rafmacalaba/datause-encoder-data huggingface dataset:
random, the (non-experimental) evaluation must overcome the difficult problem of constructing a suitable comparable group in the analysis, which overcomes the possible selection bias. The evaluation design was built on two interesting features of the program’s promotion and implementation that are likely to have substantially mitigated the scope for selection bias. The first is that it was possible to elicit the willingness of _Jefes_ beneficiaries to participate in the MEP program, using local promotional activities. In addition to the program being nationally advertised (through newspapers and radio), the local offices of the Ministry of Labor or the municipalities actively promoted the program through informational campaigns.<sup>23</sup> In this process, a registry was collected with information on those _Jefes_ beneficiaries that have showed interest in the program.<sup>24</sup> These registries were used as a sampling frame to identify the comparison group for the evaluation. Restricting the analysis to those beneficiaries who have shown interest in the program is likely to reduce the problems associated with the self-selection into the program of those with higher expected gains (for example due to their entrepreneurial ability or motivation). The assumption is that attending the promotional campaigns and eventually providing details on possible productive activities, reveals some unobservable individual characteristics, which are possibly correlated with program participation and with the labor market outcomes of interest. As a robustness check, we will also use a smaller comparison group in the evaluation which restricts the attention to those individuals that were interested in participating in the project, have actually applied for a grant but have not yet received the

> 23 The promotion of the program activities was implemented in two ways. First, the local offices of the Ministry of Labor distributed program leaflets at the payment locations for _Jefes_ ( _boca de pago_ ) and encourage beneficiaries to come to the local Ministry of Labor office to learn more about the program characteristics. A registry was kept at the local offices of the Ministry of Labor with the information on the identity of the interested beneficiaries. Second, the program was promoted with public meetings/workshops for _Jefes_ beneficiaries in given municipalities. The workshop was held by the local offices of the Ministry of Labor together with the municipality. During these sessions, the workshop organizers collected a registry with a list of all potentially interested beneficiaries.

> 24 In this process participants identify themselves, provide a description of the project and list the number of participants involved in the project.

13
are plausible.

Since the unobserved e¤ect c0i is present in equation (5), we should consider the relation between the unobserved e¤ect c0i and the explanatory variables in equation (5). Importantly, the composite unobserved e¤ect c0i is a function of v2it, where t = 1; :::; T; by construction: Thus, in order to obtain consistent estimates of the parameters from equation (5), we must take into account the relation between c0i and v2it in di¤erent time periods.

First, we use a “pure”random e¤ects approach, i.e., we assume that which can be written as c0i = �0v2i + a1i; t = 1; :::; T; where a1ijzi; y1i0; v2i � Normal(0; �<sup>2</sup> a1<sup>)</sup> T and is independent of (zi; y1i0; v2i), where <u>v2i</u> = T<sup><u>1</u></sup> Pv2it; and v2i = (v2i1; v2i2; :::; v2iT ). While t=1 a limiting assumption in many potential applications, the “pure”random e¤ects assumption (6) may be relevant for certain cases. In particular, when every individual in the initial time period is in the same state (e. g., we are interested in the population of people who smoke), assumption (6) might be appropriate. Further, since we assume that the composite unobserved e¤ect, c0i, is independent of the initial condition, y1i0, it is natural to think that v2it’s in di¤erent time periods have equal impacts on c0i. Consequently, we employ <u>v2i</u> as a su¢ cient statistic for describing the relation between c0i and v2it’s in di¤erent time periods. Then, under assumptions (1)-(4) and (6), we rewrite equation (5) as Clearly, the estimates of � = <u>�</u> , � = <u>�</u> , and �0 = <u>�0</u> can be obtained ~~p~~ �<sup>2</sup> e1 +�<sup>2</sup> a1 ~~p~~ �<sup>2</sup> e1 +�<sup>2</sup> a1 ~~p~~ �<sup>2</sup> e1 +�<sup>2</sup> a1 using standard random e¤ects probit software by including v^2i in each time period into the T ^ ^ ^ list of explanatory variables along with x1it and v2it, where v2i = T<sup><u>1</u></sup> Pv2it: t=1 As we discussed earlier, however, the assumption of independence between the unobserved e¤ect, the initial conditions and the exogenous covariates is often too restrictive. In 9
# Teacher practices - (Preprimary & Post-preprimary - LMICs)

Study countrycode Timeline Type of intervention ES (95% Cl) Outcome_variable Low & Middle Income - Pre-primary (Wolf et al. 20794) GHA 1year after TT & Coaching ~~ 0.56 (0.35, 0.77) 141, Fidelity checklist (no. of activities) (Wolf et al. 20794) GHA 1year after TT & Coaching -0.11 (0.63, 0.41) 71, Facilitating deeper learning (Wolf etal. 20719a) GHA lyear after TT & Coaching —_— 0.65 (0.16, 1.13) 71, Emotional support and behavior management (Wolf et al. 20794) GHA year after TT & Coaching —p—_ 0.52 (0.04, 1.00) 11, Supporting student expression (Wolf et al. 20794) GHA year after TT & Coaching & Parental Engagement ~~ 0.61 (0.39, 0.34) T2, Fidelity checklist (no. of activities) (Wolf et al. 20794) GHA year after TT & Coaching & Parental Engagement -0.16 (-0.73, 0.41) T2, Facilitating deeper learning (Wolf etal. 20719a) GHA year after TT & Coaching & Parental Engagement —_e— 0.65 (0.16, 1.15) 12, Emotional support and behavior management (Wolf et al. 20794) GHA year after TT & Coaching & Parental Engagement 0.09 (-0.41, 0.60) T2, Supporting student expression (Ozleretal. 2013) MW! 1.5 years after TT & Coaching 0.56 (0.01, 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, -0.13)T2, Supporting student expression Low & Middle Income - Basic (Ozleret al. 2013) MW 3 years after TT & Coaching 0.30 (-0.30, 0.90) T1, Classroom observation index (7 y.o.) (Ozleretal. 2018) MW 3 years after TT & Coaching & Teacher Stipend 0.32 (-0.25, 0.90) T2, Classroom observation index (7 y.o.) (Ozleret al. 2018) MI 3 years after TT & Coaching & Parental Engagement 0.14 (-0.45, 0.72) T3, Classroom observation index (7 y.o.) NOTE: Weights are fram random effects analysis #) -1 0 1 2
8

# **3. Data and descriptive evidence**

In this section we describe in great detail our data on workers’ remittances and current account reversals, also providing a wide range of descriptive evidence. The other variables used in the econometric analysis are listed and described in Table A1; some statistical evidence on the relationship between these variables and current account reversals is postponed to section 4, where it is useful to motivate our empirical specification.

Before digging into our data, a comment is needed. Our descriptive analysis closely follows the one by IMF (2005). However, this must not be considered a useless repetition for two reasons. Firstly, we believe that our way to look at the data has some value-added: in particular, we devote a special care in dealing with the issue of the changing sample size and composition over time, which very likely biases IMF’s analysis on the evolution of migrants’ transfers in the last decades. Secondly, since our analysis focuses on workers’ remittances, we need to be sure that the “good” properties, which characterize IMF (2005) sample of migrants’ transfers, characterize our more restricted sample of workers’ remittances, too.

According to the IMF Balance of Payments Manual Fifth Edition (BPM5, 1993), the inflows of funds that may be properly interpreted as transfers from migrants can be distinguished into three different categories: i) workers’ remittances, included among the current transfers in the current account; ii) compensation of employees, which is a component of income again in the current account; iii) migrants’ capital transfers, which instead belongs to the capital transfers in the capital account<sup>9</sup> .

Our sample, entirely drawn from the IMF Balance of Payments Statistics database, includes all available country-data on the three items above from 1976 to 2003<sup>10</sup> . After dropping

> 9 <u>Workers’ remittances</u> cover current transfers by migrants (defined as those who come to an economy and stay, or are expected to stay, for a year or more) who are employed in new economies and considered residents there. Persons who work for and stay in new economies for less than a year are considered non residents; their transactions are appropriate mainly to the component for compensation of employees. <u>Migrants capital transfers</u> are not transactions between two parties but contra-entries to flows of goods and change in financial items that arise from the migration of individuals from one economy to another (IMF, BPM5).

> 10 We have chosen to drop observations before 1976 due to the very limited data availability.
until the industry becomes internationally competitive. In short, the infant-industry argument is based principally on the idea that there are economies of large-scale production in many industries and that developing countries have difficulty in establishing such industries. Tariffs are also proposed to maintain domestic employment. The most common counter argument is the comparative-advantage theory (David Ricardo, 1817 and Shiells _et al_ , 1988),<sup>7</sup> which argues that the industry in need of such protection will likely not survive and that the resources allocated to that sector need to be transferred to occupations that have greater comparative advantage.

The empirical evidences over the past decades have left the debate open, since high levels of protection in some countries has contributed to a slowdown in production. For example, in Nigeria, a study conducted by Dorosh and Malek (2016<sup>8</sup> ) showed that an increase in the import tariff on rice from 50 percent in December 2012 to 110 percent in January 2013 did not benefit producers as incentives offered by the tariff increase were offset by disincentives from real exchange rate appreciations. On the other hand, a study conducted by Conteh, Yan, and Sankoh (2012<sup>9</sup> ) in Sierra Leone showed a positive and significant relationship between the quantity of rice produced and the domestic price of rice in the market and that a 1 percent increase in the price of domestic rice will result in a 114 percent increase in rice production in the country.

# **IV. The Model**

The model developed for the Sierra Leone rice sector is a partial equilibrium programming model which solves for a nonlinear objective function representing the sum of producers’ and consumers’ surpluses in the rice market. The sum of producers’ and consumers’ surpluses is maximized subject to inputs, state of technology and balance constraints. More specifically, the model describes domestic production, imports, domestic demand and exports for rice. It is a single period model for the 2018 base year but may also be solved for any future target year. The model is seasonally disaggregated for the two major factor inputs, land and labor. The production side of the model is disaggregated in terms of the five major ecosystems for rice. On the demand side of the model, market-clearing price is endogenous and is determined by both demand and supply conditions. The broad structure of the model is set out in figure 3 below.

> 7 Shiells, Clinton R., Robert M. Stern, and Alan V. Deardorff, "Estimates of the Elasticities of Substitution between Imports and Home Goods for the United States" 1988.

> 8 Paul A. Dorosh and Mehrab Malek, rice imports, prices, and challenges for trade policy, (IFPRI 2016) <u>https://www.ifpri.org/publication/rice-imports-prices-and-challenges-trade-policy.</u>

> 9 Alhaji Mohamed Hamza Conteh*, Xiangbin Yan, Foday Pinka Sankoh, The influence of price on rice production in Sierra Leone (2016)  https://www.scirp.org/journal/paperinformation.aspx?paperid=20164.

8
# **Table 6. Drivers of the skill content of jobs, developing countries sample (STEP index). Controlling for income group trends.**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Non‐routine<br>analytical|Non‐routine<br>interpersonal|Routine<br>cognitive|Routine<br>Manual|Non‐routine<br>manual|
|GDP (lagged, log)|0.00364|‐0.0200|‐0.0189|0.0548|0.0396|
||(0.0405)|(0.0394)|(0.0204)|(0.0341)|(0.0322)|
|Industry VA (% of GDP), change|0.164|0.154|‐0.104|0.112|0.0942|
||(0.131)|(0.127)|(0.0660)|(0.110)|(0.104)|
|Services VA (% of GDP), change|0.0225|0.0496|‐0.0548|‐0.0317|0.00334|
||(0.102)|(0.0997)|(0.0516)|(0.0863)|(0.0814)|
|Skilled (% of working age population), change|‐0.0575|‐0.0729|0.0227|‐0.0471|‐0.00529|
||(0.0480)|(0.0467)|(0.0242)|(0.0405)|(0.0382)|
|Females (% of employment), change|‐0.208**|‐0.135|0.141***|0.0405|‐0.100|
||(0.106)|(0.103)|(0.0534)|(0.0893)|(0.0842)|
|Working age population (% of population), change|‐0.0473|0.205|0.224|‐0.215|‐0.300|
||(0.401)|(0.391)|(0.202)|(0.338)|(0.319)|
|Older than 65 years (% of population), change|‐1.486|‐0.917|0.604|‐1.304|‐1.613*|
||(1.207)|(1.175)|(0.608)|(1.018)|(0.960)|
|Internet users (% of population), change|0.116**|0.126**|‐0.0762***|‐0.0507|0.0815*|
||(0.0561)|(0.0546)|(0.0283)|(0.0473)|(0.0446)|
|Imports (% of GDP), change|0.0774|0.0761|‐0.0418|0.0796*|0.0362|
||(0.0537)|(0.0523)|(0.0271)|(0.0453)|(0.0427)|
|Exports (% of GDP), change|‐0.153**|‐0.132**|0.100***|0.0238|0.00139|
||(0.0602)|(0.0586)|(0.0303)|(0.0508)|(0.0479)|
|Constant|0.511|0.157|‐0.154|‐0.677|‐0.167|
||(2.617)|(2.548)|(1.319)|(2.207)|(2.081)|
|Year x Income Group|YES|YES|YES|YES|YES|
|Observations|355|355|355|355|355|

Note: OLS coefficients from a cross-country regression using the skill content of jobs (in first differences) as the dependent variable (multiplied by 100).

The finding that the share of working age population is not significantly correlated with the skill content of jobs is surprising given that in most countries the extent of lifelong learning institutions is limited, particularly in developing countries. Thereby it is expected that most of the skill upgrading in the labor force is driven by the new entrants. Table 7 explores this hypothesis by interacting the change in the working-age population share with the change in internet use. It shows that, in fact, the roles of demography and the adoption of digital technologies are only relevant when they take place simultaneously. In other words, only countries that experience both an increase in the share of the working age population and an increase in internet penetration simultaneously witness an increase in the non-routine cognitive skill content of jobs.

25
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owner's level of education, the most common measure of skills. We divide owners into four categories of educational attainment: no formal education, completed primary, completed secondary, and completed college (including vocational and technical schools). About half the owners of informal businesses are high school graduates, ranging from about a quarter in Mozambique to over 90% in Zimbabwe. Figure 1 compares informal businesses with microenterprises in terms of the owners' education. While the two types of businesses have similar shares of owners with high school education (about half), they differ on both sides of the distribution of educational attainment. Over 10% of informal owners have not completed primary school, compared to under 5% of formal micro-enterprise owners.<sup>10</sup> Furthermore, the proportion of informal owners who only have a primary school education is more than twice that of formal micro-enterprises. Forty-one percent of formal micro-enterprise owners have either a college or vocational degree, almost three times the percentage of informal business owners. This pattern is consistent with prior work finding the relative lack of education and training possessed by informal entrepreneurs (La Porta and Shleifer, 2014; La Porta and Shleifer, 2016), although we find the difference to be less dramatic. Informal business owners also have relatively limited experience in their respective industries, with just about 7 years, less than the 9-year average for formally registered micro-enterprises.

Representation of females in the informal sector varies considerably across countries. Women are the main owner of close to 40% of these businesses; ranging from only 6 percent in Iraq to 70 percent in Lao PDR.<sup>11</sup> This pattern contrasts with our sample of formally registered micro-enterprises, in which women make up an even smaller share (27%) of firm owners. In both informal and formal (micro) businesses, women are not equally represented by sector of activity, with female ownership being mostly in non-manufacturing sectors, though these differences are not statistically significant at the conventional levels.

Female-owned informal businesses also operate slightly longer each week than maleowned businesses (62 hours on average vs. 57 hours, Figure 2). These long working hours on the business are likely added to the household responsibilities traditionally expected of women in these countries. Perhaps because of the need to juggle family responsibility and work, or safety concerns,

> 10 All the differences between the informal businesses and micro-enterprises reported in Section 3 are statistically significant at the conventional levels, unless otherwise noted. Table 2 reports the corresponding t-tests. 11 Female ownership information was not collected in the Zimbabwe survey.

12
The Role of Inventory Adjustments in Quantifying Factors Causing Food Price Inflation<sup>*****</sup> Gal Hochman<sup>†</sup> , Deepak Rajagopal<sup>‡</sup> , Govinda Timilsina<sup>§</sup> , David Zilberman<sup>**</sup> _JEL classifications:_ Q1 _Keywords:_ Inventories; Stock-to-use; Food commodity prices; Economic Growth; Agriculture productivity

> _* The views and findings presented here should not be attributed to the World Bank. We thank Will Martin, John Beghin, Mike Toman, Caesar Cororaton and Robert Townsend for their valuable comments. We acknowledge financial support from the Knowledge for Change Program (KCP) Trust Fund._

> _† Energy Biosciences Institute, University of California, Berkeley, email: galh@berkeley.edu._

> _‡ Institute of Environment, University of California, Los Angeles, email: rdeepak@ioe.ucla.edu._

> _§ Development Research Group, The World Bank, email:gtimilsina@worldbank.org_

> _** Department of Agricultural and Resource Economics, University of California, Berkeley, email: zilber11@berkeley.edu._
Tab2l 11

# RCMMNr ECFIInr1R PER STUDEWN, 1960-1981

|||||||VTE/g|eneralep|amediture|ratio|
|---|---|---|---|---|---|---|---|---|---|
||Expndi|ture<br>per s|tudent (c|onstant 198|5Insis)|Socxc|Idary|Post-se|condary|
||Prilmry|Secondary<br>teimcLAal|Secondary<br>general|Pbst-sec.<br>nruni-mv.|University|AmIal<br>(b)/(c)|Average<br>1970-77|Amnal<br>(d)/(e)|Average<br>1970-81|
|Year|(a)|(b)|(c)|(d)|(e)|(f)|(g)|(h)|(i)|
|1960|<br>587|3,941|1,412|.|.|2.8||||
|1965|989|5,075|1,941|.||2.6||||
|1970|923|1,866|1,634|4,933|8,754|1.1||0.6||
|1971|922|1,513|1,621|6,150|9,336|0.9||0.7||
|1972|1,184|1,645|1,919|4,294|7,673|0.9||0.6||
|1973|1,021|1,903|1,765|7,333|8,008|1.1||0.9||
|1974|994|1,705|1,643|6,820|6,318|1.0||1.1||
|1975|983|1,463|1,424|6,008|8,472|1.0||0.7||
|1976|956|1,426|1,287|6,973|6,008|1.1||1.2||
|1977|861|1,303|1,008|5,029|5,916|1.3||0.9||
|1978|<br>638|2,681|1,098|4,604|4,676|2.4||1.0||
|1979|<br>550|2,163|932|4,206|4,851|2.3||0.9||
|1980|<br>915|.|.|3,828|5,574|.||0.7||
|1981|<br>835|.|.|3,443|5,421|.||0.6||

Sources: Per **student enditures** were suppLUed by the Mbiostry of FAdcation **and** reported in Arregui (1988). **C)P** deflators **ire** provided by the Instituto Nacional de Estadistica **asd** reDorted in Webb (1987).

27
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