rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Effect of Trade Openness on Market Power

||_μ_<sup>_m_</sup><br>_ijt_|_μ_<sup>_m_</sup><br>_ijt_|_γijt_|_γijt_|
|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|
|_Post_1995|-0.148***|-0.164**|0.0496**|0.0386|
||(0.0412)|(0.0583)|(0.0203)|(0.0257)|
| ̃_τ_1991_× Post_1995|-0.00811*|-0.0178**|0.00954***|0.0110***|
||(0.00399)|(0.00705)|(0.00160)|(0.00238)|
|_ωit_||1.517***||0.0646**|
|||(0.165)||(0.0202)|
|Skill Ratio||0.0834||0.0310|
|||(0.132)||(0.106)|
|Small size frms||0.0715||0.299***|
|||(0.0913)||(0.0666)|
|Medium size frms||0.0340||0.0881*|
|||(0.0825)||(0.0418)|
|Inverse Mills Ratio|||-0.0425|0.161**|
||||(0.0803)|(0.0700)|
|Constant|1.680***|-11.70***|0.565***|-0.301|
||(0.0300)|(1.394)|(0.0698)|(0.255)|
|Observations|1,579|1,574|601|601|
|R-squared|0.010|0.452|0.026|0.091|
|Number of frm|223|223|152|152|
|Firm FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. The data on tariffs comes from the _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII). **_Note:_** Table normalise the tariff variable to estimate the effect on market power. Robust standard errors clustered at three digit industry level in parentheses. _∗∗∗_ Significant at 1%. _∗∗_ Significant at 5%. _∗_ Significant at 10%.

23
# **Table 4** <u>.</u> _<u>Pantawid</u>_ <u>program impact on mutually exclusive combinations of work and schooling</u>

Mutually exclusive combinations

||In school<br>only|In work<br>only|In school<br>and in<br>work|Neither in<br>school nor<br>in work|Worked<br>while<br>school was<br>in session|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|OLS only controlling for municipality and child age.|-0.003|-0.010|0.047*|-0.034**|0.031|
||(0.031)|(0.012)|(0.027)|(0.015)|(0.022)|
|Additional information:||||||
|Number of observations|1,264|1,264|1,264|1,264|1,252|
|Observations in control group|627|627|627|627|623|
|Observations in treatment group|637|637|637|637|629|
|Mean in control group|0.724|0.038|0.163|0.075|0.087|
|Mean in treatment group|0.716|0.028|0.214|0.042|0.119|

Note. Estimates of program impact on mutually exclusive combinations of work in the 12 months prior to the interview and current school attendance for children aged 10 to 14 from eligible households. Here, school refers to current school attendance and work refers to any work in the past 12 months. Standard errors are clustered at the village level. *** p<0.01, ** p<0.05, * p<0.1 52
# **4 Real Exchange Rate and Inequality**

The relationship between differences in productivity and difference in prices illustrated above can have strong distributional consequences in a very unequal society, where consumption baskets a very heterogeneous across income groups. Inequality is the defining characteristic of the South African economy. Whatever measure of inequality is used, South Africa comes always at the top of any inequality index, which is a reflection of both its history and of the structural changes after the end of apartheid.

Although South Africa’s real exchange rate is in line with comparable countries, the economy is considered expensive for the majority of the population. This is often the main explanation for strong downward rigidity of wages even in the presence of an extraordinarily high unemployment rate. The affordability of the South African economy, defined as the ability of the majority of the population to access goods and services, given the prevalent income, is a combination of relative price distribution and the level of income inequality. The South African economy is very unequal and the dynamic of productivity, as will be illustrated in the next sections, increases the burden on the poorest part of the population, who are more dependent on good produced in the less productive part of the economy.

Following Leibbrandt et al. (2016), we investigate the expenditure categories that have promoted a gap between the inflation rates of poor and non-poor households. The methodology is explained in detail in the appendix. Focusing on the period 2009-2017 and using the 2014/2015 Living Conditions of Households in South Africa, we assess price changes from the CPI basket of goods and services to which poor households were overexposed. These are typically items from the CPI basket for which inflation rates for poor households exceeded those of richer households during the period considered. Our results are consistent with the findings of Leibbrandt et al. (2016) while using a different dataset and a different and more recent time period, demonstrating that this evidence is a constant distributional issues over the last 20 years. Figure 8 plots the average annual percentage change in price relative to the ratio of weights of the bottom 4 deciles relative to the top 6 deciles. The horizontal line in the graph is the average inflation rate during the period considered, while 20
-70-

Table S: A Cmparu _of_ **Road** U.. argiug Ndm&uW

|Congson Charging<br>Mechanisms||MANU<br>12|AL||ELECTROMC<br>3|4|
|---|---|---|---|---|---|---|
|Manual and Elctonic ToU<br>CoUection<br>plus<br>Congestion Carwging<br>Mechanisms|Maa<br>Toil<br>Collecwd<br>(24 hour)|Cordon<br>Picing<br>on (Combinato<br>of Manual and<br>Reseved<br>Ln)|L<br>ing (Single<br>Zon)<br>|BElocic Toi<br> <br>Collection (24<br>hour) with AVI<br>Technlology|Electronic Road<br>Pdcing (ERP)<br>with Automat<br>Vehice<br>Idficato|Elernic<br> <br>Road<br>Pcng<br>(ERP) with<br>Smat Cad<br>Tecinology|
|Iussrativw_CareSuidy:_|Common<br>tonl plaza|<br>Bergan Toll<br>Ring|Sipore<br>Am<br>Licnsing Schm<br>ALS|Odo Toil Rig|Hong Kog<br>ERP Schmoe|Nthelands<br>ERP<br>P|
||_______||()|||rpos|
|_From User'Poaiof Vkw (14)_|||||||
|1. User-fiendines<br>(Simplicity)|0|_0_|0|0|03||
|2. Trnspaecy<br>(via Er_Ante_<br>Pricing)|O||[|O|3<br>O|0|
|3. Annymity (Potecton from<br>lnvaDionofPrivacy)|03||0|0|3|03|
|4. PrepaymentPoslpaymet<br>Optko for Charging|Pay-s-<br>YOU-go|_Pay-as-you-gP_<br>+ Prepayment|_Pnpynat_only<br>|PrPyme<br>Postpaymen|Psaymnvt/<br>Postpaymnz|Prpy-<br>_mants_|
|_Fm. Road_<br>_AnJd_t'sPet of _|_View_<br>_(5-11)_|_:_<br>|||||
|'S. B2hancedEfficscyvia<br>Ditet Chaging||E31||**U**|0|O<br>0|
|_6._Flexibility (Repoanivai s<br>toDnd)||O|O|||0|
|7. Rlbility|0O|*|7|O3|0||
> significant. Furthermore, and in line with the prediction that presence of infrastructure and other public goods decreases the cost of policing and thus the probability of civil strife, higher distance to infrastructure has indeed a marked positive impact on the probability of civil strife while location in urban areas reduces this probability, at least according to some of the specifications.

As is suggested by the descriptive statistics in table 1, there are pronounced differences across regions. While the Center has the lowest level of civil strife, the East and North have continuing high levels of violence and there has been a marked increase in civil conflict in the West, something that is likely to be related to broader regional patterns and a spillover from conflicts in Rwanda and military engagement in the Republic of Congo. While civil strife in the West was significantly lower than in the East or the North in 1992, it is now on a level equal to these regions and only the Central region continues to display a markedly lower level of civil strife than the remainder of the country.'<sup>7</sup>

> Table 1 in the appendix reports results from conducting exactly the same tobit regressions with incidence of theft and physical attacks as the dependent variables. Comparing these to what was obtained for civil strife suggests that these are indeed very different phenomena. Only very few of the coefficients discussed earlier turn out to be significant. For the ones that do, signs obtained are generally opposite from what has been observed for the case of civil strife. For example, the incidence of theft is higher close to the center of the municipality, i.e. increases directly with the availability of infrastructure in addition to being much

> more prevalent in the North, and its it is also estimated to have increased significantly over time. Physical

> attacks are estimated to increase with levels of education and wealth inequality, contrary to what was observed for civil strife, and to be more prevalent in the North and the West, as well as in coffee growing

> areas. This supports the notion that politically motivated violence and civil strife respond to factors that are different from those that underlie theft and violence aimed at individuals.

> 16 The result on human capital is very similar to what is found by Dreze and Khera who, using district level data on murder rates in India, find a significant negative correlation between education and murder.

> 17 Testing for equality of the coefficients on the different dummies rejects equality of the Westem with the Eastern and Northern dummies (F = 6.59 and 11.51 both suggesting significance at the 1% level) but can not reject such equality for the same dummies plus the dummy*year interaction (F = 1.11 and 0.81). Similarly, equality between North and East can not be rejected at the 5% level for either period (F = 1.59 and 3.11). Note that the test statistic for all of these tests is distributed F(1,727).

21
|1990|||0.032|||||
|---|---|---|---|---|---|---|---|
|1991||0.008||||||
|1992||||0.274||||
|1994|0.038|0.007|0.071|||||
|1996|0.045|||||||
|1997|0.062|0.010|||0.177|||
|1998||||0.133|||0.011|
|1999|0.046|||||0.009||
|2000|||0.079|||||
|2001|0.036|0.011||||||
|2002||||||||
|2003|0.035||||0.043|||
|2004||0.011|0.078|0.023||||
|2005|0.023|||||0.014||
|2006||0.010||0.011||||
|2007|0.017||||0.171||0.019|
|_Change in Variance_|**_-0.021_**<br>**_(-0.028)_**|**_0.002_**|**_0.046_**|**_-0.263_**|**_-0.006_**|**_0.007_**|**_0.008_**|

*The industry variable in the 1994 Sakernas is at the one-digit level; ** Lower secondary and above for Cambodia; *** Upper secondary and above for Cambodia.

Table 14: Evolution of Skill Premiums by Sector and Country

|||Indonesia|Philippines|Thailand|Vietnam|Cambodia|China|Mongolia|
|---|---|---|---|---|---|---|---|---|
|_Seni_|_or Sec. or higher_||||||||
|198|8||||||||
|-|Agriculture/Mining||0.192||||||
|-|Manufacturing||0.343||||||
|-|Trade/Services||0.539||||||
|199|0||||||||
|-|Agriculture/Mining|||1.812|||||
|-|Manufacturing|||0.642|||||
|-|Trade/Services|||0.969|||||
|199|1||||||||
|-|Agriculture/Mining||0.186||||||
|-|Manufacturing||0.322||||||
|-|Trade/Services||0.560||||||
|199|2||||-0.586||||
|-|Agriculture/Mining||||-0.102||||
|-|Manufacturing||||-0.237||||
|-|Trade/Services||||||||
|199|4||||||||
|-|Agriculture/Mining|0.809|0.161|1.148|||||
|-|Manufacturing|0.419|0.301|0.665|||||
|-|Trade/Services|0.722|0.469|1.065|||||
|199|7||||||||
|-|Agriculture/Mining||0.204|||-0.263|||
|-|Manufacturing||0.255|||0.115|||
|-|Trade/Services||0.511|||0.190|||

40
# **1. Introduction**

The traditional microeconomic textbook model assumes that the level of employment and capital used by firms is optimal at any point in time. But since adjusting employment and capital is costly to firms, they often deviate from what would be optimal in the absence of frictions. Understanding the way firms react to employment and capital shortages can shed light on many issues of microeconomic and macroeconomic nature. In this paper we analyze such adjustment process, based on a panel of manufacturing establishments in Uruguay.

The Uruguayan economy evolved from inward looking, based on state interventionism and import substitution protectionist policies, towards an outward looking orientation, with more reliance on markets as resource allocation mechanisms and exports as the growth engine. A first phase of trade reform took place in the 1970s, accompanied by a quick financial liberalization process. Later, trade liberalization was deepened in the 1990s, combining a gradual unilateral tariff reduction with the creation of Mercosur, an imperfect customs union between Argentina, Brazil, Paraguay and Uruguay.

A by-product of the trade liberalization process in Uruguay was that manufacturing firms switched to more capital intensive technologies. Specifically, in the 1990s, there were high job destruction rates as reported in Casacuberta, Fachola and Gandelman (2004). We focus on the adjustment process, and our main concern is with the effect of protection levels and trade liberalization on the adjustment functions for capital and white and blue collar employment.

The objectives of the paper are: i) to present an estimation of the adjustment functions for labor (blue and white collar) and capital, ii) to study the differences in the adjustment process for each factor of production and iii) to analyze the impact on these adjustment functions of tariff barriers, trade liberalization and international exposure.

Our main results confirm the lumpy and discontinuous nature of firms’ adjustment process. Large shortages of one factor lead to less responsiveness in adjustment in the creation side of other factors but larger adjustment in the destruction side. Adjustment costs reduce the volatility of factor usage but on average they implied an annual gap between desired and observed output of 2%. Less protected sectors adjust a larger fraction of the gap in the creation side but a lower fraction in the destruction side.

The paper proceeds as follows. Section 2 presents the policy experiments and the basic definitions of factor growth rates, factor shortages and adjustment functions. Shortages are defined with respect to some targeted desired levels. Section 3 details the methodology to obtain these desired levels. Readers not interested in the technicalities of this procedure may skip this section. Section 4 presents the results and analyzes the effects of policy changes in the adjustment process of firms and finally section 5 concludes.

2
than the results for the declining sectors. This is explained by the relatively greater use of business services and goods from imperfectly competitive sectors.<sup>38</sup> In figure 3.4, we display the frequency distributions of the output changes in the same six sectors. The pattern of which sectors expand or contract is the same as for employment, but the results are more positive. Whereas economy-wide employment is fixed by assumption, output increases overall. Output expands due to greater efficiency in the use of resources, and, more importantly, due to greater productivity of factors of production from the increase in varieties of business services and differentiated goods.

Finally, in order to display systematic sensitivity results for all industries in one figure, in the upper panel of figure 3.5 we display bars that represent fifty percent confidence intervals for aggregate output (export plus domestic sales) for all industries (the point on the bar is our point estimate). In the lower panel of figure 3.5, we show fifty percent confidence intervals for domestic output by industry. Similar figures for other variables are in Appendix B of Jensen, Rutherford and Tarr (2004).

# **3.5. Conclusions**

In this section we have developed an innovative small open economy computable general equilibrium model of the Russian economy that is capable of assessing the impact of the liberalization of barriers against foreign direct investment. Surveys and estimates of the ad valorem equivalents of the barriers against foreign direct investment were prepared for this model. We find that the source of the largest gains to Russia from WTO accession is that additional multinational service providers will reduce the quality-adjusted cost of purchasing business services in Russia and that these gains are rather substantial when compared with the typical gains from constant returns to scale models of tariff liberalization. We believe that these

> 38 Thus, variation in the values of etaf, esubs and theta_fdi  have a greater impact on these sectors.

60
degree of overlap in skills and duties of the workforce of consolidating firms (Lee, Mauer and Xu, 2018). Higher HCR implies greater potential economies of scale and therefore potential job loss for incumbent workers. We use individual-level skill data to generate a measure of human capital relatedness following an analogous approach to the occupation-based measure used in Lee, Mauer and Xu (2018).

To construct our HCR index, we flag all workers who are present at firm _j_ four years prior to the takeover and earlier (that is, we do not incorporate observations from _t −_ 3 onward). We restrict the sample to firms where we have at least ten observations of employee skills in this period. We then create firm _j_ ’s human capital profile vector _Hj_ , which indicates the number of firm _j_ ’s workers that fall into each of three given skill categories divided by the total number of workers at that firm _j_ : _Hj_ = ( _H_ low _,j, H_ medium _,j, H_ high _,j_ ). For a given takeover with acquirer _A_ and target _T_ , we then construct a time-invariant HCR index defined as The HCR index is increasing in the overlap of skill categories of the two firms; it is equal to zero if the firms employ workers of completely different skill levels and is equal to one if the distribution of workers across skill levels is identical in the two firms. As seen in Figure 8, the distribution of the HCR index is skewed with 83%, 66%, and 28% of events having an index over 0.8, 0.9 and 0.95, respectively.

Do takeovers between firms with higher HCR generate higher rates of job loss? Figure 9 shows the percentage change estimates of retention for events with above and below median HCR. We show results for both overall retention as well as hours worked at the consolidated firm, post-takeover. We see that the highest HCR events have a modestly higher impact on retention than lower HCR events: takeovers between firms with less than 50% HCR induce a statistically insignificant 7.2% and 5.9% reduction in retention and hours worked, respectively, compared to a 9.0% and 8.0% reduction for workers at takeovers that have a higher degree of HCR.

22
strategies, it might be that households are not accurate in reporting these strategies, for example, by overestimating the frequency of using these strategies to receive more assistance from humanitarian organizations.

Figure 2 shows the predicted welfare rates for the set of different poverty lines for all three models, but this time with a focus on food security. Welfare based on food security is defined in humanitarian settings as the inability to afford the minimum expenditure basket required to purchase a food basket based on basic needs. The minimum expenditure basket is defined by the WFP “as what a household requires in order to meet their essential needs, on a regular or seasonal basis, and its average cost” (WFP 2018). The results are very similar to the overall welfare results displayed in Figure 1. The results indicate that Models 1 and 2 predict the actual welfare rates well based on food security for different poverty lines and are within the 95% confidence interval for all the arbitrary poverty lines considered. The predictions are also very similar for the two different estimation models of error terms, the normal linear model and empirical error model. Again, Model 3 overestimates the poverty rates, as the predicted welfare rates are outside the confidence interval of the survey-based rates.<sup>8</sup> In summary, Figures 1 and 2, corresponding to Models 1 and 2, underestimate welfare for low poverty lines and overestimate for high poverty lines but are within the confidence intervals. Model 3 always overestimates poverty for smaller poverty lines and its predictions are outside the confidence interval. In general, Models 1 and 2 predict poverty and food security poverty well for different arbitrary poverty lines. Based on these results, we conclude that these two models provide fairly accurate aggregate welfare estimations of refugees living in Chad, and that the variables currently available in the ProGres UNHCR registration system can be combined with other survey data to predict the aggregate welfare of refugees efficiently.

The cross-survey imputation literature<sup>9</sup> stresses the importance of selecting a few key predictors, and our results from Model 1, which contains only demographic variables, are in line with this

> 8 To check for possible heterogeneity, we split the sample with respect to country of origin. The results were similar except the larger estimate variances (less precision), which might be due to sample size for refugees from the Central African Republic. 9 See, for example, Dang et al. (2017a); Dang and Lanjouw (2018); Dang and Verme (2019). See also Luca et al. (2018) for a related study on variable selection with linear models.

12
|**Table 4: Logit regression of treatment at vill**<br>|**age level**<br>|||
|---|---|---|---|
|**Variables**|**Coeff.**|**(s.e.)**|**Sig. **|
|**Village Demographics**||||
|Number of households (1000s)|-0.34|(0.27)||
|Scheduled tribe/caste (%)|1.28|(0.67)|*|
|Other backward caste (%)|2.39|(0.67)|***|
|Mean education by head (years)|-0.15|(0.15)||
|Economically active female|14.26|(2.77)|***|
|**Initial village characteristics**||||
|Availability of public bus|-1.24|(0.22)|***|
|Availability of telephone|-0.40|(0.22)|*|
|If child marriage existed|-0.55|(0.24)|**|
|If untouchability existed|0.23|(0.26)||
|If temple prostitution system existed|-0.27|(0.50)||
|If caste panchayats existed|1.20|(0.23)|***|
|**Pseudo R-squared**|0.31|||
|**Number of observations**|659|||

Significance of coefficient is as follows: * significant at 10%; ** significant at 5%; *** significant at 1% 20
|**Table 14.**QMLE par<br>with IV a<br>|ameter estimates of a fracti<br>nd inclusion ofDiDifvr<sup>+</sup>|onal Probit for|P3(Participation)|
|---|---|---|---|
|Variable<br>|MENA||OECD|
|P2(Provision)<br>|−1.95<sup>∗∗∗</sup>||2.95<sup>∗∗∗</sup>|
||(0.47)||(0.45)|
|P1(Protection)<br>|2.45<sup>∗∗∗</sup>||3.67<sup>∗∗∗</sup>|
||(0.24)||(0.24)|
|rf_rp<br>|−0.69<sup>∗∗∗</sup>||−0.84<sup>∗∗∗</sup>|
||(0.12)||(0.09)|
|rf_iu<br>|0.79<sup>∗∗∗</sup>||−0.19<sup>∗∗∗</sup>|
||(0.19)||(0.07)|
|rf_hdi<br>|−0.97<sup>∗∗∗</sup>||−1.61<sup>∗∗∗</sup>|
||(0.30)||(0.31)|
|LvD 3_DiDifvr<br>|2.55<sup>∗∗</sup>||−4.97<sup>∗∗∗</sup>|
|<br>|(1.20)||(1.05)|
|<br>(LvD 3_DiDifvr)<sup>2</sup><br>|−2.24<sup>∗∗</sup>||3.20<sup>∗∗∗</sup>|
|<br>|(1.04)||(0.69)|
|<br>p�21<br>|1.13||−4.18<sup>∗∗∗</sup>|
||(0.99)||(0.43)|
|p�22|−2.11<sup>∗∗∗</sup>||−1.00<sup>∗∗∗</sup>|
||(0.28)||(0.26)|
|Constant|−1.58<sup>∗∗∗</sup>||0.60|
||(0.40)||(0.39)|
|Wald statistic<br>|242.76<sup>∗∗∗</sup><br>||3538.62<sup>∗∗∗</sup><br>|
|Obs.<br>|112<br>||350<br>|
|+Dependent variable:P3; Stand<br>for MENA data: Domestic credit<br>|ard errors in parentheses obtaine<br>to private sector (% of GDP), Ex<br>|d using 500 boot<br>ports of goods and|strap replications; Instruments<br>services (% of GDP), Imports|
|<br>of goods and services (annual gro<br>data: Tax revenues (% of GDP),<br>capita;<sup>*</sup>:p< 0.10,<sup>**</sup>:p< 0.05,<br><br>|wth rate), FDI (net outflows, %<br>Military expenditures (% of tota<br> <sup>***</sup>:p< 0.01.<br>|of GDP), and rea<br>l government ex<br>|l GDP; Instruments for OECD<br>penditures), and Real GDP per<br>|
|<br>**Table 15.**Test of vali<br>|<br>dity and weakness of instru|ments whenD|iDifvris included<sup>+</sup>|
|H0|Statistic<br>|MENA|OECD|
||OIDR<br>|6.05|0.82|
|Instruments are valid|p-value|0.11|0.37|
|Itt k|CDME|8.54|28.92|
|nsrumens are wea|Critical value|4.32|5.44|

+ OIDR: Overidentifying restrictions test statistic (Anderson and Rubin, 1950); CDME: Cragg and Donald (1993) minimum eigenvalue statistic (Stock and Yogo, 2005); Critical value is the highest critical value relating to LIML size of nominal 5% Wald test (Stock and Yogo, 2005).

34