rafmacalaba/datause-encoder-data huggingface dataset:
|P111757|2010|2012|7/6/2011|6 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P111757|2010|2012|9/27/2011|7 Closed|Yemen,Republic of|
|P111757|2010|2012|6/30/2012|8 Closed|Yemen,Republic of|
|P111757|2010|2013|1/1/2013|9 Closed|Yemen,Republic of|
|P111757|2010|2014|12/28/2013|10 Closed|Yemen,Republic of|
|P111757|2010|2014|5/3/2014|11 Closed|Yemen,Republic of|
|P111757|2010|2015|11/7/2014|12 Closed|Yemen,Republic of|
|P111757|2010|2015|5/15/2015|13 Closed|Yemen,Republic of|
|P111757|2010|2016|11/18/2015|14 Closed|Yemen,Republic of|
|P111757|2010|2016|6/15/2016|15 Closed|Yemen,Republic of|
|P111760|2011|2011||0 Closed|Uzbekistan|
|P111760|2011|2012|7/13/2011|1 Closed|Uzbekistan|
|P111760|2011|2012|6/26/2012|2 Closed|Uzbekistan|
|P111760|2011|2013|6/25/2013|3 Closed|Uzbekistan|
|P111760|2011|2014|12/7/2013|4 Closed|Uzbekistan|
|P111760|2011|2015|7/3/2014|5 Closed|Uzbekistan|
|P111760|2011|2015|1/12/2015|6 Closed|Uzbekistan|
|P111760|2011|2016|9/15/2015|7 Closed|Uzbekistan|
|P111760|2011|2016|3/7/2016|8 Closed|Uzbekistan|
|P111760|2011|2017|9/25/2016|9 Closed|Uzbekistan|
|P111760|2011|2017|3/21/2017|10 Closed|Uzbekistan|
|P111760|2011|2017|6/19/2017|11 Closed|Uzbekistan|
|P111760|2011|2018|1/17/2018|12 Closed|Uzbekistan|
|P111760|2011|2018|3/14/2018|13 Closed|Uzbekistan|
|P111760|2011|2019|9/14/2018|14 Closed|Uzbekistan|
|P111760|2011|2019|3/11/2019|15 Closed|Uzbekistan|
|P111780|2010|2010|6/30/2010|1 Closed|Bosnia and Herzegovin|
|P111780|2010|2011|1/3/2011|2 Closed|Bosnia and Herzegovin|
|P111780|2010|2012|9/25/2011<br>|3 Closed|Bosnia and Herzegovin|
|P111780|2010|2012|5/11/2012|4 Closed|Bosnia and Herzegovin|
|P111780|2010|2013|1/14/2013<br>|5 Closed|Bosnia and Herzegovin|
|P111780|2010|2014|10/29/2013|6 Closed|Bosnia and Herzegovin|
|P111780|2010|2014|6/24/2014|7 Closed|Bosnia and Herzegovin|
|P111780|2010|2014|6/24/2014|7 Closed|Bosnia and Herzegovin|
|P111780|2010|2015|12/1/2014|8 Closed|Bosnia and Herzegovin|
|P111780|2010|2015|12/1/2014|8 Closed|Bosnia and Herzegovin|
|P111780|2010|2015|6/3/2015|9 Closed|Bosnia and Herzegovin|
|P111780|2010|2016|12/3/2015|10 Closed|Bosnia and Herzegovin|
|P111780|2010|2017|7/28/2016|11 Closed|Bosnia and Herzegovin|
|P111795|2011|2011|2/12/2011|1 Active|Nicaragua|
|P111795|2011|2011|2/26/2011|2 Active|Nicaragua|
|P111795|2011|2012|8/8/2011|3 Active|Nicaragua|
|P111795|2011|2012|3/13/2012|4 Active|Nicaragua|
|P111795|2011|2013|11/14/2012|5 Active|Nicaragua|
|P111795|2011|2013|6/5/2013|6 Active|Nicaragua|
|P111795|2011|2014|12/17/2013|7 Active|Nicaragua|
|P111795|2011|2014|6/29/2014|8 Active|Nicaragua|
<!-- Start of picture text --> 30,000 !<br>I;<br>¥ 20,000 1<br>8 i statistics<br>| Teen”<br>5 !<br>= 10,000 '<br>i) «100 os ole oe id<br>CRI<br><!-- End of picture text -->

<!-- Start of picture text --> . .<br>! 1<br>1 !<br>200,000 }<br>! 1<br>9 io<br>w2 150,000 ';}__ 11 4 statistics-<br>100,000 — | median<br>° 1 \ 1 mean<br>8<br>50,000<br>0<br>0.00 0.25! ! 0.50 0.75 1.00<br>CRI<br><!-- End of picture text -->
that male‐owned businesses in Uganda are 3.1 times larger than female‐owned enterprises and earn 2.5 times as much.

A range of factors have been discussed in the literature to explain these differences (Klapper and Parker, 2011; World Bank, 2012), including access to finance and time constraints. However, the sector in which the firm operates is consistently found to be a major determinant of gender‐observed differences in performance and growth (e.g. Hallward‐Driemeier, 2011; Bardasi, Sabarwal and Terrell, 2011; Costa and Rijkers, 2012).<sup>3</sup> For example, Hundley (2001) estimates that women’s concentration in the personal services sector explains as much as 14 percent of the gender‐based self‐employment earnings differential. De Mel, McKenzie and Woodruff (2009) find that female ownership loses statistical significance in explaining differences in performance once industry choice is controlled for. Results from Guatemala (Kevane and Wydick, 2001) and India (Kantor, 2005) suggest that constraints to female‐owned firms and their sector concentration respond to factors such as the marginal value of time for home production, and constraints on location of business, among others.<sup>4</sup> Differences in sectoral choice among men and women are large, with the vast majority of female entrepreneurs clustering in low value‐added industries. Women entrepreneurs in Africa tend to concentrate in hotels and restaurants, wholesale and retail trade, garments, textiles and leather goods, and other services (Bardasi, Sabarwal and Terrell, 2011). African men on the other hand have businesses distributed across a wider range of sectors, including construction and manufacturing.

Given that existing research has identified industry concentration as important in determining future profitability and growth, the current debate is centered on whether the women’s decision to enter these sectors is driven by constraints or preferences (see Bardasi, Sabarwal and Terrell, 2011, for an overview of this literature). Afzal, Hasan, and Parveen (2011) suggest that there are some behavioral and perception‐based determinants of lower profitability, namely that female entrepreneurs exhibit a significant amount of “intrinsic” rather than “extrinsic” motivation, and are often driven more by flexible work schedules or personal achievement rather than a raw interest in profit or growth, contributing to preference‐driven hypotheses. Similarly, Minniti (2010), among others, suggests that female

> 3 A survey of developed country literature on small and medium firms suggests that the limited growth observed among female‐owned firms is due to the over‐representation on women in a subset of sectors (Nichter and Goldmarck, 2009).

> 4 Kantor (2005) shows that 36 percent of female entrepreneurs operate home‐based firms where clients go to them for purchases, compared to 20 percent of male entrepreneurs. Household firms are smaller, and also less likely to grow (Mead and Liedholm, 1998).

3
|Appendix Table 2a:|Means and standard deviati|ons of teacher cha|racteristics var|iables (7-year ban|d pre-FPE)<br>“Permanent”|
|---|---|---|---|---|---|
||Urban|Age|Male|Born indistrict|contract status|
|Kenya|0.272|37.2|0.532|0.640|0.906|
||(0.446)|(7.60)|(0.500)|(0.481)|(0.292)|
|Madagascar|0.456|45.8|0.418|0.667|0.267|
||(0.501)|(7.95)|(0.496)|(0.474)|(0.445)|
|Mozambique|0.303|31.7|0.544|0.349|0.758|
||(0.463)|(5.56)|(0.503)|(0.482)|(0.432)|
|Tanzania|0.418|42.0|0.440|0.207|0.993|
||(0.495)|(5.19)|(0.498)|(0.407)|(0.083)|
|Togo|0.250|35.4|0.800|0.696|0.207|
||(0.437)|(6.71)|(0.403)|(0.464)|(0.409)|
|Uganda|0.182|43.3|0.588|0.549|0.811|
||(0.388)|(5.48)|(0.494)|(0.499)|(0.393)|
|Total|0.306|40.0|0.540|0.531|0.759|
||(0.461)|(7.79)|(0.499)|(0.499)|(0.428)|

Notes: Standard Deviations in parenthesis. See text for definition of variables. Sample includes only teachers hired within the 7 years prior to FPE.

A-4
in partially built cells. In other words, the additional population will not spread itself evenly on the land. Rather, it will congregate in cities. Assuming that the historical rate of population growth continues, this represents more than a tripling of dense urban areas in about 40 years, and a multiplication by 7 by the end of the century. Very similar forecast are obtained if we construct the transition matrix using only 1976 and 1986, or 2000 and 2014: the matrices are very similar, and the forecasts as well. Although our forecasts apply to buildings only, they imply a dramatic change in the distribution of population across the country, with an increased concentration in towns and cities without much of a densification of rural areas. To illustrate the magnitude of these changes, an equivalent increase in the population of Accra would imply an increase from 2.2 million in 2014 to 7.3 million people by 2053 and more than 15 million by the end of the century. These are not small changes.

# **4.3 Roads**

In Figures 3a and 3b, we repeat the exercise separately for cells that were close to a road in 1976 and those that were not. We define being close to a road as having at least one of the five categories of roads documented in 1976 either in the cell itself, or in its neighborhood _n_ 1 or _n_ 2. With this definition, 14.24% of cells at most 1.5km from a road in 1976. The year 1976 is chosen because it is close to the starting point of our data. Hence the transition matrices that are constructed on that basis can be seen as predictions based on road conditions existing in 1976. Results show a stark contrast between cells near road or not. Cells near a 1976 road were already less likely to be unbuilt in 1975: 93.7% vs. 99.3% for cells not near a road. This gap increased to 81% vs. 96% in 2014 and is predicted to grow to 61% vs. 90% by the end of the century. Furthermore, the distribution of built-up areas also varies greatly depending on the presence of a road in 1976. In 1975, only 0.05% of cells not near a road had a value of _yit_ in excess of 90%, compared to 0.73% of those near a road. By 2014 the difference was 0.46% vs. 3.71%. By the end of the century, the forecasted difference is 4% vs. 19%. Put differently, by the end of the century nearly 19% of the area within 1.5km of a 1976 road will be nearly fully built up. Of course new roads will be built that can affect that, a point we revisit below. But the main message here is that the early presence of a road is predicted to have long lasting effects on urbanization patterns. Figures 3a and 3b also show that while proximity to a road affects the probability of subsequent buildup, it 16
tions for the external validity of our results for the entire population of women in Uganda, our results are externally valid for the subset of rural women interested in increasing their income through entrepreneurship. It is also important to note that even in programs that include cash and asset transfers alongside skills, it may be feasible to allow self-selection into the skills-based components of the program.

In total, we enrolled 601 women in five different communities over the course of fifteen months (August 2018–October 2019). We worked in five communities to adequately power our study. Capacity constraints prevented us from working in more than one location at once, which is why we enroll the sample over time. While these logistical considerations were the primary motivators for our sampling frame, it enables us to effectively stratify on location, though the strata are not precisely equal in size. Our sample consists of 101 women in the first location, 153 in the second, 112 in the third, 136 in the fourth, and 99 in the fifth.

# **3.3 Timeline**

We conducted three in-person surveys with each woman in our sample: once at baseline in the two weeks following orientation, once at midline in the 2–3 weeks following graduation, and once at endline 12–18 months after graduation. Figure A2 shows a complete timeline including all data collection, implementation of the program, and COVID-19 lockdowns. In the first four locations, all treatment activities finished prior to the first COVID lockdown. The first lockdown delayed graduation in our fifth location. The timeline highlights two important considerations. First, we had originally intended to collect endline data 18 months after the baseline survey, but the COVID-19 lockdown pushed back our timeline. Therefore, our endline survey in all but the first location occurs around two years after baseline. Second, the delay in implementation for the fifth location means that the endline survey occurs around one year after midline (the same spacing as in the first location), whereas locations 2–4 have the endline 18 months after midline (two years 13
The paper is structured as follows. We proceed in Section 2 by providing a short discussion of the literature we build on, and a description of the data we use. In Section 3 we provide summary statistics on student learning outcomes and teacher content knowledge. In Section 4 we present a statistical model of cumulative knowledge acquisition, show how to estimate the key structural parameters, and discuss how we identify these parameters empirically. In Section 5 we present reduced form evidence followed by the main results, show specification and placebo tests, and present the findings from various simulations that use the estimated structural parameters. Finally, we conclude in Section 6 with a short discussion of the implications of our findings.

# 2. Related literature, data, and context

## _2.1 Related literature_

This paper is related to a growing literature on the impact of teacher quality on student learning outcomes. Several studies, primarily from the U.S. but more recently also from other countries such as Ecuador and Pakistan have demonstrated the importance of teachers using a value-added approach, with effect sizes ranging from 0.1 to 0.2 SD (Rockoff 2004; Rivkin et al. 2005; Aaronson et al. 2007; Chetty et al. 2014b; Araujo et al. 2016; and Bau and Das 2017).<sup>4</sup> While data that track individual student achievement over time, and link students to their teachers, are becoming increasingly more common in high-income countries, this is usually not the case in low- and middle-income countries.<sup>5</sup> Therefore, finding alternatives to estimating value-added models that rely on those data sets is necessary in order to provide insights into the education production function.

Our results confirm the importance of teacher quality and demonstrate the external validity of the value-added findings to a low-income environment where the average quality of teachers is low but also the variation in measured teacher quality is likely much greater.

Our work complements the literature in two additional ways. First, we estimate the causal effect of one key component of teacher quality; namely the extent to which teachers master the knowledge of the subject they are teaching. Second, we propose an alternative empirical approach that allows us to estimate the impact of teacher content knowledge using cross-sectional data. We identify structural impacts building on Dee (2005; 2007) who uses

> 4 The effect size is the impact on standard deviations of student performance from a one standard deviation improvement in teacher value added.

> 5 Singh (2019) presents grade- (but not teacher-) value added estimates for Ethiopia, India, Peru and Vietnam. Buhl-Wiggers et al. (2018) present teacher value added estimates in the context of a randomized evaluation in Northern Uganda. They find that a 1 SD increase in teacher effectiveness increases student learning by 0.09 SD.

4
**_-5 -_**

# Fig're **I**

<!-- Start of picture text --> NET OFFICIAL DEVELCPMEN-7 ASSISSTANCE<br>CAnual verage)<br>1.000<br>0.900<br>0. SW ~ ~ ~<br>0.700__ o Soo _ _ _ _<br>* 0.400<br>003<br>0.200<br>0.1100<br>0.000 m &<br>Ghana lanMs Iawl Terwania LUnda<br>(Cubsre oovv coluw are USS<br>igai-as 1 m<br><!-- End of picture text --> Source: IMF and World Bank data, and authors' estimates.
is straightforward to calculate its parameters using input-output tables. Also, consumption shares give the consumer price index weights, _θ_<sup>_i_</sup> . We use Bureau of Economic Analysis input-output and consumption share tables to calculate the parameters of the labor demand equations<sup>8</sup> . The calculated parameters are reported in Table 5.

Unlike the estimation step, we need to define all aggregate shocks for the simulations. We assume that there is an unexpected shock-therapy trade liberalization in the metal manufacturing sector which decreases the metal output price 50%<sup>9</sup> . As we discussed previously, it is practically impossible to model the exact liberalization process in the metal sector as the trade policy has been changing dynamically. Our simulation exercise is an illustrative counterfactual. We normalize initial prices to one. After the metal sector trade shock, prices in the tradable sectors adjust endogenously over the transition, while non-tradable sectors’ output prices stay constant. Thanks to uniqueness of the equilibrium, and the concavity of relevant equations, the simulations are straightforward: After we guess initial values for _Vt_<sup>_i,s_</sup> and _L_<sup>_i,s_</sup> _t_ for _t_ = 1 _,_ 2 _, .., T_ , we calculate _w_<sup>_i,s_</sup> , then implied _Vt_<sup>_i,s_</sup> and _L_<sup>_i,s_</sup> _t_ by the wage flows using equations presented in the _Model_ section. Using an arbitrarily weighted average of the initial and implied values and labor allocations, we update the guessed values. We repeat this procedure recursively until we reach a fixed point and make sure that the economy reaches free trade steady state before time _T_ . We omit the details because the simulations are easy to replicate.

We consider two different specifications. Simulation I uses the restrictions we imposed to estimate the model with CPS (i.e. the constrained model). Hence, we assume that _C_<sup>2</sup> = 0 and _C_<sup>3</sup> = 0. Therefore, we directly use the estimated parameters from CPS for Simulation I. However, Simulation II uses estimates from both NLSY and CPS simultaneously.

First, note that the model with _C_<sup>2</sup> = 0 and _C_<sup>3</sup> = 0 imposed as a constraint is called

> 8The fixed capital, _K i_ , is normalized to unity.

> 9This 50% price decrease is approximately equal to the change in steel price in the US between 1980 and 1998. Since the US uses about 10% of word metal output, a metal sector liberalization would not affect world price significantly. We experimented with an alternative specification where world price is determined endogenously and found that the qualitative implications are unchanged.

22
marketplace. The Australian numbers may be distorted because of the acquisition of Life insurers by banking groups; this creates an apparent link, but to a significant degree the source of the market share in some Australian cases did not originate in the Bancassurance sales process.

# **Where Bancassurance Has Shown Limited Growth**

Table 1.2 sets out those countries where the market share of insurance placed through Bancassurance has shown limited growth and less than either 40 percent (for Life business) or 10 percent (for non-Life business). Data are the most recently available as at 2007; sources are insurance regulators, insurance associations, Axco, Limra, and Swiss Re estimates.

# **Table 1.2 Countries Where Bancassurance Market Share Has Shown Limited Growth and Exceeds 40 Percent (Life) or 10 Percent (Non-Life)** **_percent_**

|Country|Bancassurers’ Life Share|Bancassurers’ Non-Life Share|
|---|---|---|
|Canada|1|Negligible|
|China|16|Verysmall|
|Germany|25|12|
|Japan|Verysmall|Verysmall|
|Mexico|10|10|
|Poland|14|Verysmall|
|Korea, Rep.|9|4|
|Taiwan,China|33|Verysmall|
|Turkey|23|10|
|United Kingdom|20|Less than 10|
|United States|2|Verysmall|

The penetration into Life insurance sales in many of these countries has been material but not nearly as impressive as the first group. This prompts the question asked in the next section’s heading.

# **Why Do Some Countries Show Strong Growth and Others Limited Growth?**

Numerous factors influence the differences shown by the above two tables.

At the heart of the U.S. and Canadian market’s low response to the Bancassurance concept is the regulatory situation there. Only in 1999 did the Gramm-Leach-Bliley Act remove obstacles to banks selling insurance, which had been in place since the 1933 Glass-Steagall Act. This, allied with the reality that the U.S. Life market was 10
10

**Table** 3. Popualtion in 2000 nd Stationery Population by Geographic Region nd for Selected Countries (miltions)

||Projection|<br>of 1980|Projection|of 1987-88|Current|projection|
|---|---|---|---|---|---|---|
|Geographic region<br>or country|Population<br>in 2000|Stationary<br>population|Population<br>in 2000|Statfonary<br>population|Population<br>in 2000|Stationary<br>population|
|World|6,075|9,867|6,176|10,650|6,205|10,806|
|**Africa**|805|2,071|875|2,531|**872**|2,506|
|**America**|847|1,267|824|1.216|825|1,224|
|**Asia**|3.561|5,569|3,634|5,974|3,657|6,087|
|Europe**nd**U.S.S.R.|833|922|**818**|**888**|821|882|
|**Oceania**|28|38|29|41|29|40|
|Selected cointries|||||||
|Chine|1.251|1,555|**1.274**|1,683|1,279|1,695|
|**India**|**973**|1,644|996|1,678|1,002|1,698|
|**Indorwsoa**|203|350|212|363|211|356|
|**Brazil**|201|345|178|292|180|306|
|**Nigeria**|153|425|163|529|163|529|

These revisions have affected the projected stationary populations dramatically. The eventual stationary world population now appears almost 10 percent greater than it did in 1980. For Africa in particular, the projected stationary population is now 21 percent larger than in the 1980 projections.

Aggregate changes since last year's projections, on the other hand, are minor. Projected world population in the year 2000 is up half of 1 percent this year as compared with last year. The largest revision for a geographic region is for Asia, but is barely above half of 1 percent.

a Projections for practically all countries have changed, but in the majority of cases only minimally, and about as often upward as downward. The larger changes have been due more often to revisions in current demographic indicators rather than to revisions in assumed future trends. Current indicators may have changed, for instance, because of new data or new analysis of census or survey data. Revised current population estimates account for new projections for countries like Swaziland, Costa Rica, Jamaica, Peru, and Papua New Guinea. Revised fertility estimates, often from Demographic and Health Surveys, account for new projections for countries like Nepal, Pakistan, Bolivia, Guatemala, and Nicaragua.

# Comparisons among World Bank regions

The four World Bank regions, comprising all the borrower countries
<!-- Start of picture text --> 46.6<br>41.5<br>34.4 35.1 36.7 37.1<br>Rss} & s co) s s a<br>oar]=© <=]> Bioz oS& xt£ <© BeS$ ©<br>iat (} » O < gs =.<br>a n Qo & rn o vo 2<br>1a) iS & 2 (e} <= ec<br>8 2 or 2 8 < S<br>v > h a <<br>S 3 a 3 0~<‘<G<br>5 = ° 3<br>lu<br><!-- End of picture text -->
-4 -

role in the growth of most developing economies. Most industrialization occurs in urban areas and, in the developing countries, the urban areas house increasing proportions of national populations and gross national product. Because of these trends, infrastructure investments are becoming increasingly concentrated in urban areas and urban growth is becoming strongly correlated with national growth.

It is increasingly recognized in recent years that urban concentrations of economic activity contribute significantly to economic growth (World Bank, 1991). Because urban economic activity depends heavily on infrastructure investments such as roads, power supply and water, the effects of infrastructure on the national economy can be understood better by studying first how the productivity of an urban economy responds to infrastructure services and then how increased productivity in urban areas shapes macroeconomic growth.

More importantly, the economic effects of infrastructure investments can be evaluated and analyzed better by taking an explicitly microeconomic approach. The key to such an effort is the study of the processes by which infrastructure influences households and businesses, the individual agents which drive the economy. A proper microeconomic approach would recognize that economic agents are not mere users of infrastructure services supplied by the public sector but can also act as producers of some of these infrastructure services.

Production of infrastructure services by households and businesses competes with public infrastructure provision. When deficiencies in the public infrastructure supplies or in the delivery of services are significant so as to retard the productivity of individual agents, the private sector will step in and, at least partially, fill that gap. This will happen even when privately provided infrastructure is handicapped by higher costs. The private sector will bear such higher costs if