rafmacalaba/datause-encoder-data huggingface dataset:
of the wage premium with workers and firm fixed effects occurs using information from firms that change innovation during the period - i.e., increase or decrease the number of PoTec workers, firms fixed effects will also absorb part of the innovation premium for those firms that do not change innovation intensity. As a result, in the empirical analysis we compare the estimates of Equation 2 with the results of augmenting it to include firm fixed effects.

where _δj_ represents time-invariant firm effects. As discussed in more detail below, the use of worker and firm effects will produce unbiased estimates of the wage premium under the assumption of _exogenous mobility_ or lack of sorting in firm-worker pairs.

# **3.4.2 The causal relationship between innovation and wages**

As discussed above, there are two hypotheses on why innovative firms pay higher wages. The first is related to self-selection, as those firms can pay higher wages prior to becoming innovative to, for example, incentivize workers. The other hypothesis assumes that there is a sharing of the quasi-rents from innovation between workers and firms (Reenen, 1996). To test these hypotheses, we build on the empirical literature on foreign wage premia (Hijzen et al., 2013) and the large literature on learning-by-exporting (Haidar, 2012; Yang and Mallick, 2010; Temouri et al., 2013).

To test whether high-paying firms self-select into innovation, we compare workers’ of innovative firms with workers in non-innovative firms years before there is a change in their innovative-status, defined by starting to employ a at least a PoTec worker.<sup>3</sup> In this framework, innovative firms are firms that did not have a PoTec employee in time _t_ but have at least one in time _t_ + _l_ . Non-innovative in _t_ are firms that neither have PoTec workers in _t_ nor in _t_ + _l_ . Specifically, we estimate: where _I_ is a dummy for the innovation-status (1 if firm _i_ hires at least one PoTec worker in year _t_ + _l_ , where _l ∈_ [1,3], and 0 otherwise. _wijft_ is the log hourly wage, which is regressed against a set of personal and firm characteristics, as in Equation 1.

> 3While when estimating the innovation wage premium in the previous section we used the share of PoTed workers, in the causality framework we use as the innovation variable ”treatment” whether the firm has at least one PoTec worker. We also perform some sensitivity analysis increasing the number of PoTec workers necessary to be considered as an innovative firm to two PoTec workers.

11
# **I. Introduction**

Poverty reduction consistently ranks among the most prioritized tasks of developing countries as well as the international community. For example, the Sustainable Development Goals (SDGs) recently adopted by the United Nations General Assembly call for eliminating poverty by 2030 in its very first goal. But effective and efficient poverty eradication entails the prerequisite of accurate poverty measurement, since it would be hardly possible to claim progress with decreasing poverty—or perhaps good performance with diminishing just about almost all other undesirable phenomena—if we cannot measure it well.

Three questions usually come up in the context of poverty measurement. The first question concerns, unsurprisingly, what represents the best yardstick that reliably measures poverty. For example, should it be household consumption (or income), or household wealth? Second, once we have a good poverty measure, how best to track the trends of poverty over time? Put differently, it is useful to understand the issues that can potentially affect the comparability of poverty estimates over time. Finally, what are the composition of poverty transitions over time? In particular, what is the proportion of the poor in one period that remain poor or escape poverty in the next period? Or what is the proportion of the non-poor that fall into poverty in the next period?

A sizable literature exists on the first question, and some consensus has perhaps been reached that household consumption data can be employed as a good proxy for household living standards—thus such data can provide the types of poverty estimates that are often sought.<sup>1</sup> Yet, the implementation of this idea in practice still remains a challenging task; we thus focus in this

> 1 Throughout this paper we are focused primarily on consumption as this is the measure of well-being that is most frequently used in the world for assessing poverty status, but we also note that income is used as a measure of wellbeing in many cases. For example, the 2012 global poverty estimates reported in Ferreira _et al._ (2016) are based on data from 131 countries, of which 99 use consumption as the measure of well-being and 32 use income. In an abuse of terminology, we refer to “consumption” and “income” interchangeably as the measure of household living standards in this paper. See also Ravallion (2016) for a comprehensive discussion on the history of thought on poverty and other measurement approaches.

2
analyzed in this study are female. US individual investors utilize short-trades frequently as 38% of the accounts have held a short position at some time over the sample period analyzed. 14% of US individual investors have traded options at least once over the sample period and 22% of them have traded foreign securities. 66% of the US individual investors in our transactions data set hold technical or managerial positions. The mean (median) US individual investor’s annual income over the time period studied is $76,840 ($87,500). The mean (median) US individual investor’s portfolio concentration is .52 (.48), which roughly corresponds to holding two stocks with equal dollar weights of 50%.

# **4. Transaction Costs and Holding Periods in the US**

# **4.1 Holding periods and Transaction Costs**

In this section, we provide empirical evidence in support of the first hypothesis ( _H1_ ): _Holding periods are positively related to measures of transaction costs, after controlling for investor and stock characteristics._ We begin by computing a holding period for each transaction in the data set.<sup>9</sup> The holding period is defined as the number of trading days from the first purchase of a stock to the first sale, following the approach of Seru, Shumway, and Stoffman (2010).<sup>10</sup> This generates 799,469 holding period observations, with a mean (median) of 185 (86) trading days for retail investors in the United States.

9 We also estimate portfolio level holding periods and transaction costs for each investor in the data set. Our analysis of investor portfolios yields results consistent with transaction-level analysis. As explained in the Introduction section, transaction-level analysis allows for the same investor to have different holding periods for different assets and as such is more in line with models that assume endogenous trading horizons.

10 We obtain similar results by alternatively defining the holding period as the number of trading days from first purchase till the day when all positive positions are closed as in Feng and Seasholes (2005).

14
lines constructed at a later date will still have a direct effect on demand for financial services, they will have less effect on patterns of migration. Therefore, when we use distance to the US border via the 1900 rail network as an instrument, we also include distance to the nearest rail line in 1998 as an additional control that would capture the economic benefits of being close to the railroad in modern times.

Using the historical migration network as an instrument isolates the exogenous component of remittances that comes from the historical migration patterns. We should, therefore, interpret results from this IV as identifying the long-term impact of remittances on commercial banking sector depth and breadth. Table 5A shows the results from the first-stage of the IV estimations. We report results from two specifications. The first controls only for GDP per capita, population density, and distance to the modern rail network. The second includes schooling, indigenous language, distance to the border, and distance to Mexico City as additional controls. We find that our instrument, _minimum distance_ , has a negative impact on remittances. In other words, in _municipios_ that are further away from the US through the 1900 rail network, a smaller share of households receives remittances. The effect of distance on remittances is significant in each specification. Furthermore, the Kleibergen-Paap F statistics for weak identification always exceed the Stock and Yogo (2005) critical values so we are able to reject the null that our instrument is weak.

Second stage IV results are shown in Table 5B. Consistent with our expectation that the Tobit results are biased downward, we find modestly larger impacts of remittances on banking breadth and depth when we instrument for remittances and use the more parsimonious specification. However, the IV results are very close in magnitude to the Tobit results when we include the measures of schooling, indigenous population, distance to the border, and distance to the rail. The standard errors are somewhat larger as well. While we continue to find that remittances have a positive impact on banking sector breadth (measured via the number of branches or deposit accounts), only the deposit indicator of banking sector depth (deposits to GDP) is statistically significant. Note that distance to the present-day rail network has generally weak effects on banking sector breadth and depth. Where significant (for deposits and accounts in the leanest specification), the coefficient indicates that banking sector breadth and depth is higher in _municipios_ located closer to the rail lines, as expected.

19
Table B9: Variance contributions for Uganda, by sub-group

||||Absolu|te (s.d.<br>|units)|||Rela<br>|tive (in|%)||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Strata|Level|σ�<br>�|σ��<br>�|σ�<br>�|σ�<sup>�</sup>|2Σ��|σ��<br>�|σ�<br>�|σ�<sup>�</sup>|2Σ��|σ�<sup>�</sup>|
|Female|0|0.99|0.18|0.39|0.40|0.25|3.15|15.66|15.84|6.28|59.08|
||1|1.00|0.18|0.40|0.40|0.26|3.07|15.50|15.73|6.47|59.23|
|Age|6|0.99|0.18|0.40|0.35|0.25|3.44|16.43|12.59|6.29|61.26|
||9|1.00|0.14|0.39|0.39|0.29|2.06|15.33|15.40|8.63|58.58|
||12|1.00|0.16|0.39|0.38|0.28|2.53|15.16|14.63|7.89|59.80|
||15|1.01|0.18|0.39|0.35|0.23|3.37|14.91|11.99|5.26|64.47|
|Grade level|0|0.96|0.21|0.42|0.18|0.19|4.78|19.44|3.50|3.75|68.53|
||1|0.93|0.13|0.40|0.33|0.21|1.97|17.98|12.53|4.82|62.70|
||3|1.08|0.15|0.40|0.38|0.27|2.04|14.14|12.28|6.41|65.14|
||5|0.82|0.16|0.37|0.29|0.14|3.80|19.92|12.71|2.82|60.75|
|SES tercile|1|0.97|0.13|0.38|0.41|0.25|1.75|15.87|18.01|6.50|57.87|
||2|0.97|0.10|0.40|0.40|0.25|0.98|16.77|16.52|6.60|59.15|
||3|0.97|0.18|0.39|0.39|0.22|3.35|15.79|15.95|4.99|59.91|
|Mother primary|0|0.96|0.17|0.39|0.39|0.23|3.17|16.22|16.73|5.76|58.12|
||1|0.99|0.17|0.39|0.39|0.24|3.09|15.55|15.86|6.10|59.40|
|All|.|1.01|0.19|0.40|0.39|0.24|3.58|15.44|15.12|5.77|60.09|

Note: for each stratifying variable, indicated in the first column, sub-groups (second column) are mutually exclusive and span the entire dataset; female is a dummy variable (i.e., 0 = male, 1 = female); age and grade levels are grouped (e.g., age level 6 indicates children aged 6-8; age level 15 is children 15 and above; grade level 0 contains never enrolled children; grade level 5 is all those with highest grade 5 and above); for SES tercile, level 1 is the poorest group; mother edu. takes a value of 1 if the mother has attended primary school; variance components are as per equations (3b)-(3c) and 2 the individual effects are aggregated for simplicity (denoted, σa�); absolute and relative contributions are as per earlier tables, reported in standard deviation units and percentages respectively. Source: own calculations.

54
**Table 3c: Growth in real per capita sectoral GVA and GDP by distance to major port**

||**Manufacturing**|**Construction**|**Agriculture**|**Services**|**GDP**|
|---|---|---|---|---|---|
|2004-2006 Expansion|2.92***|-2.68|7.35***|1.71***|2.05**|
||[2.82]|[-1.60]|[2.70]|[4.64]|[2.60]|
|2008-2009 Recession|-12.68***|-29.38***|8.99***|-7.82***|-7.05***|
||[-12.63]|[-21.31]|[2.99]|[-18.75]|[-11.63]|
|2010-2011 Rebound|10.06***|4.87**|7.30***|-2.47***|3.71***|
||[6.03]|[2.03]|[2.70]|[-5.88]|[4.56]|
|2013-2017 Expansion|3.53***|-6.15***|6.89***|-2.26***|0.69|
||[3.38]|[-3.09]|[3.20]|[-6.37]|[1.17]|
|2018-2019 Recession|-0.97|-38.98***|7.30***|-4.46***|-3.76***|
||[-0.63]|[-14.23]|[3.39]|[-7.10]|[-5.34]|
|2004-2006 Expansion X Distance|0.27|0.93*|0.60|-0.15*|0.12|
||[1.32]|[1.91]|[0.96]|[-1.95]|[0.76]|
|2008-2009 Recession X Distance|0.73***|0.98**|-0.05|0.18**|0.31**|
||[3.11]|[2.28]|[-0.08]|[2.53]|[2.18]|
|2010-2011 Rebound X Distance|-0.24|1.38**|0.87|0.23**|0.44**|
||[-0.62]|[2.35]|[1.41]|[2.18]|[2.34]|
|2013-2017 Expansion X Distance|-0.10|-0.07|0.28|0.06|-0.04|
||[-0.33]|[-0.12]|[0.59]|[0.76]|[-0.34]|
|2018-2019 Recession X Distance|-1.18**|0.63|-0.02|0.24|0.19|
||[-2.57]|[0.90]|[-0.04]|[1.63]|[1.25]|
|Constant|4.54***|13.08***|-6.04***|6.22***|3.66***|
||[9.77]|[16.49]|[-5.10]|[40.80]|[13.94]|
|Observations|1,134|1,134|1,134|1,134|1,134|
|R-squared|0.24|0.47|0.04|0.41|0.40|
|Number ofprovinces|81|81|81|81|81|

Notes: t-statistics based on robust standard errors (clustered by province) in parenthesis *** p<0.01, ** p<0.05, * p<0.1. All regressions use data on all 81 provinces and employ province fixed effects. Distance to major a port is computed as the shortest distance between the province centroid and the centroids of four provinces that have major ports (Istanbul, Izmir, Bursa and Mersin). This is measured in units of 100 km.

22
# **Table 2. Pairwise correlation of infrastructure measures**

## **1.) Components by sector**

|**_(a) Transport_**|||||||
|---|---|---|---|---|---|---|
||roads (in logs)|paved roads|q_roads|q_railroads|q_ports|q_air|
|roads (in logs)|1||||||
|paved roads|0.2701**|1|||||
|q_roads|0.5106**|0.5382**|1||||
|q_railroads|0.5787**|0.5787**|0.7769**|1|||
|q_ports|0.5487**|0.4610**|0.8900**|0.7579**|1||
|q_air|0.5506**|0.4737**|0.8565**|0.6957**|0.8690**|1|
|**_(b) Telecommunications_**|||||||
||ml (in logs)|cell (in logs)|telf|wl|||
|ml (in logs)|1||||||
|cell (in logs)|0.8223**|1|||||
|telf|-0.4902**|-0.5916**|1||||
|wl|-0.3950**|-0.4665**|0.1866*|1|||
|**_(c) Electricity_**|||||||
||egc (in logs)|pl|q_elec|elec_access|||
|egc (in logs)|1||||||
|pl|-0.4230**|1|||||
|q_elec|0.7331**|-0.6391**|1||||
|elec_access|0.8295**|-0.2005*|0.6069**|1|||
|**_(d) Water & Sanitation_**|||||||
||water|sanitation|||||
|water|1||||||
|sanitation|0.8112**|1|||||

_Notes:_

** denotes the significance level at 5 percent, and * at 10 percent.

## **2.) The representative component from each sector**

||roads (in logs)|ml (in logs)|egc (in logs)|water|
|---|---|---|---|---|
|roads (in logs)|1||||
|ml (in logs)|0.5727**|1|||
|egc (in logs)|0.6374**|0.8727**|1||
|water|0.4902**|0.8644**|0.7785**|1|

## _Notes:_

** denotes the significance level at 5 percent, and * at 10 percent.

17
marriage between men and women in Mali, only 48 percent of women 60 and above are married compared to 90 percent of men (Commission on Population and Development, 2000). There are considerably more widows than widowers. Indeed, 3 percent of men aged 56 and older are identified as widowers versus 52 percent of women in the same age group in the 2006 ELIM data.

Malian women tend to have different, and typically significantly inferior, human capital <u>endowments</u> to men. According to the 2006 DHS, only 17 percent of Malian women are literate compared to 37 percent of men; the breakdown for rural and urban areas is 8 versus 26 percent, and 35 versus 61 percent, respectively.<sup>6</sup> An alarming 88 percent of rural women have had no schooling at all. This is true also of 59 percent of urban women. Although the percentages of men without instruction are also high at 69 and 39 percent in rural and urban areas, they still fare considerably better in this respect than women. Far fewer girls attend school: the ratio of girl to boy attendance is 75 percent at the primary school level and only 35 percent at the secondary school level. Greater illiteracy and lower educational attainments generally, place stronger limitations on women‘s access to employment and government services. Malian women also face pronounced health risks and complications associated with the near universal practice of girls‘ circumcision in often unsanitary conditions, and repeated child bearing as adults. A woman has 6 to7 births on average, and these are associated with high rates of maternal mortality ─ 670 deaths per 100,000 live births in 2008 (Hogan et al., 2010) ─ as well as pronounced morbidity. At 250 per 1000 births, rates of infant mortality are also extremely high. Demographic and Health Surveys indicate high rates of anemia. On the whole, women are likely to suffer from lower physical resilience to shocks for a substantial part of their adult lives.

A number of studies in various countries have also suggested that men and women may have different preferences (Croson and Gneezy 2009; Tertilt and Doepke 2011) although it is difficult to disentangle preferences from constraints.<sup>7</sup> For example, men are often found to spend more of their incomes on themselves, while women tend to spend more of theirs on their children and on household nutrition and health care more generally (for example, see Schultz 1990, Thomas 1990 and Duflo 2003; Tertilt and Doepke 2011 review the literature). They may also deprive themselves more in order to protect their children‘s nutrition and health following a

> 7 Note that whether gender differences in preferences are biological or due to nurture, social norms, roles and responsibilities which can be altered does not detract from their relevance in the short to medium term.

5
MULTILINGUAL CHILD ASSESSMENT Table 4. The association of baseline caregiver literacy in English and Luo and child follow-up vocabulary scores

||E<br>|nglish recept<br>|ive vocabul<br>|ary<br>|Lu<br>|o receptiv<br>|e vocabul<br>|ary<br>|E<br>|xpressive<br>|vocabula<br>|ry<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||cg1|cg2|cg3|cg4|cg5|cg6|cg7|cg8|cg9|cg10|cg11|cg12|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Caregiver|||||||||||||
|literacy|||||||||||||
|English|0.070**||–0.057|–0.098|0.0003||–0.040|–0.077|0.049*||0.027|0.021|
||0.028||0.073|0.073|0.029||0.088|0.092|0.029||0.081|0.084|
||0.013||0.431|0.181|0.993||0.651|0.405|0.088||0.737|0.800|
|Luo||0.067***|0.109*|0.110**||0.005|0.034|0.028||0.039*|0.019|0.023|
|||0.022|0.056|0.055||0.023|0.068|0.068||0.022|0.062|0.063|
|||0.002|0.056|0.045||0.815|0.619|0.680||0.082|0.760|0.711|
|Constant|–0.190**|–0.234***|–0.224**|–0.237***|–0.003|–0.021|–0.014|–0.039|–0.142|–0.143|–0.148|–0.143|
||0.089|0.088|0.090|0.087|0.0936|0.092|0.094|0.093|0.090|0.089|0.090|0.091|
||0.034|0.008|0.013|0.007|0.9735|0.822|0.883|0.677|0.117|0.109|0.103|0.118|
|_R_-squared|0.015|0.023|0.024|0.045|0|0|0.001|0.016|0.008|0.008|0.008|0.015|
|N. of cases|442|442|442|442|442|442|442|442|442|442|442|442|

Notes. Receptive English and Luo vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years, measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Expressive vocabulary _z-_ scores were measured using a tool developed from the PPVT. Caregiver literacy is number of words (out of 5) a caregiver could read from a simple sentence at a second-grade reading level, adapted from the Early Grade Reading Assessment (EGRA). Baseline and follow-up were conducted approximately 5 weeks apart. The first two models for each vocabulary assessment (cg1 & cg2, cg5 & cg6, cg9, & cg10) are bivariate regressions. The third model for each vocabulary assessment (cg3, cg7, and cg11) include caregiver literacy in both languages. The fourth model for each vocabulary assessment (cg4, cg8, and cg12) add controls for caregiver education and household wealth. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.

38
# **Dark Costs, Missing Data: Shedding Some Light on Services Trade**<sup>_∗_</sup>

James E. Anderson<sup>_†_</sup> Ingo Borchert<sup>_‡_</sup> Aaditya Mattoo<sup>_§_</sup> Boston College and NBER University of Sussex World Bank Yoto V. Yotov<sup>_¶_</sup> Drexel University

## **JEL Classification Codes:** F13, F14, F16

**Keywords:** Gravity, Services Trade, Trade Costs in Services, Home Bias, Border Effects.

> _∗_ We would like to thank seminar participants at LSE’s CEP International Economics seminar, University of Sussex, Scottish Institute for Research in Economics (SIRE) at Edinburgh, and the 3rd IMF/WB/WTO Joint Trade Workshop at Washington DC. All remaining errors are our own. Research for this paper has been supported in part by the governments of Norway, Sweden, and the United Kingdom through the Multidonor Trust Fund for Trade and Development, and by the UK Department for International Development (DFID). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent, or any of the aforementioned individuals or institutions.

> _†_ Department of Economics, Boston College, Chestnut Hill, MA 02467, USA. Phone: 617-552-3691. E- mail: james.anderson@bc.edu

> _‡_ Department of Economics, School of Business, Management and Economics, University of Sussex, Brighton, BN1 9SL, United Kingdom, Phone: +44-1273-678731, E-mail: I.Borchert@sussex.ac.uk

> _§_ Development Economics Research Group (DECTI), World Bank Group, 1818 H Street, N.W., Washington, DC 20433, USA. E-mail: amattoo@worldbank.org

> _¶_ School of Economics, LeBow College of Business, Drexel University, Economic Research Institute, Bulgarian Academy of Sciences, CESifo Research Network, Gerri C. LeBow Hall 1020, Market Street, Philadelphia, PA 19104, USA. Email: yotov@drexel.edu