rafmacalaba/datause-encoder-data huggingface dataset:
**Table 13: Impact of Internet job search among the unemployed on employability within 5 years, using the extended definition of labor force participation**

|Dependent variable: Employed|**By g **|**ender**|**By residen**|**ce area**|**By **|**age **|**By education**|
|---|---|---|---|---|---|---|---|
||||||||Secondary|
||Men<br>(1)|Women<br>(2)|Urban<br>(3)|Rural<br>(4)|15-35<br>(5)|36-64<br>(6)|<br>or above<br>(7)|
|Internet job search|0.145*|0.078|0.121**|0.067|0.105*|-0.005|0.103*|
||[0.086]|[0.071]|[0.061]|[0.110]|[0.057]|[0.019]|[0.054]|
|Internet access at home|-0.015|0.006|-0.015|0.039|0.000|0.009|-0.003|
||[0.058]|[0.036]|[0.033]|[0.063]|[0.034]|[0.014]|[0.030]|
|Male|||0.021|0.058*|0.038|0.030|0.033|
||||[0.029]|[0.033]|[0.025]|[0.028]|[0.024]|
|Married|0.054|-0.006|0.011|0.011|0.007|0.025|0.006|
||[0.045]|[0.018]|[0.021]|[0.028]|[0.020]|[0.027]|[0.019]|
|Age|-0.003|-0.002*|-0.003***|-0.001|-0.002|-0.004|-0.003**|
||[0.002]|[0.001]|[0.001]|[0.002]|[0.002]|[0.003]|[0.001]|
|Primary or preparatory education|-0.132*|0.002|-0.021|-0.138|-0.066|-0.071||
||[0.074]|[0.018]|[0.020]|[0.085]|[0.051]|[0.069]||
|Secondary education|-0.047|0.028*|0.045*|-0.080|0.006|-0.070|-0.001|
||[0.075]|[0.016]|[0.025]|[0.081]|[0.050]|[0.073]|[0.016]|
|Above secondary education|-0.069|0.036**|0.029|-0.058|0.009|-0.083||
||[0.077]|[0.015]|[0.027]|[0.082]|[0.052]|[0.081]||
|Rural|0.021|-0.059|||-0.046|0.159|-0.061|
||[0.061]|[0.057]|||[0.046]|[0.154]|[0.044]|
|Observations|266|545|439|372|724|87|736|
|R-squared|0.061|0.048|0.075|0.042|0.050|0.250|0.049|
|Region dummies|YES|YES|YES|YES|YES|YES|YES|
|Dependent variable mean|0.071|0.029|0.043|0.043|0.047|0.012|0.045|
|Robust standard errors in brackets. *** p<0|.01, ** p<0.05,|* p<0.1||||||

_Notes._ Each cell represents a coefficient estimate using OLS regression. The analysis is restricted to unemployed individuals in 2012 that were also interviewed in 2018, aged 15 to 64 years old in 2012. Unemployed individuals are defined according to the market definition of labor force participation. The dependent variable is a dummy variable indicator equal to one if the individual is employed within 5 years after this unemployment spell in 2012 (between the years 2013 and 2017, included). The control variables refer to the year 2012 and they include: a dummy variable equal one for individuals who report that finding a job is one of the purposes of Internet use, a dummy variable for having Internet access at home, age, a dummy variable for being male, a dummy variable for being married, three dummies for the individual’s educational attainment (primary or preparatory education, secondary education either general or vocational and above secondary education either post-secondary institute or university and above, and the reference category is no educational degree either illiterate individuals or literate individuals without any diploma), a dummy variable for rural areas as well as dummies for the various geographical regions (Greater Cairo, Alexandria, and Suez cities, Urban Lower Egypt, Urban Upper Egypt, Rural Lower Egypt, and Rural Upper Egypt). The dependent variable mean is reported in the last row.

38
3

**Table 1. Key Features of the Earlier and Current Empirical Analyses**

|Feature|Ingram and Liu (1997)|The Current Paper|
|---|---|---|
|Dependent<br>variable<br>definition|* Number of vehicles per<br>thousand population;<br>* Length of roads per unit<br>area.|.<br>Total number of vehicles;<br>*<br>Total length of roads.|
|Functional<br>Specification|*<br>Log-log specification to<br>estimate constant<br>elasticities.|*Log-linear specification to<br>estimate constant<br>elasticities;<br>.<br>Quadratic log-log<br>specification to estimate<br>variable elasticities;<br>.<br>First differencing the time<br>series to control country or<br>urban area specific effect<br>(fixed effect).|
|Urban<br>development<br>history|*No explicit treatment for<br>historical spatial<br>endowment.|.<br>Attempt to take historical<br>spatial endowment into<br>account.|

log V a + b, log Y + b2 (log y)<sup>2</sup> + I ci log Xi + **EX** di (log Xi)<sup>2</sup> where V is the dependent variable; Y, per capita income; Xi, other explanatory variables; and bo, b1, b2, c; and di, parameters to be estimated. This is called a quadratic log-log equation because it includes independent variables in both linear and quadratic form. It allows non-linearity for all explanatory variables. With this specification, the income elasticity is b, +2b 2 logY and it varies with the level of income.<sup>2</sup> The results of log-linear and quadratic log-log regressions for vehicle ownership, road length, and the vehicle-road ratio at the national level (presented in Appendix Tables 1, 2, and 3) and at the urban level (presented in Appendix Tables 4 and 5) are discussed in the next section.

Non-linear effects can also be tested for by fitting piecewise linear equations. A piecewise linear equation provides flexibility for estimating different slopes for different value ranges of a given explanatory variable. For example, we are interested in knowing whether the income elasticities are different between low-income and high-income countries. We can fit a two-piece linear equation that takes the forn: 2 See Annex B for the procedure of deriving the elasticity formula from a quadratic log-log function.
# **Appendix 2: Details of Sampling Procedure**

Sampling protocols for inside and outside the market were different:

- For Danktopa market, we used a precise map of the market made by the public company managing markets in Benin (SOGEMA). This map allowed to divide geographically the market in small areas. We then randomly selected areas in the markets in which 50% of the businesses (with fixed location) where sampled for the survey.<sup>16</sup>

- For other neighborhoods of Cotonou, we were able to obtain detailed maps of each of the 144 neighborhoods in Cotonou. Those maps allowed the easy identification of _ilots_ (blocks), the official administrative unit within a neighborhood. We used this administrative unit as a reference for the listing survey sampling. We then used information given by the tax administration (and confirmed by the survey company) in order to characterize neighborhoods as high or low firm density areas. We randomly sampled 38% of _ilots_ in high density neighborhoods and 10% of the _ilots_ in low density neighborhoods. In each _ilots_ 68% of businesses where sampled for the survey in average.

Overall, 19,246 businesses were listed. The listing survey allowed us to estimate the total number of businesses operating in Cotonou (with a fixed location, excluding international and nationwide businesses and liberal professions) to approximately 68,500, including around 5,000 in Dantokpa market.<sup>17</sup> Among those 19,246 businesses, 9,938 businesses were randomly selected to be surveyed. 7,945 (80%) businesses were successfully surveyed, 1,000 (10%) businesses refused to be surveyed, and 995 (10%) businesses were dropped because the business owner was not available or not reached after 4 attempts. Figure A1 details the listing survey results inside and outside the market.

From the 7,945 businesses surveyed, a population of 3,596 businesses was then selected to participate in the study based on the following goals:

- Drop businesses already formal

- Drop businesses that will probably not cooperate in the future or which will be probably difficult to find (i.e. businesses that refused to provide information on profits or turnover during baseline survey)

> 16 Few areas were excluded from the sampling frame because they almost exclusively included businesses selling illegal products (i.e. taint oil, medicine, and voodoo products) or by large formal businesses. 17 Some sections of Dantokpa market were not included in the listing survey. Therefore, the total number of firms in Dantokpa is probably significantly higher.

54
# **3 Data**

To illustrate the testing strategy outlined in the previous section, a a unique dataset, the _Gambia Networks Data 2009,_ is used, which were collected in six out of eight Local Government areas between February and May 2009. A sample of 60 villages was randomly selected among villages with between 300 and 1000 inhabitants in the 2003 census. Restricting the sample to small villages is motivated by the desire to obtain complete dyadic data on factor transfers for each entire village. Collecting similar data in larger villages would have been impractical. For more detailed information on the data collection strategy, see Jaimovich (2015). The sample is representative of smaller villages of The Gambia, which account for 20 percent of villages in the country (Arcand and Jaimovich, 2014). Data was collected on each household in each village using a structured group interview approach. Information is available on the land _ai_ and labor _li_ endowments of each household, as well as on all land and labor transactions _wij_ within each village for an entire agricultural year. The data also contains information on pre-existing social links.

Six villages are dropped from the analysis presented here because of missing household-level information. Given that the empirical focus is on factor transfers in agriculture, three semiurban villages are dropped<sup>13</sup> and the analysis is restricted to households whose main activity is farming.<sup>14</sup> The sample used for the empirical analysis consists of 1,625 households across 50 villages, corresponding to 54,144 within-village dyads. Descriptive statistics paint a picture typical of rural Gambia.<sup>15</sup> Rural villages in The Gambia are organized around compounds. A compound is a group of buildings, used for housing, storage and other purposes, that are located in relative proximity to each other, typically around a central courtyard. Most often, a compound has a single household head that makes decisions regarding production and other daily activities. Sometimes a compound has several decision makers. In such cases, independent production units ( _dabadas_ ) can exist within the compound and independent consumption and cooking units ( _sinkiros)_ can exist within a single compound or within a single _dabada_ (von Braun and Webb, 1989; Webb, 1989). Since the interest is in production decisions, the _dabada_ is used as 15
industrial diversity, business environment includes licensing fees and taxes, and infrastructure includes the costs of transport and electricity.

The coefficients of the regression model could be biased owing to endogeneity concerns. On the one hand, although the regression assumes that the rate of growth of firm employment is a function of different factors, it is possible that employment growth drives agglomeration, availability of infrastructure and the existing business environment within a location. To mitigate reverse causality concerns, the explanatory variables are lagged (i.e. they are defined at year ��1). On the other hand, the effect of unobservable factors could continue to bias the coefficients. The underlying assumption within the model is that is a particular city offers certain inherent features that improve the profitability of firms, this will lead to employment growth. Such features could be related to the factors captured within the model, but they could also have to do with essentially un-measurable factors such as local business cultures or natural endowments. A battery of fixed effects are included in different variations of the model to control for omitted variables bias in the estimations, specifically, industry, city, year and firm fixed effects.

Localization can be measured by own industry employment in the city, which reflects disproportionately high concentration of firms within the same industrial sector within the city. This is measured as the proportion of industry _j_ ’s employment in city _k_ as a share of all of industry _j_ ’s total employment in the country. The higher this value, the higher the expectation of intra-industry concentration benefits in the city.

The Herfindal measure is used as a measure of industrial diversity within the city. The Herfindal index of a city _k_ is the sum of squares of employment shares of all industries in city _k_ . Unlike the measure of localization, which focuses on one industry, the industrial diversity index considers the industry mix of the entire city economy. The largest value for is one when the entire city economy is dominated by a single industry. Thus a higher value signifies lower levels of economic diversity.

Business environment is proxied by the amount of license fees and taxes paid by firms in given industries and cities over time. Infrastructure access is measured by the cost of transportation, electricity and bank loans, and rents proxy the cost of land. To control for time-invariant factors, year fixed effects are included. City fixed-effects control for factors common to the city. And finally, to account for firm-level differences and to understand dynamic changes, firm fixed-effects are included.

The response variable is the log of employment growth at the firm-level. In model (1) the economic geography variables are introduced, followed by business environment variables in model (2) and then by infrastructure variables in model (3). In model (4) city-level dummies are included, supplemented by year dummies in model (5) and industry dummies in model (6). Finally in model (7) firm dummies are included (which 22
In order to check for potential issues of selection in participation, we also run empirical models for all the outcomes on the sample of girls eligible to the program irrespective of their likely participation in the program The sample for this includes girls ages 12-19 that were eligible for the program even though they were never enrolled in grades 6-8 during the duration of the program. However, although impacts estimates based on this larger cohort are closer to the intent-to-treat parameter, a problem of this approach is that the results of this analysis are likely to get diluted as the units of observations may include a significant number of girls that did not participate; they could have left school before the program started and never rejoined.<sup>41</sup> Yet, some of the results from the base models remain.<sup>42</sup> A final concern for internal validity is that certain impacts on educational outcomes could be mistakenly attributed solely to the program when they are actually the combined effects of the stipend (a demand-side intervention) and changes on the supply side. This may be the case if more schools — particularly public schools — were built at the same time as the program in stipend than non-stipend districts. There is, however, no evidence of such systematic change in school supply. In fact, data from the annual Punjab school census (EMIS) for the period 2003-10 shows that the number of public schools was more or less unchanged. For instance, during 200407, the number of schools in stipend and non-stipend districts increased by only 0.34 and 0.15 percent, respectively, compared to the number of schools at baseline. Furthermore, the trends in the school supply of stipend and non-stipend districts are very similar in the period before and during the program, 2000-07 (Figure 1).

# **8. Conclusions**

This paper aims to fill the evidence gap on longer-term impacts of CCTs by evaluating a femaletargeted CCT program in Pakistan, the Female School Stipend Program. The evidence indicates that impacts are sustained beyond the short term and could help reduce gender gaps in schooling.

> 41 This bigger cohort could be problematic as it may include girls who completed grades 5, 6 and 7 but stopped going to school well before the program started

> 42 For middle school/grade 9/grade 10 completion, and middle to high school transition outcomes, average impacts on cohort age 12-17 are not statistically significant but have a positive sign across alternative definitions. However, for the cohort age 15-16, schooling outcomes are positive and statistically significant for some definitions. Impacts on labor outcomes are robust to the inclusion of eligible but not exposed girls, but with lower significance levels (5%-10%). Results for marriage and fertility outcomes (with the exception of age at marriage) have a negative sign across alternative definitions, but are not statistically significant.

29
28

# **TABLES**

Table 1: Moving to the “middle” has larger effect on poverty reduction, controlling for growth

|Change rate of the poverty headcount ratio|||
|---|---|---|
|(Poverty line)|$1|$2|
|Change rate of the share of people in the middle|‐9.7***|‐3.5***|
|Change rate of the metropolitan share of the population|‐5.4|‐2.9|
|GDP growth per capita|‐2.3**|‐1.4***|

_Note: GDP growth, flood, country fixed effects and time dummies as controls_ Source: Christiaensen and Todo (2014).
for transportation and marketing services. If perfect competition rules, the prices for these services will be their respective marginal costs, which improvements in the road network should lower. In remote and poor regions, however, it is quite possible that such improvements will lead to sharper competition among truckers and traders. In that event, the changes in prices confronting farmers will also reflect a fall in truckers’ and traders’ pure profits: in effect, there will be a transfer of ‘rents’ in favour of farmers. For this reason, it is useful to investigate the behaviour of unit transport costs and prices faced by farmers separately, even though the former reflects the organization of the transport industry.

# **3.1 The household survey**

The household survey covered marketing in considerable detail, but only for the _kharif_ crop of 2009 ( _t_ = _T_ ). Unit transport costs depend, of course, on the good in question, and all the villages marketed a diverse basket of crops, though most households had rather few transactions. We concentrate here on the marketing of paddy, which is the main commercial crop, in terms of the aggregate volume and value of transactions as well as the number of households selling a surplus.<sup>2</sup> A summary of the main results for the remaining crops of commercial importance for which there is sufficient variation in households’ transport connections, namely, cotton, various pulses and millets, is given at the close of this sub-section.

Of the 136 households selling paddy, 74 did so at the farm gate. Only for the remaining 62, therefore, are the transport costs actually observed. To deal with the censoring that results from households choosing to sell at the farm gate, we employ a Tobit specification: where _τijT_ is the (observed) unit transport cost incurred by household _i_ in village _j_ , _τijT_<sup>_∗_isanindicatorfunction,</sup><sup>_XijT_isavectorofregressorsand</sup><sup>_εijT_isthestochastic</sup> disturbance term defined in (1), whereby the common term _wT_ is absorbed into the constant in _βT_ . The household’s characteristics included in _XijT_ are its ownership holding and caste membership ; the village’s are its total area, population density and the administrative block in which it is located. Finally, and very importantly, there is the length and quality of the road connection between the farm gate and the market

> 2An early cost-benefit analysis of surfaced roads along traditional lines is Spriggs (1977).

9
**Table A8.** The marginal impacts of various factors on CO2 growth based on the LMDI

decomposition results.

|Factor|Global panel|HI countries|UMI countries|LMI countries|LI countries|
|---|---|---|---|---|---|
|**1980-2015**||||||
|_EC_|1.654|0.024|0.661|0.362|2.573|
|_ECS_|0.080|0.646|3.978|2.877|4.413|
|_EI_|4.042|1.464|8.904|4.089|0.658|
|_PCG_|2.325|0.760|2.087|1.983|1.638|
|_P_|0.724|1.086|1.724|1.846|0.911|
|**1980-1990**||||||
|_EC_|0.178|0.005|4.407|0.339|0.042|
|_ECS_|0.532|0.742|1.895|1.496|1.361|
|_EI_|1.859|1.108|5.686|0.260|2.985|
|_PCG_|2.055|0.851|3.697|1.999|0.318|
|_P_|0.578|1.072|1.070|1.181|1.088|
|**2005-2015**||||||
|_EC_|0.357|0.303|0.857|0.277|0.438|
|_ECS_|0.359|0.425|1.348|1.113|1.311|
|_EI_|3.430|1.181|2.421|1.475|0.227|
|_PCG_|2.450|1.000|1.275|1.089|0.925|
|_P_|0.764|1.161|1.143|1.086|1.048|

**_Note:_** _EC_ indicates emission coefficient; _ECS_ denotes energy mix; _EI_ describes energy intensity; _PCG_ stands for per capita GDP; and _P_ is population size.

37
23 generated no surpluses but instead incurred huge losses that resulted in additional pressures on the government's budget.

II.3 The Second Year: Failed Stabilization Programs During 1972, the macroeconomic problems continued to mount. As can be seen in Table 1 above, in that year inflation reached 217% and the fiscal deficit surpassed 13% of GDP. The rate of growth of domestic credit to the public sector reached almost 300%, and international reserves dipped below $77 million. Consumption growth dominated the expansion in demand. (See Table 2) Table 2 The Growth Rate of Real GDP and Demand (Percent per year)

||1970|1971|1972|1973|
|---|---|---|---|---|
|GDP|2.1|9.0|-1.2|-6.6|
|Consumption|||||
|Private|-0.5|13.2|7.7|-6.6|
|Government|5.9|12.4|5.7|1.7|
|Investment|6.5|-2.0|-20.1|-6.0|
|Exports|2.1|0.8|-15.1|2.8|
|Imports|0.9|8.5|3.2|-5.4|

Source: Banco Central de Chile Indicadores Economicos v Sociales 19601985.

What was even more serious was the continuous growth of the underground economy. As more and more activities moved out of the official economy, more and more sources of tax revenues disappeared. In this context, a serious vicious cycle took over. Repressed inflation encouraged
- 7 -

To estimate Equation (1), there are several empirical issues. First, one of the most important issues is endogeneity of infrastructure placement. While transport investment could generally raise productivity of the economy, transport infrastructure has already been placed where economic productivity is inherently high. In our case, for instance, land productivity may not perfectly be unobserved by the econometrician. Thus, regardless of its real impact, there tends to be positive correlation between agricultural production and transport measurement ( _TC1_ and _TC2_ ). Under the additivity assumption, such location-specific unobservables are included as _ci_ in the equation.

This is a general empirical issue to estimate an unbiased impact of large-scale transport infrastructure. The literature uses instrumental variables based on historical events in the past (e.g., Banerjee et al. 2012; Datta, 2012; Jedwab and Moradi, 2012) and applies panel regression to remove unobserved characteristics (Khandker and Koolwal, 2011). The current paper takes advantage of the latter approach. Taking the difference on both sides of Equation (1), the unobserved time-invariant location-specific effects are eliminated: Still, there remains another concern, which is autocorrelation in the error term _u_ . Our primary source of data is the SPAM, which generates spatial data at the approximately10 x 10 km land area level. Thus, even if the difference variables are taken, it is critical to deal with the possible spatial autocorrelation, i.e., _Cov_ (  _ui_ ,  _u j_ )  0 . By nature, agricultural land is a continuum of various characteristics, such as soil fertility and water availability. Weather conditions are continuous across near locations. Public infrastructure, such as railway and roads, typically forms a network, which also creates autocorrelation among neighboring areas.

To deal with this problem, the spatial autocorrelation structure is taken into account: