rafmacalaba/datause-encoder-data huggingface dataset:
engagement with children, etc.) _,_ while the second factor is mostly comprised of items from the _numeracy, literacy, and problem solving_ subscales (e.g. teaching numbers and the alphabet, reading with children, identifying shapes and colors, etc.). As a result, the findings are qualitatively the same as those in columns (1) and (2).

Columns (3) and (4) in Table 6 report changes in enrollment. Interestingly, enrollment increased significantly in treatment schools (T2 and T4), which may indicate that parents value improvements in school quality over and above the basic learning and play materials received by the control schools (T1). The effects range from a statistically insignificant 10% increase over the control group in T3 to significant increases of 17% and 21% in T2 and T4, respectively – with none of the increases in the three treatment arms significantly different than each other.

If the parents think that quality is improved at treated centers, it may be the case that these schools have more qualified and/or better-trained staff. Column (5) shows program impacts on the number of teachers trained by the program. There are no trained teachers in control CBCCs, compared with approximately 1.5 trained teachers across treatment groups, with no significant differences between any of the treated CBCCs. Given that each school nominated two teachers for training under the program, which is confirmed by administrative records of the training sessions provided by Save the Children, this indicates that roughly one out of four trained teachers was no longer teaching by the 18-month follow-up. Despite this loss of approximately 25% of the trained teachers, treatment schools had, as intended, a significantly higher number of trained teachers than the control group. Contrary to our original hypotheses, the small incentives offered to trained teachers in T3 did not improve their retention.<sup>19</sup> 19 In Appendix Table 12, we further examine changes in the characteristics of the teachers. The share of teachers who have a primary school leaving certificate (PSLC) is significantly higher in treatment schools, particularly in T3 – the intervention that provided small incentives to trained teachers. While educated teachers are more likely to have left the center by Round 2 in the control group, those without PSLCs are more likely to have left the CBCC in treatment centers. Furthermore, new arrivals in Round 2 are significantly more likely to have PSLCs in the treatment 23
_Development_ , _5_ , 31–46. https://doi.org/10.1016/j.wrr.2015.06.002 Kankwamba, H., Kadzamira, M., & Pauw, K. (2018). How diversified is cropping in Malawi? Patterns, determinants and policy implications. _Food Security_ , _10_ (2), 323–338. https://doi.org/10.1007/s12571-018-0771-x

- Karamba, R. W. (2013). _Input Subsidies and their effect on cropland allocation, agricultural productivity, and child nutrition: evidence from Malawi._ American University.

- Lewbel, A. (2018). Identification and estimation using heteroscedasticity without instruments: The binary endogenous regressor case. _Economics Letters_ , _165_ , 10–12. https://doi.org/10.1016/j.econlet.2018.01.003

- Mason, N. M., Wineman, A., Kirimi, L., & Mather, D. (2016). The Effects of Kenya’s ‘Smarter’ Input Subsidy Programme on Smallholder Behaviour and Incomes: Do Different Quasiexperimental Approaches Lead to the Same Conclusions?, (November 2015). https://doi.org/10.1111/1477-9552.12159

- Mason, N. M., Wineman, A., & Tembo, S. T. (2020). Reducing poverty by ‘ignoring the experts’? Evidence on input subsidies in Zambia. _Food Security_ , _12_ (5), 1157–1172. https://doi.org/10.1007/s12571-020-01032-z

- Matita, M., & Chirwa, E. W. (2014). _Targeting in Social Cash Transfer and Farm Input Subsidy programmes in Malawi: should they be harmonized? Discussion Paper. Evaluation of the 2013/14 Farm Input Subsidy Programme, Malawi_ . London.

- Matita, M., Chiwaula, L., Chirwa, E. W., Mazalale, J., & Walls, H. (2022). Subsidizing improved legume seeds for increased household dietary diversity: Evidence from Malawi’s Farm Input Subsidy Programme with implications for addressing malnutrition in all its forms. _Food Policy_ . https://doi.org/10.1016/j.foodpol.2022.102309

- Maxwell, D., Vaitla, B., & Coates, J. (2014). How do indicators of household food insecurity measure up ? An empirical comparison from Ethiopia. _Food Policy_ , _47_ , 107–116. https://doi.org/10.1016/j.foodpol.2014.04.003

- Miller, C. M., Tsoka, M., & Reichert, K. (2011). The impact of the Social Cash Transfer Scheme on food security in Malawi. _Food Policy_ , _36_ (2), 230–238. https://doi.org/10.1016/j.foodpol.2010.11.020

32
**Table 11.2 Investment, Savings and Capital Flows (% of GDP)**

||_85-88_|_East Asia_<br>_89-92_|_93-96_|_85-88_|_ASEAN-4_<br>_89-92_|_93-96_|
|---|---|---|---|---|---|---|
|Investment|32.1|34.9|38.2|25.7|32.6|35.0|
|National Savings|31.6|34.0|36.1|23.9|28.6|30.3|
|- Private|24.5|28.3|30.2|13.2|20.0|20.4|
|- Public|4.8|5.8|5.9|3.3|||
|Current Account Deficit|0.2|0.8|1.9|1.1|3.8|4.6|
|Total Capital Inflows|0.6|1.9|3.9|2.2|6.7|6.8|
|Reserve Accumulation|0.7|1.6|2.3|1.0|2.9|2.2|
|||_South Asia_|||_LAC_||
||_85-88_|_89-92_|_93-96_|_85-88_|_89-92_|_93-96_|
|Investment|21.9|23.6|23.6|20.5|20.6|20.1|
|National Savings|19.8|21.2|21.9|20.6|19.6|17.6|
|- Private|18.4|20.2|20.9|16.5|16.2|15.1|
|- Public|1.4|1.0|1.0|4.1|3.3|2.5|
|Current Account Deficit|2.3|2.3|1.7|1.0|1.1|2.4|
|Total Capital Inflows|1.9|2.4|3.0|0.7|2.4|3.5|
|Reserve Accumulation|-0.4|0.1|1.3|-0.3|1.3|1.0|

_5_
# Table 10: Household Structure, Children, and Inequality, Malawi (ISA 2010)

## (A) Rural Residency

<u>(A1)</u> Population Shares <u>(%)</u> by Number of Children and Dependency Ratios

|||Numb|er of Ch|ildren||Depende|ncy Ratio|
|---|---|---|---|---|---|---|---|
||0|1|2|3|_≥_4|DR_<_1|DR_≥_1|
|Population 2010|18|19|22|19|22|45|55|
|(A2) Average US$|by Nu|mber o<br>Numb|f Childr<br>er of Ch|en and <br>ildren|Depen|dency Rat<br>Depende|ios<br>ncy Ratio|
||0|1|2|3|_≥_4|DR_<_1|DR_≥_1|
|Consumption|1058|1242|1409|1417|1631|1313|1406|
|Income|905|975|1073|1212|1342|1114|1107|
|Wealth|1043|1237|1303|1274|1482|1397|1178|
|_▷_Land|444|539|592|622|620|589|548|

## (B) Urban Residency

<u>(B1)</u> Population Shares <u>(%)</u> by Number of Children and Dependency Ratios

|||Numb|er of C|hildren||Depende|ncy Ratio|
|---|---|---|---|---|---|---|---|
||0|1|2|3|_≥_4|DR_<_1|DR_≥_1|
|Population 2010|24|21|21|20|15|56|44|
|(B2) Average US$|by Nu|mber o|f Childr|en and|Depen|dency Rat|ios|
|||Numb|er of C|hildren||Depende|ncy Ratio|
||0|1|2|3|_≥_4|DR_<_1|DR_≥_1|
|Consumption|2631|3045|3109|2699|2761|3244|2349|
|Income|2041|2799|3306|2214|1880|3125|1651|
|Wealth|2573|3681|4089|2552|2256|4023|1863|
|_▷_Land|377|367|379|460|561|452|377|

_Notes:_ All variables are averages in current USD. The construction of the measures of household consumption, income, and wealth is discussed in Section 2, with further details in the online appendix.
are working.<sup>19</sup> We use the CHNS to contrast average work hours in 1991 with 2009, and also include data from the 2008 CHARLS pilot.

From the unconditional plots in Panel A of Figure 3, a gradual reduction in average work hours after age 45 is apparent for both men and women in urban areas. Also evident is a steeper decline in work hours for women between 45 and 55 and for men between 55 and 65. Turning our attention to Panel B, a gradual decline in work hours is evident for men and women who continue working beyond age 55, but it is not steep. By 2009, urban men between 55 and 60 who are employed, report spending an average of 45 hours per week working. By age 70, urban men who continue in productive employment are spending just 20 to 30 hours per week employed while their employed rural counterparts of the same age are still working more than 40 hours per week. The difference across urban and rural areas in the work-intensity of those working beyond mandatory retirement age suggests that urban residents may have the ability to retire gradually, while rural residents, if they have the physical capacity, may indeed continue to work full-time into their 70s.<sup>20</sup> When comparing work-intensity of older workers across countries, it is evident that China’s residents over 60 work fewer hours than their respective urban and rural counterparts in Indonesia and Korea (Figure 4). Conditional on working, average working hours for urban men and women (Figure 4, Panel B) are more similar to the UK, where hours drop off more quickly with age, than for Indonesia, Korea and the USA.

# **Retirement in China, Indonesia and Korea**

The descriptive patterns shown above make use of data from early stages of longitudinal studies from China and Korea which are modeled on the Health and Retirement Study (HRS) from the US and the English Longitudinal Study on Aging (UK). New survey efforts, like CHARLS in China, promise to facilitate the study of retirement and labor supply behavior in regions where pension and social security systems are not well established, and population aging is occurring at a rapid pace. In the analytical models that we estimate, we make use of the cross-sections from

> 19As earlier, we use non-parametric locally weighted regression (LOWESS) to smooth the averages across the age distribution.

> 20This has long been true of China’s rural elderly. A classic study of the rural elderly in the 60s and 70s referred to their lives as one of “ceaseless toil” (Davis-Friedman, 1991), and Pang et al (2004) characterize the retirement decision in rural China as “working until dropping.”

12
10 with one vehicle than two, indicating that motor vehicle use increases less rapidly than motor vehicle ownership.

These findings also support the earlier economic hypotheses.

# _4.2 Price elasticities of motor vehicle ownership and use13_

Motor vehicle prices affect individuals' decisions to own a vehicle. The fleet comprises a variety of vehicle types, however, which makes it difficult to construct an average price index for the entire fleet. Because of this problem, only a few empirical **14** studies include vehicle prices as an explanatory variable for vehicle ownership. Increases in vehicle prices were found to reduce vehicle registrations and prolong vehicle life-which reduces vehicle depreciation rates (table 3).15 Unlike vehicle prices, gasoline prices are generally available and thus are included in many studies. Although some analysts found that increases in gasoline prices reduce vehicle ownership,16 others found that gasoline prices had little effect on vehicle ownership.<sup>17</sup> The effect on vehicle use is clear, however: an increase in gasoline prices decreases vehicle usage and increases the average fuel efficiency of the vehicle stock by encouraging the purchase of more fuel-efficient vehicles.'<sup>8</sup> When facing higher operating costs, households with two or more cars reduce usage less than one-car households by shifting usage to their more efficient car. These results are summarized -in table 3.

A comparison of available estimates suggests that income elasticities are greater than price elasticities in absolute terms for both motor vehicle ownership and use. This finding has an important policy implication because prices are often suggested or used as an instrument to control motor vehicle ownership and use. If price elasticities are half as large as income elasticities, prices would have to grow twice as fast as incomes to stabilize vehicle ownership. In fact, prices have not increased much in real terms.

13 These price elasticities measures the percentage change in vehicle ownership or use when its price changes by one percent. A price increase usually reduces demand, so price elasticities are normally negative.

The absence of vehicle prices in a vehicle ownership model may cause bias to the income elasticity estimates. For a treatment of the problem, see Mogridge, "The Prediction of Car Ownership." Mogridge adjusted the income variable to "car purchasing income" by the cost of motoring before fitting the vehicle ownership equations.

5 Pindyck, _The Structure of World Energy Demand._ Wildhorn and others, _How to Save Gasoline;_ Pindyck, _The Structure of World Energy Demand;_ and Ingram and Liu, "Vehicles, Roads, and Road Use."

**17** Wheaton, "The Long-Run Structure of Transportation and Gasoline Demand;" and Johansson and Schipper, "Measuring the Long-Run Fuel Demand of Cars."

**18** Wildhorn and others, _How to Save Gasoline;_ Pindyck, _The Structure of World Energy Demand;_ Wheaton, "The Long-Run Structure of Transportation and Gasoline Demand;" and Johansson and Schipper, "Measuring the Long-Run Fuel Demand of Cars."
regression (4) are then used to estimate the error-correction model for each country, given that the ADF tests support the validity of the model: θ∆1 _p_ νˆ _tdt_ −1= _D_ δ _tref_ 1∆+ _pt_ θ _w D_ 2νˆ _treft_ −1 _D_ 1( _t_ ∆−+ _D_ δ _tref_ 2∆) _p_ + _tw_ ε _Dt tref_ 1(− _Dt_ ∆ ) + δ3δ∆ _ptw_ 1(− _Dtref_ ) _Dt_ ∆ +δ4∆ _ptw_ 1(− _Dtref_ )(1− _Dt_ ∆ ) + (5) All prices used in estimation are in current US dollars. The implicit assumption in the model is that the changes in the countries’ exchange rates immediately translate into changes in domestic prices in local currencies, so that the price of coffee in US dollars in unaffected by currency fluctuations. In other words, perfect transmission of exchange rates to domestic prices is assumed.

The _δs_ describe the short-term responsiveness of the domestic prices to world price increases and decreases before and after the reforms. The _θs_ are the parameters capturing the pre- and post-reform speed of adjustment to the long-term equilibrium in domestic prices. The estimated coefficients can be used to calculate how long it will take the domestic prices to fully adjust to a one-time change in the world price. With a change in the world market price occurring at time _t = 0_ , the new long-term equilibrium level of the domestic price is _γ(p_<sup>_w_</sup> _+ ∆ p_<sup>_w_</sup> _)_ . In the initial period the domestic price changes by _δ∆ p_<sup>_w_</sup> . In the following period the error-correction component is added. The degree of adjustment of the domestic price relative to full adjustment _n_ periods after the change in the world price equals For derivation see Appendix B.

# **4. The data**

The data for this exercise are monthly world market prices and prices paid to producers in approximately 20 coffee exporting countries for the past 20 years collected by the International Coffee Organization (ICO). Prices paid to growers are the farmgate prices reported to ICO by the national coffee authorities and constitute the average of all grades purchased from the farmers. The exchange rates used by the ICO to convert the prices in local currencies to US cents are the monthly average exchange rates published by the IMF.

11
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**<u>Table 7. Mechanism: the impact of the program on commune outcomes</u>**

||||||Dependent|variables|||||
|---|---|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Village has<br>an all-<br>weather<br>road<br>(passable<br>for all the<br>time)|Commune<br>has a<br>whole and<br>periodic<br>market|There is<br>electricity<br>grid in the<br>village|Village has<br>an<br>irrigation<br>system|Commune<br>has a<br>kindergarten|Commune<br>has a<br>primary<br>school|Commune<br>has a<br>secondary<br>school|Commune<br>has a<br>health<br>center|The<br>proportion<br>of in-<br>migrants<br>(in percent)|The<br>proportion<br>of out-<br>migrants<br>(in percent)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Program * Post-program|0.1602*|0.0713|0.1399**|-0.1320|0.1778|0.0030|0.1584**|0.1265**|-0.2236|0.0811|
|period|(0.0829)|(0.0958)|(0.0571)|(0.1098)|(0.1106)|(0.0092)|(0.0703)|(0.0621)|(0.2231)|(0.2842)|
|(Poverty rate – 50) * Post-|0.0018|-0.0049|-0.0106|0.0034|-0.0090|0.0003|-0.0141*|-0.0030|0.0468|0.0310|
|program period|(0.0115)|(0.0116)|(0.0080)|(0.0103)|(0.0143)|(0.0010)|(0.0072)|(0.0076)|(0.0367)|(0.0483)|
|Program * (Poverty rate –|-0.0124|0.0024|0.0103|-0.0023|0.0095|-0.0004|0.0114|-0.0005|-0.0504|-0.0379|
|50) * Post-program period|(0.0121)|(0.0120)|(0.0084)|(0.0113)|(0.0147)|(0.0010)|(0.0078)|(0.0084)|(0.0419)|(0.0526)|
|Communes in remote areas|-0.0927***|-0.0735**|-0.0286**|-0.0197|-0.0242|-0.0001|-0.0060|0.0208|-0.6171|-0.7425*|
||(0.0300)|(0.0349)|(0.0129)|(0.0272)|(0.0181)|(0.0032)|(0.0190)|(0.0208)|(0.3858)|(0.3979)|
|Log of commune area|-0.0294|0.1595***|0.0125|0.0244|0.0362**|0.0001|0.0664***|0.0705***|-1.2451|-1.4580|
||(0.0248)|(0.0452)|(0.0114)|(0.0284)|(0.0180)|(0.0022)|(0.0174)|(0.0249)|(1.1062)|(1.0997)|
|Log of population density|0.0063|0.1645***|0.0249*|0.0348|0.0447**|0.0036|0.0497***|0.0584**|-1.5236|-1.7573|
|of communes|(0.0229)|(0.0422)|(0.0131)|(0.0279)|(0.0188)|(0.0024)|(0.0161)|(0.0238)|(1.2876)|(1.2857)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.8560***|-0.9320**|0.7500***|0.4928**|0.4406***|0.9811***|0.4075***|0.5285***|12.9126|14.9616|
||(0.1986)|(0.3563)|(0.1026)|(0.2325)|(0.1427)|(0.0186)|(0.1355)|(0.1986)|(10.3237)|(10.2833)|
|Observations|2,617|2,617|2,617|2,617|2,617|2,617|2,617|2,617|2,617|2,617|
|R-squared|0.227|0.339|0.284|0.189|0.437|0.053|0.279|0.162|0.176|0.165|
|The mean value of the|||||||||||
|dependent variables before<br>the program|0.788|0.627|0.981|0.669|0.597|0.989|0.949|0.912|0.783|0.916|

Note: This table reports district fixed-effect RDD-DD regressions of infrastructures of communes using commune-level observations. Robust standard errors in parentheses. Standard errors are clustered by district level. *** p<0.01, ** p<0.05, * p<0.1.

46
AE countries (Panel B), but with larger coefficients, with the exception that in AE, corporates do not respond to VIX shocks, once we consider DID, which is the internal market debt of corporates in AE. DID seems to play a smoothing role.

Inflows for EM countries in Panel C also follow a similar pattern, with the exception of sovereign sector. As the VIX rises or as GDP falls, total and private inflows fall. This is in contrast to total debt flows to the public sector, which run counter-cyclical to domestic growth.<sup>37</sup> These results are the gross inflows analog to the results found in Alfaro, Kalemli ̈Ozcan, and Volosovych (2014) for net debt flows, who show, using the _annual_ DRS data explored in Appendix C, that net flows to public sector are counter-cyclical, due primarily to sovereign-to-sovereign flows, while debt flows to the private sector are procyclical. Our results thus complement theirs and contribute to our understanding of upstream gross capital flows in addition to net flows, at the _quarterly_ frequency.

The global financial crisis (GFC) has generated a lot of discussion about how the nature of capital flows may have changed in its wake.<sup>38</sup> Tables C1 and C2 in Appendix C show our regressions for total debt for advanced and emerging economies, split into pre-GFC (2002q4-2007q4) and post-GFC (2008q1-2014q4) periods. For advanced economies, flows are significantly associated with the VIX before the GFC with the expected negative sign, but after the crisis they are more strongly driven procyclically by GDP growth.<sup>39</sup> EM flows

> 37The results for total debt on GDP growth are robust to the inclusion of a time trend and other pull factors, as shown in Tables C3 and C4 in Appendix C. Results on the VIX are robust to the inclusion of a time trend and the TED spread, but significance declines with the inclusion of other factors capturing US monetary conditions, such as the federal funds rate and the slope of the yield curve. These results are also robust to measuring GDP growth as the differential growth over the advanced economy average growth. We show these results for total debt in Tables C5 and C6.

> 38For instance, Cerutti, Claessens, and Ratnovski (2016) find using BIS data that the VIX is significantly associated with bank lending flows to the bank and non-bank sectors, and this was especially the case after the GFC. Shin (2013) highlights how bond flows to EM have increased after the GFC.

> 39Avdjiev, Gambacorta, Goldberg, and Schiaffi (2017) similarly find that international bank lending became much less sensitive to global risk conditions following the crisis.

31
* as the size of the stock goes from the initial point _x_ ( 0 ) to the optimal point _x_ ( _t_ ) = _x_ 1 . They assume that the transfer payment flow from the sole owner to the other country is zero during the dynamic path of recruitment of the stock (this occurs if at the beginning if * the game _x_ ( 0 ) < _x_ 1 ), and that it is a constant in all the other cases (when the stock size is equal or grater than the optimum): the transfer payment has to be feasible at each stock level that will be reached during the agreement.

With this shift from binding to non-binding agreements, Munro (1990) asserts that there is a lower chance for cooperation between the agents and that this method requires additional insight, as will be seen later.

To sustain this new method, additional insights are required. Kaitala and Pohjola (1988) speak in terms of _memory strategies_ . The authors assume that the agents are able to use these strategies. This method is used to avoid the limit of the binding agreement approach and to propose a new method to guarantee an efficient equilibrium for the transfer payments. “ _The efficient memory equilibria are as follows:_

- _The harvesting countries communicate with each other and agree on a cooperative management and agree on a cooperative management program. The cooperative management must be agreeable...;_

- _The countries agree on some threat strategies to be applied, if cooperation fails;_

- _The countries start by applying the agreed cooperative harvesting policies;_

- _If the agreement is observed, then cooperation is continued during the next period;_

- _If violations against the agreement are observed, then the countries apply their threat strategies_ ” (page 111).

Of course, there are additional assumptions about the ability of the countries to monitor their respective behavior. To make such an agreement, each country should allow the monitoring and should guarantee a free access to its fishery statistics; furthermore, this monitoring involves additional costs for the countries. This is an important point that must be considered when an optimal strategy is considered for adoption.

It is important to note that the proposed approach is ‘differential’, not ‘repeated’: “the game is unique at each time moment, and hence non-repeated, since the system evolves continuously according to the state equation” (page 111).

So, we see an evolution from the first Munro’s assumption (1979) on the binding approach. Vislie (1987) proposed a two-time game and the sub-game consistency property to overcome the constraint of binding agreements. Renegotiation in his work is not however avoided. In this new time-setting, we can see a totally different formulation, which makes use of the memory strategies.

Kaitala and Pojola use the trigger strategies. They first introduce the ∂ -strategies ( ∂ is a fraction of a time unit); these are introduced in a discrete time-setting. With this decision rule, the cooperation will continue, unless either of the agents wants to deviate from it for 15