rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

This study estimates the monetary benefits and costs of public schooling in Ghana. We conduct this analysis for the quantity of schooling, or years of schooling completed, and for the gain in the quality of schooling (in terms of measured achievement) produced by specific education interventions. Our motivation is twofold: to understand the extent to which public spending on schooling provides good value for money; and to provide benefit-cost ratios that can be compared to similar estimates for other public spending in the style of the Copenhagen Consensus. Regarding the latter, our work is complementary to the recent Ghana Priorities project conducted by the National Development Planning Commission in partnership with the Copenhagen Consensus Center.<sup>1</sup> Our estimates of monetary costs of schooling are reasonably comprehensive, covering both public expenditure data drawn from government administrative sources and data on the complementary private expenditures needed to access public schooling drawn from the 2016/17 Ghana Living Standards Survey, the latest available round of the national household sample survey administered by Ghana’s statistical agency (GSS 2019). Our estimates of the benefits of public schooling are more limited, however, something that should be kept in mind when interpreting our results. In particular, we only measure the benefits to students of estimated future labor earnings increases generated by additional years of schooling. This ignores other benefits of public schooling. For example, Adu Boahen and Yamauchi (2018) find that elimination of fees for primary schooling in 1995 led to reduced teenage fertility and higher age at first marriage in Ghana. Duflo, Dupas, and Kremer (2021) find that tuition-free secondary schooling in Ghana reduced fertility as well. Other authors have posited that public schooling helps to form better citizens, though this is more difficult to measure.<sup>2</sup> There are many studies on the impact of public schooling in Ghana and around the world.<sup>3</sup> Our initial intention was to limit ourselves to studies that generate
strictions in the land market. We provide several pieces of evidence that relates to the effects of higher migration costs.<sup>25</sup> First, we find that propensity of outmigration is lower (see Figure 1 based on population census 2012) and proportion of split households where some members are left behind to hold on to the land is higher (see FIgure 2 based on HIES 2002) in villages with land retrictions. The census did not provide the out-migration data by gender. To check if female migration is also less in the areas with higher land restrictions, we use census data on female share in total population. The census data show that share of female in total population is higher when land restrictions are higher (Figure 3). This is true if we focus only on adult women (graph omitted for brevity). Using population census data from 2002 and 2012, we compute the change in female share of population for the same age cohort (15-49 year old in 2002 and 25-59 year old in 2012). Figure 4 plots this variable against proportion of land under restrictions. The positive slope confirms that more female are staying back in areas with more land under restrictions. Third, female share in the labor force –defined from HIES 2002 data – is also positively correlated with the incidence of land restrictions (see Figure 5). Taken together, these can be interpreted as strong direct evidence in favor of the proposition that women stay back in the village in response to higher migration costs arising from land restrictions. Finally, using HIES 2002 data, we find that farm size is negatively correlated with land restrictions, as one would expect if more women (and possibly some men) stay back in the village facing higher migration costs (Figure 6).

We also find suggestive evidence that support the model where heterogeneity in labor force participation and the consequences of higher migration costs depend on productivity in home goods production. Due to cultural norms, women are responsible for providing home goods in Sri
_Tax Reform in Transition_ 17 pro-poor reform). Also, the significant reduction in the number of tax brackets may well facilitate administration. Finally, the low alternative tax rates are more amenable to compliance and enforcement. But the 1993 amendment to the 1992 income tax law indicates a

# **Table 4: Alternative Tax Systems and Their Revised Revenue Potential Taking Into Account Tax Non-Compliance Index (PITNC,)**

(a) A Flat 20 Percent Income Tax With PITNC of 160 Percent

|Income interval (Lev)|Income share (%)|Tax rate_(%)_|Average tax<br>revenue (Lev)|
|---|---|---|---|
|3324<br>- 10133|20|0|0|
|10134 - 16669|40|20|1652|
|16670 - 26864|30|20|2595|
|26855 +|10|20|4582|
|Weighted average||16|1898|

(b) A Progressive Three-Bracket Income Tax System With PITNC Ranging from 160 Percent to 304 Percent

|Income interval (Lev)|Income share_(%)_|Tax rate_(%)_|Average tax<br>revenue (Lev)|
|---|---|---|---|
|3324<br>- 10133|20|0|0|
|10134 - 16669|40|20|1652|
|16670-26864|30|30|2511|
|26855 +|10|40|5910|
|Weighted average||21|2005|

_Source: Author's estimates using 1992 Individual Budget of households Survey, NSI._
as well as standard measures related to participation in the labor market: occupation, hours worked, employment conditions, search and earnings. Importantly, the ENEU is constructed as a rotating panel, where individuals are surveyed every quarter for a total of five quarters.<sup>16</sup> Worker earnings include overall earnings in the individual’s principal occupation from fixed salary payments, hourly or daily wages, piece-meal work, commissions, tips and self employment earnings. The ENEU, in its modern form, has employed a consistent survey instrument from 1987 to 2004; it is thus one of very few long-running surveys with a panel dimension in the developing world. In our study, we are able to use this 18 year span comprising a total of 72 quarters of data.<sup>17</sup> We note that while the ENEU survey records employment information on all members of the household above 12 years old, for younger workers employment is generally transient and time is often divided among schooling, unpaid support to the household and paid work. Similarly, much later in life, work again becomes more transient. In our analysis, we focus on individuals between the ages 20 and 65.

# V. Results

As discussed in the previous section, our estimation methodology proceeds in two steps. As in Carroll and Samwick (1997), we first use individual data to estimate a Mincer earnings regression. In a second step, the residuals from the Mincer regression are used to estimate income mobility parameters using (4’) and (6’). Table 2 reports the estimates from the first stage earnings regression using the ENEU data described in the preceding section. Our estimates are consistent with earlier findings in the literature. Specifically, earnings increase, but at a decreasing rate, with education. Further, earnings increase with potential experience (age) up until the age of 44 after which they decrease again. Males appear to earn 31 percent more than women, conditional on the other covariates.<
# **1. Accounting for China’s past growth performance**

In the standard growth accounting framework, growth in output per worker can be allocated into contributions from changes in physical capital, employment, human capital (the education and skills of the labor force), and a residual, total factor productivity. TFP measures gains in economic efficiency (the quantity of output produced with a given quantity of inputs), including those driven by technological progress.

Assuming a Cobb-Douglas production function, output is a function of capital, labor, and TFP: Y, A, K, and α are measures of output, TFP, physical capital, and the capital share of income, respectively. L is labor and H accounts for educational attainment and the return to each additional year of education. Dividing both sides of the production function by the labor input L, taking logarithms of both sides, and taking first differences yields: Growth in output per worker, ⁄ ), is decomposed into contributions from growth in capital per worker, ⁄ ), increases in education per worker, , and a residual measure of improvement in TFP, ∆ln(YYLL. ∆ln(KKLL ∆ln LL The data on output (real GDP), employment and the labor share of income (i.e., labor ∆ln AA compensation divided by GDP) are from China’s National Bureau of Statistics (NBS). The capital stock estimates are from Herd (2020) and the human capital variable is from Penn World Table (PWT) 9.1 (Feenstra et al., 2015).<sup>6</sup> In China, the growth contribution of TFP was particularly high in the initial stages of reform and opening, when many structural reforms were implemented. We estimate that TFP grew by 3.5 percent a year in the first decade (1979-1988) (Figure 1). Annual TFP growth averaged 3.1 percent in 1979–2008, generating 40 percent of growth
zeal, which requires firms to pay to avoid being charged with violations, or excessive sloth, requiring firms to pay to receive services. In either case, corruption represents additional costs to firms tied to regulatory enforcement. It is worth noting, however, that the negative effects associated with excessive regulatory burdens goes hand-in-hand with high levels of corruption empirically. This suggests that regulation is only problematic for investment when rent-seeking officials abuse regulatory burdens for self-enrichment using informal practices such as bribe-taking (Eiffert 2009).

# **Box 1: Uncertainty from violation of formal rules and regulations by authorities.**

One more potentially important source of regulatory uncertainty for firms could stem from the violation of formal rules and regulations by authorities. These could be e.g. violation of time limits, or limits in number of required procedures prescribed by formal rules and regulations. If _de jure_ rules and regulations are not necessarily obeyed by authorities, this constitutes not only additional costs, but also additional uncertainty for the business. While we are not concerned with regulatory uncertainty stemming from formal, written rules in our main paper, we did examine whether uncertainty in these rules mattered for investment.

We tried to estimate this type of uncertainty for Russian firms using World Bank Doing Business 2012 and BEEPS 2012 data for Russian regions. Our idea was to compare Doing Business and BEEPS data for the number of days required to get a construction permit or to obtain electrical connections in Russian regions. While Doing Business data represents _‘de jure’_ written regulations, BEEPS data reflect the situation _‘de facto’_ in the regions. Yet, the data indicate that, at least with respect to time required to get construction permits or to obtain electricity connection, no systematic violation of ‘de jure’ rules is observed in Russian regions. Only in 4 out of 26 regions covered by the data on the number of days needed to get a construction permit did _de facto_ measures exceed the _de jure_ number of days. The same situation occurs for electrical
_Table A3.2 Intention‐to‐treat effects of the original intervention on main psychological and behavioral outcomes by gender_

||(1)|(2)|(3)<br>(4)|(5)|
|---|---|---|---|---|
||ITT for|males|ITT for females|Coefficient<br>|
||(no controls)|(controls)|(no controls)<br>(controls)|equality (2)vs(4)|
||_Main o_<br>|_utcomes – Psychologi_<br>|_cal and behavioral_<br> <br>||
||0.099|0.079|‐0.071<br>‐0.045|‐0.125|
|Psychological wellbeing index|(0.204)|(0.306)|(0.216)<br>(0.414)|(0.190)|
|Risk index|0.004|‐0.003|‐0.069<br>‐0.061|‐0.058|
||(0.960)|(0.964)|(0.391)<br>(0.430)|(0.565)|
|Trust index|0.038|‐0.004|‐0.076<br>‐0.102|‐0.098|
||(0.653)|(0.963)|(0.235)<br>(0.127)|(0.407)|
|Crime and violence index|0.007|0.007|‐0.129**<br>‐0.152**|‐0.159*|
||(0.939)|(0.927)|(0
# **Introduction**

There is now broad recognition of the potential benefits of facilitating cross-border trade flows by reducing the incidence of “red tape” associated with the implementation of domestic tax and regulatory policies. This recognition is informed by a plethora of academic research, supported by business surveys, documenting that the associated costs are often a multiple of import tariffs that apply to goods without generating similar benefits for the economy in the form of government revenue.<sup>1</sup> The associated costs reflect a combination of the real resources that must be allocated by firms to satisfy administrative requirements and the uncertainty and unpredictability that often are associated with border clearance processes. Many of the provisions of the GATT are aimed at facilitating trade in the sense of reducing such costs. Examples include publication and related transparency requirements and substantive rules regarding how trade policies should be implemented – e.g., relating to the classification and valuation of products for purposes of collecting import duties or the permitted basis for charging additional fees and charges. The 2013 WTO Trade Facilitation Agreement (TFA) builds on extant GATT rules to define in much greater detail a series of good practices that all WTO members agreed should be implemented to further facilitate trade in goods (see Hoekman, 2016).

The TFA applies only to goods, not to services. In most countries, services account for 55-75% or more of total output and employment. For the world as a whole, the role of services has been increasing rapidly, reflecting a mix of technological changes and rising average per capita incomes. Efficient services are ever more critical for economic development, in part because many services are inputs into the production of other services and goods and thus the cost, quality and variety of services determine the competitiveness of firms and impact on overall economic growth. Although services account for the majority of economic activity in most economies, the share of services output that is traded is much less than for goods. However, technological changes are making services increasingly tradable.
The middle class has not only increased in size, but it has also increased the income share appropriated by this group. In Chile, this share increased 13.1 percentage points from 42.6 percent in 1992 to 55.9 percent in 2009; in Mexico it increased 7.8 percentage points from 45.2 to 52.9 percent over 1992-2008, and in Peru it expanded by 13.1 points from 42.7 to 55.7 percent over 1997-2009. These results become clear when looking at the income distribution. Figure 6 shows that the bulge in the middle increased over the analyzed period making the bell taller and confirming the increase of the middle class, partly due to improvements in income distribution (motivated by an expansion and enhancement of social policies) and a reasonable economic performance, which is evident by the movement of the bell to the right.

**Figure 6: Kernel distributions of income in Chile, Mexico and Peru**

<!-- Start of picture text --> 0 2 4 6 8 10<br>Household per capita income at PPP: log scale<br>Chile 1992 Mexico 1992 Peru 1997<br>0 2 4 6 8 10<br>Household per capita income at PPP: log scale<br>Chile 2009 Mexico 2008 Peru 2009<br>.6<br>.4<br>.2<br>0<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> _Source_ : Author‘s calculations based on data from CASEN, ENIGH and ENAHO cross-sectional databases. /a The gray line is a lower threshold of $2 a day proposed by B&D and Ravallion; while the yellow lines are the thresholds proposed in this paper.

Overall, the evidence found suggests a higher number of middle class households facing a low probability of falling into poverty in the late-2000s than in the 1990s
of partner country data is simple as data is available for all indicators in all partner countries, that of development partners needs some adjustments as not all development partners are active in all partner countries. For example, the Asian Development Bank is not operating in Africa and the African Development Bank is not operating in Asia. Assuming that development partner performance is influenced by partner country performance, the different partner country coverage in operation cannot be ignored and some adjustments are required. Therefore, standardized scores of development partners are calculated separately in all partner countries by indicators.

In this paper, the calculation of the standardized scores is unweighted. In other words, all partner countries and development partners are equally treated regardless of their size and other characteristics. This is consistent with the main purposes of this paper – which identifies key aid effectiveness constraints of FCS in comparison with non-FCS.

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103968 (2023).

- [56] Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W. & Wager, S. Synthetic difference-in-differences. _American Economic Review_ **111** , 4088–4118 (2021).

- [57] Monitoring the Illegal Killing of Elephants. Numbers of carcasses found for each site and year (2023). Downloaded on Jan 16, 2023. Available at https://cites.org/sites/default/files/MIKE/data/Numbers ~~C~~ arcasses eachMIKESite year ~~2~~ 022-03-31.xlsx.

- [58] Cornell Lab of Ornithology. eBird Basic Dataset. Version: EBD ~~r~~ elFeb-2023 (2023). Downloaded on April 10, 2023.

- [59] Liang, Y. _et al._ Conservation cobenefits from air pollution regulation: Evidence from birds. _Proceedings of the National Academy of Sciences_ **117** , 30900–30906 (2020).

- [60] iNaturalist. Dataset (2023). Downloaded on March 1, 2023. Available from https://www.inaturalist.org.

- [61] Wood, S. A., Guerry, A. D., Silver, J. M. & Lacayo, M. Using social media to quantify nature-based tourism and recreation. _Scientific reports_ **3** , 2976 (2013).

- [62] Echeverri, A. _et al._ Biodiversity and infrastructure interact to drive tourism to and within Costa Rica. _Proceedings of the National Academy of Sciences_ **119** , e2107662119 (2022).

- [63] Yeh, C. _et al._ Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. _Nature Communications_ **11** , 2583 (2020).

- [64] Kikuta, K. The Environmental Costs of Civil War: A Synthetic Comparison
is in our sample of countries facing a U.S. trade remedy. Thus failing to allow for the indirect effect of this variable could lead to biased estimates of the direct effect.<sup>25</sup> To address the concern of selection bias resulting from the idea that the set of U.S. trade remedies over which a foreign country has faced a tariff is not random, we use a Heckman (1979) style correction procedure and introduce a selection equation accounting for the U.S. government’s decision of whether or not to impose a trade remedy on a GATT/WTO member country after an industry-initiated investigation. For our purposes, the standard Heckman procedure needs to be modified to address the fact that both the selection equation (U.S. protects or doesn’t protect an industry petitioning under the trade remedy laws) and the regression equation of interest (foreign country challenges or doesn’t challenge the U.S. remedy with a formal WTO dispute) have dichotomous (as opposed to continuous) dependent variables. Therefore, we use the Van de Ven and Van Praag (1981) probit application of the Heckman (1979) selection bias correction procedure.

# **4.2 Additional Variables and Data for the Selection Equation**

Finally, in order to estimate the selection equation, we require additional explanatory variables that are not necessarily of interest to our question of the determinants of the foreign country’s decision of whether it will challenge any imposed measures with formal dispute settlement proceedings. Nevertheless, we have collected industry and country-level data on the standard political and economic variables that others (e.g., Hansen and Prusa, 1996; 1997) in the literature have shown to be determinants of the U.S. authority’s decision-making process. These include variables on the change in industry employment, level of industry employment, the import penetration ratio, the growth rate of investigated imports in the period prior to the investigation, the concentration ratio
# **Figure 6. Number of Models with Predicted Estimates That Are Statistically Insignificantly Different from the True Poverty Estimates, Within-Year Imputation**

<!-- Start of picture text --> Panel A: Headcount poverty Panel B: Near poverty<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>Bangladesh Malawi Vietnam Bangladesh Malawi Vietnam<br>Ethiopia Nigeria Tanzania Ethiopia Nigeria Tanzania<br>Panel C: Extreme poverty Panel D: Poverty gap<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>Bangladesh Malawi Vietnam Bangladesh Malawi Vietnam<br>Ethiopia Nigeria Tanzania Ethiopia Nigeria Tanzania<br>Panel E: USAID poverty gap Panel F: Consumption mean<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>Bangladesh Malawi Vietnam Bangladesh Malawi Vietnam<br>Ethiopia Nigeria Tanzania Ethiopia Nigeria Tanzania<br>Number of models Number of models<br>Number of models Number of models<br>Number of models Number of models<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. Estimates are obtained using the normal linear regression models.

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Although there is a rental market of sorts for irrigated land (paddy fields), the village model does not include a land market. When analyzing the village trade in land we found all four households groups to be net renters of land. This is due to a bias in the surveyed sample of households, which excludes households that have migrated from the village. These households are renting out land for a price below the productive value of the land. This difference can be interpreted as an insurance premium the households are willing to pay in order to maintain access to their land, which is collectively owned, in case they need to return to the village. The households remaining in the village thus get an indirect transfer of money from the migration of entire households, through having to pay less than the productive value for land rented in. Analysis of the migration of entire households is beyond the scope of our model. We therefore fix the supply of land at the level observed in the SAM, effectively removing the land market from the model. Taking again the example of the unlinked households with no draught power, the return earned on irrigated land endowment contributes 17.2 percent to its income, broken down into a shadow rent component of 12.4 percent and the above shadow rent component of 4.8 percent. This above shadow rental income results from renting-in land at a price below its marginal production value from migrant households. Non-irrigated land contributes another 7.3 percent to the household income.

A last remark on the village SAM pertains to the lack of data for modeling capital flows in the village. The SAM shows that the household group most involved in migration is a net supplier of capital to the other three groups of households. Although the survey contains some data on the conditions such funds are loaned, insufficient information is available to model a village-level capital market. We therefore assume that the household group lacking draught power but having an outside link spends a fixed share of its income on within-village shadow price for each