rafmacalaba/datause-encoder-data huggingface dataset:
exposure to PIP as an independent variable. The model also analyzes the heterogenous impact of PIP on behavioral adjustment through a series of regression analyses according to the number of siblings of the program participants (i.e. our diversity variable). We chose number of siblings as a diversity variable as it has been shown that children that have many siblings tend to display an increased odds of adverse developmental outcomes (de la Rochebrochard and Joshi, 2013).

In practice, then, compliance at Time 1 has been partialled out, leaving only the difference between Time 1 and Time 5 to be explained by the independent variable in the model (Cohen and Cohen, 1983). Using the _rnorm()_ function of the R Studio (R.Studio Team, 2020), the coefficients drawn from these analyses were applied to simulate nine datasets, each with 1,000 samples, with a child having no siblings to 8 siblings. For more details of the simulation please refer to Peng (2022).

# **4. Results**

Our regression results show that PIP led to statistically significant poor behavioral adjusted outcomes for participants with no siblings (β = 10.45, p < .001), 5 siblings (β = 9.10, p < .001), 7 siblings (β = 12.27, p < .001) and 8 siblings (β = 10.83, p < .001) as revealed by the change in RBRI score post intervention. The PIP, however, was estimated to be beneficial to children with 1 (β = -7.00, p < .001) or 2 siblings (β = -2.09, p < .001), but had no statistically significant effect on children with 3 (β = 0.57, p > .1), 4 (β = 2.01, p > .1) or 6 siblings (β = - 0.15, p > .1).

Based on these results, we further estimate the number of individual children who potentially may be triaged into the aforementioned alternative treatment and behavioral management programs (as described in Table 1). The difference in proportion of children with an RBRI score of 20-29 (category 1), 30-39 (category 2), 40-49 (category 3) and 50-60 (category 4) were observed between the intervention group (with PIP) and control group (without PIP). Such a difference in proportion was applied to the actual data collected by Homel et al. (2015), suggesting either a potential reduction or increase in the number of children who may require future behavioral management interventions. The estimated number of children who require additional behavioral management interventions is based on the percentage of children sorted into one of the four RBRI score categories before and after the intervention. This is calculated by applying the percentages derived from the simulated dataset to the actual number of participants in the Homel dataset. We present these results in 26
Africa. The analysis uses a dataset of 8500 household farms collected across 10 countries in Africa (Dinar et al., 2008).

This paper differs from previous adaptation papers in that it quantifies adaptation measures appropriate for each of the 16 Agro-Ecological Zones in Africa. Based on the AEZ classification by the FAO (1978), the paper examines the likelihood each farm type is adopted in each AEZ. Since a single adaptation policy is never relevant to all AEZs, it is important to quantify how adaptation measures vary across AEZs depending on agroclimatic conditions.

In the next section, a multinomial logit model of choice of farm type is provided. Data used in the study are described in the third section. The fourth section provides the empirical results. The paper then simulates climate change impacts on the distribution of farm types across AEZs for two climate scenarios in 2100, a hot and dry scenario and a mild and wet scenario. The paper concludes with discussions and policy implications.

# **2. Theory**

We assume that farmers manage their farms to maximize the net revenue from various farming activities, taking the existing climate as given. We define net revenue broadly to include the value of own consumption. So the model provides an appropriate description of both commercial and household farms. Farmers first consider a type of farm and then which combination of crops or livestock species, inputs, and timing that would maximize the net revenue they obtain. We assume that farmers have five choices about the type of farm they can select: crop-only rainfed, crop-only irrigated, mixed (crop and livestock) rainfed, mixed irrigated, and livestock-only farms<sup>7</sup> . Farmers will examine exogenous factors relevant to their farm but beyond their control when making these decisions. Most specifically, they will consider climate but also soils and elevation. Because these factors determine which AEZ a farmer is in, we hypothesize that the farmers’ choices will vary by AEZ.

Let the net revenue from these five alternative farm types, _j_ , be written in the following

> 7 The number of farms that specialized in livestock is too small to divide them into rainfed livestock only farms and irrigated livestock only farms. Most of them are, however, raise livestock on dryland.

4
# **3. The Formal Sector**

# **3.1 The Pre-ERP period**

# **3.1.1 Employment**

Ghana's employment in the formal sector has been dominated by the public sector for a long time, and it seems to be more true in the post ERP era. The employment series are summarized in Table 3.1 reproduced from ISSER 1994 (Table 8.4).

**Table: 3.1: Formal Sector Employment, 1960-1991 ('000)**

|||||Annual|Change (|percent)|
|---|---|---|---|---|---|---|
|Year|Private<br>Sector|Public<br>Sector|All Sectors|All<br>Sectors|Public|SOPSE(<br>percent)|
|1960|149|184|333|-|-|44.7|
|1965|118|278|396|+3.8|+10.2|29.8|
|1970|110|288|398|+0.1|+0.7|27.6|
|1975|137|318|_455_|+2.9|+2.1|30.1|
|1980|46|291|337|-5.2|-1.7|13.6|
|1985|67|397|463|+7.5|+7.3|_14.5_|
|1986|66|347|414|-10.8|-12.6|15.9|
|1987|79|315|394|-4.8|-9.2|20.0|
|1988|55|252|307|-22.1|-20.0|17.9|
|1989|38|177|215|-30.0|-29.8|17.7|
|1990|40|189|229|+6.5|+6.8|17.5|
|1991|31|136|186|-18.8|-17.5|16.1|

_Source: Ghana Statistical Service Quarterly Digest of Statistics_ SOPSE= Share of private sector in total formal sector employment Ghana ISSER 1994, Table 8.4, p. 139.

The major expansion in public sector employment took place before 1965. Over the next two decades the public sector increased its work force by 40 per cent-about the same relative increase as it had achieved in a single five-year period between 1960 and 1965. In the decade of 1965-75 the public sector employment increased at about the same rate as the private sector. Although the growth rate of employment in the formal sector had slowed down considerably, the public sector did not try to inflate it by overzealous expansion. It was during the severe decline in formal employment during the crisis years of 1975-80 that the share of the public sector in formal employment

**8**
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**Table 3: Robustness checks: alternative party institutionalization measures and alternative timing assumptions (System-GMM)**

|Dependent variable:<br>Central government<br>expenditure (except<br>column 3)|(1)<br>Base sample|(2)<br>Original<br>B&D sample|(3)<br>Dep var:<br>Gen. gov. final<br>consumption<br>expenditure|(4)<br>Base Sample,<br>Election dummy<br>adjustment for<br>elections in first<br>month of fiscal<br>|
|---|---|---|---|---|
|||||year|
|Election|0.496**|0.488*|0.978**|0.640**|
||(0.232)|(0.264)|(0.460)|(0.263)|
|Avg. party age||||-0.002<br>(0.005)|
|Ruling party age|0.001<br>(0.006)|0.005<br>(0.009)|||
|Party confidence|||0.063<br>(0.464)||
|**Respective party**|**-0.005***|**-0.007****|**-0.802****|**-0.009****|
|**variable X Election**|**(0.003)**|**(0.003)**|**(0.394)**|**(0.004)**|
|Lagged dependent<br>variable|||0.788***<br>(0.110)|0.929***<br>(0.023)|
|Log GDP per capita|0.514**|0.705|0.328|0.577***|
||(0.204)|(0.492)|(0.220)|(0.194)|
|Output gap/ Growth|-0.121***|-0.261**|-0.001|-0.119***|
||(0.031)|(0.118)|(0.026)|(0.031)|
|Ruling coalition size|0.073|-0.597|-0.112|0.119|
||(0.852)|(0.913)|(0.774)|(0.855)|
|Unified government|1.016***|0.703|0.148|1.033***|
||(0.317)|(0.494)|(0.189)|(0.318)|
|Constant|-0.887|-5.063|2.580**|0.666|
||(0.897)|(3.891)|(1.284)|(0.852)|
|Time dummies|Yes|Yes|Yes|Yes|
|Hansen statistic (p-<br>value)|1.00|1.00|1.00|1.00|
|AR (1)|-4.20***|-4.06***|-2.29**|-4.282***|
|AR (2)|0.46|1.24|-0.47|0.387|
|Number of observations|1,128|998|419|1,128|
|Number of countries|65|65|57|65|

*** p<0.01, ** p<0.05, * p<0.1  System-GMM specification analogous to explanation in table 1.
nominal interest rates is expected to be much lower than that witnessed in the years leading to the debt crisis. Then, the sharp increase in real interest rates in international markets resulted from a combination of tight monetary policies in some industrial countries to fight domestic inflation, and expansionary fiscal policies to boost their domestic economies. Between 1977 and 1981 the nominal LIBO rate rose from 6.5 to 16.7 percent (the average expost real LIBO rate in U.S. dollars, using the U.S. wholesale price index, rose from about 1.8 percent in 1978 to about 11.3 percent, 1982). By contrast, IMF estimates put the sixmonths dollar-denominated LIBO rate at 6.0 percent for 1995, and World Bank estimates put the rate at 5.8 percent for the next ten years. **7** Two major reasons stand behind the assertion that the expected increase in international interest rates will be minor. First, current inflation rates in industrial countries are much lower than those in the late 1970s. In fact, the average inflation rate (using the consumer price index) for all industrial countries was 2.9 percent in 1993, and it is forecasted to be slightly lower in the next several years. By contrast, during 1979-81 that rate of inflation ranged from 9 to 12 percent.<sup>8</sup> Second, the recent deficits run by the governments of the major industrial economies will force them to pursue a prudent fiscal policy in the future. In fact, these deficits are expected to be lowered by almost two percentage points of GDP during the next three years.9 Thus, although the increase in international interest rates represents a serious threat for developing countries, in real terms the increase will probably not be as great as the increase in the early 1980s and, therefore, will not be the major cause of a reversal of flows.

> 7 IMF (1994: 17); World Bank, (1994, 7). 9 IMF (1994: 17); World Bank (1994: 7); IMF list in references (1986: 188).

> 9 The General Govemment Fiscal deficit of the major industrial countries increased from 1.7 percent of aggregate output in 1979 to about 4 percent in 1983-84; it then steadily decreased to a record low of 1.3 percent in 1989. It began increasing thereafter to reach 3.8 and 4.3 percent in 1992 and 1993, respectively. It is expected to fall to 3.9 and 3.5 percent of GDP in 1994 and 1995, respectively, and to be about 2.6 percent for the following three years. IMF (1986: 196, 1994: 57).

6
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regional assessments, which if made comparable to international assessments, would provide further insight into achievement in developing regions. For example, Latin American countries participate in the UNESCO Laboratorio Latinoamericano para la Evaluación de la Calidad de la Educación (LLECE) and many African countries participate in the South and Eastern African Consortium for Monitoring Educational Quality (SACMEQ) or the Programme d’Analyse des Systèmes Educatifs de la CONFEMEN (PASEC). Altinok and Murseli (2007) use a similar anchoring approach as Hanushek and Kimko (2000) – creating an index to adjust for scaling and difficulty. Instead of doing this over time, they do this across assessments, linking Regional Standardized Achievement Tests (RSATs) and ISATs. The first database of this kind produced by Altinok and Murseli (2007) included 105 countries. Extensions by Angrist, Patrinos and Schlotter (2013) included 128 countries for any test score from 1965-2010 and Altinok et al. (2014) included up to 103 countries in primary education and 111 countries in secondary education .

In this paper, we build on datasets constructed by Altinok et al. (2014), Angrist et al. (2013) and Altinok and Murseli (2007). We deploy a similar methodology linking international assessments such as PISA, TIMSS, PIRLS, and their precursors, as well as include more regional student achievement tests (RSATs), such as MLA, LLECE, SACMEQ or PASEC<sup>3</sup> , which are only partially included in previous papers. This enables us to obtain original data on the quality of student achievement for the largest set of countries to date, and the largest number of developing countries. To our knowledge, this paper presents the largest globally comparable panel database of cognitive achievement, including 163 countries and regions, 32 of which are from Sub-Saharan Africa, over the last 50 years (1965-2015).

The size of our database has a few ramifications beyond sheer coverage. Most notably, we can include many developing countries – the countries that have the most to benefit from educational reform and educational progress. Second, because the methodology we use to link assessments hinges on the existence of enough overlap in countries which take _both_ an RSAT and an ISAT, the larger the database, the more overlap, and the more robust _all_ transformations. Thus, a larger database enables both the inclusion of developing countries as well as enhances the robustness of the methodology used to include them, making each update significant. Finally, since this database has rich panel data over time and across countries, it can be used to

> 3 Respectively the Monitoring Learning Achievement (MLA), the Latin American Laboratory for Assessment of the Quality of Education (LLECE), the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) and the Program on the Analysis of Education Systems (PASEC)

4
This paper contributes to a large body of literature on the trade effects of preferential trade arrangements by including the notion of “depth” in the analysis of PTAs.<sup>8</sup> Previous work in this area suffers from a well-known measurement error problem (Baier and Bergstrand, 2007). Due to lack of data, most studies use dummies to identify the presence of a PTA or distinguish between broad types of trade arrangements (e.g. partial scope agreements, free trade agreements or custom unions, as in Baier et al., 2014). This approach does not adequately capture the variation in the content of preferential trade agreements. Indeed, we show that this variation has important implications for the effects of PTAs both on members’ and nonmembers’ trade flows. Our analysis also has relevant implications for the longstanding debate on regionalism versus multilateralism (Bhagwati, 1993). A key question in this debate is whether PTAs are building blocks or stumbling blocks of the multilateral trade system. Both formal models and empirical studies in this literature assume that PTAs are mostly about tariff liberalization.<sup>9</sup> The positive impact of deep PTAs on members’ and non-members’ trade that we find in our analysis supports the view that deep provisions in trade agreements can complement rather than undermine the world trading system (WTO, 2011).

The rest of the paper is organized as follows. The next section discusses the database on the content of trade agreements and the other data used in the analysis. Section 3 presents the empirical strategy. Econometric results are presented in Section 4. Concluding remarks follow.

# **II. Data**

Our measures of depth of preferential trade agreements are based on detailed information on the content of PTAs from a new database (Hofmann et al., 2017). The database covers 279 treaties, which are all the preferential agreements notified to the WTO and in force up to December 2015.<sup>10</sup> Following the methodology proposed by Horn et al. (2014), the focus is on 52 policy areas (see Table A1 in Appendix A), divided into areas that are currently under the mandate of the WTO such as tariffs, antidumping duties and subsidies (referred to as “WTO

> 8 For recent surveys, see Freund and Ornelas (2010), WTO (2011), Head and Mayer (2014) and Limão (2016). There is a small literature on deep agreements. Osnago, Rocha and Ruta (2017a) look at the impact of deep agreements on countries’ participation in global value chains. Mulabdic, Osnago and Ruta (2017) study the effect of Brexit (i.e. the undoing of a deep agreement) on future EU-UK trade relations. Other studies that have looked at the impact of deep agreements based on a more limited database covering around 100 PTAs are Orefice and Rocha (2014) and Osnago, Rocha and Ruta (2015 and 2017b). 9 Informal arguments on the relationship between deep PTAs and the multilateral trade system have been made in Baldwin and Low (2009) and WTO (2011).

> 10 The data are freely accessible at <u>http://data.worldbank.org/data-catalog/deep-trade-agreements.</u>

5
Putting these two components together, the instrument for the level of emigration of individuals of skill group _s_ (where _s ∈{low, high}_ ) from source district _i_ at time _t_ is: where _Mj,_<sup>_s_</sup> _−i,t_<sup>arethetotalnumberofmigrantsofskilllevel</sup><sup>_s_whomoved</sup> to district _j_ between _t_ and _t −_ 5 years from all districts other than _i_ (this is the the shift component). The share component _Nj,i,t_<sup>_s_</sup> _−_ 5<sup>isthehistorical(5</sup> years ago and earlier) share of destination district _j_ in the total migrants of skill level _s_ who left district _i_ (capturing the exposure of district _i_ to a shift in _j_ ). The instrumental variable regression uses both high-skilled and low-skilled instruments.

Madhok et al. (2024) and Anelli et al. (2023) are two recent studies which also employ shift-share instruments for out-migration. Like our study, Madhok et al. (2024) is in the context of internal migration in India. However, unlike Madhok et al. (2024) where the share component is based on distance, we exploit bilateral migration data in constructing the share component.

# **Residual network IV specification**

We present a unique variation on the share component which takes into account the fact that migrants tend to move to nearby districts (Kone et al., 2018).

One concern in this case is that nearby districts can be affected by the same agronomic shocks driving both land and labour market. Given that migrant networks are biased towards nearby districts, this could make our instrumental variable strategy invalid. While controls for local weather conditions might mitigate some of these concerns, we present a more demanding first-stage specification in which we use the variation in the historical migrant network that remains after the distance between them has been taken into account. In other words, we exploit that part of the historical migrant network that is not explained by distance (specifically, travel time, as detailed in the Appendix).

27
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