rafmacalaba/data-use-mentions-extended huggingface dataset:
past variability of income. Summary statistics from the baseline survey are presented in Table 1, and variable definitions are provided in Online Appendix C.<sup>11</sup> After the completion of the survey, credit history training, and fingerprinting of the treatment group, the names and locations of the members that applied for loans along with their treatment status were handed over to MRFC loan officers so that they could screen and approve the clubs according to their protocols. Among other standard factors, MRFC conditions lending on the club’s successful completion of 16 hours of training. MRFC approved loans for 2,063 out of 3,206 customers (in 121 out of 239 clubs). Of the customers approved for loans, some failed to raise the required down payment and others opted not to borrow for other reasons. The final sample consists of 1,147 loan customers from 85 clubs.<sup>12</sup> These loan customers received loan packages with an average value of MK 16,913 (US$117).<sup>13</sup> During the months of July and August, farmers harvested the paprika crop and sold it to CP at predefined collection points. CP then transferred the proceeds from the sale to MRFC who then deducted the loan repayment and credited the remaining post-repayment proceeds to an individual farmer’s savings account. This garnishing of the proceeds for loan repayment essentially allows MRFC to “seize” the paprika crop when farmers sell to CP (and for most farmers it is the only sales outlet).<sup>14</sup> Farmers could also make loan repayments directly to MRFC at their branch locations or during credit officer visits to their villages; this occurred, for example, among the small number of farmers who sold to paprika buyers other than CP.

We also implemented a follow-up survey of farmers in August 2008, once crops had been sold and income received. The sample size of this follow-up survey is 1,226 in total
one. Second, we set the depth of non-mapped RTAs as the value of the closest-in-time (positive) RTA depth observed for the considered country-pair.

The Exporter Dynamics Database (EDD) takes the form of multiple databases: one per country.<sup>8</sup> So, as a last step in the construction of the final data set, each country-specific EDD is merged with the CEPII gravity database. Finally, we merge the tariffs data from MacMap (CEPII), and pool all the 55 country-specific databases to get a single proper and complete firm level base. This dataset initially contains 29,009,865 observations (firm-destination-productyear specific) spanning from the late 1990s to 2020. However, the DTA database covers only the period 2000-2020; so we keep only firm export data over that period. It must be noted that the coverage of the EDD varies by country: for some countries we have data for the entire 2000-2020 period, for some other countries we have a more limited time period (cf. table A1 in appendices). Although original firm-level exports data are (also) product (HS6) specific, our main variable of interest is not, so we aggregate (sum) export data by firm-destination-year across HS6 products.

As described in Table 1, the majority of exporters included in the EDD have at least one active RTA in force (i.e. 48 out of 55). However, only a sub-sample of countries (and countrypairs) have changed the depth of their RTAs in the period 2000-2020 (i.e. a newly signed RTA or amendment of pre-existing RTAs). Namely, 31 exporting countries signed new RTAs or amended a pre-existing one, giving _within_ variation to our measures of RTAs depth. This translates into 701 country-pairs having time variation in the depth of RTAs in the period 2000-2020. This time variation comes mainly from the entry into force of new trade agreements.<sup>9</sup> Since in the empirical strategy we rely on the within
<mark>on social media or crowd sourcing. For example, it has been shown that in Indonesia, food price related tweets correlated with real events, and that the number of tweets on food prices correlated with food price inflation (Global Pulse 2014). Twitter, blogs and similar social networks may also provide early signs of emerging disasters. Extracting these early signs requires processing of huge amounts of data, a process where analytic tools developed for Big Data analysis will be useful. The potential of Big Data in agricultural research is in combining large amounts of data collected from various sources for making predictions over time, e.g., pest outbreaks, livestock behavior, or soils and weather.</mark> <mark>Sophisticated precision farming systems are more commonly applied at technologically advanced farms and plantations, with the underlying logic to combine various remote sensing data and satellite imagery for a given farm parcel to provide precise information for growth (e.g., sensors for soil conditions, ground water level, and rain water precipitation detectors combined with irrigation optimization systems). As this can be done remotely, it saves significant time and labor when compared to manual sampling, and the use of calibrated technology makes the system less prone to error when assessing appropriate growth conditions (Hamrita and Hoffacker 2005). The benefit of the system is the resource optimization that can be done with the help of the information acquired. For example, the system can detect where there are nutrient deficiencies in the soil and thus additional fertilizers can be distributed to the areas where there is the most acute need for fertilizers instead of fertilizing the entire farm land as has traditionally been done. Irrigation or pesticide needs can similarly be detected and precisely applied. Digital tools can be applied to irrigation systems such as pumps that can be automated and controlled via mobile phones, such as the Nano Ganesh system in Pune, India where the farmers are able to save water, energy, and time by remotely controlling their irrigation pumps (Tulsian and Saini
<!-- Start of picture text --> Min scores in HS GPA or national entry test is an admission requirement e<br>Research skills are very important when hiring faculty<br>* Percent of faculty working in the industry<br>HEI provides job market information @<br>Somebody is in charge of industry relations @<br>HEI trains students for job interviews e@<br>Test required for graduation @<br>Teaches a foreign language e<br>Collect student satisfaction data more than once per year e@<br>Teaches numerical competencies e<br>-20 0 20 40<br>Percent<br>@ Training and curriculum = @ Link with productive sector Faculty @ Other Practices<br><!-- End of picture text --> <!-- Start of picture text --> Research skills are very important when hiring faculty<br>Students and peers' informal comments are very important in faculty evaluation<br>* Percent of faculty with SCP degree<br>* Percent of <40 years old faculty<br>* Percent of faculty working in the industry<br>* Percent of faculty with 5yrs+ industry experience<br>Classroom observation is very important in faculty evaluation<br>HEI coordinates job interviews with firms @<br>HEI has agreements with private firms to hire graduates .<br>Industry lends or provides equipment to program for student training @<br>HEI trains students for job interviews és<br>HEI provides job market information ©<br>Government scholarships are used by some students €<br>Analyze student performance to solve problems more than once per year &<br>Time assigned to practical training (%) 3<br>Collect student satisfaction data more than once per year @<br>Student feedback is very important to update program @<br>Teaches numerical competencies @<br>Government standards are very important to update program
24 equation (13). Therefore, we simply set _φ_ to 1.05%, the lowest reported food expenditure share in our 2019 household data. Parameter _ν_ is set to 0.12, following Eckert and Peters (2023).

To estimate the Engel elasticity _η_ , we take logs of equation (13), add household subscripts, collect relevant terms into a region fixed effect, and add a household size control variable and an error term, thus obtaining the following equation, which is estimated on 2019 data from Sri Lanka’s Household Income and Expenditure Survey (HIES): where _h_ indexes households, _n_ indexes regions, _ξnh_<sup>_A_is the share of income the household spends on</sup> food, _ynh_ is household income, _hhsizenh_ is household size, _ωn_ is a region fixed effect, and _εnh_ is an error term.<sup>55</sup> To allay concerns about the potential endogeneity of household income (e.g. due to seasonality in prices and employment opportunities), we follow an IV approach. We instrument household income _ynh_ with two arguably exogenous sources of income: income obtained from lottery (and other _ad hoc_ gains) and income obtained from disasters and other relief payments.<sup>56</sup> To our knowledge our paper is the first to estimate the Engel elasticity off exogenous income variation generated by a lottery.<sup>57</sup> The resulting IV estimate for _η_ , shown in Table II, is 0.656.<sup>58</sup> To estimate the elasticity of substitution across crops ( _σA_ ), we take logs of equation (17), add household subscripts, collect relevant terms into crop- and household-specific fixed effects, and add an error term, obtaining the following equation, which is estimated on 2019 HIES data: where _k_ indexes crops, _βnhk_<sup>_A_is the share of crop</sup><sup>_k_in the household’s total
|_β_<sup>_rbf_(</sup><sup>_re_)+</sup><sup>_ta _</sup>_−β_<sup>_rbf_+</sup><sup>_ta_</sup>|-0.07%|0.65%|-4.41%|-1.27%|-4.18%|-0.32%|
|**Spending Defnition**|per student|per capita|per capita|per capita|per capita|per capita|
|**Dep. var. mean**|8.193|6.021|4.834|4.417|3.204|7.466|
|**Dep. var. SD**|0.375|0.504|0.948|0.843|0.988|0.276|
|**# of municipality Observations**|4,163|4,160|4,256|4,256|4,256|4,163|
|**Adj. R**_−_**Squared**|0.702|0.223|0.065|0.13|0.078|0.381|

**Notes.** _∗∗∗_ denotes significance at 1 percent, _∗∗_ at 5 percent, and _∗_ at 10 percent level. The dependent variables come from National Treasury Bureau ( _Secretaria do Tesouro Nacional_ (STN). All regressions restrict the sample to the 330 municipalities at the border between Ceará and adjacent states. The definition of border considers the three imediate municipalities at each side of _Ceará_ and _Piaúi_ , _Rio Grande do Norte_ , _Pernambuco_ and _Paraíba_ . municipality controls include the number of births in the municipality _−_ year according to the _Sistema de Nascidos Vivos_ (SINASC), and GDP per capita as provided by
Table A.4: Average treatment effects using alternative measures of foreign ownership, excluding exits

|Dep. var.:|(1)<br>Employment (log)||(2)<br>Employment (log)|
|---|---|---|---|
|Post*German ownership (binary)|0.156***<br>(0.041)|Post*German shareholders (num)|0.088***<br>(0.020)|
|Post*French ownership (Binary)|0.154**<br>(0.066)|Post*French shareholders (num)|0.080**<br>(0.037)|
|Post*British ownership (binary)|0.098<br>(0.064)|Post*British shareholders (nun)|0.083<br>(0.056)|
|Year FE|Yes||Yes|
|Firm FE|Yes||Yes|
|Observations|270919||270919|
|Firms|61073||61073|

Notes: Authors’ calculations based on Orbis data. These regressions exclude firms that exit after accession. Each cell is obtained from a different specification where the number of employees is regressed on a time-varying indicator of foreign ownership indicated on the row. All regressions include firm and year fixed effects. Coefficients show the average treatment effects of foreign ownership. Results refer to firms that were observed both before and after accession. Data exclude the year 1999 due to anomalies in the data. Foreign ownership in column (1) is measured by a binary indicator taking value one when a firm had a foreign shareholder. Column (2) uses the number of foreign shareholders as an alternative measure of foreign ownership. Standard errors in parentheses are clustered at 2-digit industry level. *10% level, **5% level
. Section 3 presents the results and robustness tests. Section 4 discusses policy implications and concludes.

# **2 Empirical approach**

In this section, we first describe the data, sample, and variables used in the analysis. We then illustrate the empirical model.

# _2.1 Data and sample_

We gather information from multiple rounds of the World Bank Enterprise Surveys (WB ES) for 104 countries over the period 2009-2018.<sup>2</sup> The WB ES collects information from formal enterprises in the manufacturing and services sectors on a variety of aspects, including access to finance, corruption, infrastructure, crime, competition, and performance measures. We focus on the manufacturing sector

> 2 The Enterprise Survey data is available at the following link: <u>https://www.enterprisesurveys.org/en/enterprisesurveys.</u>

5
because of the low educational level in the population during their study period.

The evidence on education mobility across generations by sex is mixed. Lam (1999) finds that the effect of a mother’s education on a child’s education is no different relative to that of a father in South Africa. In contrast, Kwenda et al. (2015), Thomas (1996), and Branson et al. (2012) on South Africa, Ranasinghe (2015) and Crook (1995) on Australia, and Bj ̈orklund et al. (2006) on Sweden present empirical evidence that the schooling of mothers has a bigger effect on children’s educational attainment than that of fathers, while Girdwood and Leibbrandt (2009), Plug (2004), and Behrman and Rosenzweig (2005) find the paternal effect to be strong in the United States. The current study aims to extend the evidence for on nine Sub-Saharan African countries with recent data and provide an in-depth analysis of intergenerational education mobility over the long term. The study therefore complements the growing literature on international comparisons of intergenerational education mobility and fills the hitherto overlooked aspect of intergenerational mobility in developing countries particularly in Sub-Saharan Africa.

# **3 Analytical framework**

There are several theoretical arguments on how parental educational background affects children’s education. Educational decisions on children are determined by parental preference and credit constraints

> 4To the best of our knowledge, Hertz et al. (2007) are the only exception, their sample includes three Sub-Saharan African countries. This study also updates the data on Ghana using recent survey data.

4
, 1.10) 11, Classroom observation index (5.5 y.o.) (Ozleret al. 2013) MW 1.5 years after TT & Coaching & Teacher Stipend —_—~eo— 4.21 (0.64, 1.77) T2, Classroom observation index (5.5 y.o.} (Ozleret al. 2013) MW 1.5 years after TT & Coaching & Parental Engagement —>— 1.00 (0.45, 1.54) 13, Classroom observation index (5.5 y.o.) (Wolf and Peel 2019)GHA 2 years after TT & Coaching 0.21 (-0.01, 0.44) 141, Fidelity checklist (no. of activities) (Wolf and Peel 2019)GHA 2 years after TT & Coaching 0.30 (-0.77, 0.16) 11, Facilitating deeper learning (Wolf and Peel 2019)GHA 2 years afler TT & Coaching 0.36 (-0.04, 0.76) 11, Emotional support and behavior management (Wolf and Peel 2019)GHA 2 years afler TT & Coaching -0.34 (-0.90, 0.21) T1, Supporting student expression (Wolf and Peel 2019)GHA 2 years after TT & Coaching & Parental Engagement 0.25 (0.02, 0.47) T2, Fidelity checklist (no. of activities) (Wolf and Peel 2019)GHA 2 years after TT & Coaching & Parental Engagement -0.42 (-0.92, 0.09) T2, Facilitating deeper learning (Wolf and Peel 2019)GHA 2 years after TT & Coaching & Parental Engagement 0.14 (0.92, 1.19) T2, Emotional support and behavior management (Wolf and Peel 2019)GHA 2 years after TT & Coaching & Parental Engagement —_—— 0.66 (-1.23
performance of those approaches that strongly rely on data from previous surveys, e.g. adjusting poverty lines and cross-survey imputations.

# **3. Results**

We assess the performance of the rapid approach vis-à-vis alternative approaches (Table 1). The long questionnaire of the full-consumption approach can increase unit and item non-response but is nevertheless used as benchmark due to its _de facto_ standard for consumption surveys. The rapid approach compromises on the long list of items by introducing a subset of core items and distributing remaining items in optional modules, reducing the questionnaire length with beneficial impacts on unit and item non-response but at the cost of additional estimation error. The reduced approach further decreases questionnaire length by simply dropping items altogether, creating substantial bias in the resulting estimates. The bias can be minimized by adjusting the poverty line based on data from a previous consumption survey, called the adjusted reduced approach below. Finally, the largest time savings are generated by completely abandoning consumption data and using a structural model to impute consumption from a baseline survey using common co-variates. The time gap with the baseline survey as well as shocks and other structural changes, for example from the implementation of a project to reduce poverty, invalidate the structural model leading to substantial bias in the estimates.

**Table 1: Comparison of consumption methodologies and sources of error.**

||**Unit non-response**|**Item non-response**|**Implementation issues**|**Error inherent to method**|
|---|---|---|---|---|
|**Full**<br>**consumption**|Elevated levels of<br>non-response,<br>particularly<br>among<br>urban and wealthy<br>households (Korinek,<br>Mistiaen,<br>and<br>Ravallion 2006; Osier<br>2016)|Long list of items<br>increase item non
Most of the difference, however, is “unexplained.” However, “unexplained” in this case refers to the returns to observable characteristics. That is, for the same level of resources, Jordan’s schools are producing more output (student test scores); or put another way, Jordan’s teachers are able to add more value with a given level of resources. More than two-thirds of the improvement is due to improved effectiveness of the use of resources, or increased value-added of Jordan’s teachers. This is reflected in the fact that the urban advantage disappeared over time. Moreover, while the student: teacher ratio increased slightly over time, the effectiveness of teachers to handle a large class improved; that is, the system became much more efficient, thus being able to educate more children, and to improve their test scores at the same time. Female advantage more than doubled over this short period of time.

Table 2: TIMSS scores decomposition (1999-2003)

||||||Determinants of test sc<br> <br>|ore differentials<br>||
|---|---|---|---|---|---|---|---|
||||||Explained<br>Unexplained|as % of total te|st score diff.|
||_b1999_|_b2003_|_X1999_|_X2003_|_b2003(X2003-X1999)_<br>_X1999(b2003-b1999)_|Explained|Unexplained|
|Constant|450.3|476.8|1.0|1.0|0.0<br>26.5|0.0%|38.7%|
|Public school|-36.2|-32.6|0.8|0.8|0.3<br>2.9|0.5%|4.2%|
|Urban|15.7|
42

**Table 5: Regression results for neonatal mortality in 2003 and 2008 in Kenya**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Intermediate malaria risk zone (IMRZ)|0.0086|0.0089|0.0131|0.0120|0.0124|0.0114|0.0081|
|High malaria risk zone (HMRZ)|0.0058|0.0086|0.0169|0.0156|0.0164|0.0141|0.0093|
|Interaction LMRZ and ITN ownership|0.0142|0.0139|0.0088|0.0098|0.0072|0.0077|0.0103|
|Interaction IMRZ and ITN ownership|-0.0143|-0.0141|-0.0179|-0.0175|-0.0160|-0.0160|-0.0154|
|Interaction HMRZ and ITN ownership|-0.0054|-0.0056|-0.0104|-0.0088|-0.0105|-0.0091|-0.0082|
|2008 survey|0.0050|0.0011|0.0062|0.0056|0.0066|0.0061|0.0038|
|Full immunization (district level)||0.0250|0.0197|0.0265|0.0290|0.0273|0.0364|
|Born in a facility|||0.0296***|0.0325***|0.0302***|0.0316***|0.0322***|
|
economic effects that we attribute to the fertilizer ban.

# **VIII Conclusion**

Trade policy and geopolitical events increasingly affect the ability of many countries to access critical intermediate inputs. This paper quantifies the costs of a nationwide fertilizer shortage and explores its interaction with domestic agricultural policies, such as subsidies. It does so by leveraging an unprecedented natural experiment, an abrupt and unexpected import ban that disrupted the supply of chemical fertilizers in Sri Lanka. Relying on novel high-frequency firm-level trade data, detailed agricultural ground production data, crop yield estimates from state-of-the-art remote sensing techniques, and cutting-edge event study designs, we show that the fertilizer import ban led to dramatic declines in fertilizer imports, agricultural production, and exports of fertilizer-intensive agricultural products.

To better understand the mechanisms and welfare implications of the ban’s effects, we propose and estimate a spatial general equilibrium model of agriculture and trade with quantitative restricexpenditure to aggregate income, as predicted by our model. This metric captures both the relative importance of the agricultural sector in the district’s economy and the agricultural sector’s degree of specialization in fertilizer-intensive crops.
The audit function in the URA remained weak in spite of the reform, though the Large Taxpayers Department is better positioned than the rest of the agency. The LTD reported a comprehensive audit presence of about 11% in 2001 and also reported using third party information to cross-check taxpayer returns (Table 3 summarizes large taxpayer audit coverage across several countries). The Internal Revenue Department registered an audit presence of about 2.6% in 2001 for filing corporate income tax and VAT taxpayers.

# **TABLE 3. AUDIT COVERAGE OF LARGE TAXPAYERS**

|RevenueAuthority|Audit Coverage|
|---|---|
|KRA(2001)|38%|
|SENIAT(1999)|6%|
|SUNAT(1999)|11%|
|URA(2001)|11%|

_Source: Author’s calculations based on ARA and MOF data._ In sum the cases show improvement in taxpayer registration, especially in Peru, Kenya, and South Africa. The cases show that the more autonomous ARAs have invested their resources wisely in their large taxpayers offices. SUNAT and the KRA have focused on their LTOs and have managed to improve performance considerably as a result. SARS has made progress in expanding the tax rolls and improving filing compliance in the income tax. The KRA has also made some progress on expediting refunds. The cases show declining performance in Venezuela and stagnation at best in Uganda. There seems to be a positive association between the greater autonomy of SUNAT, KRA, and SARS, and their relatively better performance.

With respect to compliance management SUNAT has demonstrated progress in comparison to the pre-reform situation, though its performance has not been exceptional in this regard. The Mexican case suggests that while the SAT’s performance rates positively in comparative perspective, there has not been much improvement as a result of the reform. Though SENIAT made some progress with its large taxpayers office, the impact of the reform was limited. The KRA made significant progress since the reform, instituting new audit
, while women’s usage of the internet to buy goods is 7 pp less than men’s usage. Finally, (Ono and Zavodny, 2003) examine differences in men’s and women’s usage of the internet using data from several surveys during the period 1997 to 2001. They find that the frequency and intensity of internet usage by women is less than that of men, however, women’s likelihood of having access to the internet is not significantly different from that of men since the year 2000. Other, more recent, studies document that women have lower digital literacy (Rowntree, 2018), and are on average, 14% less likely to own a mobile phone than men (De Paz Nieves et al., 2021). The literature, however, has remained relatively silent on whether such potential gender gaps exist during periods of large unanticipated economic shocks, which is the focus of this section.

> 21Women’s internet usage is 24% lower than that of men in low-income countries, 14% lower in lower-middle-income countries, 8% lower in upper-middle-income countries and 5% lower in high-income countries. On mobile phone gender gaps, women’s mobile phone usage is 21% lower than that of men in low-income countries, 10% lower in lower-middle-income countries, 4% lower in upper-middle-income countries and 2% lower in high-income countries.

24