rafmacalaba/datause-encoder-data huggingface dataset:
The probability of a child attending school is positively associated with the household’s education and wealth levels. One additional year of education of the most educated person in the household is associated with a 1.3 percent increase in the probability of school attendance. An increase in one point in the wealth index is associated with a 2.3 percent increase in the probability of an individual attending school.<sup>14</sup> We also include a dummy variable indicating if both parents are permanently living in the household. Children that grow up in households where both parents are present are 9.5 percent more likely to attend school.

Neither urban dummy nor the average value of the wealth index at the cluster level appears as a statistically significant determinant of school attendance, once observed household and individual characteristics are controlled for.<sup>15</sup> This result suggests that the simple correlation between attendance and urban residence reflects individual and household characteristics rather than an overall area effect.<sup>16</sup> To assess the relationship of school supply with attendance we use the 2003 school census, merged with each round of the DHS, only considering the schools that appear to be founded on or before the year in which the survey was taken. We consider a set of variables that approximate the school supply in several dimensions: number of schools, number of classes, total number of teachers, and number of trained teachers. All the variables are divided by the number of children in school age in the locality.<sup>17</sup> These variables are intended to account for both the availability of schools and teachers as well as the quality of the teachers. Additionally, we include in our regressions the average fees charged by schools in the locality. Finally, from the community module in the 1994 and 2000 DHS, we include information on the average time to travel from a central point in the cluster to a primary school.

Table 4 shows results from the analysis of school determinants taking into account school supply variables. The availability of schools in the urban/rural commune (both in terms of the number of schools and classes) is positively associated with the probability of attending school. A greater number of teachers is associated with a higher probability of school attendance,

> 14 The standard deviation of the wealth index is 1.96.

> 15 In order to reduce endogeneity problems with this variable, we use the left out mean for every household. 16 Table A.5 and Table A.6 in the appendix show the results from Table 3, run separately for urban and for rural areas.

> 17 The population of school age is defined as the number between 5 and 19 years old who lived in the urban/rural area of the commune at the time of the 2003 population census.

11
Then, we apply logarithm to both sides of equation (20) to disentangle one effect from the other. The resulting expression is as follows: We proxy the imitation effect with _ln_ (1 + _mσ_ ).<sup>7</sup> Our estimating equation becomes as follows: _Zs_ is a vector of control variables at sectoral level. It includes variables such as the logarithm of workers, the logarithm of the volume of exports, capital intensity of the sector, and skill intensity of the sector.<sup>8</sup> We include these variables to be sure that the coefficients of interest are capturing the effect we want to measure and not the impact of one of these variables if they are omitted from the model. In particular, capital and skill intensive sectors may have an advantage in the production of high quality products. Sectors with large volume of exports frequently supply better goods to appeal to the standards of international markets, and large sectors innovate strongly in both dimensions.

Since data on imitation are only available for the period 2001-2003, our results correspond to that period. According to the theory, coefficient _β_ 1 is expected to be negative, and coefficient _β_ 2 is expected to be positive. Table 3 presents the estimation results.

> 7We add 1 to the logarithm of _mσ_ to avoid missing observations for which _m_ = 0.

> 8We employ the Bartlesman and Gray[9] data set to construct the capital and skill intensity indices.

16
<!-- Start of picture text --> AlgerN geria i a Botswana<br>Iran, Islamic Rep. Malawi Zam<br>Burkina FasoGhana Syrian Arab RepublicEcuadorParaguay<br>BangladeshMadagascar Trinidad and Tobago<br>Bolivia NorwayTogoGambia, The<br>Senegal<br>Honduras PhilippinesCosta Rica<br>Peru ChileNicaragua ZimbabweJordan<br>Kenya El Salvador Ireland<br>Pakistan Guatemala Colombia Israel Dominican Republic<br>Argentina AustraliaSouth AfricaMoroccoNew ZealandUruguayTun siSr i L a nka Panama Malaysia<br>MexicoCanada<br>Japan Indonesia<br>Brazil India FranceUnited KingdomSpainPortugalTurkeySweden Thailand Belgium<br>China Denmark<br>United States<br>Netherlands<br>Italy<br>.6 .7 .8 .9 1 1.1<br>Trade openness<br>Source: Authors’ calculations<br>(a) Openness<br>NigeriaAlgeriaBotswana<br>MZ a mbialawiIran, Islamic Rep.<br>Burkina FasoGhanaSyrian Arab RepublicEcuadorParaguay<br>MB a ngladeshdagascar Trinidad and Tobago<br>TogoGambia, TheBolivia Norway<br>Senegal<br>HondurasPhilippinesCosta Rica<br>ZimbabweNicaJoPeru r aguadan Chile<br>KenyaEl Salvador Ireland<br>Pakistan Guatemala ColombiaDominican Republic Israel<br>Sri LankaMoroccoTunisiaSouth AfricaMalaysiaPanamaUruguayArgentina New Zealand Australia<br>Mexico Canada<br>Indonesia Japan<br>IndiaThailandTurkeyBrazil PortugalSpain FranceBelgiumUnited KingdomSweden<br>China Denmark<br>United States<br>Netherlands<br>Italy<br>0 10000 20000 30000 40000<br>GDP per capita in initial period (2000 prices)<br>Source: Authors’ calculations<br>(b) Income per capita<br>1<br>.8<br>.6<br>Export concentration<br>.4<br>.2<br>1<br>.8<br>.6<br>10 product concentration .4<br>.2<br><!-- End of picture text --> Figure 3: Distribution of countries by (10 product) diversification and (a) openness (b) initial GDP per capita. The maroon line indicates the threshold level of diversification, with countries below the line benefiting from increased openness. Open countries feature both above and below the threshold, while high income countries tend to be clustered below the threshold.

the threshold value we identified, implying that they are likely to enjoy the benefits of trade openness while being well shielded against global shocks via the participation in a large number of global value chains. Yet, we also see that the vast majority of countries above the diversification threshold are low income countries, although a large number of 21
varying reference periods of one week, month, or year to demonstrate sensitivity of employment indicators to this factor.

# 3. **Trends**

Figures 2 to 9 illustrate trends and patterns in male and female employment in South Asia. We used multiple surveys in the aggregate graphs to look at employment rate sensitivity to recall periods. For the disaggregated graphs, we only used the weekly recall period to determine employment status and restricted ourselves to surveys in which pertinent information was available. All estimates use individual sample weights.

# _Aggregate_

As shown in Figure 2, the female employment rate—the number of employed women as a share of the working-age population—remained stable since 2001 in Sri Lanka, Pakistan, and Bangladesh, regardless of survey instrument or recall period. Also, there is a persistent gap of approximately 42 and 55 percentage points between male and female employment rates in Sri Lanka and Pakistan, respectively.

In Nepal, India, and Bhutan, however, we observe greater sensitivity in employment indicators. As expected, yearly reference periods yield higher employment rates compared to when respondents are asked about activities only in the last week. For Nepal, a clear trend does not emerge; using the NLSS, the female employment rate declines based on yearly recall (95 to 75 percent between 2003 and 2010) but slightly increases when considering weekly recall. The category “extended economic activities” is not included in this estimate to remain consistent with employment definitions in other surveys. In India, we observe a consistent pattern of falling female employment rates across surveys.

In Bhutan, women are about 9 to 15 percentage points less likely to be employed than men (depending on the year) when respondents recalled activities in the past week rather than the last year. However, women’s employment clearly declined from 69-78 percent in 2003 to 50 percent in 2012, a sharper decline for women than for men.

In Afghanistan, the male employment rate is similar to other countries, and male employment also experienced a relative decline towards 2013. On the other hand, female employment is high relative to the region, with rates around 50 percent, except for a sharp fall in 2011 when using a weekly recall period.

# _Age_

Employment rates differences between men and women are not as large for the youngest bracket as they are for older ones. According to Figure 3, male employment rates are similar across countries in the two oldest age brackets, while female employment rates vary significantly between countries. For instance, the employment rate for women between 25 to 54 years-old varied by 50 percentage points

# **Figure 2: Comparing employment rates computed from different surveys and recall periods**

9
**Table 1. Descriptive statistics of explanatory variables**

|**Household characteristics**|Mean*|
|---|---|
|Household size|6.37 (2.30)|
|Number of household members under 15|3.47 (1.86)|
|Number of females 15 to 64|1.39 (0.83)|
|Number of household members 65 and older|0.13 (0.41)|
|Wealth quintile 1 (reference group)||
|Wealth quintile 2|0.16|
|Wealth quintile 3|0.14|
|Wealth quintile 4|0.11|
|Wealth quintile 5|0.05|
|Head has at least secondary education|0.03|
|Head is female|0.24|
|Main livelihood: Salaried employment (reference group)||
|Main livelihood: Daily laborer|0.04|
|Main livelihood: Agriculture|0.72|
|Main livelihood: Manufacturing, trade, services|0.12|
|Main livelihood: Pension, remittances, donations|0.04|
|Ability to raise 300 birr within a week|0.79|
|Rural household|0.82|
|**Woreda level characteristics**||
|Average distance to road|16.58 (22.50)|
|% of households in agriculture|0.77 (0.27)|
|% of household heads employed|0.79 (0.15)|
|% of household heads with at least secondary school<br>education|0.04 (0.09)|
|Average dietarydiversity|4.32(0.86)|

*Standard deviations are provided in parenthesis for continuous variables. Source: Author calculations from 2016 HCES/WMS.

13
47

# TABLE 17

AVERAGE RATE OF GROWTH OF<sup>TFP FOR ALL INDUSTRIES</sup> D01O FOR 1976-80. D1-1 FOR 1981-85 DO-PR FOR PRIVATE FIRMS AND<sup>DOPU</sup> FOR PUBLIC FIRMS

|VARIABLE|MEAN|MINIMUM<br>VALUE|MAXIMUM<br>VALUE|
|---|---|---|---|
|--------------|------<br>DO-PR|D1-0 -----------|----------|
|MTFP|-0.041|-0.108|0.043|
|--------------<br>-OOPR|------|D1-1 -----------|----------|
|MTFP|0.034|-0.078|0.098|
|--------------|------<br>DO=FU|D1-0 -----------|----------|
|MTFP|-0.075|-0.227|0.044|
|--------------|------<br>DO-PU|D1=1 -----------|----------|
|MTFP|0.057|-0.077|0.209|

TABLE 18 CORRELATION COEFFICIENT BETWEEN<sup>THE RATES OF</sup> GROWTH OF LABOR (GL),<sup>CAPITAL STOCK (GK). REAL</sup> OUTPUT (GCX), INTERMEDIATE INPUTS<sup>(GCI), AND TFP</sup> PEARSON CORRELATION COEFFICIENTS /<sup>PROB>:R: UNDER HO:RHODO/NUMBER OF OBSERVATIONS</sup>

||GL|GK|GCX|GCI|TFP|
|---|---|---|---|---|---|
|GL|1.00000<br>|0.11411<br>|0.40099<br>|0.39308<br>|0.09879<br>|
||0.0000<br>|0.0057<br>|0 0001<br>|0.0001<br>|0.0298<br>|
||585|585|487|484|484|
|GK|0.11411<br>|1.00000<br>|0.13911<br>|0.04138<br>|-0.05818<br>|
||0.0057<br>|0.0000<br>|0.0021<br>|0.3636<br>|0.2014<br>|
||585|588|487|484|484|
|GCX|0.40099<br>|0.13911<br>|1.00000<br>|0.74356<br>|0.67615<br>|
||0.0001<br>|0.0021<br>|0.0000<br>|0.0001<br>|0.0001<br>|
||487|487|487|484|484|
|GCI|0.39308<br>|0.04138<br>|0.74356<br>|1.00000<br>|0.08072<br>|
||0.0001<br>|0.3636<br>|0.0001<br>|0.0000<br>|0.0761<br> <br>|
||484|484|484|484|<br>484|
|TFP|0.09879<br>|-0.05818<br>|0.67615<br>|0.08072<br>|1.00000<br>|
||0.0298<br>484|0.2014<br>484|0.0001<br>484|0.0761<br>484|0.0000<br> <br>484|
be a useful approach. Further, preconception consultation programs, similar to those implemented in the Islamic Republic of Iran and Saudi Arabia (Hamamy, 2012), might focused on carrier detection and genetic counseling. While such screening and genetic-counseling might prevent certain types of disabilities, other negative effects of consanguinity on children’s cognitive ability might not be necessarily addressed by these interventions.

# **References**

Agarwal, Bina. 1994. _A Field of One’s Own: Gender and Land Rights in South Asia_ . Cambridge: Cambridge University Press, 1994.

Barbour, Bernadette and Pascale Salameh. 2009. Consanguinity in Lebanon: prevalence, distribution and determinants. _Journal of Biosocial Science_ , 41(4), 505-517.

Bashi, Joseph. 1977. Effects of inbreeding on cognitive performance. _Nature_ , 266, 440-442.

Becker, Gary S. and Nigel Tomes. 1976. Child Endowments and the quantity and Quality of children. _Journal of Political Economy_ , 84(4), 143-162 Becker, Gary S. 1981. _A Treatise on the Family._ 424pp. Cambridge: Harvard University Press.

Birnbaum, Allan. 1967. "Some Latent Trait Models and Their Use in Inferring an Examinee’s Ability." In Lord, Frederic M. and M.R. Novick, eds., Statistical Theories of Mental Test Score. London: AddisionWesley Publishing Company.

Bittles, Alan H. 1994. The role and significance of consanguinity as a demographic variable. _Population and Development Review_ , 20(3), 561-584.

Botticini, Maristella and Aloysius Siow. 2003. Why Dowries? _American Economic Review_ , 93(4): 1385– 1398.

Caldwell, J.C., P.H. Reddy and P. Caldwell. 1988. _The Causes of Demographic Change: Experimental Research in South India_ . Madison: University of Wisconsin Press.

Cameron, Colin A. and Douglas L. Miller. 2011. “Robust Inference with Clustered Data,” in A. Ullah and D.E. Giles eds., _Handbook of Empirical Economics and Finance_ , CRC Press.

Cameron, Colin A. and Douglas L. Miller. 2015. “A Practitioner’s Guide to Cluster-Robust Inference.” _Journal of Human Resources_ 50(2): 317-372.

Das, Jishnu and Jeffrey S. Hammer. 2005. Which Doctor? Combining Vignettes and Item Response to Measure Clinical Competence _. Journal of Development Economics,_ 78(2), 348-383.

17
# **4.3.2 Excluding pioneers from Silicon Valley**

Another potential concern about the results from the baseline analysis is that these pioneers might exhibit specific features, both with regard to their innovative capacity and the network to which they belong. For example, one might be concerned that a few leading technology firms in Silicon Valley might be driving the overall patterns observed in the data, and that the results for these firms might not extend to those of other innovative regions or technologies. To address this concern, in Table 13 we exclude pioneer firms located within Silicon Valley from our linkage sample. We observe that the empirical results remain similar to those in the baseline analysis, suggesting that our findings apply more broadly to other pioneer regions as well. In particular, the coefficients on pre-existing buyers and innovation partners of pioneers in Column (5) remain positive and statistically significant. The main qualitative difference relative to the baseline analysis is that the coefficient on affiliates in Column (5) is now also positive and statistically significant.

Table 13. Firm-to-firm linkages excluding Silicon Valley pioneers

||Dep v|ariable: Norm|alized share o|f technology-|related job ad|verts|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Pioneer|4.269***|4.135***|4.223***|4.140***|2.761***|2.743***|
||(0.420)|(0.417)|(0.418)|(0.418)|(0.482)|(0.483)|
|Pioneer_×_years since emergence|-0.177***|-0.168***|-0.175***|-0.170***|-0.093*|-0.093*|
||(0.038)|(0.038)|(0.038)|(0.038)|(0.050)|(0.050)|
|**Linkages of pioneer frms**|||||||
|Buyer||1.921***||||1.539***|
|||(0.299)||||(0.319)|
|Supplier|||0.690***|||0.065|
||||(0.168)|||(0.166)|
|Innovation partner||||1.654***<br>(0.268)||1.038***<br>(0.284)|
|Affliate|||||1.722***|1.523***|
||||||(0.519)|(0.519)|
|**Linkages of pioneer frms over time**|||||||
|Buyer_×_years since emergence||-0.052*||||-0.052*|
|||(0.029)||||(0.030)|
|Supplier_×_years since emergence|||-0.003<br>(0.018)|||0.027<br>(0.018)|
|Innovation partner_×_years since emergence||||-0.037<br>(0.026)||-0.019<br>(0.026)|
|Affliate_×_years since emergence|||||-0.092*|-0.081|
||||||(0.053)|(0.053)|
|Technology FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.040|0.040|0.040|0.040|0.040|0.040|
|Observations|10,057,442|10,057,442|10,057,442|10,057,442|10,057,442|10,057,442|

Notes: Standard errors clustered at the firm level in parentheses. The normalized share is capped at the 99<sup>_th_</sup> percentile of non-zero observations. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 30
Table B7: Summary statistics by country

||AGO|BFA|CIV|ETH|GAB|GHA|GIN|GNB|
|---|---|---|---|---|---|---|---|---|
|Number of households|11,822|6,651|11,589|30,255|7,914|14,009|8,243|5,291|
|Survey years|2018_/_19|2018_/_19|2018_/_19|2015_/_16|2017|2016_/_17|2018_/_19|2018_/_19|
|Lowest geographic unit avail-|Bairro|PSU|PSU|HH|HH|PSU|PSU|PSU|
|able<br>|||||||||
|**DOU urban classifcation:**|||||||||
|Urban center|0.47|0.23|0.30|0.75|0.42|0.07|0.33|0.34|
|Urban cluster<br>|0.08<br>|0.08|0.25<br>|0.21<br>|0.23|0.08|0.16|0.15|
|Rural|0.45|0.69|0.46|0.04|0.35|0.85|0.51|0.51|
|**DB urban classifcation:**|||||||||
|Core|0.47|0.22|0.28|0.60|0.57|0.02|0.31|0.30|
|Suburb|0.11|0.06|0.06|0.07|0.17|0.05|0.09|0.04|
|Town|0.11|0.13|0.35|0.24|0.10|0.10|0.28|0.21|
|Other rural<br>|0.43|0.72|0.66|0.33|0.26|0.92|0.59|0.65|
|**Consumption**<br>**(per**<br>**day**|||||||||
|**USD in 2017 PPP terms):**|||||||||
|Real per capita consumption|5.68|4.42|5.12|3.70|12.32|3.39|4.44|4.11|
|expenditures|||||||||
|Nominal per capita consump-|5.90|4.67|5.20|0.99|12.37|3.45|4.49|4.23|
|tion expenditures|||||||||
|**Demographic:**|||||||||
|Household size|6.44|8.93|6.21|5.76|5.90|5.64|6.26|11.34|
|Age of household head|42.87|48.63|44.92|22.53|44.88|46.97|46.28|49.21|
|Household head is male|0.74|0.91|0.84|0.80|0.71|0.71|0.83|0.83|
|**Marital status (of house-**|||||||||
|**hold head):**|||||||||
|Married|0.10|0.90|0.78|0.84|0.27|0.65|0.89|0.80|
|Never married|0.04|0.02|0.15|0.02|0.25|0.06|0.02|0.06|
|Living together|0.66|0.01|0.00|0.00|0.38|0.10|0.00|0.01|
|Divorced/Separated|0.12|0.01|0.02|0.04|0.03|0.09|0.01|0.02|
|Widowed|0.09|0.06|0.06|0.09|0.07|0.10|0.08|0.10|
|**Education (of household**|||||||||
|**head):**|||||||||
|No education|0.16|0.76|0.50|0.51|0.19|0.27|0.55|0.40|
|Primary incomplete|0.30|0.10|0.19|0.33|0.09|0.11|0.13|0.28|
|Primary complete|0.05|0.03|0.03|0.05|0.08|0.04|0.00|0.09|
|Secondary incomplete|0.40|0.08|0.18|0.04|0.42|0.41|0.17|0.07|
|Secondary complete|0.02|0.00|0.03|0.02|0.07|0.06|0.02|0.08|
|Post secondary but not uni-|0.00|0.00|0.03|0.03|0.00|0.07|0.00|0.00|
|versity|||||||||
|University incomplete and<br>complete|0.06|0.03|0.05|0.03|0.16|0.05|0.12|0.07|
|Can read and write|0.75|0.32|0.53|0.46|0.91|0.52|0.40|0.57|
|**Employment (of house-**|||||||||
|**hold head):**|||||||||
|Not in labor force|0.09|0.07|0.08|0.08|0.21|0.02|0.06|0.09|
|Unemployed|0.06|0.00|0.00|0.00|0.04|0.02|0.01|0.00|
|Employed in Agriculture|0.34|0.66|0.52|0.72|0.14|0.42|0.41|0.47|
|Employed in Industry|0.09|0.08|0.09|0.05|0.14|0.14|0.15|0.10|
|Employed in Services|0.42|0.18|0.31|0.15|0.48|0.40|0.38|0.34|
|**Access to basic services**|||||||||
|Improved water|0.68|0.81|0.78|0.57|0.88|0.59|0.79|0.78|
|Improved sanitation facility|0.46|0.30|0.33|0.04|0.32|0.20|0.29|0.37|
|Access to electricity|0.47|0.53|0.80|0.36|0.91|0.81|0.44|0.58|

Source: International Urban Poverty Database. Note: Real per capita consumption expenditures are deflated using a spatial deflator calculated based on the GHSPOP 1km dataset and the DOU method. 64
quantity of subsidized exports ( industrial countries by 21 percent and developing countries by 14 percent) over the implementation period.<sup>6</sup> The new rules on export subsidies under the Round, while important, were not as sweeping as those in market access, in that the Round did not outlaw export subsidies but only imposed linmits on their application.

_Domestic Support._ New limits were established on the aggregate level of trade-distorting domestic agricultural support. Based on fixed external prices in the base period (1986-88), the agreement specifies the determination of the Aggregate Measure of Support (AMS) and requires developed countries to reduce their total AMS by 20 percent (13.33 percent in developing countries) over the implementation period. Individual commodity support is not limited except that if support exceeds the 1992 level. For several reasons, these concessions on domestic support reductions are considerably less effective than those of border measures.' First, the constraint on aggregate, rather than commodity specific support, leaves much scope for continued support policies and domestic policy action on particular commodities. Second, the "green box"<sup>8</sup> will allow many policies to continue unreduced in participating countries. Last, the AMS calculations are based on the outlays during 1986-88, which was a period of relatively low world prices for agricultural products and generally high expenditures on domestic support to farmers.

# B. ESTIMATES OF PRE- AND POST-URA IMPORT RESTRICTIONS

Previous studies (Ingco, 1995; Hathaway and Ingco, 1996) have examined the ways in which individual countries took liberties with the generalized rules of the GATT agreement in tabling their tariff commitments. Ingco (1995) found that countries took liberties in converting former quantitative restrictions to tariff equivalents, with the result that non-tariff restrictions were replaced by very high over-quota tariffs, which in many cases has effectively limit trade in 1995 to the minimum access quota levels and in some cases, allow the country to insulate domestic prices from world prices. Most developing countries took an option that allowed them to bypass tariffication of non-tariff barriers and merely declared bound tariffs for products where non-tariff barriers had been used. While several developing countries in Latin America and East-Asia

> 6 Six years for industrial countries and ten years for developing countries.

> 7See Ingco (1995) and Hathaway and Ingco (1996) for detailed discussion.

> 8 This includes general services involving expenditures which provide services and do not involve direct payments such as research, pest and disease control, training, extension, marketing and promotion, and infrastructure services. These measures shall not involve price support to farmers. In developing countries, government measures to promote agricultural and rural development such as investment subsidies, input subsidies provided to low income farmers (cash or kind) are exempted.

7