rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2**

Dependent Variable: Unemployment Rate

|VARIABLES|(1)<br>OLS-FE|(2)<br>OLS-FE|(3)<br>IV-FE|(4)<br>IV-FE|(5)<br>IV-FE|(6)<br>IV-FE|(7)<br>IV-FE|(8)<br>IV-FE|(9)<br>IV-FE|(10)<br>IV-FE|
|---|---|---|---|---|---|---|---|---|---|---|
|POST|1.00*|1.12*|-6.52**|-5.11|-1.54*|-1.56*|-7.30|-5.17|-1.40|-1.42|
||(0.52)|(0.60)|(3.03)|(3.13)|(0.90)|(0.92)|(6.78)|(5.27)|(0.91)|(0.93)|
|Time|0.00|-0.02|0.49**|0.34|0.12*|0.07|0.55|0.34|0.11|0.06|
||(0.10)|(0.11)|(0.24)|(0.26)|(0.07)|(0.08)|(0.50)|(0.39)|(0.07)|(0.08)|
|Timesq|-0.00|-0.00|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|
||(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|WorkingAgePop||-0.00||-0.00||-0.00||-0.00||-0.00|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|rGDP||0.00||0.00||0.00||0.00||0.00|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|LFPRate||-0.29*||0.06||0.07||0.06||0.07|
|||(0.16)||(0.17)||(0.17)||(0.17)||(0.17)|
|Observations|849|765|514|514|514|514|514|514|514|514|
|Number of Countries|67|59|48|48|48|48|48|48|48|48|
|R-squared|0.01|0.03|||||||||
|Partial R-squared|||0.03|0.03|0.42|0.42|0.02|0.02|0.42|0.42|
|Hansen J p-value|||0.86|0.65|0.49|0.58|0.43|0.43|0.58|0.54|
|Kleibergen-Paap F<br>statistic|||5.45|5.42|500.47|466.94|3.07|3.18|381.01|368.91|

Note: Robust and clustered standard errors at the country level are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. POST is instrumented with the debt-to-GDP ratio, terms of trade, and five-year change in democracy score in columns (3) and (4). In columns (5) and (6) the instruments also include lagged POST. POST is instrumented with external debt-to-GDP ratio interacted with the change in world interest rates, terms of trade, and five-year change in democracy score in columns (7) and (8). In columns (9) and (10) the instruments also include lagged POST.

28
# **– 6. SPECIAL SAFEGUARDS (SSM) THE PROPOSALS**

Several factors lie behind the Doha Round’s consideration of a new Special Safeguard Mechanism (SSM). One of these was developed countries pressure for increased access to developing country markets as quid pro quo for developed country concessions on agriculture and on manufactures. Significant reductions of developing country bound rates would be required, developed countries insisted, because developing countries’ bound rates were on average higher than those of developed countries, and much higher than their applied rates. (Table 6-1) Developing countries, on the other hand, had experienced significant increases of agricultural imports over the 1990s and into the 21<sup>st</sup> century.<sup>39</sup> They insisted that they would need a safeguard mechanism to help with the adjustment to lower tariff rates.

In the background was the continuing concern to pay particular attention to issues of concern to developing countries, this signaled by the Members having accepted the label “Doha Development Agenda” for the work program.

Table 6-1: Average post Uruguay Round applied and bound tariff rates on <u>agricultural products; ad valorem equivalents</u>

|<br>Country group|<br>Applied|Bound|
|---|---|---|
|High income countries|15.0|41.9|
|Developing countries except LDC|13.4|53.9|
|Least developed countries|12.5|94.1|
|Source: Martin and Mattoo (2008, 5)|||

A possible alternative to creating a second agricultural safeguard was that developing countries obtain access to the SSG by, say, allowing a new period for notification of the tariff lines on which Members reserve SSG application. This alternative however attracted little support. There were complaints that the SSG’s complex formulas were difficult to apply and that the 1986-88 base for price triggers was increasingly out of date. Furthermore, a sense of fairness suggested that while the developed countries had had more or less exclusive access to the SSG during their period of adjustment, developing countries should likewise have an exclusive instrument during theirs.

## **The proposals**

The major proposals tabled have been those of the draft agricultural modalities working document for the July 2008 WTO meetings, the December 2008 high-

> 39 FAO 2003 and Jales 2005 and references cited in these papers.

25
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Table 17: Variable Definition and Source

|**Variable**|**Description**|**Source**|
|---|---|---|
|_DEATH_|Total number killed by a natural disaster|EM-DAT, CRED (2008)|
|_AID_|Aid per capita (current US Dollars)<br>Total real ODA commitments in recipient country per<br>capita|World Bank,<br>WDI|
|_GDP p.c._|Real GDP per capita (US Dollars in 2000 prices)|Penn World Table<br>Version 6.2|
|_GINI_|Measure for income inequality|gruen03|
|_POP_|Total Population expressed in thousands|World Bank,<br>WDI|
|_POPDENS_|People per square kilometer|World Bank,<br>WDI|
|_OPEN_|Exports plus Imports of goods and services divided by<br>GDP|Penn World Table<br>Version 6.2|
|_MAGNITUDE_|Disaster magnitude expressed in:<br>_Storms:_Wind speed measured in Kilometers per hour<br>_Floods:_Number of months per year in the country,<br>with a precipitation sum that is 1 standard deviation<br>above the long time precipitation mean in the country<br>_Earthquakes:_Richter scale|EM-DAT, CRED (2008)<br>NOAA (2008)<br>EM-DAT, CRED (2008)|
|_NATHAZ-RISK_|GIS-DATA on spatial mortality risk<br>Country mean (0-10)|Dilley et al. (2005)|
|_LATITUDE_|Absolute Latitude in degrees|Kahn (2005)|
|_ELEVATION_|Elevation in meters|Kahn (2005)|
|_CORR CONT_|Perception of the extent to which public<br>power is exercised for private gain, including<br>both petty and grand forms of corruption,<br>as well as influence of elites.|Kaufmann et al. (2008)|
|_GOV EFF_|Perception of the quality of public<br>services, the quality of the civil service<br>and the degree of its independence from<br>political pressures, the quality of policy<br>formulation and implementation, and the|Kaufmann et al. (2008)|

40
o

# Table A2.2: ALL EMPLOYEES EARNINGS FUNCTION

# WITH DIFFERENT DEPENDENT VARIABLE

||ln|W2|In (W2 p|er hour)|In (raw|data W2)|
|---|---|---|---|---|---|---|
|Intercept|9.967|(82.8)|4.650|(34.8)|9.986|(73.8)|
|Years of Primary Schooling|.042|(1.6)|<br>.026|(0.9)|<br>.049|(1.7)|
|Years of Secondary Schooling|.141|(6.0)|<br>.163|(6.2)|<br>.133<br>|(5.0)<br>|
|Years of Higher Schooling|.160|(4.4)|<br>.189|(4.7)|<br>.203|(4.9)|
|Years of Current Job Experience<br>|.091|(7.0)|<br>.094|(6.4)|<br>.096|(6.6)|
|(Years of Current Job Experience)<br>2|-.0017|(3.7)|-.0017|<br>(3.3)|-.0019|(3.6)|
|Years of Previous Job Experience<br>|.040|(1.6)|<br>.032|(1.2)|<br>.044|(1.6)|
|(Years of Previous Job Experience)<br>2|-.0011|(0.7)|-.0008|(0.4)|-.0015|<br>(0.8)|
|Years of Apprenticeship|.019|(0.9)|<br>.018|(0.8)|<br>.031|(1.3)|
|Years of VTE|.061|(2.2)|<br>.066|(2.1)|<br>.058|(1.9)|
|CEPE|.122|(0.8)|<br>.231|(1.3)|<br>.086|(0.5)|
|BEPC|.087|(0.4)|<br>.143|(0.5)|<br>.027|(0.1)|
|VTE Viploma|.309|(1.6)|<br>.389|(1.8)|<br>.285|(1.3)|
|Other Diploma|.275|(1.1)|<br>.611|(2.2)|<br>.067|(0.2)|
|Sex (1-female)|-.213|(2.4)|-.105|(1.0)|-.227|(2.2)|
|Non-Ivorian|-.284|(2.8)|-.326|(2.9)|-.322|(2.9)|
|Seasonal Job|-.634|(3.0)|-.520|(2.2)|-.684|(2.9)|
|Abidjan|.014|(0.2)|-.003|(0.1)|-.026|(0.3)|
|Rural Areas|-.276|(2.2)|-.045|(0.3)|-.225|(1.8)|
|R2|.58||.57||.52||
|F|37.3||35.8||29.5||
below 3,000.<sup>13</sup> We then used data from the 2015/16 integrated household survey to exclude two districts with the lowest shares of households receiving remittances, to ensure we focused on areas with higher likelihoods of migration. We then randomly selected 404 settlements from a pool of 580 eligible settlements to conduct a listing exercise. Figure 2 plots the settlements by treatment assignment (see below).

# _Selection of households and individuals_

Settlements are divided into enumeration areas (EAs), consisting of at most 500 individuals (50 households), and most settlements consist of only one EA. We randomly chose up to 2 EAs per settlement and conducted a door-to-door listing exercise of all households in these 404 settlements to record whether they have at least one male aged 18 to 30. In addition, a quick survey was conducted among village elders in each of these settlements to elicit village level characteristics such as the fraction of households in the village that have a migrant in Europe, in Senegal, and in Banjul; whether any vocational training program had been made available in the village; the cost of a bus to Dakar from that village; and whether any information campaign against irregular migration had already been conducted in the village. Interviewees were also asked to select the 11 males aged 18-30 most likely to migrate from the village in the next 12 months out of a list of 20 males 18-30.<sup>14</sup> Listing took place from March 26, 2019 to April 11, 2019 (see study timeline in Appendix A5) and listed 5,597 households with males aged 18 to 30 in the 404 villages. The baseline survey then took place between April 19, 2019 and May 18, 2019.<sup>15</sup> Data collection was conducted in 391 settlements, after dropping 13 small settlements that had fewer than 4 eligible households. Within the eligible households, the baseline interview took place with a male 18 to 30. We focused on this age as the most at risk of migrating the backway. If the household had more than one male in this

> 13 These excluded settlements include one city (second largest city in the country, Basse) and the rest are predominantly Sarahuli communities all with low irregular emigration rates.

> 14 Our initial plan was to use this information to oversample the youth that community leaders identified as most at risk of migrating. However, the EAs ended up with smaller numbers of youth listed than anticipated, and so in order to meet our sampling target of 10 youth per EA, we did not have much scope for oversampling.

> 15 The baseline survey was conducted by 2M Corp/RTA using survey tablets. It collected youth’s socioeconomic characteristics, labor supply, migration history and network, intentions to migrate, beliefs and expectations, knowledge questions, preference questions, and follow-up contact information. Interviews were conducted in the local languages of Mandingo, Fula, and Wolof/Serer.

12
cheap credit, and that the financial returns to cash-starved entrepreneurs are so high that interest rates are a minor concern. Even though the arguments torture both logic and evidence (see Morduch 2000), the arguments have convinced policy makers and practitioners, creating a broad defense of commercialization in social terms: High interest rates are necessary for a microfinance institution’s profitability, it is argued, and, in principle, that then attracts investment to allow portfolio growth and far greater reach. It also frees donors of the need to perpetually support the sector.<sup>10</sup> In theory, moral hazard and adverse selection in financial markets impose limits on the ability to raise interest rates, since raising fees worsens incentives to repay. Although, all else the same, higher interest rates increase revenues generated by loans, the higher prices can undermine repayment discipline to such a degree that expected profit falls. In some cases, it is impossible to find a profit-making interest rate at all.<sup>11</sup> The promise was that new credit contracts (particularly group lending and installment lending) could attenuate the incentive problems, permitting microfinance to succeed and expand on a commercial basis while serving the poor (Ghatak and Guinnane 1999). The theory works well, but the bottom line is ultimately empirical.

The empirical difficulty with the vision is not with the claim that some poor households are willing to pay high interest rates. The difficulty is instead with the assertion that there is no trade-off between pursuing commercial and social goals. With respect to interest rates, evidence 10 See Helms (2006) for an overview of the donor vision from CGAP, the microfinance donor consortium. See Hudon and Traca (2011) for important questions around subsidy and efficiency. 11 See Stiglitz and Weiss (1983) for a classic paper along these lines. See Armendariz and Morduch (2010) for a review of theory as it relates to microfinance. See Field et al. (2013) for an example of a structural empirical model of these mechanisms, calibrated to data from India. See Karlan and Zinman (2009) for an experimental approach to “observing unobservables” in the context of South African consumer finance.

13
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _8_ gaps, and projecting future values. To accomplish this, a predictive model is constructed for severe food insecurity that uses covariates that describe macroeconomic drivers of food insecurity and structural vulnerabilities. The model is of a simple contemporaneous form: where _yit_ is the prevalence rate of severe food insecurity for country _i_ at time _t_ , and _Xit_ is a vector of length _d_ that describes several attributes of country _i_ at time _t_ . For estimation purposes, the individual country data can naturally be stacked as vectors so that the prediction function can be estimated from _Y_ = _f_ ( _X_ ) where _Y_ is a vector of length _T × N_ , and _X_ is a _d_ -column matrix with _T × N_ rows. The annual predictions at the national level are optimized for out-of-sample squared correlations. The optimized model _f_<sup>ˆ</sup> can then be used to reconstruct historical food insecurity databased on official macro-economic statistics, fill missing observations, or produce preliminary estimates and projections by leveraging preliminary assessments of the macro-economic figures.

# _C. Covariates_

Since the outlook is intended to inform broad-based economic policies that can be defended in front of a non-expert audience, parsimony and straightforwardness in input variables carries a premium. A critical constraint for specifying _X_ is that credible outlooks or plausible scenarios for the future values _XH_ : _{Xt_ +1 _, t_ +2 _,..., t_ + _h}_ across a long horizon _h >_ 3 will be required to produce meaningful food insecurity outlooks. The empirical application here applies the model to the WEO database to project food insecurity through 2027, and considers the following variables for model estimation purposes.

- 1) The poverty rate at US $ 1.90

- 2) GDP per capita, ppp adjusted

- 3) The 3-year average real GDP growth rate

- 4) The 3-year average population growth rate

- 5) The 3-year average CPI (Consumer Price Index) inflation rate

- 6) Agriculture, forestry, and fishing, value added (% of GDP)

- 7) Food imports (% of merchandise imports)

- 8) Fuel imports (% of merchandise imports)

- 9) Agricultural land cover (% of land)

- 10) Forest land cover (% of land)

- 11) Historical child mortality (1995-2015 average rate)

All the data, including the dependent variable, are taken from the WDI (accessed by API on September 1, 2022). All the data comes from standard sources
To further operationalize our estimation, we assume that the linear projection of household consumption on household and other characteristics ( _x_ ) for survey 1 is given by a cluster random-effects model Were the household consumption data y2 available in survey 2, we assume the same linear projection of household consumption on household characteristics<sup>11</sup> where, conditional on household characteristics, the cluster random effects and the error terms are assumed uncorrelated with each other and to follow a normal distribution μ _j_ | _x j_ ~ _N_ (,0 σμ 2 _j_ )

and ε _j_ | _x j_ ~ _N_ (,0 σε 2 _j_ ) . Equation (1) thus provides a linear random effects model that can be

straightforwardly estimated using most available statistical packages.

We are most interested in the poverty estimates for survey 2, where the consumption data are missing. Let z2 be the poverty line in period 2, if y2 existed the poverty rate P2 in this period could be estimated with the following quantity _P_ ( _y_ 2 ≤ _z_ 2 ) (3) where P(.) is the probability (or poverty) function that gives the percentage of the population that are under the poverty line z2 in survey 2. P(.) is thus non-increasing in household consumption.

We further make the following assumptions that underlie the theoretical framework.

**Assumption 1** : _Let xjt denote the values of the variables observed in survey j at time time t, for j= 1, 2, and t= 1,..., T; and let Xt denote the corresponding measurements in the population. Then xjt=Xt for all j and t._

> 11 This assumption assumes that the returns to the characteristics _xj_ are captured by equation (2) and precludes the (perhaps exceptionally) rare situations where there could be no correlation between these characteristics and household consumption due to unexpected upheavals in the economy or calamitous disasters. Contexts where there are sudden changes to the economic structures (e.g., overnight regime change) may also introduce noise into the comparability of the parameters in equation (2).

13
<!-- Start of picture text --> South Africa: Projected Production of Capacity and Flows of Electricity Kilowat Hours per capita, 2003-<br>2010<br>7000 250<br>6000<br>200<br>5000<br>150<br>4000<br>3000<br>100<br>2000<br>50<br>1000<br>0 0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Electricity Production -- left scale Changes in Electricity Production -- right scale<br><!-- End of picture text --> Figure 5a: 2003-2010 Forecast of Electricity Infrastructure Demand <!-- Start of picture text --> South Africa: Projected Stocks and Flows of Telephone Lines, 2003-2010<br>(number per 1,000 people)<br>700 50<br>45<br>600<br>40<br>500<br>35<br>30<br>400<br>25<br>300<br>20<br>200 15<br>10<br>100<br>5<br>0 0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Stock of Telephone Lines - left scale Change in Stock of Telephone Lines--right scale<br><!-- End of picture text --> Figure 5b: 2003 – 2010 Forecast of Telephony Infrastructure Demand.

15
Yang, J. (2009). Expectation, Excess Liquidity and Inflation Dynamics in China.

_Economic Research Journal,_ (1), 106-117.

Yang, N. (2008). Ministry of Agriculture alert: attach great importance on land transfer conflict when migrants return. _First Financial Daily, 9 Dec.,_ A3.

Zhang, L., Huang, J., Li, X. & Rozelle, S. (2008). _China’s Labor Transition and the_ _Future of China’s Employment and Wages._ Working Paper. Center for Chinese Agricultural Policy, Chinese Academy of Sciences.

27
To perform the analysis, we combine a large annual panel of manufacturing plants (Statistik Industri – SI henceforth) for the period from 2002 to 2014 with the village census data (PODES), which contains information on the number of preschools available per village. As village identifiers are not available in the Manufacturing Survey, we aggregate the number of preschools at the district level, taking advantage of the representativeness of firm-level data at such low level of geographic disaggregation. We use a triple-difference framework exploiting variation in preschool expansion geographically and across time, as well as sector-specific baseline reliance on female labor supply to identify the impact of preschool availability on plants. We argue that plants that relied more on female labor at baseline would benefit more from preschool expansion. This relies on the assumption – which we confirm empirically - that preschool expansion is more likely to affect female than male labor supply given women’s greater role in childcare responsibilities. This approach allows us to control for all time-varying confounders at the district and sector levels, thus addressing the main concerns to the causal interpretation of the estimates.

We find a significant positive impact of the expansion of preschools on plant-level total factor productivity – whether measured in revenue (TFPR) or quantity (TFPQ) terms. Our preferred specification suggests economically substantial impacts: an increase of 1 additional preschool per 1,000 eligible age children (approximately 1/3 of standard deviation) increases TFPR by 11 percent for plants in sectors with 41 percent female employees (which is the average in our sample). We also explore two mechanisms through which institutional childcare may impact firm productivity via the increased female labor supply: (i) improvements in allocation of talent across jobs; and (ii) increase in job stability due to reduction in turnover.

First, preschools may alleviate one of the constraints that women face when joining the labor market - childcare responsibilities - enabling them to choose between productive and reproductive work based their ability and earning potential. This is likely to improve allocative efficiency of talent across jobs, potentially leading to an increase in productivity. We provide evidence in support of this mechanism by demonstrating that preschool expansion had a positive impact on the share of women hired by manufacturing firms using SI data. We complement this evidence with an analysis based on individual Labor Force Survey data (SAKERNAS), which shows 4