rafmacalaba/datause-encoder-data huggingface dataset:
Porta and Shleifer, 2014). Yet study after study has found an overlap in the distributions of measures of revenues and labor productivity, indicating that parts of both sectors are operating at the same points in these distributions.<sup>10</sup> While not definitive by any means, such over-lapping distributions suggest that some portion of the informal sector is operating more freely out of choice (perhaps avoiding a costly process of formalizing into a still-low-productive formal sector). The data for Harare’s informal and formal sectors fit this pattern. Figure 3 shows kernel densities of the log of labor productivity (in USD) for both formal and informal businesses. Since the values for informal businesses are recorded as of the last completed month, this value is multiplied by 12.<sup>11</sup> Values for the formal sector are calculated from the World Bank’s Enterprise Surveys.<sup>12</sup> _<u>Figure 3: The Relative Productivity of Harare’s Informal and Formal Sectors</u>_ <!-- Start of picture text --> Labor Productivity, Sales per Worker (log)<br>.3<br>.2<br>.1<br>Formal<br>Informal<br>0<br>0 5 10 15<br>Harare only. Log of revenues with adjustment. Informal monthly values multiplied by twelve to annualize value.<br><!-- End of picture text --> Formal and informal sectors may vary in terms of the sectoral composition, which may account for the differences shown in Figure 3. Table 1 does show that there is a notable difference in the types of activities engaged in by the formal and informal sectors. Nearly half of Harare’s informal sector is dedicated to food provision (sales of food, beverages, and bakeries). Only 10% of

> 10 Graphs in this vein have made frequent appearances in the literature on informality, including Ulyssea, 2018. Graphs produced in Stata using Daniel Bischof’s plotplain scheme.

> 11 The densities shown are for the log of revenues+1, as last month’s sales can be 0.

> 12 <u>www.enterprisesurveys.org. The typical Enterprise Survey (ES) includes only enterprises with 5+ employees, but a</u> supplemental survey for micro (1-4 employees) was conducted at the same time in 2017. Calculations use both micro and typical ES.

(10)
A further source of ambiguity stems from the fact that differing levels of IPRs protection may affect a firm's decision on its preferred mode of serving a foreign market. A firm may choose to serve a foreign market by foreign direct investment (FDI) or by licensing its intellectual asset to a foreign firm instead of exporting the product in an environment characterized by strong IPRs (Ferrantino 1993).3 Thus, strengthened IPRs protection may have a further negative effect on trade flows in this respect.

# _Welfare implications_

The implications of tighter IPRs on economic welfare are highly complex. The simple fact that trade flows rise or fall in response to tighter IPRs is not sufficient for drawing conclusions regarding economic welfare. Both static and dynamic effects need to be considered. Moreover, in this paper, we are primarily concemed with the effects of IPRs on international trade flows. In a different paper (Primo Braga and Fink 1997), we discuss how tighter IPRs affect economic welfare through FDI, the transfer of technology, and domestic R&D. The following paragraphs give a brief summary of the associated static and dynamic costs and benefits for two trading economies that may arise only in response to changes in trade flows fostered by stronger IPRs.

From a **static,** partial-equilibrium point of view, the source country of the trade flow is likely to gain from tighter protection, because it can capture increased monopoly profits from the sale of its goods abroad. In contrast, the static effects on the welfare of the destination country are likely to be negative: increased market power by foreign title holders leading to deadweight losses.<sup>4</sup> In this view, many small, innovation-consuming countries fear that increased patent protection will only lead to a rent transfer to developed, innovation-producing countries.<sup>5</sup> From a static, general-equilibrium point of view, tighter IPRs tend to be further detrimental to the destination country of the trade flow because the reallocation of production, i.e. the shift of product lines 3 FDI as a mode of serving a foreign country is of special relevance because the existence of intangible assets such as intellectual property is a major rationale for the existence of (horizontally integrated) TNCs (Caves 1996). The importance of FDI is also highlighted by the fact that in 1992 world wide sales of foreign affiliates (USS 5,325 billion) exceeded global exports of goods and services (US$ 4,570 billion). See The World Bank (1996) for further details.

4 See, for example, Deardorff (1992). Nogues (1993), Maskus and Konan (1994) and Subramanian (1995) try to estimate these deadweight losses for the pharmaceutical industry in several developing countries.

5 This scenario assumes that the destination country is able to imitate the source countries' products in the absence of IPRs. If this is not the case, i.e. if technology is not freely available, the introduction of IPRs creates consumer surplus in the form of newly available products which may partly offset the deadweight loss. In this view, tighter IPRs are beneficial in that they transfer technology.

3
<!-- Start of picture text -->
-.4 -.2 0 .2 .4<br>Rainfall residuals<br>Growth residuals Fitted values<br>.4<br>.2<br>0<br>-.2<br>-.4<br><!-- End of picture text -->

Figure 4: Scatterplot between rainfall residuals (horizontal axis) and growth residual (vertical axis), after removing outliers After we remove outliers, the relationship becomes more significant (at 1% level). A 1% increase in rainfall leads to an increase of 0.058% in real per capita GDP growth. F-test for rainfall equals 17.26. For the rest of the paper we will work with this sample.

|VARIABLES|Full sample|Without outliers|
|---|---|---|
|log(rainfall)|0.038***|0.051***|
||[2.85]|[4.15]|
|F-test (rainfall)|8.12|17.26|
|Time fe|Y|Y|
|Country fe|Y|Y|
|Country time trend|Y|Y|
|R2|0.2215|0.2436|
|No of countries|37|37|
|No of Obs|1216|1198|

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Table 1: Regression results between real per capita GDP growth and rainfall (note: standard errors are clustered at the country level) 18
order polynomial in age is included to take into account the fact that three waves of the DHS survey are being used, and _τy_ is a set of birth-year-specific fixed effects that capture any cohort-specific effects of the reform; _δz_ is a vector of zone-specific fixed effects that capture any time-invariant characteristics of the different areas throughout Ethiopia, and _δz_ Trend _y_ is a set of zone-specific linear trends that captures secular changes over time within each zone of Ethiopia.<sup>6</sup> This first-stage equation is used to estimate the exogenous increase in schooling generated by the removal of school fees in Ethiopia. The predicted increase in schooling can then be used in the second stage to estimate the causal relationship between schooling and births or any other outcome of interest: The dependent variable _Bizy_ is the outcome of interest, initially the number of births to person _i_ from zone _z_ born in year _y_ . The second-stage equation uses the same set of control variables as equation (8), and the coefficient on the predicted years of schooling, _β_ , captures the causal impact of one additional year of schooling exogenously generated by the education reform. The baseline specification used throughout the article includes all women born between 1970, the first fully pre-reform cohort, and 1988, the fully postreform cohort. Standard errors are clustered by zone to allow for within-zone correlation (Bertrand et al., 2004).

The ordinary least squares (OLS) relationship between schooling and fertility can be studied using a modified version of equation (9), where the predicted level of schooling is replaced with each individual’s actual level of schooling, YrsSchl _izy_ . However, the OLS estimates are likely biased if schooling is correlated with unobservable characteristics that also affect the number of children women choose to have. If women who are more likely to achieve higher levels of schooling also have higher economic ambition and lower levels of desired fertility, the OLS estimates would be biased upward, overstating the true relationship. Alternatively, measurement error in schooling could lead to a downward bias of the OLS estimate that may even be larger than the ability bias that is more often discussed (Card, 2001). In fact, causal work in sub-Saharan Africa that directly compared OLS and instrumental variables (IV) estimates found evidence that OLS estimates significantly underreport the relationship between schooling and fertility (Osili and Long, 2008).

The central assumption underlying this identification strategy is that education reforms in Ethiopia, such as the removal of school fees, impact women’s fertility decisions only through the effect on their level of schooling. This requires that contemporaneous changes in government policy and the conclusion of the

> 6The set of fixed effects and trends is similar to what was used in the empirical strategy employed by a number of previous studies to evaluate education reforms, including Black et al. (2005), Bleakley (2010), Lucas and Mbiti (2012a,b), Fort et al. (2016), Holmlund et al. (2011), and Lundborg et al. (2014).

9
40 In (8), the estimated mill-specific fixed effects from the fixed effects regression in (7) ∧ ( _fe j_ )<sup>42</sup> become the dependent variable and the cooperative and geographic dummy coefficients serve as explanatory variables. Note that the Central Valley is the omitted category so that the interpretation of geographic dummy variable coefficients is relative to the Central Valley region.

# _Econometric Results_

Estimating (7) using the panel data set on the Costa Rican coffee market yields the following results:

**<u>Table 6 Fixed Effects Estimates for (7)</u>**

|**Variable/Statistic**|**Coefficient**<br>**[std. error]**|
|---|---|
|hbl|-0.080<br>[9.072]|
|shb|-0.070*<br>[3.725]|
|ft|-5.631*<br>[2.997]|
|ftcomp|2.289<br>[3.347]|
|Own|-19.271***<br>[5.299]|
|multi|10.654*<br>[5.603]|
|nyears|-0.424*<br>[0.222]|
|_1975_76|-11.654***<br>[2.142]|
|_1976_77|64.201***<br>[4.535]|
|_1977_78|22.457***<br>[3.243]|
|_1978_79|-1.433<br>[2.028]|
|_1979_80|4.135<br>[2.596]|
|_1980_81|-34.877***<br>[3.443]|
|_1981_82|22.092***<br>[2.378]|

> 42 The mill-specific fixed effects are recovered from the data by αˆ j = y j − x jβ<sup>ˆ</sup> where yj is the dependent variable and β<sup>ˆ</sup> is the vector of estimated coefficients. STATA, however, calculates the fixed effects as αˆ = y − α − x β<sup>ˆ</sup> , where ~~α~~ is the overall constant term for the regression (the average j j j value of all fixed effects). Programming with matrices within STATA does, however, permit the estimation of correct mill fixed effects ( αˆ = y − x β<sup>ˆ</sup> ) ‘by hand’, as it were. All mill specific fixed j j j effects referred to in the thesis are calculated this way and double checked against STATA estimates. This also permits the calculation of a full set of variances for the fixed effects (not possible with an unbalanced data set in STATA) with which to perform a WLS in the second stage regression.
Thus, a moderate relaxation of barriers to South-North labor migration as considered here generates substantial net gains for natives of low- and middle-income countries. However, the simulation results suggest that the additional migration flows from LMI to HIC do not contribute to a convergence of real incomes between natives of LMI and HIC regions, as the latter gain more than the former in both absolute and relative terms (Table 3).

**Table 3: Higher South-North migration flows benefit new migrants from developing countries and the natives of all countries the most**

Real income <u>gains 2020-30,</u> deviations from baseline in US$ Billion

|**Year**||**Incumbent**|**Migrants**|**Na**|**tives**|**T**|**otal Change**||
|---|---|---|---|---|---|---|---|---|
|**N**|**ew Migrants**|**from LMIs**|**from HICs**|**Host Region**|**Origin Region**|**LMI Natives**|**HIC Natives**|**Global**|
|**2020**|89.919|-42.935|7.227|151.350|59.617|106.600|158.577|**265.177**|
|**2025**|177.388|-81.539|14.849|361.094|125.342|221.192|375.943|**597.135**|
|**2030**|193.840|-93.607|16.353|454.249|135.748|235.981|470.603|**706.584 **|
|Deviations f|rom Baseline in|Percent|||||||
|**2030**|+125.1|-4.2|+0.7|+1.0|+0.4|0.55|1.03|**+0.82 **|

Source: Authors’ simulations

# **4. South-South Migration Reversal**

To obtain a broad indication of the order of magnitude of aggregate welfare impacts associated with current levels of South-South migration, a deliberately extreme hypothetical reverse migration scenario is considered. In this scenario, all bilateral LMI-LMI migrant labor stocks drop gradually over the period 2016 to 2025 so that by 2025 these stocks are 90 percent lower than in the baseline and then remain 90 percent below baseline beyond 2025. Leaving migrants return to the region of origin and take dependent household member with them. In line with the purpose of this scenario, it is assumed that the productivity of return migrants is the same as that of existing native non-migrant workers in the new equilibrium.

Table 4 highlights the largest 2030 baseline South-South migration stocks and thus serves to identify the migration corridors along which the largest absolute international movements of labor are assumed to occur. In this scenario, South-South migrant stocks have dropped by 2030 by 21.5 million workers vis-à-vis the baseline scenario. In particular, cross-national intra-SSA migrant worker stocks are 8 million below the baseline in 2030, 2.1 million Bangladeshi workers have returned from India, in turn 1.5 million Indian workers have returned from Pakistan, and 1.9 million international intra-Latin-American migrant workers have moved back home by 2030 compared to the baseline in the same year.

20
Studies rarely explain the choice of methodology. However, some papers shed some light on this issue. Fackler and Goodwin (2001) and Barret (2001) acknowledge that most empirical tests for food markets reject the law of one price. Failure of empirical tests to support the law of one price for food markets are believed to stem from problems with measurement, especially of transport and transaction costs, policy and market factors. Yet, this implies that long-run, co-integration or error-correction parameters may yield insignificant results. Adam (2011) summarizes these points by presenting the relationship between the export parity price, the import parity price and domestic food prices: where _c_ and _t_ denote transport costs and tariffs on exports _x_ and imports _m_ ; _E_ is the nominal exchange rate, is the international price of food, and is the domestic price of food. The higher the costs associated with trade across borders are, the greater the wedge between domestic and world prices of food, and the greater the influence domestic market conditions will have on domestic prices, implying lower passthrough from world food to domestic food prices. In the case of MENA, where most of the countries are consistent net importers, the domestic prices will follow closely the import parity price, modified here to reflect the presence of subsidies _s_ used widely in the MENA region: Price subsidies can be used to insulate completely domestic prices from world food price and exchange rate fluctuations, but when international food prices are increasing at a rapid pace this means that subsidies will also have to increase rapidly in order to keep domestic prices stable. This is not always possible, especially in countries with limited fiscal space and high import food dependency. Take for example the case of MENA‟s oil importing countries where fiscal deficits have grown from an average of about 4 percent of GDP in 2008 to 7.5 percent of GDP in 2011, while government debt has declined, but has remained above 70 percent of GDP since 2008. In such a country it would be difficult to accommodate escalating subsidy costs that absorb the difference between international and domestic food prices.

Empirical information on subsidies from developing MENA supports this view. As mentioned earlier, food subsidy data is patchy, but for a few countries there

-13-
9

# **III.** Regression Results

Table 5 presents the first set of cross country growth regressions. The inequality variables are added to a "Barro type" growth regression including variables to proxy political instability levels, levels of human capital, size of government and initial GDP per capita.

Table 5: Ordinary Least Squares Results for Barro Type Regression with Inequality Variables.

|Dep. Var|(1)<br>L0PC7088|(2)<br>L0PC7088|(3)<br>LOPC7088|(4)<br>L0PC7088|(5)<br>L0PC7088|
|---|---|---|---|---|---|
|# of Obs|81|74|74|74|81|
|Constant|0.0154<br>(1.47)|0.0533*4<br>(3.14)|0.0394**<br>(3.10)|0.0537**<br>(3.53)|0.0255 *<br>(2.29)|
|SUDPPC70<br>l_______________|-0.0023*<br> <br>(-1.84)|-0.0026*<br>(-1.79)|-0.0026*<br>(-1.83)|-0.0Q27*<br>(-1.89)|-0.0025**<br>(-2.04)|
|REVC7085<br>________________|-0.0017<br>(-0.18)|-0.0040<br>(-0.43)|-0.0044<br>(-0.47)|-0.0050<br>(0.55)|-0.0030<br>(0.31)|
|ASSP7085<br>l______________|-0.0385**<br>(-2.03)|-0.0511**<br>(-2.27)|-0.0527**<br>(-2.37)|0.0531 4*<br>(-2.41)|-0.0405**<br>(-2.20)|
|PP170DVN<br>______________|-0.00534<br>(-1.95)|-0.0059**<br>(-2.28)|-0.0062**<br>(-2.42)|-0.0063**<br>(-2.48)|-0.0058*<br>(-2.18)|
|CPRIM60<br>l_______________|0.0134<br>___ (1.22)|0 9116<br>(1.05)|0.0127<br>(1.16)|0.0121<br>(1.12)|0.0175<br>(1.63)|
|_CSEC60_|0.0282<br>(1.57)|0.0193<br>(0.94)|0.0152<br>(0.74)|0.0122<br>(0.59)|0.0178<br>(0.98)|
|SGOV7088|-0.0426<br>(-1.51)|-0.0595**<br>(-2.00)|-0.0606**<br>(-2.06)|-0.0604**<br>(-2.08)|-0.0479*<br>(-1.74)|
|GIN!|l________________|-0.0691**<br> <br>(-2.59.)||||
|THEIL|||-0.0438**<br>(-2.91)|||
|COEFFVAR||||-0.0298*<br>(-3.15)||
|RTP40|||||-0.0022**<br>(-2.26)|
|R-Squared|0.23|0.32|0.34|0.35|0.28|

4 t datistic significant at 10% level 44 t stAtistic significant at 5% level
Table D.11. Comparison of Baseline Characteristics Between Census Tracts In and Out the RD-Sample

|In RD-Sa|mple|Out of RD-|Sample|
|---|---|---|---|
|_Baseline Characteristics_<br>Mean|Obs|Mean|Obs|
|_Geographic Characteristics (Before 1980)_||||
|Altitude<br>488.319|3,681|499.802|8,752|
|Slope<br>8.624|3,681|6.968|8,751|
|Ruggedness<br>12.381|3,681|10.300|8,751|
|Hydrography<br>0.320|3,681|0.266|8,754|
|_Infrastructure Characteristics (Before 1980)_||||
|Roads and Railway<br>0.401|3,681|0.366|8,754|
|Had a City/Village<br>0.128|3,681|0.073|8,754|
|Distance to City/Village<br>1.024|3,681|1.285|8,754|
|Distance to Comms<br>1.199|3,681|1.257|8,754|
|Comms Density<br>0.328|3,681|0.334|8,754|
|Had Land Reform<br>0.081|3,681|0.112|8,754|
|Cultivated Area<br>0.665|3,681|0.676|8,754|
|Had a Parish<br>0.011|3,681|0.011|8,754|
|Distance to Parish<br>4.309|3,681|4.055|8,754|
|Distance to School<br>16.980|3,681|21.771|8,754|
|_Population Demographics (Before 1980)_||||
|Total Population<br>158.233|3,667|161.574|8,735|
|Population Density<br>1,418.195|3,666|2,060.920|8,735|
|Years of Education<br>3.493|3,666|4.227|8,737|
|Natality Rate<br>0.174|3,664|0.175|8,730|
|In-migration (Share)<br>0.108|3,636|0.147|8,646|
|Out-migration (Share)<br>0.006|3,446|0.008|8,272|
|Highly Populated Area<br>0.674|3,681|0.722|8,754|
|_Agro-Climatic Potential Yield (1961-1979)_||||
|Z-Potential Yield<br>-0.012|3,681|0.005|8,754|
|Bean Potential Yield<br>4.056|3,669|4.068|8,632|
|Coffee Potential Yield<br>1.678|3,669|1.670|8,632|
|Cotton Potential Yield<br>0.709|3,669|0.709|8,632|
|Maize Potential Yield<br>9.827|3,669|9.990|8,632|
|Wet Rice Potential Yield<br>8.714|3,669|8.591|8,632|
|Sugarcane Potential Yield<br>6.408|3,669|6.307|8,632|
|_Crops’ High Suitability (1961-1990)_||||
|Bean High Suitability<br>0.858|3,691|0.942|8,736|
|Coffee High Suitability<br>0.086|3,691|0.146|8,736|
|Maize High Suitability<br>0.980|3,691|0.983|8,736|
|Sugarcane High Suitability<br>0.108|3,691|0.194|8,736|
|_Confict (Before 1981) and Incarcerations (19_|_80-198_|_5)_||
|Number of War Events<br>0.037|3,681|0.018|8,754|
|Number of War Victims<br>0.155|3,681|0.056|8,754|
|Number of Incarcerations<br>0.018|3,681|0.107|8,754|

_Note:_ The table compare the mean and number of observations of outcomes in Table 1 between census tracts in the RD-sample and census tracts outside the sample. 85
**Table A6: Individual characteristics of ever-married women, by current marital status.**

||Once-<br>married|Remarried<br>widows|Remarried<br>divorcees|Widows|Divorcees|
|---|---|---|---|---|---|
|Lives in a rural area|0.54|0.61*|0.47**|0.49**|0.29***|
|Age|34.87|49.02***|40.15***|62.16***|40.29***|
|Muslim|0.96|0.95|0.98|0.95|0.92**|
|Wolof/Lebou|0.42|0.38|0.40|0.36**|0.41|
|Serere|0.12|0.14|0.14|0.13|0.11|
|Poular|0.29|0.26|0.29|0.29|0.26|
|Other ethnic group|0.17|0.23*|0.17|0.23***|0.23**|
|Age at first marriage|19.09|17.79***|18.27**|18.43**|19.50|
|Fostered before age 15|0.14|0.20**|0.19*|0.09**|0.15|
|Polygamous father|0.59|0.68**|0.63|0.59|0.53|
|Ever been to a French school|0.30|0.17***|0.32|0.12***|0.45***|
|Ever been to a Koranic school|0.16|0.21|0.15|0.19|0.12|
|Total number of children (alive)|3.46|4.74***|3.91**|4.47***|2.71***|
|Has a son 18 or older|0.49|0.65***|0.45|0.77***|0.52|
|Log of total household consumption per<br>capita (CFA francs per year)|12.37|12.24|12.40|12.42|12.60***|
|Log of total cell consumption per capita<br>(CFA francs per year)|12.31|12.22|12.33|12.43**|12.55***|
|_In a leviratic union_||12.06||||
|_In a non-leviratic union_||12.36||||
|Household size|11.72|10.88|9.98***|10.67***|10.24**|
|Number of adults living in household|6.74|6.25|5.91***|6.61|6.54|
|Cell size|4.23|3.70***|3.86***|4.07|3.71***|
|Share of kids in cell|0.44|0.28***|0.42|0.19***|0.31***|
|Household head|0.05|0.25***|0.11***|0.34***|0.26***|
|Cell head|0.76|0.85**|0.89***|0.52***|0.72|
|Belongs to head’s cell|0.08|0.36***|0.14***|0.75***|0.48***|
|N|2153|155|247|430|170|

Note: All characteristics are expressed as percentages of the marital status group except for ages (years), number of children, consumption aggregates, household and cell sizes and number of adults living in the household. 1 dollar = 522.9 CFA francs in 2006. Divorced includes separated women. All significance tests are relative to once-married women, where *** p<0.01, ** p<0.05, * p<0.1.

Source: Authors’ calculations using PSF1.

53
> (2001) that the linear segment estimator outperforms the covariance estimator in small samples.

Table 2.3: Altemative Estimates of the Extended Gini Coefficient

|Aversion Parameter|CovarianceMethod|LinearSegments|
|---|---|---|
|1.00|0.00|0.25|
|1.50|0.14|0.47|
|2.00|0.25|0.62|
|2.50|0.33|0.73|
|3.00|0.38|0.81|
|3.50|0.40|0.87|
|4.00|0.41|0.91|
|4.50|0.40|0.93|
|5.00|0.39|0.95|
|5.50|0.37|0.97|
|6.00|0.35|0.98|

> Source: Computed

# **_Decomposition by Factor Components_**

Let's suppose for simplicity's sake that the welfare indicator can be decomposed into two components. For instance, household expenditure may be divided into food, xl*, and nonfood expenditure, _xh_ . Using expression (2.3), average social welfare may be written as follows.

(2.14) _Vai = (,u_ + **P 2 ) +** **_[COV(X.,_** _O) +_ Cov(x 2,)] **_=_** _V 1 + V2_ In the general case where there are m components, the above expression becomes: Suppose now that we choose social weights according to the maximin principle where individuals (or households) are ranked in increasing order of total expenditure. Using the weights defined by (2.4) leads to the following expression for average social welfare.

where **Cj(v)** is the extended concentration index of component j with respect to the overall level of expenditure.

> Given that _,u_ **_=_** _Uj_ , expression (2.16) may also be written as: **_j=1_**

12