rafmacalaba/datause-encoder-data huggingface dataset:
Table 3B: Impact on Social Conformity Outcomes (Position Effects) Gender Statement Game

||**Sw**<br>**(A**|**itch in Pub**<br>**ll Position**|**lic**<br>**s)**|**Swi**<br>**(Po**|**tch in Pub**<br>**sitions 2**|**lic**<br>**-5)**|**S**<br>**(Positions 2-**<br>**before int**|**witch in Publi**<br>**5 and respons**<br>**eracted with t**|**c**<br>**e in position**<br>**reatment)**|
|---|---|---|---|---|---|---|---|---|---|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|CHOICES (0/1)|-0.045**|-0.044|-0.049**|-0.043**|-0.031|-0.056**|-0.016|0.008|-0.041*|
||(0.02)|(0.03)|(0.02)|(0.02)|(0.03)|(0.03)|(0.02)|(0.02)|(0.02)|
|Number of CHOICES peers in team (0-4)|-0.003|0.003|-0.010|0.003|0.018|-0.013|0.003|0.018|-0.012|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Mixed-gender teams|0.085***|0.000|0.082***|0.076***|-0.007|0.070***|0.039*|0.007|0.040*|
||(0.02)|(0.03)|(0.02)|(0.02)|(0.03)|(0.02)|(0.02)|(0.02)|(0.02)|
|Boys only team|0.086**|||0.083**|||0.039|||
||(0.03)|||(0.04)|||(0.03)|||
|Response of position before different to i's private response|||||||0.513***|0.613***|0.420***|
||||||||(0.04)|(0.05)|(0.06)|
|CHOICES*Response of position before different|||||||-0.078*|-0.202***|0.038|
||||||||(0.05)|(0.06)|(0.07)|
|Control group mean|0.24|0.28|0.21|0.25|0.27|0.23|0.25|0.27|0.23|
|Adjusted R-Squared|0.10|0.07|0.11|0.10|0.07|0.13|0.30|0.29|0.31|
|Observations|2937|1446|1491|2352|1151|1201|2352|1151|1201|

Notes: * significant at 10% level ** significant at 5% level *** significant at 1% level (1) CHOICES=1 if adolescent was assigned to CHOICES only or CHOICES+EMB treatment groups; 0 if in control group.

(2) Standard errors are clustered at the individual level and are reported under the coefficient in parentheses. All OLS regressions includes district fixed effects and controls for whether household has multiple adolescents, the age of respondent, and peer effects given by the number of CHOICES adolescents in the adolescent's team excluding the respondent (0-4). Regressions control for randomized team gender composition (whether team is mixed-gender, boys only, and girls only) where girls only is the omitted category in the regression, statement fixed effects and enumeratorgender fixed effects.

(3) Outcomes: Switch in Public is whether the adolescent changed her privately held belief when in a public group setting (=1 if adolescent changed their private response in public; 0 if stuck with their private response). A negative coefficient of being less likely to change in public is indicative of demonstrating greater non-conformist behavior.

(4) Table 3B columns (7) to (9) analyzes the influence of position that you play the game by interacting the CHOICES treatment dummy variable with a dummy variable that equals 1 if the public response of the individual seated in the position before adolescent i is different to i's private response.
investment climate in Africa are simply not good enough. Foreign firms deciding to outsource part of their production face a trade off between lower costs of production and increasing time costs, transport costs, and coordination costs generally. With poor infrastructures, cumbersome bureaucratic practices, poor supporting services or even corruption, outsourcing to Africa will be associated with high costs. Clearly under status quo where nothing is done about these problems, Africa will continue to experience a large amount of missed opportunities. It is hard to see how such a strategy can yield growth and create jobs on a scale necessary for significant poverty reduction.

The third broad policy option is to create incentives for African firms to strive to participate and be competitive in international markets. In our view, this is the most likely route to better economic performance. This view is directly supported by some of the findings based on the firm data. Harding, Söderbom and Teal (2004) find that the strongest explanatory factor of productivity growth over five years among firms in Ghana, Kenya and Tanzania, is the competitive pressure faced by the firms. Firms that face little competitive pressure, and have high profits as a result, record slower subsequent productivity growth than firms that face a lot of competitive pressure. Thus, opting to embrace, rather than to shy away from, increased competition is likely to improve the performance of manufacturing firms in Africa. Secondly, as discussed in Section 3.5, there is relatively strong evidence that participation in exports markets is a source of improved firm performance. We do not yet understand fully why this is so – it could be that international competition “disciplines” firms to get better organized and become more efficient, or that contacts with firms and customers in developed countries help to speed up the rate of technology transfer. Nevertheless, we know from the data that these positive effects exist. Apart from the productivity effect, international trade leads to concentration of investments in the more efficient sectors according to comparative advantages as well as the exploitation of scale advantages.

While the rewards to participation in international markets may be significant, pursuing such a strategy also puts big demands on both firms and governments in Africa. Without more investment in modern technology and without a drastically improved business environment, we cannot see how African firms will be able to compete internationally on anything but a very modest scale. Results will not come 35
**Annex 1: The Basic Methodology**<sup>**25**</sup>

The basic methodology draws on<sup>the approachof Nicita,OlarreagaandSoloaga (2002)in</sup> their study of the impact of trade reform<sup>in Cambodia. Let the incomeY of a household</sup> be given by: where p,. is the price of output j; _qo_<sup>isthequantity of output j;</sup><sup>_p'_and</sup><sup>_qk_arethe</sup> corresponding input prices and<sup>quantities;wf is the wage rate for factorf,Lf is the net</sup> sale of factorfby the household;<sup>and</sup><sup>_Tmn_is the net transfer received by household</sup> member n from source m.

Note that the first term in equation<sup>(1)is the value-addedof all production (whetherfrom</sup> farming or non-farm enterprises).<sup>This includes both marketed productionand own</sup> consumption. The second term is<sup>the value of netfactorsales by thehousehold -in the</sup> case of most poor households this<sup>simply meansnet labour sales (i.e.employmentincome</sup> minus payments for hired labour)<sup>sincethe onlyfactor which most poor households can</sup> sell is their own labour. The final<sup>term representsthe net transfers received by the</sup> household.

Similarly we can write the consumption<sup>of the household simply as:</sup> where _pc_ is the buying price of good<sup>i and</sup><sup>_qc_is thequantity consumedof good i.Note</sup> that _qc_ includes own consumption as<sup>well as goodspurchasedfrom the market.</sup> It is then possible to simulate the impact<sup>on householdincome of price changesinduced</sup> by structural reforms. In the short<sup>run wecanassume thatall quantities remain fixedso</sup> that Similarly the change in consumption assuming<sup>thatquantities remainfixed is</sup>

> 25 This methodology draws heavily on "A<sup>simple methodology toassessthe poverty impact of economic</sup> policies using household data. An application<sup>to Cambodia. Nicita,Olarreagaand Soloaga (2002).</sup>

35
products. For example, Denmark’s 1976 population census relied on a population register, a business register, and a set of tax registers (Jensen 1983). By contrast, administrative data in lowercapacity countries are just beginning to become available for statistics.

High-capacity NSSs use a variety of big data applications to official statistics.<sup>10</sup> With more sophisticated production processes that deliver a wider array of statistics, staff in high-capacity NSSs are highly specialized. For example, dedicated teams develop supply-use tables, the foundation for rebasing GDP estimates. In contrast, lower-capacity NSSs produce supply-use tables less frequently, and the work is done by nondedicated teams. Unlike lower-capacity NSSs, higher-capacity NSSs also have staff devoted to developing analytical and innovative products.

These and other differences suggest that technical assistance works best when high-capacity advisors are familiar with the circumstances in low-capacity environments. The IMF Regional Technical Advisory Centers provide a range of statistical support to developing countries. In 2016, they delivered 563 technical assistance (TA) products and 120 training events (IMF 2016). These centers are typically staffed by statisticians with long experience in high-performing NSSs, who coordinate efforts and sensitize foreign experts to local circumstances. Relocating these statisticians to immerse them in the working circumstances in their countries likely results in better advice and more sustainable outcomes. Leading bilateral providers of TA are adopting a similar approach. The presence of in-country strategic advisors has helped improve donor coordination on NSSs and sensitize experts with little experience in low-capacity environments (UK Foreign, Commonwealth, and Development Office 2020).

The gap between the cluster of high-performing NSSs and the rest of the world shown in Figures 1 and 2 also suggests that expanding South–South knowledge exchange could improve the impact of capacity building. Cooperation among developing countries is not new. The Statistical, Economic, and Social Research and Training Center for Islamic Countries (SESRIC) and AFRISTAT are examples of capacity-building programs for member states. Broadening South– South support to improve official statistics remains relevant in the face of common new challenges faced in lower-capacity NSSs.

> 10 For examples from Statistics Netherlands, see https://www.cbs.nl/en-gb.

16
10 The Household Budget Survey (POF) is nationally representative and has extensive and detailed information on consumption, expenditure, and income. We use the 2002/2003 round, which is the only pre-shock detailed survey with the appropriate information for our analysis, and which surveyed 48,470 households throughout the country. The 2008/2009 round is post-crisis, and therefore unsuitable for the purpose of estimating the compensating variation of the 2007-2008 price change. The welfare measure is household per capita consumption expenditure, calculated excluding pensioners, household workers, and relatives of household workers. The unit of analysis is the individual. The concordance between food groups in the INPC and POF classifications works extremely well at the level of the 16 food groups, with 97% of total household food consumption expenditures in the 2002-03 POF matched to these groups.

Although the POF records some income information for household members as well, it does not include detailed data on two variables that are important for our estimation of the market and transfer income effects of rising food prices. The first variable is a disaggregation of the type of activity exerted by workers, with detailed information on the kind of crop or agricultural production performed by individuals reporting positive incomes from that sector. The second is information on the receipt of social assistance transfers. Both of those variables are therefore obtained from the National Household Income Survey (PNAD), an annual, nationally representative survey of income, employment and living conditions. Since the 2007 survey, fielded in September, would already have been contaminated by rising food prices, we use the 2006 wave. The PNAD survey lacks a comprehensive measure of expenditure, but is thought to capture wage incomes comprehensively, and it contains information on whether households are beneficiaries of social programs, specifically Bolsa Família and BPC. The method used to combine information from these different, unmatched surveys is explained in the next section, which describes our analytical framework and empirical approach.

# **4. Analytical Framework and Empirical Approach**

The conventional starting point for an analysis of the impact of price changes on household welfare is to estimate the resulting change in consumer surplus. The most commonly used concept of consumer surplus is the Hicksian compensating variation:
Table 28. Regressions with fiscal variables – disaggregated local spending per capita

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|∆Total Personnel Spending per capita|0.00232<br>|-4.38e-05<br>|||
||(0.0241)|(0.0262)|||
|∆Total Social Security per capita|-0.0338*|-0.0329|||
||(0.0190)|(0.0224)|||
|∆Total Purchases on Goods and Services per capita|0.0239|0.0504**|||
||(0.0216)|(0.0221)|||
|∆Total Interest Payments per capita|0.00528|-0.000144|||
||(0.00472)|(0.00494)|||
|∆Total Current Transfers per capita|0.0140*|0.0198***|||
||(0.00723)|(0.00663)|||
|∆Total Personnel Spending per capita*West|||-0.0173|-0.00854|
||||(0.0200)|(0.0214)|
|∆Total Personnel Spending per capita*East|||0.0457|-0.0774|
||||(0.0482)|(0.120)|
|∆Total Social Security per capita*West|||-0.0286|-0.0284|
||||(0.0185)|(0.0224)|
|∆Total Social Security per capita*East|||-0.0867**|-0.00957|
||||(0.0338)|(0.0569)|
|∆Total Purchases on Goods and Services per capita*West|||0.000195|0.0247|
||||(0.0198)|(0.0235)|
|∆Total Purchases on Goods and Services per capita*East|||0.0660|0.106*|
||||(0.0419)|(0.0521)|
|∆Total Interest Payments per capita*West|||0.000387|-0.00474|
||||(0.00451)|(0.00618)|
|∆Total Interest Payments per capita*East|||0.0173**|-0.000668|
||||(0.00657)|(0.00348)|
|∆Total Current Transfers per capita*West|||0.00948|0.0107|
||||(0.00681)|(0.00759)|
|∆Total Current Transfers per capita*East|||0.0205***|0.0227**|
||||(0.00708)|(0.00851)|
|Constant|-0.00905***|-0.00564**|-0.00788***|-0.00540**|
||(0.00271)|(0.00236)|(0.00243)|(0.00228)|
|Sector Shares||X||X|
|Observations|156|104|156|104|
|R-squared|0.104|0.302|0.195|0.424|

_Notes_ : W1=West, W0=East. Results from Equation 3 are shown. Regressions without sector controls are over the period 20072013. Regressions using sector controls are limited to 2007-2011. + p<0.10, * p<0.05, ** p<0.01 _Source_ : Ministry of Finance, LFS, SILC 2007-2011; authors’ calculations.

57
32 _I_

# by 6-13 percentage points. (If one includes MFA products, this share increases substantially for all CEE-5

countries.<sup>43</sup> ) Table 13: The Significance of the Copenhgen Concessions: Net Change In "Liberalized" hnports, Relative to the ITA Schedule.

||1993|1994<br>_nmil_|1995<br>_aion ofUS_|1996<br>_dollars)_|1997|1998|
|---|---|---|---|---|---|---|
|Bulgaria|n.a.|6|10|11|83|0|
|FCSK|87|131|148|841|0|0|
|Hungary|48|87|103|245|0|0|
|Poland|95|200|218|499|0|0|
|Romania|n.a.|6|0|114|0|0|
|CEE-5|229|431|<br>479|1710|83|0|
|||_(share i_|_n indusia_|_I import_|_s, in perc_|_ent)_|
|Bulgaria|n.a.|1|1|1|9|0|
|FCSK|1|2|2|13|0|0|
|Hlmgary|1|2|3|6|0|0|
|Poland|1|3|3|6|0|0|
|Romania|n.a.|0|0|0|7|0|
|CEE-5|1|2|2|8|1|0|

Note: The canges in customs status in each fiutre year are weighted according to the commodity composition of flows in 1992.

Source: See Table 6.

# B. Reliabit of Nwtv-Free Share Esimates: How "Free" is a Free Trade Regime?

The esimates of the free trade share in CEE-5 exports to the EC are based on assumptions tbat: (i) once tariff and nontariff measures are removed ihese products will contine to enjoy free access to EC markets; and (ii) the export baskets will remain essentily unchanged over the next four to five years. bhe threat of managed trade-to borrow an apt phrase from Ostry (1993)-is _writ_ large in the ITAs whose provisions contain a rich array of loosely defined safeguard clauses and, thus, '...open to virtually 4 The reason for not taking account of the concessions on texiles and cloting products (duty-free access to EC markets after five years instead of six years) is that MFA trade remains managed. Its futun is tied to the Uruguay Round. Including MFA products mises the net gain as follows: for Bulgaria (29%), FCSK (14%), Hungary (22%), Poland (19%), and Romania (39%).
unemployment rates, gross domestic investment, or the existence of war. These, however, yielded a lower explanatory power than the indicators above.

**Box 1: Expenditure Reuression Summary**

All the following regressions apply to 47 countries and do not include anv transition countries. TIhe t-statistics is in parentheses

|**gg**=||||||**A**|**dj R-sqr**|**Root MSE**|
|---|---|---|---|---|---|---|---|---|
|**(1)**|**.193 sec**<br>(2.46)|+|**.131 mor**+<br>(1.961)|**.396 old**<br>(1.63)|+<br>**.455 oldeu**<br>+<br>(3.98)|**5.094**<br>(0.74)|.78|7.0|
|Comp|arator witho|ut the|regional dummies|:|||||
||.158 sec<br>(1.75)<br>Problem: Inf|**+**<br>ant mo|**.153 mor**<br>+<br>(1.99)<br>rtality has but should no|**1.118 old**<br>(5.985)<br>t have a positive|-<br>coefficient.|**4.690**<br>(-.63)|.71|8.1|
|(2)|.402 gdp<br>(2.45)|+|16.877 eu<br>-<br>(6.00)|**9.695 las**<br>(4.00)|+<br>**31.062**<br>(12.72)||.81|6.5|
|Comp|arator withou|t the|regional dummies|:|||||
||**1.367 gdp**<br>(6.30)||||+<br>**21.987**<br>(7.78)||**.46**|**11.0**|
|**(3)**|**.142 sec**<br>**.(360)**|4**+**|**.066 d**<br>+<br>(2.17)|**.429 oldeu**<br>(6.10)|-<br>**8.354 las +**<br>(-3.67)|**21.110**<br>(5.63)|**.85**|_5.8_|
|Comp|arators:||||||||
||**.154sec**|**+**|**.108d**<br>+|**.540 oldeu**|<br>**_-+_**|**12.690**|||
||(3.458)||(3.35)|(7.49)||(3.77)|**.81**|**6.6**|
||**.065 sec**<br>(93)|+|**.106 d**+<br>(2.73);0|.934**old**<br>(5.13)|**+**|3.363<br>**(.91)**|.73|7.8|
||||**.104d**<br>+<br>(2.67)|**1.069 old**<br>(9.61)|**-+**|**4,919**<br>_(1.50)_|.731:|<br>**7.8.**|
|Notes:|sec.- seconda<br>mor-infant m<br>old - old age<br>eu= I ifcoun|ry edu<br>ortali<br>depen<br>trybel|cationenrollment ratio<br>ty<br>dency ratioif<br>ongstoEurope; eu=Oot|gd<br>d-<br>las<br>herwise<br>old|p**-**per capita GDP evaluated a<br>publicdebtoto0tD? ratio:<br> <br>1= if country belongs to Latin<br>eu-oldeu|t PPi:<br>Ameica or Asia;|las 0 O other|wise:|

For further expenditure analysis in this section, we use the results of regression (3). For its results see Figures 2 and 3.

15
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of non-traded items themselves, as well as distribution costs. I do not have direct information on the size of these distribution margins in the case of Egypt, although in principle these can be extracted from input-output tables for Egypt. In industrial countries, these distribution margins are typically quite substantial, averaging 30-50 percent of the prices paid by consumers (Campa and Goldberg (2006)).

Unfortunately, however, I cannot simply estimate Equation (3) econometrically since I do not directly observe the price of the non-traded component of each good, P<sup>N</sup> . I also cannot ignore this term and treat it as part of the error term in a regression since movements in the non-traded component of goods prices might be spuriously correlated with movements in the exchange rate. In particular, during much of the period of interest there were across-the-board increases in nominal prices in Egypt together with a depreciation in the exchange rate, and at least part of these price increases were likely driven by purely domestic factors.

To address this problem I assume that the growth rate of the non-traded component of the price of each item in each region consists of a common component and an idiosyncratic component that is orthogonal to movements in the exchange rate: I assume further that I can approximate the common component of non-traded goods prices with a simple average of a few items in the consumer price index that appear to be primarily non-traded on _a priori_ grounds. These are Domestic Services, and Restaurant and Hotel Services.<sup>8</sup> These two assumptions (of a common component in non-traded goods prices, approximated by these two particular prices) are clearly strong ones and open to debate. However, it is not clear what good alternatives might be available. Although the results that follow are based on this assumption, I have tried three alternatives, and found that the estimates of pass-through are not very different. One possibility is to try to model explicitly purely domestic sources of inflation, for example by including measures of growth in the money supply in the regression. I

> 8 Other clearly largely-non-traded items are rent and education.  However prices of these items are tightly controlled in Egypt and movements in them are unlikely to properly reflect movements in overall non-traded goods prices.

6