rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Descriptive statistics for Ecuadorian firms (in percentage)**

|**Group**|**Number of firms**|**Exporters (percent)**|**Non-Exporters (percent)**|
|---|---|---|---|
|_Region_||||
|Azuay|31|42|58|
|Guayas|158|28|72|
|Manabi**|17|59|41|
|Pichincha|213|31|69|
|Tungurahua***|22|5|95|
|_Industrial Sector_||||
|Oil products|49|39|61|
|Food and beverage|113|29|71|
|Apparel, textiles and leather|95|30|70|
|Wood and furniture|33|39|61|
|Chemical products|77|26|74|
|Metallic products|74|27|73|
|_Size_ _(Employees)_||||
|Less than 10***|40|2|98|
|From 10 to 20***|91|13|87|
|From 21 to 50***|136|20|80|
|From 51 to 100*|77|39|61|
|More than 100***|97|66|34|

Notes: Sample equals 441 firms. Data for year 2002. *Exporters’ share is statistically different from the rest of the sample at the 10 percent level. **Exporters’ share is statistically different from the rest of the sample at the 5 percent level. ***Exporters’ share is statistically different from the rest of the sample at the 1 percent level.

Source: ICS-Ecuador.

Another possible way of comparing exporters with non-exporters is to verify the means of some variables for each of them. As previously discussed, on average, exporters hire almost four times more employees (and pay wages that are, on average, 45 percent higher) than non-exporters. In addition, on average, exporters are six years older than non-exporters. Thus, it seems that older firms are more likely to explore business opportunities in foreign markets because they either accumulated experience over time or needed new markets to sell their products. With respect to quality certification, 31 percent of exporters had a quality certificate in 2002, a share three times higher than the one for non-exporters. This may reflect requirements to comply with international quality standards in order to supply foreign markets. Finally, comparing exporters to nonexporters in 2002, we have: (i) the share of exporters that used the web was 50 percent larger than the share of non-exporters; (ii) on average, 83 percent of exporters offered training to their permanent staff, a share 32 percent larger than the share of non-exporters offering it; (iii) the share of exporters with loans was 20 percent higher than the share of non-exporters, which can be related to a larger share of exporters that are listed companies; and, (iv) the share of exporters with plants abroad was four times larger than the share of non-exporters (Table 2).

Table 3 describes exporters in terms of location, industry, and size. With respect to region, most exporters were located either in Pichincha (49 percent) or in Guayas (33 percent). In Pichincha, 36 percent of exporters belong to the apparel, textile, and leather sector. Pichincha is the region where Quito, Ecuador’s capital, is located and where its textile cluster developed “(...) Succeeding in creating a _maquiladora_ industry within this 9
(“long-run” estimates); (2) static estimates obtained using annual data with various specifications for the lag operators; and (3) dynamic estimates, also using annual data.

_“Long-run” estimates._ An attractive feature of (1) is that it is consistent with the fact that much of the effects of the independent variables upon per capita agriculture GDP are likely to be embodied through processes that may take considerably more than one year to have an effect. Working with several year averages instead of annual data might help in elucidating what can be considered long-run effects. A drawback of the approach is that one loses degrees of freedom by reducing the number of observations. Alternatively, we used 3 or 5 year simple averages.<sup>22</sup> In general the absolute values of the coefficients tend to be higher when 5-year averages are used, but their signs and degree of significance change very little.

Table 2 provides the Random Effects (RE) and Fixed Effects (FE) estimators of the per capita agricultural GDP regressions using 5-year simple averages as units of observation. The most important message emerging from all these estimates (as well as from the several alternative specifications used that we do not report here) is that while government expenditures have a positive and highly significant effect on agriculture per capita income, the structure or composition of such expenditures is quantitatively much more important and also of great statistical significance. In fact, the results show that increasing the share of (non-social) subsidies keeping the total public expenditures constant has a large negative impact on agricultural per capita GDP. According to the estimates, a reallocation of just 10% of the subsidy expenditures to supplying public goods instead may cause an increase in per capita agriculture income of about 2.3%. And this is obtained _without_ increasing the total government expenditures.<sup>23</sup> By contrast, increasing government expenditures without altering their composition is much less effective in raising per capita agriculture incomes: A 10% expansion of government outlays causes on average only a 0.6% increase of agriculture income.

In 1996–2000, the 50% of the countries spending the least in rural areas spent about $35 per capita, while the 50% spending the most spent about $74 per capita. Thus, if the average country in the bottom half increased its per capita outlays to levels comparable to the average country in the top half, per capita agricultural GDP in that country could increase by more than

> 22 We also use moving averages.

> 23 It is important to emphasize that this result is purely a composition effect under the assumption that the efficiency in use of resources in public goods (which several analysts have judged to be low) remains unchanged. Of course if the efficiency of public good programs increases, the change in the composition of public expenditures would have an even greater impact.

18
# _UhiawNV hi the Amb ComWiw_

|_cap"y_|_Uniwi!y & Lcaton_|_Year_|_Langug_|_Acad_<br>_Staff_|_Stu_|
|---|---|---|---|---|---|
||hbmic U,<br>Mbdina|1961|A|c. 350|G2,740|
||ISlami U. ofImam.<br>M<br>_wamad<br>Ib-Sd,<br>Riy I|1974|A|1983<br>946|1983<br>7,532|
||U. of Petrolum and<br>Minema, Dhahran|1963/<br>1975|E|84-85<br>780|8<br>4,500|
||Kig Abdul Azi Univ,<br>Jeddah|1967/<br>1971|A, E|1,840|c.14,570|
||Umm al4-Qua Univ.,<br>MUka|1979|A|||
|Syri|U. of Damacus,<br>Damasc|1903/<br>1923|A|c<br>960 F.T.<br>440 P.T.|cGSi6,260|
||U. of Abppo, Aleppo|1960|A|c. 760<br>280 P.T.|c. 33,300|
||Al-Bath Univasity,<br>HoInS|1979|A|84-85<br>101 F.T.<br>143 P.T.|8485<br>8,560|
||U. of October<br>(TishfLeen), Lattaka|1971|A|134 F.T.<br>335 P.T.|84-85<br>14,108|

a MOB-Mily of Edu aio MOHESR-Mwisuy of Hge Eduction nd _scitifc_ Reseah b Ag-Agriclture; Ar-Achtectude; A-AMs Cs-Computer Sc4 D-Denc;atz Bd-Educatlon; E-Eeer; Ft-Food Techn4 Ln-Langu-eg L-La, M-Medkibn P-Pbarnac R-Rciouw Stdies; S-Sdcences; T-Tebnoy, Vm-Vetina Mcd _48_
Table 6: Tests of food poverty convergence across municipalities, 1992–2014

||**(1)**<br>**1992-2014**|**(2)**<br>**1992-2000**|**(3)**<br>**2000-2014**|**(4)**<br>**2000-2005**|**(5)**<br>**2005-2010**|**(6)**<br>**2010-2014**|
|---|---|---|---|---|---|---|
||||**a. All mu**|**nicipalities**|||
|_lnPit−τ_|_−_0_._012***<br>(0.002)|0.014**<br>(0.006)|_−_0_._031***<br>(0.002)|_−_0_._055***<br>(0.006)|_−_0_._038***<br>(0.005)|_−_0_._048***<br>(0.006)|
|Obs.|2,361|2,361|2,361|2,361|2,361|2,361|
|R<sup>2</sup>|0.206|0.025|0.565|0.333|0.175|0.164|
||||**b. Urban m**|**unicipalities**|||
|_lnPit−τ_|_−_0_._013***<br>(0.002)|0.014**<br>(0.007)|_−_0_._031***<br>(0.002)|_−_0_._057***<br>(0.007)|_−_0_._038***<br>(0.006)|_−_0_._044***<br>(0.006)|
|Obs.|944|944|1,017|1,017|1,022|1,022|
|R<sup>2</sup>|0.229|0.024|0.575|0.353|0.193|0.142|
||||**c. Rural m**|**unicipalities**|||
|_lnPit−τ_|_−_0_._017***<br>(0.001)|_−_0_._032***<br>(0.007)|_−_0_._033***<br>(0.006)|_−_0_._077***<br>(0.015)|_−_0_._031***<br>(0.007)|_−_0_._114***<br>(0.009)|
|Obs.|1,417|1,417|1,344|1,344|1,339|1,339|
|R<sup>2</sup>|0.192|0.064|0.375|0.276|0.031|0.426|
|||**d. Municipa**|**lities in stat**|**es along the **|**U.S. border**||
||_−_0_._023***|_−_0_._044***|_−_0_._027***|_−_0_._058***|_−_0_._097***|_−_0_._038*|
|_lnPit−τ_|(0.004)|(0.008)|(0.004)|(0.011)|(0.013)|(0.023)|
|Obs.|267|267|267|267|267|267|
|R<sup>2</sup>|0.375|0.185|0.281|0.194|0.392|0.031|
|||**e. Munic**|**ipalities in n**|**on-U.S. bor**|**der states**||
|_lnPit−τ_|_−_0_._009***<br>(0.002)|0.019**<br>(0.008)|_−_0_._028***<br>(0.003)|_−_0_._053***<br>(0.010)|_−_0_._020***<br>(0.003)|_−_0_._055***<br>(0.006)|
|Obs.|2,094|2,094|2,094|2,094|2,094|2,094|
|R<sup>2</sup>|0.114|0.041|0.507|0.282|0.067|0.243|

_Source_ : World Bank calculations. _Note_ : The table presents estimates of the parameter _β_ in equation (6), weighted by municipal population at the initial year of each period under study. The dependent variable is the annualized growth in the food poverty headcount ratios of municipalities over the period. _lnPit−τ_ are municipalities’ initial poverty headcount ratio. All variables are in log-scale. Urban (rural) municipalities are defined as those with more (fewer) than 15,000 inhabitants. The intercepts are shown in table 34 in the ancillary file. Robust standard errors are in parentheses. *** _p < ._ 01, ** _p < ._ 05, * _p < ._ 1 incomes in their richer counterparts were disproportionately affected by the economic contractions that characterized these years.

25
Polay nnirng, and Rewch Wotid Dolopmnt Report A key question for stabilization programs is this: How do governments get into fiscal difficulty in the first place?

Three views predominate: the political deficit, the structural deficit, and the inflation tax. In the first view, the state is forced toward taxing too little and spending too much, both to pay off specific interest groups and to sustain employment thrugh aggregate demand. In the secord view, the e,inomy suffers a contraction- ary shock such a,, falling terms of trade or interest rate incirsions on extemal debt - which the govenmmen **t** ries to offset by fiscal means in the short run. In the third view, the state indulges the desire to use revenue from the inflation tax in the absence of other sources.

* Maintaining self-reliance in trade and external finance?

It makes each of these tasks harder, because of the incomplete understanding of the likely effects of fiscal measures.

First, policymakers need to know more about the specific effects of different policies. For example, cuts in public investment may also cause private capital formation to decline. Changes in the prices that public enterprises charge for food or essential services can have strong distributional repercussions. And the bidding up of interest rates can, if accompanied by other incentives, make capital repatriation and emigrant reiidittance more likely.

Fiscal deficits thus have numerous causesnot all of them irrational, not all adding to aggregate demand. Reducing the deficit is nevertheless the sine qua non of orthodox stabilization packages. How does such austerity affect an economy's chances of achieving th,e normal policy goals of:

* Maintaining socially acceptable capacity use and growth?

* Keeping inflation tolerable?

* Altering the distributions of income and wealth?

Second, fiscal measures should not be independent of other policy moves. If devaluation causes contraction, teaming it with fiscal re- straint may lead to extreme losses of output the overkill for which orthodox programs are often criticized.

Third, fiscal measures can sometimes substitute for other (less savory) policy changes. For example, the narrower and more directed fiscal interventions can avoid many of devaluation's unpleasant economywide effects (political visibility, output contractions, and price inflation because of the higher costs of imported inputs).

This is a background paper for the 1988 World Development Report. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Rhoda Blade-Charest, room S13-060, extension 33754.

T _PPR_ Working Paper Series dibeminates the findings of work under way in the Bank's Polcy, Plaing, Che and Research Complex. An objective of the series is to get these fundings out quickly, even if presentations are less than fully polished. T,he findings. interpretations, and conclusions in these papers do not necessarily repTesent official policy of the Bank.

Copyright 0B 1988 by the International Bank for Reconstruction and Developmenttlle World Bwik
3 results in faster absorption of technological progress.6 Despite the common sense appea3. of these arguments, their theoretical basis is not very robust. Indeed under the right set of assumptions some of these results can be reversed.7 It is therefor,; ultimately an empirical issue to establish whether trade liberalization leads to greater productive efficiency and lesser monopolistic distortions or not.<sup>8</sup> The present study tests the empirical relevance of these arguments to Turkey's experience of trade liberalization of the 1980s. .~Ating in 1980-81, the Turkish government introduced a series of major foreign trade liberalization measures that significantly increased wile share of import and export trade in Turkish industrial output and the degree of exposure of domestic firms to international competition.

The remainder of the paper is divided into two parts. Part iI provides a brief background on trade and other economic reforms in Turkey in the 1980s. Part III provides an analysis of industry-level relationships between trade liberalization, firms' competitive behavior as measuLed by changes in price-cost margins, and efficiency gains as

- 6.See Nishimizu and Robinson (1984), Nishimizu and Page (1987), Romer (1989) and Edwards (1989).

- 7.See for example Rodrik (1988) and Bhagwati (1988).

- 8.For a recent survey of empirical literature linking the growth in total factor productivity to changes in the trade regime, see Havrylyshyn (1989). For a recent survey of empirical literature linking economic growth to trade policy see Edwards (1989).
**Table S12:** Primary outcomes with multiple testing adjustments <u>(Original p-values and FDR q-values)</u>

||||Baseline S|choolgirl|||Baseline|Dropout|
|---|---|---|---|---|---|---|---|---|
|_Outcomes_|CCT vs|. Control|UCT vs|. Control|CCT v|s. UCT|CCT vs.|Control|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||p-value|q-value|p-value|q-value|p-value|q-value|p-value|q-value|
|Highest Grade Completed|0.136|1.000|0.465|1.000|0.850|1.000|0.000|0.007|
|Competencies Score (Standardized)|0.269|1.000|0.147|0.478|0.630|1.000|0.263|0.237|
|=1 if Ever Married|0.206|1.000|0.829|1.000|0.613|1.000|0.001|0.007|
|Age at First Marriage|0.940|1.000|0.016|0.289|0.032|0.465|0.006|0.022|
|=1 if Ever Pregnant|0.471|1.000|0.980|1.000|0.614|1.000|0.054|0.099|
|Number of Live Births|0.580|1.000|0.600|1.000|0.410|1.000|0.007|0.022|
|Age at First Birth|0.292|1.000|0.997|1.000|0.436|1.000|0.100|0.145|
|= if HIV Positive|0.955|1.000|0.938|1.000|0.980|1.000|0.649|0.504|
|=1 if Anemic|0.699|1.000|0.053|0.299|0.068|0.465|0.263|0.237|
|Opportunity Cost of Time|0.617|1.000|0.120|0.478|0.550|1.000|0.641|0.504|
|Typical Daily Wage in Last Three<br>Months|0.847|1.000|0.726|1.000|0.665|1.000|0.041|0.090|
|Proportion of Hours Spent in Self-<br>Employment or Paid Work in Past|0.502|1.000|0.831|1.000|0.842|1.000|0.221|0.237|
|Super Index of Overall<br>Empowerment (Standardized)|0.551|1.000|0.051|0.299|0.052|0.465|0.890|0.504|
|Change in Subjective Wellbeing<br>from Five Years Ago to Today|0.143|1.000|0.354|1.000|0.650|1.000|0.263|0.237|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, an indicator for never had sex, and whether the respondent participated in the pilot phase of the development of the testing instruments. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.

92
# _The estimation strategy_

In most household level studies that measure the vulnerability to poverty, vulnerability is related to the propensity or probability of a household experiencing an episode of poverty or a shock that brings its welfare below a socially accepted level (often established as a poverty line). As a result, assessing vulnerability requires information on the mean and variance of the household welfare collected ideally from many repeated observations in different periods of time. In reality, there are very few countries, in particular in the developing world, where panel data tracks the same households for more than 15-20 years, and this prevents the construction of a reliable estimate of the mean and variance. Therefore, the most feasible approach is to estimate vulnerability using cross-sectional data by making some strong assumptions (Naudé, Santos-Paulino & McGillivray 2009; Skoufias, Vinha and Beyene 2021).

In this study, the method used is based on Gunther and Hartgen (2009) who integrated a two-level hierarchical model (Goldstein, 1999) into Chaudhuri’s (2002) method. This allows one to differentiate unexplained variance at the household level (related to idiosyncratic household specific shocks) from unexplained variance at the community level (covariate community level shocks). This method also corrects for inefficient estimators that might occur if Chaudhuri’s (2002) method is applied to hierarchical data with household and community level variables used simultaneously in consumption modelling (Gunther and Hartgen, 2009).

The main hypothesis the method makes is that unexplained variance in the consumption of otherwise equal households captures the impact of both household-specific and community-specific shocks on households’ consumption. This variance is assumed to be correlated and can be explained by observable household and community characteristics.

Following Gunther and Hartgen (2009) and Skoufias, Vinha and Beyene (2021), let i =(1,... , n) denote households at level one and j =(1,... , j ) denote communities at level two, with households being nested within communities. Consumption, c, of household i in community j is specified as The coefficients (both constant terms and slopes) of each community are assumed to be affected by community observed characteristics (Z) and community unobserved factors denoted by u0j and u1j Thus, there are three error terms to be estimated u0j , u1j and eij where eij captures the idiosyncratic shocks and u0j and u1jXij capture the community covariate shocks. In particular, u0j captures the direct effect of covariate shocks affecting the intercept of each community and all households in the same community in the same manner, while u1jXij captures the indirect effect of covariate shocks. Equation (4a) may be rewritten as equation (4b) 5
- Linking a worker's pay to firm performance. Supposedly a close link should increase employee incentives.

Understand internal incentives may help policy makers provide better advice on private sector reforms. For instance, suppose surveys reveal little correlation between pay and firm performance in a particular country. Such a finding may imply that the labor force is not properly motivated, perhaps due to poor performance measurement or the many layers of hierarchy. Alternatively, if all employees perceive their jobs as permanent, increasing the share of contract workers may be a valid option to motivate workers.

Many World Bank surveys do not measure internal incentives at all. Some have a minimum number of measures, such as the percentage of work being contracted out, employment benefits, bonuses, payment method (e.g., piece rate or hourly rate), total wages, non-wage costs, and labor turnover rates. The RPED covers internal incentives most extensively. It asks many useful questions, including the payment scheme (piece rate, time rate, or based on firms' sales), the number of people fired, the presence of a union, overtime pay, and detailed wage data by worker category. An especially innovative feature of the RPED is its survey of a subsample of workers in the surveyed firms, which asked workers about issues such as bonuses, wages, and housing allowances.

Despite the strong advances made by the RPED, Bank surveys leave many questions unexplored. For example, we still know little about pay differences between different levels of workers, and between managers and production workers. Likewise, in few countries and industries do we have any sense of the involuntary turnover rate. Without such data we cannot understand the effects and determinants of firm internal incentives, and whether performance improves with better internal incentives in different countries.

# **B. Human Capital**

Human capital theory emphasizes the importance of schooling, labor market experience, seniority, and training in explaining individual productivity (Willis 1986). In the context of firms we should then expect these human capital variables to positively impact productivity. The lack of data has thus far prevented us from learning how education and training affect 9
poverty. In contrast, the World Bank tracks the evolution of poverty following an absolute poverty measurement approach. Two main elements are part of this exercise: the welfare indicator and the poverty line. Household welfare is measured using consumption per capita obtained annually from the HBS surveys, whereas the poverty threshold of reference for Turkey is the upper-middleincome poverty line of $5.50 per capita per day expressed in 2011 PPP. The standard practice to convert this line into real Turkish lira values is to update it with the annual inflation rate derived from the overall national CPI. However, as evidenced above, the overall CPI likely underestimates the inflation experience of lower-income households, above all in cases where inflation is driven by fast growth of food prices. Indeed, this was the case in the rapid price hikes that occurred in 2013, 2015 and 2019.

In order to check the sensitivity of the “standard” poverty calculations to price adjustments to the poverty line that take into account the actual prices changes borne by the poor and vulnerable, we recalculated the real Turkish lira value of the $5.50 (2011 PPP) poverty threshold in 2019 using the CPI for the 1st decile as opposed to the overall CPI. The result shows that 209,000 additional individuals would have fallen under the poverty line relative to the poverty incidence computed based on the “standard” price adjustment (8,229,895 people were in poverty with the standard approach). The magnitude of this increase in the poverty rate is 2.5% or 0.3 percentage points (rising from 10.2% to 10.5%). Similarly, calculations using decile-specific inflation for 2015 translate into 342,000 extra poor, namely an increase of 4.2% or 0.4 additional percentage points (reaching 11.1%, up from 10.7%).

These differences are not trivial. On average, the measurement gap is equivalent to between one-fourth and one-third of the average annual reduction in poverty achieved in a typical year of robust and broad-based economic growth in Turkey. In fact, we find that extending the “standard” poverty methodology using a measure that more closely reflect the inflation burden on low-income households yields a higher poverty rate in every single year analyzed for the period 2011-2020. Along the same lines, the values of the Gini coefficient, a standard measure of inequality, are slightly (0.6%) but consistently higher when estimated using the inflation experience of each decile (Figure 6). Other inequality measures often used in the literature, such as percentile ratios (90th/10th or 80th/20th) yield results that point in the same direction.

17