rafmacalaba/datause-encoder-data huggingface dataset:
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32
Salvadoran migrants reside are valid instruments (i.e., we cannot reject exogeneity of the other instrument conditional on one instrument being exogenous).

We find that our main results are robust to controlling for the potential endogeneity of remittances. Namely, while remittances have a positive effect on the likelihood that a household has a deposit account, neither the fact that a household receives remittances nor the actual per capita amount received has a statistically robust impact on the likelihood of applying for and receiving a loan.

# **V. Conclusion**

As the importance of remittances for developing countries has grown, a sizeable literature has flourished examining the impact of remittances on various aspects of countries’ development. An issue which has received little attention is the effect of remittances on the financial system. This issue is important given the evidence that financial development matters for growth and poverty alleviation and financial inclusion has many beneficial effects for households.

This study examined the impact of remittances on financial inclusion using data from a four-wave rural household survey for El Salvador. In particular, we analyzed the impact of remittances on the likelihood that households use financial services such as deposit accounts and loans. Overall, we find that remittances have a positive impact on financial inclusion by promoting the use of deposit accounts. These results hold controlling for unobserved household characteristics and using instrumental variables regressions to correct for the potential endogeneity of remittances. On the other hand, remittances do not have a significant effect on credit from formal financing institutions. Hence, we conjecture that by relaxing credit constraints, remittances might reduce the need for external financing by financial institutions, while at the same time increasing the demand for savings instruments.

There are a number of potential avenues for future research. First, it would be interesting to analyze the extent to which remittance recipients that have accounts, actively use these accounts to save and manage their daily transactions. Second, it would be important to go deeper into the reasons why those that receive remittances do not seem to have a higher demand for credit. In particular, it would be useful to analyze whether indeed this is due to the fact that 14
2

|1.3.|Types of households living in privately owned (i) and non-privatized (ii)<br>housing|18|
|---|---|---|
|2.1.|<br>Satisfaction with housing conditions (% of household polled)|20|
|2.2.|<br>Household motivations in changing housing (% of households polled)|21|
|2.3.|Households' plans to improve their housing condiitons in the near future<br>(% of households polled)|21|
|2.4.|Needs for better housing, actual plans to move and structure oif demand<br>(%of household polled)|23|
|2.5.|Household plans to improve housing conditions in the near future (by quantiles<br>based on per capita provision with floor space; % of each quintile)|24|
|2.6.|Households' plans to improve housing conditions in the near future (by quintiles<br>based on per capita income; % of each quintile)|25|
|2.7.|Share of Moscow households who plan to improve housing conditions by income<br>(by deciles based on per carita income; % of each decile)|25|
|2.8.|Willingness to take a loan for the purchace of housing, by level of provision<br>with housing (by quintiles based on per capita provison with total floor space,<br>% of each quintile)|28|
|2.9.|Willingness to take a loan for the purchace of housing (by quantiles based on<br>per capita income; % of each quintile)|28|
|2.10.|Willingness to take out a long-term loan for the purchase of housing,<br>by housing plans, %|29|
|2.11.|Household mobility in Russia (% of households by year of move into current<br>unit)|30|
|2.12.|Household mobility (% of households who changed housing in the four||
||years of housing reform, by quantiles based on per capita incorne)|31|
|2.13.|Ratio of moves to new and previously occupied units in Moscowv<br>(% of mover households, by year)|33|
|2.14.|Methods of obtaining housing in the primary and secondary housing<br>markets in Moscow (% of mover households in 1993-1995)|33|
|2.15.|Search methods in Moscow (by quantiles based on in capita income,<br>% of mover household in 1993-1995)|34|
|2.16.|Change in mover households' living conditions in Moscow by year of||
||<br>the move (% of mover households in each year)|34|
|2.17.|<br>Change of housing conditions when moving into new and previously<br>occupied housing (% of mover households in 1993-1995)|35|
|2.18.|<br>Change of mover household living conditions in Moscow by methods of||
||obtaining housing (% of mover households in 1993-1995;<br>same legend as Fig.2.17)|35|

- 2.19. Change in mover households' living conditions in Moscow (by quantiles based on per capita income) 36
WTPAve= 10 Rupees. Thus, if 10 individuals die each year from the health risk illustrated above, the cost to society is 10* VSL = 10*100 thousand Rupees = 1 million Rupees.

A number of VSL studies have been conducted in India. Table A2 presents a summary.

# **Table A2:** **<u>Value of Statistical Life in India</u>**

|**Name of Study**|**Method of**<br>**Estimation**|**Value**|**Adjusted**<br>**Value (2010)**|**Adjusted**<br>**Value in $**<br>**(2010) using**<br>**` 47.5 = $1**<br>**exchange**<br>**rate**|
|---|---|---|---|---|
|Shanmugam K.R. (1997|Compensating-<br>wage differentials|` 12,084,000|` 18,932,020|$398,569|
|Simon et al. (1999)|Compensating-<br>wage differentials|` 6,417,341 - `<br>15,040642|` 16,197,563|$341,001|
|Bussolo and O'Connor<br>(2001).|PPP and income<br>elasticity Brandon<br>(1995) estimate|$ 202,000 -<br>$343,860, use the<br>central value of<br>$273,000|` 19,109,280|$402,301|
|Madheswaran (2007)|Compensating<br>wage differentials|` 15,000,000|` 16,939,353|$356,618|

Source: prepared by A. Sagar.

The average VSL from these comes out at about $375,000 (Rs. 17.8 million) and this figure was applied in the report. From Table 6.2 it can be seen that the ratio of VSL/HCA is about 16 times for children and 44 times for adults.

In this report we used the average of the VSL and HCA values for adults (i.e. $192,000 or Rs. 9.1 million). For children we do not use the VSL value at all as none of the VSL studies are for children. Hence we take only the HCA value of $24,168 or Rs. 1.148 million (Table 6.1). This conservative approach is also consistent with other costs of degradation studies that have been conducted.

31
� � log( _τs,b j,t_ ) = _σ_ log( _TravelTimes,b j,t_ ) _._ We leverage the Covid-19 lockdown as an exogenous shifter that only influences travel time between locations of seller _s_ and buyer _b_ , as reflected in Equation (17) below.

Detailed derivations and estimating equations are in Appendix D.1.4. _Redo_ ( _s_ ) _d_ ( _b_ ) and _Orangeo_ ( _s_ ) _d_ ( _b_ ) are the share of districts designated as _Red_ and _Orange_ , respectively, along the route between seller _s_ and buyer _b_ . We construct these variables using the Dijkstra algorithm for least-cost routes. Details about the implementation of this algorithm are in Appendix C.

Finally, we instrument the changes in relative prices in Equation (15) to estimate _ζ_ . Potential unobservable product-level demand shocks could again induce an upward bias to OLS estimates of _ζ_ . To construct our instruments, we leverage the seller-level and seller-buyer-level instruments we used to estimate _ε_ and calculate weighted averages across suppliers to instrument for the change in relative prices for buyers. The intuition is that buyers that purchased inputs either from a larger share of sellers in _Red_ zones, or from sellers located in districts such that the trading routes comprised of many _Red_ zone districts were more exposed to supply disruptions induced by the Covid-19 lockdowns. More details of the instruments are in Appendix D.2.3.

**Discussion of instruments.** The instruments induce buyers of certain types to be more affected than others based on their production networks. The Local Average Treatment Effect (LATE) may not represent the Average Treatment Effect (ATE) if buyers in _Red_ , _Orange_ , and _Green_ zones already traded intensively with sellers in certain lockdown zones, and there is heterogeneity in responses. For instance, if buyers in _Red_ traded mostly with sellers in _Red_ , then our instrument may estimate effects on firms induced by having more _Red_ sellers, thus upweighting effects on buyers in _Red_ . In Figure A4, we run two distributional checks to investigate these patterns. These figures show that, in general, sellers from _Red_ , _Orange_ , and _Green_ zones had similar interactions with buyers from _Red_ , _Orange_ , and _Green_ zones.

We also consider whether certain products are sourced intensively from firms located in certain zones. For instance, if all the rubber supply of firms in this production network comes from suppliers in _Red_ zones, then buyers of rubber would find it increasingly difficult to find suppliers. Once again, if there is heterogeneity in responses by product category, our estimated LATE elasticity would weigh rubber products higher than non-rubber products. While not a source of bias, it does affect the interpretation of the estimated parameter. In Figures A4g and A4h, we 25
productivity at the micro level. To the extent that aggregate productivity is a function of the sum of individual firm productivities, the findings should prove relevant for assessing whether the rise of exports helped, hindered or was irrelevant for country’s aggregate productivity trends.

The literature analysing the relationship between exports and productivity using micro data has expanded to cover a wide range of different countries.<sup>2</sup> Two important findings arise from this literature: exporters are generally more productive than non-exporters, and there is self-selection into export markets. According to the self-selection mechanism firms need to reach a minimum productivity threshold to enter the more competitive foreign markets (Melitz, 2003). Thus, only the ex-ante most productive firms are able to sell abroad. If this mechanism dominates, then the rise of exports in the Brazilian economies might have yielded negligible effects on aggregate productivity.

At the same time, theory also suggests that once firms enter export markets they can also experience further productivity gains (Clerides _et a_ l., 1998): this is the so-called learning-byexporting hypothesis (LBE, hereafter).<sup>3</sup> On theoretical grounds, the potential productivity gains arise from growth in sales that allows firms to profit from economies of scale, knowledge flows from international customers (that provide information about process and product innovations, reducing costs and improving quality) and from increased competition in export markets that forces firms to improve their efficiency. The evidence for the learning by exporting effect is, however, less compelling in the existing literature.

2 See Greenaway and Kneller (2007a) and Wagner (2007 and 2012) for thorough reviews of this literature. Further, ISGEP (2008) provides an international comparison across 14 countries.

> 3 Silva _et al._ (2010) provide a detailed survey of the learning by exporting literature. Further, Martins and Yang (2009) provide a meta-analysis of 33 empirical studies. Singh (2010) concludes that studies supporting self-selection overwhelm studies supporting learning-by-exporting.

3
# **Table of Contents**

|I.|Background|_5_|
|---|---|---|
|II.|Labor Market|6|
|III.|Education and Training|10|
|IV.|CONALEP|13|
|V.|Evaluation of the CONALEP System: Students and Graduates|16|
|VI.|Data|22|
|VI.1|Methodology|24|
|VI.2|Results|26|
|VII.|Benefits from CONALEP's Reformed Program|39|
|VII. 1|. Methodology|39|
|VII.2|. Results|39|
|VIII.|Cost-Benefit Analysis|45|
|IX.|Conclusions|45|
|Selec|ted References|48|
|Anne|x 1|51|
|Anne|x 2|58|
|Anne|x 3|65|
|Anne|x 4|69|
|Anne|x 5: List of Tables and Figures|73|
experts advised the team on which issues to prioritize and how to incorporate the comments to further improve the tool. This updated version of the tool was applied in four settings, where observers were given a certification exam that ensured they could reliably code using _Teach_ . In Mozambique, 74% of the observers passed the certification exam; in Pakistan and the Philippines, 96% passed; and in Uruguay, 100% passed. The observers also provided comments on the tool and training that was considered during the revision process.

Finally, the development team analyzed the psychometric properties of the tool (Molina et al., 2018c). Based on this analysis and feedback from the trainers and observers, the development team revised each element’s structure and complementary examples to improve the tool’s consistency and clarity. As part of this process, the “Time on Learning” element was modified to capture teachers’ time on instruction and students’ time on task through a series of snapshots. This process resulted in a tool that comprised one low-inference and nine high-inference elements. The final stage involved testing these revisions using the _Teach_ video library.

# 3. Evidence

This section provides the evidence for Teach’ framework.

## **3.1 Time on Learning**

Effective teachers maximize the amount of time students spend on learning (WhartonMcDonald, Pressley, & Hampston, 1998; Bruns & Luque, 2014; Stronge, 2018). These activities can involve different instructional contexts, such as large group, small group, and individualized work, but the general consensus is that time lost in classroom instruction is associated with behavioral problems and poorer student academic outcomes (Bruns & Luque, 2014; Dobbie & Fryer, 2013; Lavy, 2010, 2015). This problem is even more acute in lowand middle-income countries, with high absence rates and low instructional time (Bold et al., 2017; World Bank, 2018). Thus _Teach_ deemed it important to include the following two observable teacher behaviors to maximize time on learning in the classroom: _1 Teacher provides learning activity to most students_ _2 Students are on task_ 8
# **1.  Introduction**

Following the G20 summit on November 15, 2008, the leaders of the world's largest economies issued a statement explaining how they intended to restructure the world's economic architecture. On the very first page of this statement, they stated: _“Our work will be guided by a shared belief that market principles, open trade and investment regimes, and effectively regulated financial markets foster the dynamism, innovation, and entrepreneurship that are essential for economic growth, employment, and poverty reduction.”_ This statement highlights the relevance of entrepreneurship as a force behind economic development. It is therefore critical that governments be able to measure and understand the entrepreneurial activity of their countries. However, the mere concept of entrepreneurship, and how to measure it, still lacks a common language among scholars and academia.

The World Bank Entrepreneurship Survey (WBGES) was conceived to answer the demand of scholars and government for a reliable and internationally comparable indicator to measure entrepreneurial activity. In its third year, data for 100 countries were collected directly from the registrars of companies around the world on the number of total and newly registered corporations over an eight-year period (2000-2007).

The WBGES data continue to show that a good regulatory environment can boost entrepreneurial activity in developing countries. After controlling for economic development (GDP per capita), higher entrepreneurial activity is significantly associated with cheaper, more efficient business registration procedures (Doing Business, 2009) and better governance (Kaufmann and others, 2008).

The 2008 WBGES includes new data on the impact of modernization of business registries on business creation. It gathers extensive data on the functioning and structure of business registries in 71 countries from the registrar of companies. The data show that registry modernization is associated with shorter incorporation time and markedly lower cost of entry, thus reducing barriers to formal business formation and entrepreneurial activity, and relatively higher business entry rates. While the degree of progress in the modernization of business registries varies greatly, countries usually have a common goal 2
born is much higher than the number of foreign citizens (twice as large in countries such as Hungary, the Netherlands, and Sweden)<sup>11</sup> . Another reason is that the concept of country of birth is time invariant (contrary to citizenship which changes with naturalization) and independent of the changes in policies regarding naturalization. The OECD statistics report that 14.4 million foreign born individuals were naturalized between 1991 and 2000. Countries with a particularly high number of acquisitions of citizenship are the US (5.6 million), Germany (2.2 million), Canada (1.6 million), and Australia and France (1.1 million). Despite the fact that they are partially reported in traditional statistics (OECD, 2002), the number of foreign-born can be obtained for a large majority of OECD countries. In a limited number of cases, the national census only gives immigrants' citizenship (Germany, Italy, Greece, Japan and Korea). As it will appear in Table 2, 88.3 percent of working-aged immigrants can be characterized in terms of country of birth in 2000 (11.7 percent in term of citizenship). Contrary to common belief, data availability is not significantly different in 1990, even among European states. We obtain information about country of birth for 88.0 percent of working-aged immigrants in 1990 (12.0 in terms of citizenship).

� It is worth noting that the concept of foreign born is not fully homogeneous across OECD countries. As in many OECD countries, our main criterion relies on both country of birth and citizenship at birth: we define foreign born as an individual born abroad with foreign citizenship at birth. For example, the U.S Census Bureau considers as natives persons born in the US (as well as in Puerto Rico or U.S. Island areas), or born abroad from a U.S. citizen parent<sup>12</sup> . Other residents are considered foreign born. France and Denmark use a similar concept. Statistics Netherlands defines first-generation immigrants as persons who are born abroad and have at least one parent who is also born abroad (Alders M., 2001). However, in a couple of countries (Australia, New Zealand, and Belgium), the "foreign born" concept used by the Statistics Institute essentially means "overseas-born," i.e. an individual simply born abroad. While it is impossible to use a fully comparable concept of immigration, we have tried to maximize the homogeneity of our data sources. It is worth noting that our definition clearly excludes the second generation of immigrants. A couple of countries offer a more detailed picture of immigration, distinguishing the foreign born

> 11 By contrast, in other OECD countries with a restricted access to nationality (such as Japan, Korea, and Switzerland), the foreign population is important (about 20 percent in Switzerland). 12 See Malone et al. (2003) for more details.

8