|1 if number of rooms divided<br>by household size is less than<br>1, 0<br>if number of rooms divided by<br>household size is more than 1|Available|
||Does your household have cement walls?|1 if no, 0 if yes|Available|
||Does the household use a flush to pit<br>latrine?|1 if the answer is no, 0 if the<br>answer isyes|Available|
||Does the household use a bottle/sachet<br>water for drinking?|1 if the answer is no, 0 if the<br>answer isyes|Not Available|
|**Work Status**|Does the head of the household/main<br>income-earner have a contract with his/her<br>employer?|1 if the answer is no, 0 if the<br>answer is yes)|Not available. We use information<br>on whether respondent has a formal<br>contract as aproxy|
||What is the occupation of the household<br>head/main income-earner of the family?|Validation question|Available|
|**Illness/Disability**|Does anyone in your household suffer from<br>illness or disability?|1 if yes, 0 if no|Available|
|**Total possible score**||10|9|

_Source_ : Authors’ calculations based on administrative data from NaCSA (2020) and CIMS(2020). _Note_ : LPMT variables and threshold were calibrated by analyzing the overlap the LPMT score and poverty level in the SLIHS (2018) household survey data.

32
# 3 The data

Having described the conceptual framework and estimation strategy, we now present the data. The data used in this paper come from two sources: living standard household surveys, and the population census.

The living standard data come from two rounds of Nepal Living Standards Survey (NLSS). The fi rst round was conducted in 1995/96 while the second took place in 2002/3. The NLSS surveys collected detailed information on households and individuals using nationally representative samples. The 1995/96 NLSS survey is used as source of detailed information about locally available amenities. It is also used to estimate the income regression (1).

Survey data are complemented with information from the 2001 population census. The short population census questionnaire was administered to the whole population. It contains information about ethnicity and caste. For a randomly selected 11% of the census population, additional information was collected using a second, longer questionnaire. This questionnaire collected information on district of current residence, district of residence 5 years prior to the census, and district of origin. Detailed information is also available on gender, age, education, unemployment, occupation, and motive for migration, if any. The Nepalese Central Bureau of Statistics was kind enough to merge the short and long questionnaire datasets for the 11% of the population covered by the long questionnaire. This provides a very large data set on which we estimate the migration regression (3).

Nepal is divided into 75 districts and further subdivided into 3,915 VDCs and 35,235 wards. The 11% population census covers approximately 2.5 million individuals in 520,624 households. 345,349 of these individuals are living in a district other than their district of residence and 119,475 have moved in the fi ve years preceding the census, that is, in the period between the 1995/96 NLSS and the 2001 census. Most of these individuals have moved for reasons other 12
tiary and the rest) with assumptions assigned for each category. For the main roads in the baseline, 35 km per hour is assumed on the primary network, and 20 km per hour for the rest of the main network (secondary and tertiary roads). While trucks may move faster on certain segments than others, the overall limited quality of the roads as well as activities along side the roads slow down land-based freight movement.

In addition, frictions at the border increase travel time between pairs of locations. Delays are added at the border in two ways: a transit time for crossing countries which are neither the origin or the destination, and a final delay when reaching the country of destination. Delays for transiting a country are assumed to be 20 hours for each border crossing. A same time is assumed across all borders, given the lack of consistent data for time delays at borders in West Africa. Delays when reaching the country of destination are assumed to be 50 hours, which represent the time needed to go through the administrative trading processes, not included in the transit time. The choice of the transit and final border times is motivated by Table A1 which provides cross-country time estimates, but given the unreliability of some numbers across different sources, we settle on a same number across all countries and pairs of countries. No additional delay is assumed for trade within countries.

In the counterfactuals, we increase the driving speed to 80 km per hour on the upgraded segments.<sup>5</sup> In complementary scenarios, transit delays are reduced to zero and final border delays are halved.

# **4.2 Scenarios**

We consider three types of scenarios that we incrementally implement to compare the differential effects of each policy.

**Transport investments** The nature of the upgrades along the corridors vary across segments. Upgrading existing roads consist in either increasing the number of lanes, improving road quality, or removing obstacles on the road. Some sections require little additional investments while other sections categorized into secondary or tertiary roads are to be completely rebuilt. The construction of bridges or other infrastructure than roads are not considered. Figures 1 and 2 shows the location of the new transport segments included in the corridor investments.

5While this speed is relatively high, it is the driving speed for trucks observed in richer economies and expected on newly renovated segments.

10
The left-hand side of (7) is TFP growth as computed (not estimated) in the standard growth accounting approach. An alternative route to a full estimation of (6) is thus to estimate the reduced form (7) using the (year by year) available results from (2) in terms of TFP growth rates _A_<sup></sup> ( ). This is convenient as these are indeed readily available from standard growth accounting _A_ exercises for a number of countries, including MENA countries (see below).

We will therefore use (7) below to estimate  , the pure externality effect of infrastructure.

# **2.2. Data and estimation**

There are two main options for estimating (7). One is based on regional panel data, while the other one is a country-per-country approach based on time series data.

The panel estimation technique rests on the assumption that a common production function exists for the countries under analysis, with individual country effects to be controlled for. While this approach has been extensively used with state / provincial panel data for India (Hulten et al. 2006), Italy (La Ferrara & Marcellino, 2000) and the US (Holtz-Eakin, 1994), it remains to be seen whether it can work when applied to a set of countries, albeit in the same region<sup>2</sup> . We report below panel estimations suggesting that indeed this modeling of growth accounting could be fruitful in the specific case of MENA countries.

We then perform individual country estimations, which more realistically do not assume that there is a common underlying technology for all countries. This has been the approach used by most non-infrastructure growth accounting studies.<sup>3</sup>

> 2 In a similar exercise for East Asian countries, Straub et al. (2008) found that panel models could not cope with cross-country heterogeneity.

> 3 See for example Barro & Sala-i-Martin (2003).

7
We find that assignment to the program significantly reduces dropout by 1.3 percentage points (3.6 percent of the baseline dropout rate) in the subsequent school year. The effect size is similar across the three treatment arms, ranging from 1.1 to 1.5 percentage points, and we cannot reject the hypothesis that the impacts are equal across treatment arms. This suggests that the core intervention of providing information on _how_ dropout can be prevented is driving the results. Impacts are heterogeneous, with dropout reduction concentrated among higher-risk students, students attending larger primary schools (and therefore likely facing fewer supply constraints), and male students. Furthermore, imperfect compliance, both in terms of incomplete take-up of treatments and contamination of the control group, suggest that intent-to-treat (ITT) estimates are likely a lower bound of the effect of the program on the treated. We therefore also estimate impacts on program compliers and find that the local average treatment effect is a 3.1 percentage point reduction in dropout (about 8 percent of the baseline dropout rate), roughly double the ITT estimate but much less precisely estimated.

To further understand the mechanisms driving the results, we collected data on several intermediate outcomes and conducted a series of focus groups with program actors. Across treatment arms, school principals report an increase in their own expectations of the returns to education for their students, in their beliefs about their own ability to change students’ outcomes, their self-reported prioritization of dropout prevention among their responsibilities, and a large increase in targeting their efforts to at-risk students.

While the magnitude of the estimated impacts is modest, the program leverages the Ministry of Education’s existing administrative data structures, management systems, and personnel, with limited additional costs. Specifically, total costs per student are estimated at USD 2.91, 36% of which corresponds to the design of the program— i.e., the cost of implementing the program amounts to USD 1.85 per student— and 45% to the opportunity cost of principals and teachers. Estimates of the monetary benefits accruing to students who do not drop out from higher labor market earnings suggest an internal rate of return of 28% and a benefit-cost ratio of almost 19-to1, suggesting that ENTRE is a highly cost-effective, sustainable, and scalable approach to addressing short-run drivers of dropout. Moreover, ENTRE may be a platform program on which additional interventions to reduce dropout can be developed and rolled out, presenting a promising 5
**Figure 3: Cost-effectiveness of improving learning – Percentage of times each program is in the bottom and top quartile of cost-effectiveness, across 100,000 draws**

<!-- Start of picture text --> Percentage of<br>Percentage of draws<br>draws in top<br>in bottom quartile of<br>quartile of cost-<br>cost-effectiveness<br>effectiveness<br>Minimum conditional cash transfers, Malawi<br>Read-a-Thon, Philippines<br>Girls' merit scholarships, Kenya<br>Individually-paced computer assisted learning, India<br>Extra contract teacher + streaming, Kenya<br>Village-based schools, Afghanistan<br>Camera monitoring, India<br>Remedial education, India<br>Textbooks for top quintile, Kenya<br>Teacher incentives (year 2), Kenya<br>Electing school committee and linking to local<br>government, Indonesia<br>Linking school committee to local government,<br>Indonesia<br>Streaming by achievement, Kenya<br>Providing earnings information, Madagascar<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> Source: Data adapted from J-PAL (2014a). Calculations by authors.

26
**Table 4. The impact of DSM on farmers’ decision to purchase maize and wheat seeds, by cohort and survey year aggregations**

|**Maize**||||**Wheat**||||
|---|---|---|---|---|---|---|---|
|**Cohort**|**ATT(g)**|**Survey**<br>**year**|**ATT(t)**|**Cohort**|**ATT(g)**|**Survey**<br>**year**|**ATT(t)**|
|2013|-0.073<br>(0.062)|2016|-0.041<br>(0.072)|2013||2016|0.102<br>(0.137)|
|2014|0.169*|2019|0.208*|2014|0.066|2019|0.094|
||(0.086)||(0.061)||(0.160)||(0.088)|
|2015|0.056<br>(0.072)|||2015|0.046<br>(0.115)|||
|2016|0.172|||2016|0.349|||
||(0.236)||||(0.186)|||
|2017|0.271*|||2017|0.089|||
||(0.063)||||(0.096)|||
|2018|0.272*|||2018|0.135|||
||(0.113)||||(0.094)|||
|2019|0.095<br>(0.079)|||2019|-0.024<br>(0.113)|||
|ATT|0.153*||0.084|ATT|0.092||0.098|
||(0.054)||(0.057)||(0.079)||(0.099)|

_Source_ : Authors estimation based on the ACC 2012, 2016, and 2019 survey data.

_Note_ : P-value for a pre-test of parallel trends assumption is 0.192 for maize and 0.169 for wheat. Comparison group: “never treated.” Anticipation periods: 0. Estimation method: Doubly Robust. Standard errors are reported in parenthesis. * _p <_ 0.10.

Figure 1 presents a summary of ATTs based on the event-study (dynamic) aggregation and shows results nearly identical to those from aggregation by cohort and survey year. On average, DSM led to a 14-percentage-point increase in the proportion of farmers purchasing maize seed during posttreatment years. Specifically, DSM significantly increased the share of farmers buying maize seed in the second, fourth, and fifth years after its introduction in a district. Though positive, the effect during the same year and the year after is estimated with noise, making it difficult to statistically distinguish from zero. Consistent with the discussion above, the event-study aggregation indicates no evidence of DSM’s impact on the percentage of farmers purchasing fresh wheat seed.

16