rafmacalaba/datause-encoder-data huggingface dataset:
value added, manufacturing value added, and the GDP share of exports are from WDI (2011). Data on the agricultural labor force, the share of the agricultural labor force in the total labor force, and an index of agricultural production are from FAOSTAT (2011). For some summary statistics see Table 1.

# **3. Baseline Estimation Strategy**

To examine the effects that within-country changes in distortions to relative agricultural prices (Δabs( _rrai,t_ )) have on real GDP per capita growth (Δln( _GDPi,t_ )), we estimate the following econometric model: (1) Δln( _GDPi,t_ ) = _αi_ + _βt_ + ηΔabs( _rrai,t_ ) + ΓΔX _i,t_ + _ui,t_ where _αi_ are country fixed effects that account for cross-country differences in geography, history, ethnicity and other time-invariant determinants of economic growth such as initial income per capita levels. The year fixed effects, _βt_ , capture common year shocks that affect both GDP per capita growth in Africa and changes in agricultural product price distortions (for example, common shocks to economic growth that are due to changes in the world business cycle or political events such as the end of the Cold War). The vector X _i,t_ includes additional within-country controls such as variations in a country-specific international export price index, rainfall, political institutions, the government expenditure share, and civil war incidence.

We note that our main measure of within-country changes in relative price distortions is the change in the _absolute_ rate of assistance. The motivation for using the change in the absolute RRA value is that – as argued in Section 2 – economic policy reform which reduces inter-sectoral bias is characterized by movements of the RRA towards zero: either from below if an anti-agricultural policy bias had been in place, or from above if a pro-agricultural policy bias had been in place. By using the change in the absolute value of the RRA, we impose the restriction that the effects of changes in the RRA are symmetric for an anti-agricultural (negative RRA) and a pro-agricultural 9
|Owned|77.68|79.54|
|---|---|---|
|Sharecropping|4.42|5.45|
|Rented|3.82|3.30|
|% hholds w/outstanding loan|4.18|1.75|
|Remoteness (proportion of HHs in cate|gory)||
|Low|0.37|0.26|
|Medium|0.43|0.57|
|High|0.20|0.17|
|Expenditure per capita|||
|Thousand Malagasy Francs|102.92|126.15|
|US dollars at PPP|27.78|30.32|
|Share of livestock sales in cash income|0.22|0.06|
|Share of crop sales in full income b/|0.14|0.32|
|First-crop share in total harvest|0.58|0.49|

Source: EPM, 1993-2001 Further information on vanilla farmers can be extracted from the household surveys by looking at both “participation” decisions (whether or not to grow vanilla at all, a binary variable) and output decisions (how much of vanilla to grow, a continuous variable). For this we estimate a standard two-stage selection model of the form where _yi_ is the log of farm _i_ ’s vanilla output and _Ii_ is equal to one when farm _i_ grows vanilla. _X_ is a vector of household characteristics likely to influence a farmer’s portfolio choice whereas _Z_ is a vector of household and community characteristics ―including location― likely to influence a farmer’s decision or ability to grow vanilla at all. Identification of the selection equation comes from the location and community characteristics.<sup>7</sup> Results are shown in Table 3.

> 7 We chose to estimate a selection model because omitted variables such as a farmer’s ability or equipment may influence both his decision to grow vanilla and how much to grow, which implies that cov ( _u_ 1 , _u_ 2 ) ≠ 0.

11
tariff measures, it would appear that developed country markets in 2011 were definitely no less and probably more open than they were in the mid-1990s.

The major product groups which are the object of anti-dumping investigations are much the same for developed as for developing countries: Table 11 shows that Basic Metals, and Chemicals, are the two sectors which account for the bulk of anti-dumping investigations, between 40 and 50 percent, of all anti-dumping investigations in both developed and developing countries. Over the last thirty years, little has changed in this respect which may simply reflect generic pricing practices by firms operating in these sectors.

**Table 11: Sectoral Distribution of Antidumping Initiations by Developed and Developing Countries**

|||1987-|1994|||1995-|2001|||2002|-2011||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Developed|Countries|Developi|ngCountries|Develope|d Countries|Developin|gCountries|Develope|d Countries|Developing|Countries|
|HS Section|No. of Cases|<br>% Share|No. of Case|s<br>% Share|No. of Case|s<br>% Share|No. of Case|s<br>% Share|No. of Case|s<br>% Share|No. of Cases|% Share|
|I Animals and products|8|0.6|17|2.2|11|1.3|13|1.2|10|1.6|20|1.4|
|II Vegetable products|11|0.8|18|2.3|12|1.4|16|1.5|8|1.2|20|1.4|
|III Fats, oils and waxes|4|0.3|13|1.7|2|0.2|2|0.2|3|0.5|7|0.5|
|IV Prepared foodstuff and tobacco|52|3.7|9|1.2|17|2.0|20|1.9|5|0.8|13|0.9|
|V Mineral products|41|2.9|11|1.4|20|2.3|26|2.5|9|1.5|18|1.2|
|VI Chemical products|210|15.0|157|20.2|140|16.4|183|17.1|154|25.0|348|23.6|
|VII Plastics and rubber articles|147|10.5|102|13.1|105|12.2|130|12.2|81|13.2|197|13.4|
|VIII Hides, skins and leather products|8|0.6|1|0.1|1|0.1|2|0.2|0|0.1|2|0.1|
|IX Wood and pulp|16|1.1|8|1.0|13|1.5|15|1.4|18|3.0|45|3.0|
|X Paper, paperboard and articles|65|4.6|47|6.0|42|4.9|48|4.5|36|5.8|82|5.6|
|XI Textiles and articles|95|6.8|55|7.1|61|7.1|77|7.2|43|7.0|122|8.3|
|XII Footwear and headgear|24|1.7|9|1.2|8|0.9|10|0.9|4|0.6|10|0.7|
|XIII Glass, stone and ceramic|59|4.2|15|1.9|26|3.0|31|2.9|29|4.7|67|4.6|
|XIV Pearls and precious stones|0|0.0|0|0.0|0|0.0|1|0.1|0|0.0|0|0.0|
|XV Base metals and articles|374|26.7|178|22.9|274|32.1|342|32.0|148|24.1|339|23.0|
|XVI Machinery and electrical equipment|212|15.2|83|10.7|82|9.6|103|9.6|44|7.1|120|8.2|
|XVII Vehicles, aircraft and vessels|27|1.9|7|0.9|9|1.0|11|1.0|6|0.9|15|1.0|
|XVIII Instruments, clocks and recorders|19|1.4|21|2.7|12|1.4|13|1.2|6|1.0|17|1.1|
|XX Other miscellaneous manufactures|27|1.9|27|3.5|20|2.3|25|2.3|12|1.9|29|2.0|
|Total -- above all sectors|1,399|100.0|778|100.0|855|100.0|1,068|100.0|615|100.0|1,472|100.0|

Note: Exclude arms sector XIX Source: WTO antidumping database.

51
equality and development is crucial to external validity, particularly to ensure that policies are effective in various contexts.

Third, most of the literature focuses on the effect of inequality on individual outcomes, such as schooling, rather than the effect of laws and institutions that generate disparities between the genders, such as laws that grant better property rights to men compared with women. This distinction has important policy implications because policies related to gender equality may affect laws and institutions directly, whereas these policies may only indirectly influence some of the determinants of individual outcomes.

It is important to clarify that although the macrolevel evidence cannot be used to justify inequality-reducing policies, it also cannot be used to argue that these policies are ineffective. In other words, the lack of evidence of a causal link is not in itself evidence that the causal link does not exist. Rather, we suggest that more detailed microlevel studies that shed light on the mechanisms through which gender inequality affects development are needed to inform the design of effective policies.

This paper is organized in five sections. The next section reviews the cross-country evidence on the link between different measures of inequality and development. Section 3 discusses the three main weaknesses of cross-country research and their implications for policy design. Section 4 presents the most recent statistics on gender inequality in education, health, labor force participation, and political participation to assess the validity of the predictions based on evidence from the cross-country literature within the past two decades. We illustrate the sensitivity of cross-country estimates to theory-based specifications and sample selection. We also show that although the gender-education gap has closed, the channels through which it was intended to increase income or other forms of economic growth (such as women’s labor force participation and the average level of human capital) have largely failed. Section 5 concludes by discussing the possible reasons for low levels of women’s representation in economic and political life, especially at the highest levels, in developed and developing countries. We suggests questions that future researchers must address to design effective policies that eliminate the root causes of gender inequality.

# **2. Evidence from cross-country studies**

3
an econometric model estimated at the household level. Urban and rural sectors are modeled separately, because they differ in fundamental ways.<sup>8</sup> The model draws on four rounds of past Indian National Sample Survey data, namely the 61st, 66th, 68th, and 72nd rounds, which were fielded in 2004-05, 2009-10, 2011-12, and 2014-15 respectively.<sup>9</sup> The 2014-15 survey, unlike the three previous rounds used in this exercise, did not collect information on aggregate household consumption and therefore cannot generate direct estimates of poverty. The 2014-15 survey, however, contains information on several household characteristics that are also present in past rounds and are reasonably well-correlated with per capita consumption.<sup>10</sup> These common characteristics include household age, size, caste, religion, a few labor market variables, and expenditure on three categories of services that are asked in the same way as previous rounds.

The analysis estimates a regression model that predicts household per capita consumption using these common household characteristics, plus contemporaneous rainfall shocks and a linear time trend. Most coefficients are allowed to vary over time in a linear way, slightly relaxing the usual strong assumption that the relationship between the explanatory variables and log welfare remains constant over time.<sup>11</sup> The model is estimated using data from the first three rounds of the survey. The coefficients from this regression model were then applied to measured values of the same common characteristics in the 2014-15 survey, as well as 2014-15 rainfall levels, to repeatedly simulate levels of welfare and poverty. The results of these simulations were aggregated to generate estimates of poverty headcount rates and their standard errors.

This exercise is similar in spirit to the adjustments for non-comparability in the consumption aggregate that led to “the great Indian poverty debate”.<sup>12</sup> Since then, this type of nowcasting exercise has also been carried out in several other countries.<sup>13</sup> While this method has not yet been successfully applied to estimate changes in inequality, a significant body of evidence now exists that out of sample predictions

> 8Also, India is one of three countries for which separate poverty estimates are presented by the World Bank for urban and rural areas.

> 9Unfortunately, data between 1993 and 2004-05 were unavailable because the welfare aggregate in the 1999-2000 round was not comparable.

> 10In the pooled regression including 2004-05, 2009-10, and 2011-12 data, the _R_ 2s are 0.57 for urban areas and 0.46 for rural areas (Table 14 (Appendix).)

> 11Nguyen and van der Weide (2018).

> 12Deaton and Dreze (2002) and Kijima and Lanjouw (2003) Deaton and Kozel (2005).

> 13See, for example, Christiaensen and Stifel (2007) in Kenya, Newhouse, Shivakumaran, Takamatsu, and Yoshida (2014) in Sri Lanka, Douidich, Ezzrari, Van der Weide, and Verme (2016) in Morocco, and Dang, Lanjouw, and Serajuddin (2017) in Jordan.

4
**A5: Explanatory variables used**

||Variable name|Type of variable|Unit|Source|
|---|---|---|---|---|
|Pit|Real price|Continuous|FCFA/kg|SONAGESS|
|Pit−1|Lagged realprice|Continuous|FCFA/kg|SONAGESS|
|RERt|Real exchange rate|Continuous|FCFA/USD|IMF database|
|IPt|Real international Price of Maize|Continuous|FCFA/kg|IMF database|
|_TC_|Time and kilometer distance<br>between local market and main<br>consumption center|Continuous|Minutes|Google maps|
|_Border_|Time distance between local market<br>and main border crossing points|Continuous|Minutes|countrystat|
|_Surplus_|Surplus of production|Dummy|1(Surplus of<br>production)/0|Countrystat|

All prices are deflated by Consumer Price index.

30
industries with i, the specification takes the form: The set D contains three distance bands with respect to the GQ network: a nodal district, 0-10 km from the GQ network, and 10-50 km from the GQ network. The excluded category includes districts more than 50 km from the GQ network. The f3j coefficients measure by distance band the average change in outcome Yd,i over the 1999-2009 period compared to the reference category.

We consider two outcome variables Yd,i expressed in logs: log loan counts and log average loan size. District x industry cells are only included if they have measured loan activity in both periods. All estimations control for industry fixed effects Ti, which is equivalent to including industry-x-year fixed effects in a panel regression. We thus control in our estimations for any industry-wide changes in loan activity, due for example to a growth or decline in sector activity or financial dependency. Regressions further control for the baseline level of financial development of each district to flexibly capture issues related to economic convergence across districts.

Estimations include 12,225 observations and are weighted by the log population of the district recorded in the 2001 population census, the year prior to the implementation of GQ. We cluster standard errors by district. We winsorize outcome variables at the 1%/99% level to guard against outliers.

# 4.2 Baseline Estimations

Table 1 reports the core results with specification (1). The dependent variable for Columns 1 and 2 is the change in log loan counts for a district-industry over the 10-year period; the dependent variable in Columns 3 and 4 is the change in log average loan size.

Regressions in Columns 2 and 4 further add state fixed effects, which is equivalent to including state-x-year fixed effects in a panel regression. This is a much more aggressive empirical approach than the baseline estimation as the augmented regression only considers variation within states (and thus we need to have districts located on the GQ network and those farther away together in individual states). By validating our results with the restricted variation, we can show that state policies, business cycles, and so forth 9
proxy for the "new sector" account for<sup>about46 percentof totalemployment(Table</sup> 7). This means that the new sector is<sup>still underdevelopedin Croatia,withimportant</sup> implications for economic and employment<sup>growth.Asnotedin World Bank(2002),</sup> "[...] there appears to be a threshold<sup>-</sup> of<sup>around40percentforthesharesof small</sup> enterprises in employment and value<sup>added-below which economiesdo not takeoff</sup> in terms of growth." (p. 40). Croatia<sup>hasbarely passed thisthreshold,andis lagging</sup> behind leading reformers where the<sup>shareof employmentinsmallenterprisesis over</sup> 50 percent. This means that the new<sup>sector-comprisedof smallprivatefirms--</sup> needs to grow further and faster in order<sup>to enhancethe jobcreation potentialof the</sup> Croatian economy. As it will be shown<sup>later,itissmallprivatefirmswhoarethe</sup> engine of job creation in Croatia,<sup>similarlyasinother transitionandmarket</sup> economies.

**Table 7 Employment by firm size,**<sup>**2001**</sup>

|Firmsize<br>(numberofemployees)|Shareintotalemployment,<br>%|
|---|---|
|1- 9|23.4<br>|
|10-19|9.4<br>|
|20-49|12.8<br>|
|50-99|10.5<br>|
|100-199|11.0<br>|
|200-499|12.4|
|500+a)|20.4|

a) Including respondents who did not<sup>know thesize of their firm(2.1%).</sup> Source: Labor Force Survey, 2001.2,<sup>CBS;Authorscalculations.</sup> _-16-_
(3) yijy = aTreatment1; + BTreatment2; + SKCBDistance; + Xjy + sj + &ij (4a) Treatment; = aZ; + Z; x KCB Distance;o + 6KCBDistance; + Xjy + 5; + &j (4b) Treatment2; = aZ; + Z; x KCB Distance;o + 5KCBDistance; + Xjy + sj + &j it j i ij ii ii i i i
37 economy, since an efficiently functioning labor market requires social benefits to be portable. At the same time, the market risks increased for employees as a result of recurrent structural changes and related job losses. New high-risk economic agents, such as private entrepreneurs and the self-employed, also emerged. Moreover, while the previous agricultural communes, many of which comprised thousands of households, could achieve some pooling of income risk, family farms cannot easily do so – at the same time as new market risks emerged for the farm population as well.

During an early phase of the reform period, the decline in firms’ financial surplus also made it difficult for many of them to live up to their social responsibilities.<sup>63</sup> In Chinese parlance, work units “no longer guarantee either the iron rice bowl or the iron chair” (Warner and Zhu, 2000) – a formulation referring to the fact that firms were no longer able to take responsibility for either income or jobs. _Ad hoc_ selective subsidies and soft loans to state-owned firms with financial problems functioned as a “stop-gap solution” to the problem. In other words, loans by state banks have to some extent functioned as a substitute for traditional social policies. As a result, the discrimination of private firms in credit and capital markets was accentuated, which further reduced their ability to expand production and employment. The “double bind” – state firms constrained in shedding labor, and private firms constrained in acquiring loans – has entailed a kind of catch-22 situation during much of the reform period. It is difficult to remove this “double bind” until non-state firms can expand their employment sufficiently to absorb a much larger fraction of the redundant labor force, and before a more comprehensive system of income security is in place.<sup>64</sup> Inadvertently, households have also helped finance firms’ social obligations, since households’ deposits in state banks (at low, and during some periods even negative, real interest rates) have been intermediated into loans to state firms. As a result, the social

> 63 According to official statistics, the financial surplus is reported to have fallen from about 20 percent of firms’ aggregate revenues in 1978 to about 4 percent in 2003 (Chinese Statistical Yearbook, 1996 and 2004). There has, however, probably been a substantial recovery of these surpluses from about year 2000; see, for instance the survey of papers on this issue in The Economist, 2006, October 21. Agarwala (2004) has argued that statistics of surpluses (or profits) in Chinese firms are not fully comparable with statistics in western countries, largely because investment spending in China is not periodized in the same way.

> 64 Although the employment level in the non-state sector in urban areas has increased considerably, from 12 to 43 million between 1990 and 2003 (Chinese Statistical Yearbook, 1990-2004), this has clearly not been sufficient to absorb redundant workers in state-owned firms and collective firms, new entrants into the labor force, and the flow (in particular the potential flow) of labor from agriculture.
29 index of the subjectively perceived quality of each public facility (school or health clinic) can be constructed27.

The HRDS asked a series of questions about the level of parental and community involvement in the schools which allow us to construct an index of parental participation<sup>2</sup> '. The HRDS also asked individuals about their attendance at 'meetings where issues important to the community, such as health and education, could be discussed.' Table 7 shows that social capital in the SCPS survey is associated with higher reported levels of parental participation in schools and attendance at community meetings in the HRDS data (which surveyed different individuals than those used to measure social capital). Moreover, higher social capital was associated with higher levels of school quality. These fmdings trace out a possible chain of causation from greater social capital to more parental and community involvement in schools to better quality schools. There is, however, no link at all between health facility quality and social capital. This is perhaps not surprising as the major factors for health clinics were drug

**27** Arithmetically, quality index in the **ji** village based on the ik household's assessed importance of the characteristic a. and household's ranking of that characteristic R is defined

as: _Q'i_ Nx (¢l I _c *R6 ) .The_ principal difficulty with this measure at the cluster as:J _NJ_ level is that there is little coherence among household's rankings of the same facility. That is, on each of the five rankings of school quality, within cluster differences account for more than 85 percent of the total variation, which raises some questions about reliability or interpretation of the rankings.

2' The questions asked about the closest government primary school were: "Are parents asked to participate in decisions affecting the school [NAME]?" "Does the school have an active parents/teacher committee?" "Does the school [NAME] have open days for parents to visit?" "Does the school report grades?"