rafmacalaba/datause-encoder-data huggingface dataset:
they are present in half of the sectors in Egypt. Second, connected firms should receive large privileges (i.e., cost advantages exogenous to their productivity performance) that do not benefit unconnected firms. The policy privileges of the politically connected firms should drive a wedge between the prices of inputs and outputs that they confront compared to those faced by unconnected firms.

We test for the existence of such a wedge, showing that the policy privileges that connected firms receive account for their higher profit rates relative to unconnected firms. Specifically, two of the privileges enjoyed by connected firms, protection from import competition and energy subsidies, account for their higher profitability, just as they should if these firms exhibit large and exogenous cost advantages. To see this, we use the following specification to estimate whether connected firms are more profitable, i.e. have higher rents for a given pattern of input use, because their products are more frequently protected from import competition or because they absorb more energy subsidies.

Yist = βB connectedist + βR Regulationst + βBR connectedist ∗Regulationst + βX lnXist + βt T+ εist (2) Yist is the profitability (log of profits per assets) of firm _i_ in the 4-digit sector _s_ at time _t_ . _Connected_ is equal to one if firm _i_ enjoys political connections of any type in the 4-digit sector _s_ . The variable _regulation_ measures either the number of NTMs (Class B) protecting the firm’s products in sector _s_ from import competition or a dummy variable equal to one if sector _s_ is high energy-intensive (according to UN classification) enabling access to energy subsidies in Egypt, and zero otherwise. Xis is a matrix of firm level control variables, and _T_ is a matrix of year dummies.

The analysis is based on Orbis firm survey data; this is the only database that allows us to identify individual connected firms. Profit and revenue data are available for 250 larger manufacturing and mining firms of which approximately 50 are politically connected by any type; our analysis is therefore focused on these 250 firms.<sup>37</sup>

> 37 We cannot include firm size as an additional control variable since the sample of firms that report profit data is small and many of these firms do not report their (consolidated) number of employees in Orbis. Thus, we only observe employment and profits jointly for eleven politically connected manufacturing firms and 40 firms overall.

24
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# (e) Equilibrium

We use an iterative search procedure to solve for the equilibrium combination of rural to urban employment in the model. From this, commodity demand and supplies are determined as are net trades. Because of the small, open economy assumption, equilibrium in the model involves factor market clearing and government budget balance, with trade ba,ance a property of such an equilibrium. We begin by making an initial estimate of the wage rate in the urban sector and of the return to labor in the rural sector. We then vary the parameters until an equilibrium is found that produces a set of factor prices that clears goods and factor markets, that holds external balance conditions and that equalizes utility across the two sectors.

# **IV.** USING THE GENERAL EQUILIBRIUM MODEL TO ANALYZE THE ROLE OF TAX POLICIES IN KOREA'S OUTWARD ORIENTED GROWTH STRATEGY

We have used the model described above in counterfactual equilibrium analysis to assess the contribution of tax policy to growth in Korea. As indicated above, we calibrate the model to a microconsistent data set for a given base year incorporating a number of outward oriented growth policies used in that year, including tax policies. Because of data difficulties, we have built data sets for two years only, 1962 and 1982, representing recent and early years in Korea's growth process. This yields two alternative models, a 1962 and a 1982 base year model.

Using each base year model, we perform a series of counterfactual equilibrium calculations. First, we remove the export subsidy component of the policy mix used in the base year, yielding what we term an "export policy neutral equilibrium" (in other words, tariffs remain present). **This**
from the margin of formalizing and will have lower treatment effects. Second, some businesses already have access to other forms of registration that offer partial benefits and for which the added benefits of the _entreprenant_ status will be lower. This includes two groups – those in the Dankopta market who are registered with the public company in charge of all markets (“ _Société de Gestion des Marchés Autonomes,_ ” or SOGEMA), and traders who have a access to a “trader card”. Third, we use our baseline data on formal and informal firms together with the species classification technique of de Mel et al. (2010) to identify which informal firms look similar to the formal “species”, and predict that they will be closer to this formalization margin. Fourth, we consider directly size and owner education, believing smaller, less productive firms are likely to be further from the margin where formalization can benefit them, so will respond less. Finally, if avoiding problems with tax inspections is a benefit of formalizing, we predict that firms that are less frequently inspected will see less benefit from formalizing.

Finally, the framework predicts that the informal firms that formalize as a result of our added interventions will be further from the margin than those who are already formal and those who would formalize without the added help. That is, the interventions should be bringing in smaller, and less like the formal type to begin with, firms.

# **5.2 Estimation**

To analyze the impact of the program on formalization rates, our estimation is at the firm level and involves the following specification for firm _i_ : Where ��,��� is the outcome variable (formalization), �1� is an indicator for being assigned to treatment group 1, �2� an indicator for being assigned to treatment group 2 and �3� an indicator for being assigned to treatment group 3. �� is a vector of strata dummy variables (one dummy variable for each triplet of businesses) (Bruhn and McKenzie, 2009) and ��,��� is the error term. ��, �� and �� provide the intent-to-treat effect of being assigned to treatment groups 1, 2 and 3, respectively. This is the effect of being a business assigned to treatment 1, 2 or 3 relative to being a business in the control group.

16
of value placed on schooling and the frequency with which they communicate this priority is *s*ociat*d with higher achievement in reading and mathematics independent of parents' occupational and educational own schooling (at least among young children in Japan and the U.S.; Hess et al. 1980). This communication of value may take the form of expectations for higher levels of school attainment, greater support (both material and non-material) for learning, or communication regarding the relevance of education for adult occupational attainment. Yet these factors have received slight attention from researchers working within developing countries. A notable exception is Jamison and Lockheed's (1987) research on determinants of child schooling in Nepal, which examined a number of such variables, including p_..atal attitudinal modernity and demand for child education; both were found to have strong and posit..ve effects on child school participation. In a parallel study undertaken in Thailand, Cochrsne and Jamison (1982) found that parental aspiration for educa,-ion was the most important predictor of male educational attainment, while parental landholding (an indicator of wealth) was the most important predictor of female educational attainment. Another example is provided by a study of urban Grade 10 students from Sri Lanka, (Niles, 1981) which found that family social class (parental education and occupation) and parental interest in the child's progress and parental aspirations were strongly associated with student achievement, and taken together with material possessions in the home (non-significant) and school factors (only modestly significant) accounted for 49% of the variance in student achievement.

Second, pupils vary in the effort they expend on school work and the extent to which they feel efficacious in performing well. This is

-6-
# **2. The 2005 ICP round and its implications for global poverty measures**

International comparisons of economic aggregates have long recognized that market exchange rates are deceptive, given that some commodities are not traded; this includes services but also many goods, including some food staples. Furthermore, there is likely to be a systematic effect, stemming from the fact that low real wages in developing countries entail that laborintensive non-traded goods tend to be relatively cheap. In the literature, this is known as the “Balassa-Samuelson effect,”<sup>6</sup> and is the now widely-accepted explanation for an empirical finding known as the “Penn effect”—that GDP comparisons based on market exchange rates tend to understate the real incomes of developing countries.<sup>7</sup> Similarly, market exchange rates overstate the extent of poverty in the world when judged relative to a given $US poverty line. Global economic measurement, including poverty measurement, has relied instead on PPPs, which give conversion rates for a given currency with the aim of assuring parity in terms of purchasing power over commodities, both internationally traded and non-traded. World Bank (2008a) is the source of our PPP’s based on the 2005 ICP round. Here we only point to some salient features relevant to measuring poverty in the developing world.<sup>8</sup> The 2005 ICP is the most complete and thorough assessment to date of how the cost of living varies across countries. The world was divided into six regions with different product lists for each. All regions participated, although the participation rate was lower for Latin America. The ICP collected primary data on the prices for 600-1000 (depending on the region) goods and services grouped under 155 “basic headings” deemed to be comparable across countries; 110 of these relate to household consumption. The prices were typically obtained from a large sample of outlets in each country. The price surveys were done by the government statistics offices in each country, under supervision from regional authorities. The number of countries participating in the 2005 ICP is larger than in 1993, the last ICP round we used for global poverty measurement; 146 countries participated, as compared to 117 in 1993. This was the first time that a number of countries, including China, participated in the ICP. And the surveys were implemented on a more scientific basis. The 2005 ICP used stricter standards in defining internationally

> 6 See Balassa (1964) and Samuelson (1964).

> 7 The term “Penn effect” stems from Penn World Tables (Summers and Heston, 1991), which provided the price level indices across countries that were first used to establish this effect empirically.

> 8 Broader discussions on PPP methodology can be found in Ackland et al. (2006), World Bank (2008a) and Deaton and Heston (2009).

5
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# Table 6: Spatial Distribution of Rural Manufacturing Bouake Region, Ivory Coast, 1970 (Employment per 1,000 Population)

|Type of<br>Manufacturer||Kilom|eters fro|m Bouake *|/||
|---|---|---|---|---|---|---|
||0-10|10-15|15-20|20-25|25+|Region|
|Basketmakers|6.24|8.75|16.42|19.46|40.74|<br>11.71|
|Weavers|9.73|11.70|13.76|15.72|17.75|<br>10.14|
|Potters|2.65|4.62|3.83|3.38|4.22|<br>2.97|
|Builders (cement)|<br>2.75|2.86|1.89|1.77|1.90|<br>1.79|
|Builders (banco)|1.68|1.76|2.21|1.25|1.06|<br>1.33|
|Dyers|0.00|0.53|2.30|6.79|1.90|<br>1.58|
|Tailors|1.56|1.19|2.30|1.53|1.48|<br>1.24|
|Wood carving|0.59|1.35|0.86|0.64|1.06|<br>0.74|
|Carpentry|0.59|0.33|0.54|0.88|0.84|<br>0.43|
|Shoemakers|0.00|1.47|0.09|0.00|0.84|<br>0.43|
|Mechanics|0.25|0.20|0.90|0.76|0.42|<br>0.40|
|Blacksmiths|0.25|0.25|0.63|0.64|0.23|<br>0.31|
|Jewelers|0.00|0.08|0.00|0.12|2.74|<br>0.25|
|Hairdressers|0.00|0.00|0.14|0.00|0.00|<br>U.03|
|Total|26.21|35.17|46.00|53.06|75.24|<br>33.37|

g/ Bouake had a population of 110,000 in 1970.

Source: Ancey (1974), p.<sup>116</sup>
# **Figure 6: High South-North migration flows can lead to faster capital formation in migrantreceiving high-income countries**

Impact on aggregate real capital stocks, percent difference relative to baseline <!-- Start of picture text --> Australia, New Zealand and Other Oceania<br>Canada<br>European Economic Area<br>High-Income Asia<br>High-Income Middle East<br>Russian Federation<br>USA<br>0.0 0.2 0.4 0.6 0.8 1.0<br>2020 2025 2030<br><!-- End of picture text --> Source: Authors’ simulations

**Figure 7: Higher inflows of migrants from developing countries tend to depress the wages of developing-country migrant workers already present**

Impact on wages incumbent developing country migrants by host region in 2030, percent difference relative to baseline <!-- Start of picture text --> Australia, New Zealand and Other Oceania<br>Skilled<br>Unskilled Canada<br>European Economic Area<br>High-Income Asia<br>High-Income Middle East<br>Russian Federation<br>USA<br>-25 -20 -15 -10 -5 0<br><!-- End of picture text --> Source: Authors’ simulations For most LMI countries, real returns to capital drop relative to the baseline (Figure 7). Together with the decline in aggregate GDP against the baseline, this effect entails a decline in aggregate saving and net investment volumes, and aggregate capital stocks fall slightly below baseline levels in these regions. For example, in Mexico and OLAC the capital stock is 0.4 to 0.5 percent below the baseline and 0.6 to 0.8 percent below baseline in Central Asia and Turkey by 2030. However, 17
# **Appendix Table 4: Budgeting - IV Specification**

||(1)|(2)|(3)|
|---|---|---|---|
||Budgeting<br>Composite Score|Q1: Have a<br>Written Budget|Q2: Adhere to<br>Budget|
|Takeup in Autonomous Trainer Selection (T1)|0.154**|0.148**|0.160**|
||(0.062)|(0.073)|(0.075)|
|Takeup in Fixed Trainer Selection (T2)|0.043|0.062|0.024|
||(0.048)|(0.061)|(0.057)|
|R-squared|0.056|0.025|0.056|
|N|7885|7885|7885|
|Dependent Variable Mean in Control Group|0.341|0.155|0.526|
|F-test(p-value): T1 = T2|0.091|0.297|0.059|

This table presents instrumental variable analysis for budgeting using household survey data across two endline rounds. All regressions control for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level)
The countries in this sample are admittedly very diverse. As already explained, for some of them return and risk considerations may not be the only or most important factor behind the changes in their net foreign asset positions. Non-market forces **--** related to, for instance, geopolitical interests, humanitarian aid, or developmental purposes **--** may drive to some extent the transfer of capital resources across countries. In addition, some countries use capital and current account restrictions to prevent market forces from 'undoing' net foreign asset positions based on non-market factors. These considerations have the practical implication that the longrun impact of risk and return on net foreign assets may not be the same for all countries (which in turn would imply that the long-run restrictions imposed **by** the PMG estimator would hold only for specific country groupings).

> To explore this issue, we break the overall country sample according to two criteria. First,

> we separate high- and upper-middle-income countries from lower-income countries. Specifically, using the World Bank's World Development Report income classification we form one group

> consisting of **29** industrial, high- income and upper- middle income developing economies, and a matching group of **25** low and lower- middle income developing economies.

> Second, we separate countries that feature low capital controls from those that have high capital controls. The only source of data on this topic with broad time-series and cross-country coverage is the IMF's Exchange Rate Restrictions, which includes qualitative information on

> four kinds of measures that hamper international portfolio diversification. 14 We combine them into a summary measure **by** adding them up, and compute the average for each country over the period **_1965-97._ If** for a country the average is greater than or equal to three (implying that, on

> average, restrictions exist in at least three of the four categories throughout the sample period), we classify the country as having high capital controls. This procedure yields a subsample of 20

> countries with low capital controls and 34 with high capital controls. The countries included in

> each subsample are listed in Table **Al** in the Appendix. An inspection of the list of countries in each group shows that almost all countries with low capital controls belong to the group of high and upper-middle income countries (the exception is Thailand).

> **14** These are: (a) multiple exchange rate practices, **(b)** current account restrictions, (c) capital account restrictions, and **(d)** mandatory surrender of export proceeds.

**13**
# **Table 8:** Household Member Education

||**_All C_**|**_hildren_**|**_G_**|**_irls_**||**_Boys_**|
|---|---|---|---|---|---|---|
|**Outcome**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|
|**Panel A: Households with children**|**6-16 years**|**old**|||||
|Children currently attending school|0.48|0.02<br>(0.02)|0.52|0.02<br>(0.02)|0.47|0.02<br>(0.02)|
|Education assessment score|-0.01|0.05<br>(0.04)|-0.00|0.01<br>(0.06)|0.01|0.06<br>(0.05)|
|**Panel B: Households with children**|**who have**|**attended sch**|**ool**||||
|Students dropped out of school|0.10|-0.02<br>(0.01)|0.09|-0.00<br>(0.02)|0.11|-0.03<sup>_∗_</sup><br>(0.02)|
|Education assessment score|0.44|0.06|0.41|-0.02|0.47|0.07|
|||(0.06)||(0.08)||(0.08)|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

24
1 **Un** t Table 1 The Value and Destination of French Iron and Steel Exports (SITC 67): 1962 to 1987

||French Iron a|nd Steel Expo|rts (SITC 67)|Share of A<br>Develo|ll French<br>ped Countr|Iron an<br>ies|d Steel Expo<br>Devel|rts Destined For<br>oping Cour.tries|Differ|ent Country Gro|ups (%) 2/|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Year|All<br>Products<br>(Smillion)|Sampled<br>Products 1/<br>ISmillion)|Sampled<br>Products<br> <br>Share<br>(per cent)|All<br>Developed<br>Countries|of whi<br>EEC(10)|ch:<br>EFTA|Total Less<br>Fr2nch<br>Associates|of which:<br>Latin America|<br>Asia|French<br>Associates 3/|Socialist<br>Countries|
|1962|786.8|461.1|60.0|68.4|48.0|11.4|<br>12.7|4.7|2.9|11.7|6.0|
|1965|966.4|556.6|57.6|74.7|46.4|11.2|<br>13.3|4.0|3.5|7.8|3.6|
|1968|1,013.1|561.0|55.3|73.9|48.0|9.7|10.7|3.7|2.0|8.2|6.0|
|1971|1,532.1|814.0|53.1|77.6|48.8|9.0|10.5|3.3|2.2|7.0|4.4|
|1974|3,978.5|2,181.6|54.8|73.8|48.4|8.4|<br>11.2|3.1|1.4|7.2|6.8|
|1977|4,279.3|1,938.3|45.2|68.8|46.4|5.7|<br>12.3|3.4|1.5|8.8|9.3|
|1980|7,290.0|3,035.2|41.6|69.9|51.7|6.8|<br>14.4|5.0|2.5|7.5|8.0|
|1983|4,854.1|1,933.9|39.8|69.0|46.4|6.3|<br>15.2|3.3|4.7|6.4|7.4|
|1986|6,152.5|2,446.5|39.8|75.7|53.0|6.2|12.2|2.7|4.1|4.8|7.1|
|1987|6,642.7|2,619.0|39.4|76.9|53.8|6.3|<br>11.7|2.4|3.9|3.6|6.6|

1/ The sampled iron and steel products consist of the four and five-digit SITC products listed in appendix tables 3 to 13.

2/ The developed, developing and socialist co3untry trade shares may not sum to 100 since some French exports are unallocated in terms of final destinations.

3/ These countries consist of Tunisia, Morocco, Guinea, Madagascar, Ivory Coast, Central African Republic, Chad, Niger, Senegal, Mauritania, Mali, Algeria, Burkina Faso, Cameroon, Gabon, Congo, Togo, Benin, Reunion ana Mautitius. The declining importance of these countries as a destination for France's iron and steel exports is due primarily to major reductions in France's share of the associate's total iron and steel imports. An additional factor was that the growth in total import demand in these countries generally lagged well below that of other regions. See Appendix Table I for statistics on France's share of the associated countries iron and steel imports over 1962-1985.
communities that are considered to be treated are only those in proximate downstream areas. Households located directly upstream, or downstream but far away from the work site are in the same water catchment area and therefore have the same major water source, agro-climatic conditions, and similar social and economic conditions of the treated locality, but would not be directly affected by the rehabilitation.

To supplement this strategy, we use extensive qualitative information to ensure comparability between the control and treatment groups; it provides evidence to inform the empirical strategy, particularly related to program targeting, the context and the implementation. Qualitative information shows that soil quality does not vary much within communities, but can vary greatly across WUAs and coastal regions. Thus, households located in downstream communities within a designated boundary of influence that are both within the catchment area and within the appropriate water users’ associations are in the treatment group. Households located in upstream communities within the same WUA, or in downstream households in the same WUAs but very far from the rehabilitation site, are in the control group. As the specific project location along the canal provides for a somewhat arbitrary cut-off point between the treatment area and the control area, the project is the main factor that differentiates them. Figure 2 demonstrates the methodology of determining the treatment and control area. The specifics of how the GIS data were used are described below.

# [INSERT FIGURE 2]

We first used the longitude and latitude coordinates of the beginning and end of each canal refurbishment project, which were obtained from the project team in Lima to 11