rafmacalaba/datause-encoder-data huggingface dataset:
for opening a charter school or not. The prospective entrant receives a random draw of the nonpecuniary net benefit from operating a charter, based on whether it decides to submit an entry application to the regulator. To decide whether to authorize the entry, the regulator forecasts the prospective entrant’s enrollment by predicting household _equilibrium_ choice of school. In addition, the regulator decides whether incumbent charters can remain open based on their financial and academic viability.

Thus, our model has multiple stages of charter, regulator and household actions. We start with the household choice stage.

# **4.1 Household Choice of School**

We use _J_ to denote total number of schools in the economy. Households in the economy have one child each. In what follows, we use “household”, “parent”, “child” and “student” interchangeably. Student _i_ is described by variables ( _D_ ,l, _I_ , _g_ , _ε_ ), where: • _D_ is a row vector describing student demographics. In our data this vector contains � _D_ = 3 binary elements indicating whether the household is white, Hispanic (omitted category is black) and non-poor, respectively.

• l, l ∈{1,..., _L_ } is household location in one of the economy’s _L_ neighborhoods.<sup>12</sup>

- _I_ is annual income of the student’s family.

• _g_ is the child’s grade, ranging from _g_ = 0 (kindergarten) to _g_ = 12 (12th grade).

• _ε_ is a vector that describes the student’s idiosyncratic preference for each school.

Subscripts _j_ and _t_ denote a school campus and school year respectively. We treat multiple campuses of the same school as separate units because they are often run as such. In what follows, we use “school” and “campus” interchangeably as well as “school year” and “year”. When making its choice for year _t_ , household _i_ takes into account the following characteristics for school _j_ :

- _κ jt_ is the set of grades served by the school, or “grade level.”

- _xi jt_ is the geographic distance from the household’s residence to the school.

• _y j_ is a row vector with time-invariant school characteristics such as type (public, charter, Catholic, other religious, nonsectarian) or focus (Core, Language, Arts, Vocational, Other). For brevity we refer to _y j_ as “focus.”

- _p jgt_ is tuition. It is always equal to zero for public and charter schools.

• _D_<sup>~~�~~</sup> _jt_ is the row vector of households’ beliefs about the school’s peer (or student body) composition at _t_ . As explained below, in equilibrium these beliefs are consistent with the realized schools’ peer composition. Empirically we use another variable, _D jt_ , which

> 12A child’s location determines her travel distance to each school. We take student location as given and do not model household residential choice. For models of joint residential and school choice, see Nechyba (2000) and Ferreyra (2007). In estimation we measure distance as network distance, expressed in miles. We use Census block groups as household locations for demand estimation, and neighborhood clusters as the locations used to define entry points for supply estimation.

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16 Under this rule, real depreciation will relax the fiscal adjustment requirement if the export elasticity with respect to the real exchange rate exceeds one.

Cohen (1988) has argued that the proper resource base ought to be a weighted average of GDP and exports, with the weights chosen in such a way that the resulting ratio is insensitive to the real exchange rate. The actual chcice does not matter too much unless changes in the real exchange rate are anticipated. In the empirical application presented below, we use the Cohen approach (see Anand and van Wijnbergen (1988) for details on the derivation of the weights).

# 3 EmDirical Preliminaries: The Structure of Money Demand in Turkev

Reserve money consists of currency in circulation and reserves held by the commercial banks against demand and time deposits. To derive demand for base money we therefore estimate asset demand functions describing the private sector's portfolio choice over currency, demand deposits and time deposits as a function of inflation, income and interes3t rates. All data on prices and financial assets are taken from the Monthly Bulletin of the Central Bank of Turkey; real output data are from International Financial Statistics (IMF, various issues). All equations are estimated using OLS. Consider demand for currency first.
# 4. CONCLUSIONS ON EXPECTATIONAL ISSUES AND REFORM DYNAMICS

Hisperceptions of reform costs and benefits add another uistortion (of an intertemporal nature) to the many already identified and investigated in the literature, which prevent the economy from reaching a first best solution. Some results from the literature on credibility, partially summarized in section 3.2, point to the necessity of addressing credibility problems directly through preannounced institutional changes acting as signals that a new policy orientation is being followed.

In fact, bold moves in trade reforms such as Turkey's elimination of quantitative restrictions to imports, Venezuela's abolition of the agency in charge of foreign exchange allocation, Chile's elimination of quotas associated with a drastic reduction in the highest tariff rates are reported as examples of credibilityenhancing measures in World Bank (1990, p.50).

However, even taking for granted that the mentioned cases are success stories, some doubts can be cast on such seemingly widely held view. This is not to deny the relevance of the concept of credibility in the analysis of policy reforms, but rather to emphasize that credibility considerations do not necessarily justify reforming Governments to take on too risky attitudes towards reform implementation, especially in the presence of only partial and not yet consolidated policy prescriptions.

On the stabilization side, the quoted example of heterodox shock policies reminds that "signals" not respecting the budget constraint or too long restraining the functioning of a market economy, such as price freezes, rapidly undermine, rather than reinforce, the credibility of a reform, witnessing the difficulty of directly targeting credibility by itself. They may succeed in making 38
require much more individualized intra-household information or they will need to be nearly-universal in coverage.

This dispersion of undernourished individuals across the distributions of household wealth and consumption entails that countries with a higher overall incidence of undernutrition tend to be countries where a larger share of the undernourished are found in non-poor families. This suggests that the need for broad coverage in social policies (rather than policies finely targeted to poor households) is especially great in countries with a high incidence of undernutrition. Rather than folding nutrition schemes into household-targeted antipoverty programs in such countries, emphasis should be given to nutritional interventions with near universal coverage, such as comprehensive school feeding (with explicit nutrition supplementation), maternal health care and universal sanitation services.

In addition to documenting the limitations of relying on household poverty data to reach nutritionally deprived individuals, we throw some light on why those limitations are so severe. For the subset of countries for which we also know adult male BMI, we have shown that the extent of intrahousehold inequality entails that the bulk of underweight women and undernourished children are found in households where the male head appears to be adequately nourished. In exploring the cross-country patterns, we find that richer countries (in terms of GDP per capita) within Africa tend to have child stunting more concentrated among the wealth-poor, suggesting greater scope in those countries for targeting wealth-poor households as a means of reaching children with longer-term nutritional deficiencies. But this is not so for child wasting. In countries with a higher female literacy rate one tends to find a greater concentration of underweight women in poor wealth strata. By contrast, female literacy has little power for predicting whether children’s undernutrition is more concentrated among the wealth poor. There is no sign that countries with lower average access to improved water and sanitation tend to have undernourished women and children more concentrated among the wealth poor; while there is little doubt that improved water and sanitation makes for better nourished people, intra-household inequalities appear to be a more plausible explanation for our main findings on the relationship with household wealth than these aspects of the health environment. In all cases, the size of the wealth effect—how much undernutrition falls as the wealth index rises—is a significant predictor of how effectively one can expect to identify nutritionally-disadvantaged individuals by targeting poor households. However, as we have also emphasized, it is better to focus directly on the relevant conditional probabilities for this purpose, rather than the wealth effect.

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# **Annex B: Methodology**

## **Labor income regression**

For the analysis of correlates with individual labor income, a Heckman’s selection model is estimated. The baseline OLS model is set as follows: ln( , where is the log monthly wage or labor income of individual yyii) = αα+ ββXXii + εεii _i_ , is a vector of covariates including various demographic characteristics (for example, age, age squared, gender, education ii ii attainment), characteristics of jobs (for example, economic sectors, public/private, and yy XX formal/informal), and geographic characteristics of an enumeration area (for example, distance to the CBD). is an iid error term. In order to take account of the selection of individuals reporting labor income, Heckman’s selection model is estimated by adding a first-stage selection model ii based on individual’s demographic characteristics. The results are presented in Table A.1. εε We also employed a regression analysis to identify factors that determine unemployment and informality of jobs. The outcome variable is a binary variable coded 1 if unemployed or working in the informal sector. An OLS (linear probability) model was used. The same set of demographic characteristics and geographic characteristics, as described above, was adopted for these regressions as well, although we also introduced interaction terms between education levels and sex to explore if there are any differential effects of education on employment conditions by sex. The results are presented in Table A.2.

## **Job accessibility index**

The index of job accessibility that we used in this report measures the share of job opportunities that can be reached within a certain travel time (Nakamura and Avner 2018). This index can be formalized as follows: where _i_ indexes each location (or origin), _Oj_ represents the number of job opportunities in location _j_ , tij is the travel time between location _i_ and location _j_ , and is a time threshold. 1[.] is an indicator function that takes the value of 1 if the statement in the brackets is true, 0 otherwise. In other words, this index captures the share of jobs that are accessible from location aa̅ _i_ within travel time = . The index is computed for each 30-arc seconds grid (roughly squared kilometers) where travel aa̅ time by walk between the centroid of each grid and the locations of jobs is derived based on OpenStreetMap and Open Source Routing Machine (OSRM).<sup>15</sup>

> 15 ‘osrmtime’ in STATA is used to compute travel time between the centroid of each grid and the locations of jobs as recorded in the 2017 JICA CS.

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leads to 19 different sectors, but only 10 sectors are manufacturing sectors. Moreover, the skills data generated from the GTAP IO tables cover only 3 years: 2004, 2007, and 2011. These data are matched with our GVC data according to these 3 years and for corresponding manufacturing sectors. A panel of three years of data is generated accordingly and used in the analysis. Our underlying assumption is that, we expect skilled labor content in exports to have a positive and significant impact on countries’ participation in GVCs while unskilled labor is expected to have less impact on GVC participation for a given country.

# **_b. Human capital index_**

The Human Capital Index (HCI), developed by the WBG, measures the human capital of the next generation. It captures the amount of human capital a child born today could expect to attain by age of 18, in view of prevailing risks associated with poor health and poor education in the country where she/he lives (WBG, 2018). The index has three components: survival, expected years of learning-adjusted school and health. The survival component is measured using the mortality rate of child under 5 years. This component reflects the fact that children born today need to survive before they can benefit from human capital accumulation through formal education. For expected years of learning-adjusted school, this component measures how much children learn in school based on countries’ relative performance on international student achievement tests. Finally, the health component combines adult survival rate and the rate of stunting for children under 5 years old to measure a country’s overall health environment. In this study, the HCI, which runs from zero to one, is used as a proxy to measure a country’s initial human capital endowment. A score of 1 means that a child born today in a country can expect to achieve both full education potential (i.e. 14 years of high-quality school by age of 18) and full health (i.e. no stunting and 100% adult survival). A score of 0.80 for instance means that the future productivity of a child born today is 20% below what could have been achieved with complete education and full health. We assume the HCI does not change much in the short and medium term since the benefits of investing in people can take a long time to materialize (WBG, 2018). Since we consider HCI as a measure of initial human capital endowment, we expect countries with high HCI endowment to perform better in GVC participation. Therefore, we expect a positive and significant relationship between HCI and countries’ participation in GVC.

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**Table 3: Truncation Bias in Relative Mobility: All Children Sample**

|||**Rank**|**Correlation**|**I**|**GC**|**IGRC**|
|---|---|---|---|---|---|---|
|||**Full**|**Coresident**|**Full**|**Coresident**|**Full**<br>**Coresident**|
||||**BA**<br>|**NGLADE**<br>|**SH **<br>|<br>|
|**Father's Schooling**||0.523|0.483|0.506|0.459|0.546<br>0.422|
||**Bias**|**-8**|**.31%**|**-1**|**0.4%**|**-29.40%**|
|**Mother's Schooling**||0.548|0.492|0.465|0.421|0.842<br>0.616|
||**Bias**|**-1**|**1.36%**|**-1**|**0.3%**|**-36.67%**|
|**Average Schooling**|**Bias**|0.531<br>**-5**|0.504<br>**.26%**|0.50<br>**-5**|0.47<br>**.15%**|0.71<br>0.58<br>**-22.09%**|
|||||**INDIA**|||
|**Father's Schooling**||0.448|0.409|0.439|0.397|0.483<br>0.419|
||**Bias**|**-9**|**.42%**|**-1**|**0.8%**|**-15.36%**|
|**Mother's Schooling**||0.453|0.382|0.367|0.318|0.569<br>0.454|
||**Bias**|**-1**|**8.77%**|**-1**|**5.5%**|**-25.52%**|
|**Average Schooling**||0.467|0.420|0.456|0.403|0.654<br>0.543|
||**Bias**|**-1**|**1.39%**|**-1**|**3.1%**|**-20.37%**|

Notes: (1) The sample consists of daughters and sons of 13-60 years age. (2) IGC stands for Intergenerational correlation, IGRC stands for intergenerational regression coefficient.

(3) Bias is Percentage Bias = [(Coresident estimate - Full estimate)/Coresident estimate] * 100

**Table 4: Bias in Relative Mobility for Father-Sons** **<u>and Mother-Daughters</u>**

|||**RankC**|**orrelation **|**IGC**||**IGRC**|
|---|---|---|---|---|---|---|
|||**Full**|**Coresident**|**Full**<br>**Coresident**|**Full**|**Coresident**|
||||**BA**<br>|**NGLADESH **<br> <br>|||
|**Father-Sons**||0.516|0.499|0.488<br>0.449|0.565|0.444|
||**Bias**|**-5**|**.19%**|**-8.61%**||**-27.03%**|
|**Mother-Daughter**||0.577|0.505|0.529<br>0.490|0.845|0.597|
||**Bias**|**-14**|**.27%**|**-8.10%**||**-41.60%**|
|||||**INDIA**|||
|**Father-Sons**||0.426|0.421|0.420<br>0.409|0.457|0.427|
||**Bias**|**-1**|**.26%**|**-2.65%**||**-7.22%**|
|**Mother-Daughters**||0.549|0.481|0.466<br>0.428|0.688|0.571|
||**Bias**|**-14**|**.18%**|**-8.70%**||**-20.44%**|

Notes: (1) The samples consist of daughters only (mother-daughters) and sons only (father-sons) for the 13-60 years age range. (2) IGC stands for Intergenerational correlation, IGRC stands for intergenerational regression coefficient. (3) Bias is percentage bias.
year horizons; correction for policy risk assumes that health and education outcomes will repeat the smallest increase on record over any ten-year period; correction for labor market reforms risk assumes that female labor force participation rate will repeat the smallest increase. E. Impact of natural disasters assumes that the number of climate disasters in 2022-30 will increase as much as it rose between 2000-10 and 2011-21 for each country, that is, from once every two years to twice every three years, on average.

F. Recession impact based on estimated impact of recessions in Kilic Celik et al. (2023), table 1F.15. E.F. Orange whiskers display one standard deviation of the impact of climate disasters and recessions, respectively.
Table 6, panel B, presents the results on profitability for the entire sample and separately for farms with areas that are less than or greater than 120 ha, highlighting two points. First, the area cultivated remained approximately constant, consistent with the results from the remotely sensed data. Physical output for key crops decreased by between 6% and 21% for rapeseed and maize or sunflower, respectively, in the first year of the war but was only marginally (between 1% and 6%) below 2021 levels in 2023. These results support the focus on area rather than yield in our earlier analysis.

Second, revenue per hectare decreased by 34% between 2021 and 2022, from US$1,088 to US$714, with a further decline to US$597 (or 56% of the 2021 level) in 2023. Combined with an increase of 26% in input costs over 2021 levels in 2022, this implied a drop in profit per hectare by 60%, from US$778 to US$313. The share of output sold to the market decreased from 78% to 68% in 2022. While the level of market participation recovered to the original level in 2023, the share of output sold in 2023 remained at 62%, 20 percentage points below the share attained in 2021. Although lower input prices implied a small recovery of profits to US$397/ha, this was still only 54% of the 2021 level.

# **4.2 Farm-level analysis and variation of impacts by farm size group**

The farm survey panel data can be used to run farm-level fixed effect regressions for the level of market participation, output value, input cost, and profits overall and by farm size group. In the absence of other confounding factors, year dummies for 2022 or 2023 can be interpreted as conflict impact and the difference between the two years as the result of farmers’ adaptation overall or by farm size group. Columns 1, 3, 5, and 7 in table 7 present the results of the regressions for the overall sample. They suggest that the war resulted in a 30% reduction in output sold ( _α1_ ), but that adaptation reduced this effect by about 7% ( _α2 - α1_ ) in 2023. Similarly, the value of output and the cost of inputs in 2022 were 47% below and 23% above the pre-war levels, respectively, with output value decreasing by a further 14% and input cost falling by 79% from the 2022 level in 2023. Profits, which had decreased by US$477 from 2021 to 2022, improved by US$86 in 2023.<sup>12</sup> An exogenous increase in the cost of transport together with better access to European markets due to the opening of accession negotiations with the European Union on December 14, 2023, could benefit (small) producers of high-value crops and foster rural diversification. To assess whether the conflict-induced changes differed across farm size groups, we interact year dummies for 2022 and 2023 with pre-intervention farm size, classifying farms into large and small, based on whether they farm more or less than 120 ha. The magnitudes and significance levels in the bottom panel in table 7 are reported for whether small and large

> 12 As they are strictly positive for all producers, we use the log of the dependent variable for regressions of output value and input cost, and absolute values for the share of output sold and profits.

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was mostly nominal government bonds, followed by mortgage bonds, inflation-linked bonds, and a small allocation in foreign bonds. Real estate assets accounted for just over 3 percent of total assets.

In 1999, a major review of asset allocation strategy was undertaken. This took into account historical data on the level, volatility and covariance of asset returns, the longterm nature of pension liabilities, the size of ATP assets, and the relative size, prospects and supply of securities of different markets. A decision was reached to increase further the allocation to equities and to diversify internationally in both equities and bonds. Investments in real estate were also set to rise to 5 percent.

International diversification in equities was justified by the large size of ATP funds relative to the local market and the possibility of raising average returns and especially lowering risk. Increased international diversification in bonds was predicated on the decision to increase their interest sensitivity to achieve a better alignment with the interest sensitivity of pension liabilities in conjunction with the limited availability of long-dated bonds in the local market, relative to the potential demand from ATP. Increased investments in foreign bonds focused on other European countries, because of the limited exposure to currency risk, given the policy of aligning the Danish crown to the euro.

No currency hedging was undertaken initially but later on, as investments in non-euro bonds started to grow, a very clear policy on currency hedging was stipulated. This required full hedging of foreign bonds denominated in non-euro currencies and a significant but varying degree of hedging of euro-denominated bonds. Clear limits by foreign currency, covering all assets including equities, were also imposed on unhedged positions.

ATP introduced in 2001 an active liability hedging program that has been expanded over the years. This is designed to protect pension liabilities from interest rate changes. The hedging program achieved 100 percent after-tax hedging of liabilities in 2005. A new department that specializes in hedging its pension liabilities was created in 2006.

The investment department now deals with the portfolio of bonds and equities and also engages in hedging activities that aim to manage the risk of investment assets. The investment portfolio was divided into two components in 2005: the Beta portfolio, which focuses on strategic allocation strategies with a longer horizon in assets of varying levels of risk and earning returns commensurate with their risk; and the Alpha portfolio, which focuses on active management that seeks to achieve excess returns.

In recent years, ATP established a group structure that includes 2 companies that invest in real estate, five companies that specialize in private equity, and two companies that offer administrative services to other pension funds on market terms. Investments in private equities and foreign real estate funds are carried out through Danish limited partnerships because this corporate form is better suited for the use of incentive packages 11