rafmacalaba/datause-encoder-data huggingface dataset:
## **Table 2. Comparison: commune-level characteristics**

|_Indicator_<br>_Risk-i_|_nduced vulnerability (pove_|_rty-induced vulnerability = low) Chronically poor_|
|---|---|---|
|<br>|<br>_Low_|<br>_High _|
|_Household characteristics_<br>|||
|No formal education, household head<br>|0.50|0.77<br>0.85|
|More than secondary education, head|0.19|0.06<br>0.02|
|Dependency ratio|0.40|0.50<br>0.55|
|Rural|0.12|0.62<br>0.96|
|_Employment, head_|||
|Employed<br>|0.22|0.10<br>0.04|
|Self-employed<br>|0.36|0.46<br>0.56|
|Unemployed|0.31|0.34<br>0.35|
|_Assets_|||
|Wealth index|0.92|-0.36<br>-1.30|
|Mobile phone|0.87|0.78<br>0.75|
|Computer|0.21|0.07<br>0.02|
|Car|0.13|0.07<br>0.02|
|Fridge|0.38|0.16<br>0.03|
|TV|0.79|0.47<br>0.16|
|_Dwelling characteristics (improved)_|||
|Walls|0.93|0.73<br>0.40|
|Floor<br>|0.86|0.73<br>0.54|
|Roof|0.80|0.45<br>0.54|
|Electricity|0.86|0.50<br>0.14|
|Sewer|0.54|0.14<br>0.03|
|Flush toilet|0.31|0.04<br>0.16|
|Running water|0.56|0.29<br>0.12|
|_Administrative data measures_|||
|Coverage 3G mobile network (pop. shares)|0.98|0.92<br>0.78|
|Average nighttime light luminosity (2018)|15.00|1.30<br>0.03|
|Exposed to fluvial flood risks (pop. shares)|0.02|0.05<br>0.03|
|Exposed topluvial flood risks(pop. shares)|0.02|0.03<br>0.52|

_Sources:_ Calculations using commune vulnerability rates from the poverty mapping exercise; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics and Demography, Dakar, Senegal, https://www.ansd.sn/enquete-et-etude/recensement-generalde-la-population-et-de-lhabitat-de-lagriculture-et-de-lelevage. _Note:_ Low and high poverty- and risk-induced vulnerability are defined by national medians. Variables are population weighted.

# Robustness checks

This subsection assesses the difference in the variance estimations and the associated implications for the predicted vulnerability rates between the Günther and Harttgen (2009) approach and the small area estimation method. To render the results comparable between survey estimates and imputations into the census, the vulnerability predictions across communes are aggregated by the department and nationwide rates.

Despite the use of a slightly different econometric technique and the limitation in the covariates available in the census and household survey, the results of the analysis closely matches the estimates based directly on the household survey. In the analysis, the vulnerability predictions by department from the small area estimations in the census were aggregated and compared with the results of the Günther and 15
# **2 The Environment**

Each firm _i_ produces output ( _Qit_ ) using capital ( _Kit_ ), labor ( _Lit_ ), intermediate inputs ( _Xit_ ) and energy inputs ( _Eit_ ) in period _t_ . The firm has a Cobb-Douglas (CD) production function with a nested Constant Elasticity of Substitution (CES) function for intermediate inputs.

where _e_<sup>_ωit_</sup> is the productivity of the firm. The intermediate input bundle is a CES aggregator of horizontally differentiated material inputs similar to Gopinath and Neiman (2014).<sup>7</sup> The input varieties are imperfect substitutes for each other and the aggregate input bundle is increasing in the number of varieties employed in production.<sup>8</sup> The intermediate input bundle is a CES aggregator of a bundle of domestic intermediate inputs ( _Xdit_ ) and a bundle of imported intermediate inputs ( _Xfit_ ).

The elasticity of substitution between the domestic input bundle and the imported input bundle is given by 1 _−_ <u>1</u> _ρ_<sup>,where0</sup><sup>_<ρ<_1.Thedomesticinputbundleandtheimportedinputbundleare</sup> again CES aggregators of domestically available varieties and imported varieties of intermediate inputs, respectively.

where _nit ≤ Nt_ . Firm _i_ ’s use of domestic variety _h_ is represented by _dhit_ and use of foreign variety _a_ is represented by _fait_ . _Ht_ is the number of input varieties available domestically within the country, _Nt_ is the number of varieties available in the rest of the world and _nit_ is the number of foreign inputs imported by firm _i_ .

Note that while _Ht_ is common to all firms, _nit_ is sub-scripted by _i_ . This is because the number of varieties of inputs imported from abroad varies across firms since the firm pays a fixed cost of importing each variety of the foreign intermediate input. The elasticity of substitution within

> 7Intermediate inputs are commonly modeled as CES functions in the trade and growth literature. See Ethier (1982), Kasahara and Rodrigue (2008), Halpern et al. (2011) and Kasahara and Lapham (2013).

> 8An alternate approach in the growth literature has been that of quality ladder type models where intermediate inputs are vertically differentiated (see Aghion and Howitt (1992) and Chapter 4 of Grossman and Helpman (1991)). The data set used in the paper does not have information about firm-specific actual varieties and prices of imported materials. This makes it difficult to make inferences about the quality of imports. The horizontally differentiated varieties framework allows me to derive an expression relating varieties to import intensity (observed in the data) to use in the production function. Moreover, empirical evidence points to the importance of the extensive margin or number of varieties in imports. Goldberg et al. (2010), using HS6-level data on imports into India, find that between 1987 and 2000, the extensive margin (varieties not imported in 1987) accounted for over 67 percent of the growth of imported intermediates in India.

5
Table 47

|Dependent variable: Fisca|l adjustment|when need|ed based o|n debt/rev|enue||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|-0.024***|-0.022**|-0.025***|-0.022**|-0.018|-0.011|-0.022|-0.047*|
|structural as % GDP|(0.007)|(0.009)|(0.008)|(0.009)|(0.012)|(0.028)|(0.029)|(0.025)|
|Primary gap (t-1)|-0.000|0.004|-0.001|0.003|0.003|0.003|0.003|0.003|
||(0.003)|(0.005)|(0.003)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.445||-0.271|-0.216|-0.257|-0.271|-0.317|
|||(0.838)||(0.918)|(0.939)|(0.922)|(0.951)|(0.921)|
|Inflation (t-1)||0.064||0.131|0.154|0.141|0.131|0.066|
|||(0.209)||(0.202)|(0.212)|(0.208)|(0.209)|(0.213)|
|U.S. Interest rate||-0.035**||-0.035*|-0.037*|-0.037*|-0.035|-0.037*|
|||(0.017)||(0.020)|(0.021)|(0.021)|(0.023)|(0.021)|
|**Political factors**|||||||||
|Gini|||0.005|0.005|0.005|0.008|0.005|0.006|
||||(0.007)|(0.007)|(0.007)|(0.009)|(0.007)|(0.007)|
|Rule of law|||-0.044|-0.008|-0.005|-0.007|-0.008|-0.006|
||||(0.062)|(0.075)|(0.076)|(0.076)|(0.089)|(0.075)|
|Margin of Majority|||0.508**|0.491**|0.502**|0.495**|0.491**|0.135|
||||(0.239)|(0.237)|(0.238)|(0.238)|(0.238)|(0.323)|
|Election's year|||-0.029|-0.027|-0.028|-0.028|-0.027|-0.024|
||||(0.043)|(0.044)|(0.044)|(0.044)|(0.044)|(0.043)|
|**Interactions**|||||||||
|EMEs * Deviation|||||-0.011<br>(0.016)||||
|Gini * Deviation||||||-0.000<br>(0.001)|||
|Rule of law * Deviation|||||||0.000<br>(0.007)||
|Majority * Deviation||||||||0.045|
|||||||||(0.041)|
|Constant|0.548***|0.675***|0.298|0.242|0.234|0.139|0.242|0.433|
||(0.070)|(0.108)|(0.474)|(0.506)|(0.509)|(0.574)|(0.519)|(0.517)|
|Observations|534|531|480|479|479|479|479|479|
|R-squared|0.015|0.025|0.027|0.037|0.038|0.037|0.037|0.039|
|Number of ifscode|83|83|74|74|74|74|74|74|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard errors clu|stered by co|untry in pa|rentheses||||||
|***p<0.01,**p<0.05,*p|<0.1||||||||

- 76 -
# _Trends in productivity_

Table 4 summarizes various measures of productivity. Both land and labor productivity have increased at about 2.5% annually. Land productivity has risen more slowly since the boost during the Green Revolution period, while labor productivity has generally increased in successive periods due to rapid mechanization. An important result is the overall decline in water productivity, which reflects inefficient use of irrigation water in part due to subsidies on canal water prices and fixed rates on electricity used for tubewells (Faruqee, 1995).

The overall growth in TFP for the crop sector was 1.26% per annum. Contrary to most views, TFP increased little in the Green Revolution period due to high investment costs, especially in tubewells, and was most rapid in the post-Green Revolution period." There have also been sharp differences by system with the highest rate of growth in TFP in the wheat-cotton and wheat-mungbean systems (Figure 2). Both systems experienced rapid growth in the post-Green Revolution period due to successful technological innovations (especially new cotton and mungbean varieties).

> Similar results have been observed in a recent study of the Indian Punjab (Murgai, 1999). The reason for this decline may be high investment on tubewells which enters TFP as a capital flow cost. As most of tubewells are not utilized to their full capacity, the cost does not fully reflect their contribution in productivity. In particular, installation of tubewells is probably a risk reducing strategy, due to uncertain water supply from the canal system.

14
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Vertical segregation may be an explanation why women earn less than men even within the same occupation. Horizontal segregation may substitute for gender wage gaps as it may allow discriminatory employment practices without violating the widely accepted equal-pay-for-equal-work norm. Hence, horizontal and vertical integration should be taken into account when explaining gender wage gaps.

Horizontal occupational segregation is measured by the index of dissimilarity that is by far the most commonly used inequality index. It is defined as one-half of the summation over all occupations of the absolute differences between the proportion of all females and the proportion of all males in each occupation. It has a minimum value of 0 (no segregation) and a maximum value of 1 (complete segregation) (see Anker 1998, p.69). We use information on the number of female and male workers at the 3-digit ISIC level from the Trade and Production Database to calculate the index of dissimilarity. This index at the sector level is subsequently linked with the ILO October Inquiry at the occupational level given that each occupation belongs to a specific sector (ILO 1995).

It is more problematic to calculate a proxy for vertical segregation as this information is often unavailable at the country level, let alone for a large country dataset. We therefore use a rough proxy, namely the number of firms in a sector, assuming that vertical segregation is more likely as the sector expands with an increasing diversity of firms. Once again, we use information from the Trade and Production Database on the number of firms at the 3-digit ISIC level and link this information with the ILO October Inquiry at the occupational level.

Columns 3 and 7 show the results if we include our proxies for horizontal and vertical occupational segregation in the regression. The only significant effect is for the number of firms in richer countries – the occupational gender gap tends to be higher in countries with more firms in a sector.<sup>30</sup> This finding confirms our hypothesis that an expansion of the number of firms may increase the opportunities for vertical segregation, and therefore contributes to the gender wage gap _within_ occupations. However, the trade variables are all insignificant now, because of the stark reduction in sample size. If we rerun the same regression but without the segregation variables on the same smaller

> 30 No significant effect is found in poorer countries possibly because many firms are unregistered and informal firms.

27
survey year (61 percent) but not in 2016. For wheat seed purchases, consistent with the lack of impact on the extensive margin, there is no statistically significant impact on the intensive margin.

**Table 5. The impact of DSM on the quantity of maize and wheat seeds farmers purchase per hectare, by cohort and survey year aggregations**

|**Maize**||||**Wheat**||||
|---|---|---|---|---|---|---|---|
|**Cohort**|**ATT(g)**|**Survey**<br>**year**|**ATT(t)**|**Cohort**|**ATT(g)**|**Survey**<br>**year**|**ATT(t)**|
|**2013**|0.024<br>(0.184)|2016|-0.080<br>(0.269)|2013||2016|0.536<br>(0.667)|
|**2014**|0.602*<br>(0.230)|2019|0.605*<br>(0.186)|2014|0.331<br>(0.785)|2019|0.469<br>(0.413)|
|**2015**|0.090|||2015|0.284|||
||(0.214)||||(0.534)|||
|**2016**|0.479|||2016|1.648|||
||(0.991)||||(0.961)|||
|**2017**|0.683*|||2017|0.339|||
||(0.203)||||(0.509)|||
|**2018**|0.835*|||2018|0.656|||
||(0.291)||||(0.461)|||
|**2019**|0.261<br>(0.300)|||2019|0.064<br>(0.507)|||
|**ATT**|0.449*<br>(0.192)||0.263<br>(0.190)|ATT|0.464<br>(0.370)||0.503<br>(0.474)|

_Source_ : Authors’ estimation based on the ACC 2012, 2016, and 2019 survey data. _Note_ : P-value for a pre-test of parallel trends assumption is 0.230 for maize and 0.278 for wheat. Comparison group: “never treated.” Anticipation periods: 0. Estimation method: Doubly Robust. Standard errors are reported in parenthesis. * _p <_ 0.10.

The results from the event-study (dynamic) aggregation on the quantity of maize and wheat seed purchased by farmers show coefficients similar to those from the cohort and survey year aggregations. DSM led to a 46 percent increase in the quantity of maize seed purchased per hectare, while no statistically significant impact was observed for wheat seed purchases (Figure 2).

18
We explore the types of investment received in Table 8.<sup>14</sup> Firms in the two-year long follow-up survey were asked about whether they had made deals with different types of outside investors, and if so, what type of deal. We see that the most common deals occurred with other business owners (17%), angel investors (10%), and venture capital funds (10%).<sup>15</sup> Most of these deals were for a share of equity in the firm, with royalty deals, convertible notes, and licensing deals not very common. Both the type of investor and type of deal are similar across the treatment and control groups. We have only partial data on the amount of these deals, but know that 16.8% of firms (69% of those receiving an outside investment) received an amount of at least 25,000 euros, with this again not differing significantly by treatment status (appendix Table 9.5).<sup>16</sup> Finally, firms in the long survey were asked what is the main challenge their business faces in their ability to grow over the next two years. Getting financing is seen as the main challenge by 41% of the control firms and 24% of the treatment firms, with this difference statistically significant (p=0.002). In addition to the positive point estimate on getting access to external financing, it is possible that the investment readiness program led some treatment firms to realize that they need to improve other areas in their business first.

# **6. Conclusions**

Investment readiness programs have been offered in a range of developing and emerging markets, based on the idea of a gap between the quality of ideas entrepreneurs have, and their readiness to attract and receive outside investment in those ideas. Despite their growing use, there has not been any rigorous study of their effectiveness. Our five-country randomized trial enables measurement of the effect of such a program. We do find that investment readiness increases, as measured by scores in a pitch competition, and that these scores are in turn predictive of future investment readiness and outcomes among firms. Nevertheless, despite finding positive point estimates, our estimates of the treatment effects of the investment readiness program on these firm investment outcomes over the next two years are not statistically significant. Our analysis suggests that this in

> 14 Note that this table was not pre-specified, and is intended to explore the higher than anticipated rate of outside funding received by firms.

> 15 Our measure of receiving external financing excludes financing received from family and friends. 10.8% of firms in the long survey received financing from family and friends, but in less than half of the cases this was for an equity stake- terms tend to be less formal in such cases. 16 Using the long follow-up survey only, 43% (42% treatment, 44% control) of those receiving outside funding received at least 100,000 euros in investment. This information is not available for firms doing the short survey.

28
planned economies. Given that most transition economies had small market-oriented service sectors, the last type of agreement is unlikely to have been seen as a useful lock-in or signaling device. This paper therefore focuses on the two major options: the WTO and the EU. We explore to what extent trade agreements were used as commitment devices for services policies by comparing countries in terms of their participation in and use of agreements to commit to services policy openness and relating commitments made to actual policies applied, as well as the changes in actual policies over time. Our interest is in part to determine whether there are common or consistent differences between the set of countries that were and that were not EU accession candidates, and more specifically, given that the EU is a deep integration agreement, whether the WTO could play a similar role as a focal point/lock-in mechanism as the EU for those countries where EU accession is not in the cards. Another question of interest is whether transition economies – both accession candidates and others – had caught up with the European Union in terms of liberalization of services markets as of 2004. Even for recent accession countries this is not a redundant question as the EU is not (yet) a true common market for services.

The paper starts with a discussion of service sector commitments made by the European Union in the GATS. Descriptive statistics are generated on the extent to which EU members (EU-15) made market access and national treatment commitments in the GATS. We characterize average EU commitments and the variance between the schedules of each member state, and assess to what extent EU offers in the context of the Doha Round negotiations as of end 2004 would move the EU towards greater liberalization and uniformity. In Section 2 the same descriptive exercise is undertaken for 16 transition economies that are WTO members, including a determination whether their commitments go beyond those of the EU. Section 3 assesses trends in the actual policies pursued by the transition economies, and compares these to the index of GATS commitments. We find that the 16 countries can be divided into different groups on the basis of actual policies/GATS commitments: countries that have better applied policies but have weak GATS schedules (mostly EU accession candidates); countries with good policies and high GATS commitment indices (again many EU accession candidates), and a group of countries that have weak policies and are relatively closed in practice but that did make extensive GATS commitments. In the last group actual policies have become worse over time or else not improved as rapidly as in other transition countries.

2
# **Table 10: Determination of Employment and Daily Wage at the National Level (2003-2007)**

|**Estimation of income equation**|_Reg_<br>**(dependen**|_ression 1_<br>**t variable = dail**|_Re_<br>**y wage in**|_gression 2_<br>**log)**|
|---|---|---|---|---|
|age|0.05|(47.38)***|0.05|(50.55)***|
|age * age|-0.0004|(35.15)***|-0.0004|(37.40)***|
|year of shool|0.09|(238.45)***|0.08|(236.98)***|
|sex (female=1)|-0.22|(51.80)***|-0.23|(56.71)***|
|value added per employee(in log)|||0.03|(13.00)***|
|share of industry (% of GDP)|||0.004|(17.37)***|
|share of service (% of GDP)|||0.0001|(0.44)|
|% of SMEs|||-0.06|(39.49)***|
|Island group dummies (NCR as reference)|||||
|Luzon|-0.21|(55.26)***|-0.21|(29.88)***|
|Visayas|-0.40|(85.08)***|-0.38|(60.84)***|
|Mindanao|-0.38|(87.89)***|-0.30|(35.58)***|
|year dummies (2003 as reference)||yes||yes|
|constant|3.69|(171.35)***|9.35|(55.38)***|
|**Estimation**|**of selectio**|**n equation**|||
|age|0.15|(134.02)***|0.15|(134.18)***|
|age * age|-0.002|(133.37)***|-0.002|(133.51)***|
|year of shool|0.02|(33.05)***|0.02|(31.67)***|
|sex (male = 0, female=1)|-0.06|(9.79)***|-0.06|(9.65)***|
|househoold head (household head =1)|0.32|(40.01)***|0.31|(39.46)***|
|married (married =1)|0.22|(26.68)***|0.22|(26.46)***|
|child of younger than 5 years old|-0.04|(7.61)***|-0.04|(7.48)***|
|female * married|-0.73|(68.11)***|-0.73|(68.14)***|
|female * headcount of child(ren)|-0.07|(32.08)***|-0.07|(31.81)***|
|value added per employee(in log)|||-0.005|(1.32)|
|share of industry (% of GDP)|||0.01|(27.23)***|
|share of service (% of GDP)|||0.01|(15.47)***|
|% of SMEs|||0.02|(7.11)***|
|Island group dummies (NCR as reference)|||||
|Luzon|-0.04|(6.64)***|0.08|(7.65)***|
|Visayas|-0.01|(0.72)|0.09|(9.36)***|
|Mindanao|-0.05|(6.88)***|0.13|(10.29)***|
|year dummies (2003 as reference)||yes||yes|
|constant|-2.75|(141.86)***|-5.10|(19.31)***|
|Log likelihood|-3|96834.1|-3|94553.5|
|Number of observations||467,501||467,501|

Note: *, ** and *** stand for significance at 10 percent, 5 percent and 1 percent, respectively.

14