rafmacalaba/datause-encoder-data huggingface dataset:
One reason for non-comparability of adjusting and non-adjusting countries is that the samples are not drawn from populations witfi the same characteristics. More specifically, it has been argued (notably by Goldstein and Montiel, 1986) that countries adopting stabilization programs are more likely to be in a situation of economic crisis. To the extent that current economic performance is determined by lagged economic performance then there will be inherent sample selection bias in the control group approach - since on average the pre-programn performance of adjusting countries will be worse than that of non-adjusters (a view supported by the data on initial conditions presented in Table 2.1). Goldstein and Montiel proposed the Table=2. Economic variables for selected county pairs

|Change in<br>Change in<br>**-** .: .;<br>.<br>.<br>;terms .of trade- - terms of trade,<br>:-<br>-:-<br>.<br>1<br>J975-81<br>1981-86<br>-(%pa)<br>(pa)|GNP per<br>capita($),<br>1981|Real GDP<br>growth,<br>197081(9%<br>--<br>pa)|
|---|---|---|
|Bolivia .<br>-7.7<br>4.0.|600|7l|
|Colombia<br>0.6<br>3.7|1,380|3.6|
|halawi<br>-4.3.w.8200<br>*-aw<br>-4-D--<br>f -s<br>.<br>--0 :,*<br>Zimbabwe<br>-RI<br>~~~na|<br>870O|-'2.5<br>'|
|-akistan<br>:....6<br>-.0<br>AEgyt<br>-294.61|-<br>50<br>:1.9.<br>-:650|4.9.|
|Thailand<br>~~~~<br>~~<br>~<br>7.0<br>-639|770|4.|
|<br>.<br> <br> <br> <br> <br>Malaysia<br>-0.1<br>-6.3|<br>1,840|5<br>.|
|-Turky<br>:-<br>-.6<br>-na||:21540<br>2.|
|.. :--.--.~Greece.:---<br>. ..**.**....<br>_-2 .~_<br>-<br>:-**-**-~44<br>-:<br>na<br>* <br>|..<br> <br>:--~.: --<br>..4420|.<br>. ~--<br>-.3.3|
|Source: Mosley .._a..(-t_<br>1991:. 1<br>.<br>.|||

; O1 Ec.M -a - ,, ,X .-:-:

# modified control group approach to overcome this problem.

## Modified control roup auDroach

The modified control group approach endogenizes the policy response in order to model what policies would have been in the absence of adjustment. The effrct of these policies is removed to give the net impact of the program itself The presentation that follows is based on Goldstein and Montiel (1986), who laid the foundations for subsequent work in this area.

9
over time in log points of the instrument can be expressed as The same intuitions from Section 2.2 carry over to our comprehensive formulation of the SNAP formula, although they are more difficult to see here. As in Section 2.2, changes in the numerator no longer depend on realized trends either in rents or in incomes of program participants. The simulated variable is also free from the influence of participants joining the program after 2006. Again, note that we find it necessary to include a housing-price control to net out the influence of these new participants on the actual benefits, which crucially affects the first stage. The simulated benefits depend only on the intensive-margin changes based on the levels of demographic characteristics of participants _Xi_ 0 realized prior to the Great Recession.

# **C Adjustments to the SNAP Formula Made by Individual States**

Although the SNAP formula is set at the federal level as described above, a number of adjustments to the formula occur at the state-level during implementation that introduce cross-state idiosyncrasies in final disbursement levels. These include, most importantly, the standard utility allowance program (SUA).

## **C.1 Standard Utility Allowance**

First, all states participate in the SUA. States set their own utility-deduction standards, which are designed to reflect the average within-state utility costs of households. According to Holleyman et al. (2017), states’ methodologies fall into two categories: (1) methodologies that rely on state-specific recent utility data and (2) methodologies that adjust a base number using an inflation measure such as the Consumer Price Index (CPI) of utility costs. Some states use a methodology that combines both approaches. The first set of states recalculate the SUA yearly while the second set of states update the SUA based on the change in fuel CPI. Considerable variation exists within these methodologies. For example, some states use only data for low-income households, whereas others gather data for all households. States incorporate a variety of fuel types, and some assign weights to the different fuel types, whereas others do not. Over time, FNS has found some variation between established SUA values and average household utility expenses in many states. Over 40 states make the usage of these standards mandatory, and the standards are generally larger than utility expenses to encourage usage and simplify application procedures. Furthermore, 15 states participate in a related program called “Heat and Eat,” which allows households participating in the Low-Income Home Energy Assistance Program (LIHEAP) to receive the highest-bracket utility deduction without having to provide utility-cost verifications. The “Heat and Eat” program especially benefits households with elderly or disabled members, because LIHEAP gives priority to these households during application.

Based on some additional documents from Holleyman et al. (2017) that supported the released report, we find that for the states on which we have additional information, they use 50
Correll, S. (2004). Constraints into Preferences: Gender, Status, and Emerging Career Aspirations. _American Sociological Review, 69_ (1): 93-113.

<mark>Conti, G., Heckman, J., & Urzua, S. (2010). The education-health gradient.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>100</mark>_ <mark>(2), 234-38.</mark> Costa, P., Terracciano, A., and McCrae, R. (2001). Gender Differences in Personality Traits Across Cultures: Robust and Surprising Findings. _Journal of Personality and Social Psychology, 81_ (2): 322-331.

<mark>Credé, M., Tynan, M. C., and Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature.</mark> _<mark>Journal of Personality and Social Psychology, 113</mark>_ <mark>(3), 492– 511. https://doi.org/10.1037/pspp0000102axzxazxxzx</mark> Croson, R. and Buchan, N. (1999). Gender and Culture: International Experimental Evidence from Trust Games. _<mark>American Economic Review</mark>_ , <mark>89 (2): 386-391.</mark> Croson, R., and Gneezy, U. (2009). Gender Differences in Preferences. _Journal of Economic Literature, 47(2): 448–474._ Cross, C. P., Copping, L. T., and Campbell, A. (2011). Sex differences in impulsivity: A metaanalysis. _Psychological Bulletin, 137_ (1), 97–130. https://doi.org/10.1037/a0021591 Cunha, F. Heckman, J. and Schennach, S. (2010). Estimating the Technology of Cognitive and Noncognitive Skill Formation. _Econometrica_ , _78_ (3), 883–931.

Cunningham, W., Torrado, M., & Sarzosa, M. (2016). Cognitive and non-cognitive skills for the Peruvian labor market: Addressing measurement error through latent skills estimations. _World Bank Policy Research Working Paper_ , (7550).

Cunningham, W., and Villasenor, P. (2016). Employer Voices, Employer Demands, and Implications for Public Skills Development Policy Connecting the Labor and Education Sectors. _<mark>The World Bank Research Observer, 31</mark>_ <mark>(1): 102–134.</mark> <mark>Das, S., & Kotikula, A. (2019). Gender-based employment segregation: Understanding causes and policy interventions. World Bank.</mark> DiPrete, T. A., & Jennings, J. L. (2012). Social and behavioral skills and the gender gap in early educational achievement. _Social Science Research_ , 41(1), 1-15.

<mark>Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: perseverance and passion for long-term goals.</mark> _<mark>Journal of personality and social psychology</mark>_ <mark>,</mark> _<mark>92</mark>_ <mark>(6), 1087.</mark> <mark>Duckworth, A. L., & Seligman, M. E. (2005). Self-discipline outdoes IQ in predicting academic performance of adolescents.</mark> _<mark>Psychological science</mark>_ <mark>,</mark> _<mark>16</mark>_ <mark>(12), 939-944.</mark> 33
cover (Hansen et al. 2016) to manage protected areas, or as measures of evapotranspiration to help manage water resources (Anderson et al _._ 2012).

# **3. Data, Descriptive Statistics, and Analytical Approach**

Treatment variables, annual information on which is provided by the project monitoring system, are the share of a micro-watershed covered by SWC measures on cultivated or degraded land and new tree planting, respectively. We define a 5-km buffer around treated watersheds to serve as a comparison and source of control pixels and use a DID approach with pixel-level fixed effects to assess how the cumulative area treated affects mean photosynthetic activity, vegetation cover, and plant or soil water content in different seasons. Outcome data are aggregated from different indices based on biweekly Landsat 7 imagery for roughly 2.8 million pixels at 30m resolution. For the five micro-watersheds where daily information on water flow and sediment load was collected, this is complemented with a before-after assessment of how treatment affected these variables.

# **_3.1 Nature of Intervention and Study Area_**

From 2009 through 2015, TBWIRD supported watershed planning and institutional strengthening in the entire Tana-Beles sub-basin and specific SLM activities in five critical watersheds covering some 860 km<sup>2</sup> (see map, figure 1). Table 1 illustrates that, at mean elevations of 2,300 to 3,000 m with average slopes of about 10 degrees, project watersheds seem susceptible to soil erosion. Each critical watershed comprises 25 to 40 micro-watersheds, defined as geographical units with a single point of water discharge. Microwatershed boundaries and the drainage from each based on a 30m x 30m resolution ASTER digital elevation model (DEM) are illustrated in figure 2.<sup>6</sup> The figure also illustrates the location of five hydrological stations. At each of these, daily measurements of water flow and turbidity were taken by community members who had been trained in the use of simple techniques and were regularly supervised by expatriate staff—part of an initiative to build local capacity for better understanding factors leading to soil erosion and downstream impacts of SWC measures. Although only collected after 2009, these readings provide outcome measures that can help cross-check results based solely on remotely sensed data.

We analyze the impact of three types of TBIWRD-supported interventions: (i) establishment of SWC structures, such as terraces, bunds, and physical vetiver grass barriers to limit runoff on land already cultivated; (ii) construction of micro-basins, terracing, bunding, area closures, and other conservation structures on non-cultivated land, such as hillsides and land used for grazing and forestry; and (iii) new tree plantings to establish agroforestry systems to stabilize landscape and produce timber and fuelwood. Panel

> 6<sup>In addition to imagery, we generate elevation for each pixel using the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM)</sup> data (Farr et al. 2007) with a 1 arc-second (30m) resolution.

8
export shares and firm performance in the post-accession period. In column (2), we include simultaneously the pre-determined export shares for Poland and Germany. The results reveal that Germany’s initial export share in the sector is a significant predictor or the evolution of firm TFP and revenue in the post-2004 period, while that of Poland is not.

## [Table 4 about here.]

In columns (3)-(8), we further assess the validity of our interpretation of the econometric results by conducting a set of placebo tests for Russia, the second major trading partner after Germany, and two other neighbouring countries: Lithuania and Ukraine. Since EU accession did not entail deeper integration with Russia and Ukraine, we would not expect to observe systematic positive effects on firm performance. Lithuania also joined the EU in 2004, but unlike Germany did not have significantly higher levels of income per capita. The econometric results suggest that the export shares for these countries do not have a systematic positive effect on the dynamics of firm performance in Poland after accession to the EU. Finally, in column 10, we also consider the influence of France, the second major trading partner. While the effect of France’s export share is positive, the effect disappears when including also Germany’s export shares. We interpret this evidence as providing further support to the hypothesis that deeper integration with Germany following EU accession was an important driver of firm performance in the post-accession period. We also find no evidence of firms located in regions bordering Germany benefitting more from German specialization (Table A.10), which argues against alternative explanations, such as the potential role for border trade or improvements in transport infrastructure. Taken together, these results reported above indicate that EU accession led to a differential increase in German acquisitions and performance improvements in sectors in which Germany had a comparative advantage at the moment of accession.

# **6 Concluding remarks**

The belief that integration with high-income markets can help firms in less developed countries to reduce efficiency gaps is a key argument for pursuing deep agreements. In this paper, we studied the interplay between industrial structure and foreign acquisitions in shaping the effects of deeper integration on firm performance. We examined the extent to which this interaction played a role in driving the patterns of catch-up growth observed in Poland following accession to the European Union in 2004. Drawing on detailed data on Polish manufacturing firms 17
in the case of at least five of these countries (table 2.3, chapter 2), these are not the investment vehicles best suited to pension funds and other institutional investors that tend to adopt longer-term investment horizons that seek to maximize returns, particularly when portfolios are heavily weighted in these investment vehicles. They can, however, play a role in a balanced portfolio.

A relatively low portion of pension fund assets were held in corporate bonds, while holdings in equities range widely by country. For the six countries (Cape Verde, Kenya, Namibia, Nigeria, Tanzania, and Uganda) reporting some portion of pension system assets invested in corporate bonds, the allocation ranges from only 0.1 to 7.3 percent at a maximum. Investment of pension system assets in equity ranged widely among the 11 countries reporting these securities in their asset portfolios, from 4 percent (Madagascar) to a high of 58 percent (Namibia). For Namibia, a significant amount of these equities are known to have been issued abroad, mostly by firms in South Africa, given Common Monetary Area links.

For those focus countries for which time-series data on pension system asset allocation are available (Ghana, Kenya, Madagascar, South Africa, and Zambia), there are signs of a somewhat more diversified portfolio approach to asset allocation and a shift away from large holdings in assets generating little or no returns, although not consistently (figures 4.2, 4.3, and 4.4). In three of these five countries (all except Madagascar and South Africa), the percentage allocation of pension system assets to equities and other non-government-issued securities has increased over the past several years. In South Africa’s case, the percentage allocation of these assets to equity securities declined over the period, but the amount was still large at 23 percent. In three of the five countries (Ghana, Madagascar, and South Africa), the portion of pension system asset holdings in real estate declined, by more than 15 percentage points, in the case of Ghana and Madagascar. In South Africa, the allocation declined further from already low levels.

**Figure 4.2 Pension system assets invested in equities: Signs of a more diversified portfolio approach**

Percent of total <!-- Start of picture text --> 35<br>30<br>25<br>2001<br>20 2006*<br>15<br>10<br>5<br>0<br>Ghana Kenya Madagascar South Africa Zambia<br>%<br><!-- End of picture text --> Sources: Pension-system asset data sourced from national pension funds and financial authorities.

* End-2006 or most recent available; end-2005 for Madagascar and South Africa; end-2007 for Zambia.

Patterns in pension system asset allocation varied among the five countries for which these timeseries data are available, reflecting different levels of capital markets development, among other factors. In Zambia, the share of pension system assets invested in equities increased from 8 percent in 2001 to 24 34
Levinsohn, 2002).<sup>5</sup> They also have the advantage of being easier to understand than general equilibrium modeling. As seen above, partial-equilibrium/cost-of-living analysis often requires a researcher to abstract from the income side of the issue, or limit the analysis to consideration of a single factor (typically labor). The historical focus on commodity markets is due in part to lack of good data on household earnings, since in household surveys, income information tends to be less complete and less reliable than expenditure information (Cockburn, 2001; Lipton and Ravallion, 1995). However, as noted above, recent studies provide evidence that – regardless of the data limitations – this abstraction is not innocuous.

General equilibrium (GE) models, by contrast, can assess the impact of economic shocks that reverberate across sectors and, in some cases, regions of a country or the world. They are capable of producing disaggregated results at the microeconomic level, while providing a consistency check on macroeconomic accounts. GE models are typically benchmarked to a Social Accounting Matrix and based on neoclassical theories of firm and household behavior. While most are comparative static in nature, dynamic versions have also been developed, and a few of these will be discussed below in the section on economic growth.

One caveat with GE models is that they can become quite complex, thereby making it harder to distinguish the extent to which results are driven by particular modeling assumptions, or whether they are robust to model specification and largely data-driven. While this type of criticism can be leveled at almost any empirical modeling exercise, the fact is the work of GE modelers is very assumption-intensive, and they therefore bear a greater burden in ensuring that their results are robust to variation in these assumptions. Of course, the payoff from GE analysis is that it offers the prospect of being able to examine a wider range of issues that are critical for policy-making.

GE analysis of income distribution and poverty issues in developing countries has only recently become widespread. However, its origins go back fairly far -- to the pioneering work of Adelman and Robinson for Korea (1978) and Lysy and Taylor for Brazil (1980). In addition, Bourguignon led an important series of studies on the topic of macro-economic adjustment and

> 5 Of course to conduct _ex ante_ analysis of policy reforms, this approach must still be combined with a simulation model with which to generate prospective price changes.

13
Annual Report 2001-2002, available at: www.ird.govt.nz/ aboutir/reports/annual02.pdf; and Canada: Canada Customs and Revenue Agency, 2001-2002 Annual Report to Parliament, Financial Statements, available at www.cra-arc.gc.ca/agency/ annual/2001-2002/. For Guatemala: simple average of adjusted figures from Mann (2002). Ghana: revenue-weighted average of figures cited by Terkper (1995). Namibia: statistics provided by Klaus Schade of the Namibian Economic Policy Research Unit. Tanzania: statistics provided by Odd-Helge Fjeldstad of the Chr. Michelsen Institute. Remaining countries: Taliercio (2004). We are very grateful to Klaus Schade, Odd-Helge Fjeldstad and Robert Taliercio Jr. for their assistance in obtaining these data.

# **C Model Calibration**

Benchmark quantities of goods and factors (entries in the SAM) are calculated using the following equations: 33
in the combined treatment arms. Column 2 in Table 5 shows that no one in the control group or the IPT-G arm received cash, while 86.5% of those in the combined treatment (IPT-G+) arm received their lump-sum cash transfer. We report intention-to-treat (ITT) estimates throughout the paper, and refer the readers to Appendix Table A4 for estimates of the local average treatment effect (LATE) of IPT-G.

# **3.2 Treatment effects on mental health**

## **3.2.1 Impact of IPT-G**

Table 6 presents the impact of offering IPT-G on mental health.<sup>20</sup> Immediately following the end of IPT-G (Panel A), approximately a quarter of the control group had no or minimal depression as measured by the PHQ-8 (25.6%, column 1). This share is increased by 3.1 pp in the pooled IPT-G group, but this is not statistically significant (p-value = 0.172). The share of study participants with minimal psychological distress as measured by the GHQ-12 also increased in the IPT-G group (by 6.1 pp over a mean of also 25.6%, column 2), which is a modest and statistically significant improvement (p-value = 0.011; q-value = 0.023). The discrete PHQ-8 and GHQ-12 scores tell a similar story (columns 3 & 4): IPT-G reduced PHQ-8 scores by less than 0.1 standard deviation (SD, q = 0.590) and GHQ-12 scores by more than 0.15 SD (q = 0.069). It is possible that the participants that attended more sessions benefited more from IPT-G: Appendix Table A2 shows a positive correlation between the number of IPT-G sessions attended and psychological well-being, but we cannot ascribe a causal interpretation to this finding.

At the 12-month follow-up (Table 6 Panel B), we find that the modest but statistically significant effects of therapy on primary mental health outcomes remained: the likelihood of minimal depression was higher in the IPT-G arm (no longer pooled with the combined treatment arm of IPT-G+) by 5.4 pp over a control group mean of 18.4% using the PHQ-8 (q = 0.017) and by also 5.4 pp over a control group mean of 27.8% using the GHQ-12; q = 0.030). The reductions in the discrete scores are both less than 0.1 SD and not statistically significant. Figure 3 shows that the improvements in the scores largely occurred around the cutoff values for minimal depression, explaining the effects on the primary outcomes.<sup>21</sup> Table A5 presents the impact of IPT-G on binary indicators for moderate (PHQ-8 _≥_ 10) and severe depression (PHQ-8 _≥_ 15) in each follow-up survey round: the impact estimates are

> least 14 (or 87.5%) of the 16 total sessions, compared with only 28% of the participants in our study, who attended at least 12 (or 85.7%) of the 14 total sessions.

> 20Table A3 replicates the same table with covariate adjustments.

> 21Data on other secondary outcomes were not collected during this round as the questionnaire had to be shortened for the phone survey that was implemented during the COVID-19 lockdown.

14