rafmacalaba/datause-encoder-data huggingface dataset:
It is important to mention, however, that the framework presented here is for illustrative purposes only, as it is based on relationships observed in historical and highly aggregated data, which should be ideally supported by microeconomic evidence, to draw stronger conclusions. Moreover, a more complete macro-model would need to incorporate the balance of payments and foreign exchange shortages.

# **7. Concluding Remarks**

We have analyzed the determinants of inflation in Ethiopia by estimating error correction models for cereal, food and non-food consumer prices and the CPI using monthly data from January 1999 to November 2008. The resulting models are highly parsimonious and appear reasonably well specified.

Our results show that inflation in Ethiopia is heavily associated with the dominant role of agriculture and food in the economy. In fact, Ethiopia’s inflation is practically synonymous with food price inflation, and prices for major cereals, such as wheat, maize, teff, sorghum and barley matter most. However, contrary to the prevailing view, we find that the growth of food prices is determined in the external sector. In other words, the exchange rate and international prices explain a large fraction of Ethiopia’s inflation: a one percent increase in world food prices eventually increases domestic food prices by one percent, unless the exchange rate changes. A realistic time span is about three years. Similarly, non-food price inflation appears to be determined in the external sector, but the evidence for the long run is not as strong.

There are large periodic deviations from the equilibrium relationship between domestic and foreign prices. These are mainly due to domestic agricultural supply shocks, but also to inter-seasonal fluctuations, which probably are induced by expectations about future harvests. Therefore, the influence of world market prices explains the presence of both food price inflation and high growth rates in agricultural production after 2004. On the other hand, the mid-2008 rapid food price increases are most likely due to pessimistic expectations about the harvest of cereals. Osborne (2004) documents several similar episodes in Ethiopia.

The key role played by world food prices is surprising, given the limited role of the private sector in formal grain imports. It was not the purpose of this study to evaluate the transmission mechanism from world to domestic food prices, but we highlight a few issues.

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consisting of 271 separate industries that span all economic activity in the formal sector. Unfortunately, if an employee leaves the formal (tax-registered) sector, we are unable to observe if the employee becomes unemployed or finds a job in the informal sector.

Individual records in the raw data contain an identifying number for the person, an identifying code for the establishment, the daily wage, the date when the information of this record became valid, and the date when the information stopped being valid. If the worker leaves the establishment, the old record is closed. If the worker’s salary changes, the old record is closed and a new record is opened with the updated wage information but with the same identifier for the establishment. Importantly, we have both an establishment identifier and a person identifier that are consistently coded over time. Our first step was to convert this information into annual information. We chose December 31 as the date for which we would extract the relevant information each year from 1985 to 2001.<sup>8</sup> For each December 31 of the 17 years for which we have data, we selected the records that were applicable to the particular date. If a person had two apparently applicable records from the same establishment, we chose the record with the later start date. If a person had two applicable records from different establishments, we assumed the person really was working in both establishments. We only selected workers with strictly positive wages. This restriction mainly excludes students from the database, many of whom are insured by the IMSS although they are not really employees.

The files mentioned above include wage and employment histories of all workers registered with the IMSS. The files also contain the age and gender for nearly all workers. We also merged in industry and location information of the establishment using separate files provided by the IMSS. The match rate was nearly 100%.

4
# **METHODOLOGY**

The present analysis draws on three years of data from four villages, two where _Jeevika_ has been operating since 2006 and two where it has not intervened. The “treatment” villages were selected at random from the set of treated villages in two different districts (counties) – Muzaffarpur and Madhubani. Each “treatment” village was then matched with a set of “control” villages using propensity score matching methods (Imbens and Rubin 2015) on the basis of village level data from the 2001 government census on literacy, caste composition, landlessness, levels of outmigration, and the availability of infrastructure. The statistical matching method gave us a choice of three possible controls for the treatment village in Muzaffarpur and two possible controls for the treatment village in Madhubani. In order to find the closest treatment-control match, field investigators then visited the set of possible controls for two-days for visual inspection and qualitative assessment. This combined quantitative and qualitative matching method gave us two matched pairs of treatment and control villages, with each pair located within the same district.<sup>13</sup> For the purpose of keeping their identity anonymous,<sup>14</sup> we have named the villages in Madhubani district Ramganj (treatment) and Virganj (control) and villages in Muzaffarpur district Saifpur (treatment) and Bhimpur (Control). See table 1 below. This method of sample selection allows us to compare the village with the intervention with its “untreated” statistical clone, allowing us to draw causal inferences about the effects induced by _Jeevika_ .

> 13 This method is similar to Barron et al.’s (2011) sociological analysis of the impact of a development intervention in Indonesia.

14 We have also kept the names of informants anonymous in the paper.
Parker, 2005; Koszegi, 2006; Koszegi and Rabin, 2009). While this literature is mostly theoretical, this paper takes a stylized model of anticipatory utility to actual data. In this sense, it also contributes to the literature that studies the impact over time of life events on SWB (see, e.g. Kimball et al., 2015; Galiani et al., 2018). These papers study the degree to which SWB eventually returns to the level prior to the event, often exploiting variation found in the data in the time elapsed since the event. In contrast, this paper studies adaptation by relying on multiple measurements after the event for the same household. More importantly, this paper explicitly addresses anticipation effects, and it is the first to highlight that unexpected delays can lead to losses in subjective measures of well-being.

# **2 Experimental design and data**

In collaboration with the cash transfer program ProSoli of the Government of the Dominican Republic, we designed and implemented a randomized controlled trial of a home upgrading program for ProSoli beneficiaries. The program upgraded dirt (and badly damaged cement) floors with new cement floors and painted the windows and exterior walls of the house. The program is based on Piso Firme in Mexico (Cattaneo et al., 2009), but unlike Piso Firme which mostly focused on peri-urban neighborhoods or slums, the program in the Dominican Republic also included rural areas.<sup>2</sup> Study households were grouped into 336 small geographical areas or clusters. Eighty percent of the clusters were assigned to receive the program (treatment group) while the 20% were to the control To assess remaining assigned group. potential spillovers, 20% of the study households in treatment clusters were not targeted to receive the program. As a result, the average distance between untargeted households in treatment and control clusters and the closest targeted household was 0.5 km and 1.4 km, respectively, (p-value of t-test of equality of means is 0.000).

Panel A of Figure A1 shows a map with the location of all study households while Panel B zooms in on a map of two study provinces with the location of study households in different colors depending on their treatment status.

Figure A2 describes the timeline of data collection and interventions. The baseline data was collected in-person between October 2019 and February 2020, with a total of 1,192 households. The preferred respondent was the head of household, and if unavailable, the primary holder of the Prosoli beneficiary card.<sup>3</sup>

> 2The program was implemented in the provinces of Santo Domingo, San Cristóbal, Santiago, Monte Plata, Barahona and Baoruco.

> 3The initial list used beneficiary data from 2018 and contained 2,172 households. By the time of the

4
# **3.3 Adult health**

This section provides the other leg of interpreting health improvements in the demographic transition by analyzing an increase in adult survival. There is again an effect on the savings rate originating from the intertemporal price structure of consumption: the prices are seen to depend directly on _μ1_ and _μ2_ in the intertemporal budget constraint (12). Consequently, when their ratio changes so will the optimal intertemporal smoothing of resources for consumption (i.e. the savings rate). As such, the increasing price ( _μ2/R_ ) of second period consumption produces a decline in the savings rate (Figure 6). A counteracting effect is present when _μ2_ increases, however, because the marginal utility of second period consumption increases, exerting upward pressure on savings.<sup>17</sup>

**Figure 6. Adult Survival & Saving Rate Figure 7. Adult Survival & Fertility**

<!-- Start of picture text --> n<br>s<br>μ2 μ2<br>Source: Author Source: Author<br><!-- End of picture text --> The net impact upon fertility when _μ2_ increases is illustrated in Figure 7. The increase in the price of second period consumption automatically reduces the relative price on children, ( _κwt_ - _zt+1wt_ + _1μ2/Rt+1_ ), which increases the fertility rate. When the increase in _μ2_ is small, fertility will initially fall, and will then, due to the quadratic terms in _μ2_ in _n_ ( _μ1_ , _μ2_ ) in (23), increase the larger _μ2_ is.

Since the savings rate initially fell, the wage rate will also fall, providing households with less income for all types of goods: _c1_ , _c2_ and (1+ _n_ ). This will initially reduce fertility, but the more _μ2_ increases, the less income is available in old age and the savings motive for having children exerts upward pressure on altruistic gifts and thus fertility. As a strategy to finance old age consumption, workers therefore increase fertility because children now become relatively cheaper than capital savings. An amplifying impact on fertility and savings again originates from the PAYG system. There is a counteracting effect on pension benefits, though: there will be more retirees per household, for any given number of children, and this will generate fewer benefits to be distributed among retirees. The net effect on benefits is ambiguous, but the effect on fertility, to at least keep benefits unchanged, is clearly positive and increasing in the size of _μ2_ .

> and a higher productivity growth rate. These dynamics can be elaborated on analytically by reformulating the PAYG system in terms of fixed benefits, but this is outside the scope of this paper to consider. 17 This is the most well-known effect on savings, which is analogous to the result from the literature on ageing, where workers save more to finance a longer retirement period (see Chakraborty, 2004; and Finlay, 2006; Jensen and Jorgensen, 2008).

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Table 17. Indonesian Exports, ICT use, and Aggregate Labor Market Indicators. All workers. 2011-2015

|Dependent variable:|La|bor force pa|rticipation ra|te||Employ|ment rate|||Unemplo|yment rate||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.00786||0.00585|0.00288|0.0575||0.0539|0.0320|0.00848||0.00812|0.00806|
||(0.0302)||(0.0291)|(0.0523)|(0.0316)*||(0.0311)*|(0.0454)|(0.0116)||(0.0112)|(0.0170)|
|Share of hhlds with computer||-0.0461|-0.0479|-0.0548||-0.0701|-0.0863|-0.137||-0.00617|-0.00861|-0.00875|
|||(0.0554)|(0.0589)|(0.107)||(0.0573)|(0.0620)|(0.0908)||(0.0218)|(0.0224)|(0.0390)|
|IDN exports per worker *||||0.0324||||0.238||||0.000657|
|Share of hhlds with computer||||(0.407)||||(0.368)||||(0.138)|
|Observations|165|165|165|165|165|165|165|165|165|165|165|165|
|R-squared|0.927|0.927|0.927|0.927|0.975|0.975|0.975|0.975|0.967|0.968|0.968|0.968|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

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the U.S. as their first, second, or third main trade partner; (ii) exporters claiming an Andean Community country as their first, second, or third main trade partner; (iii) nonexporters; and, (iv) the whole sample. Exporters whose main markets are either the U.S. or the Andean Community have higher averages for all variables in comparison to nonexporters and the total sample. Focusing on exporters whose main market is the U.S. and those whose main market is the Andean Community, we verify that, on average, the share of the former that accesses the internet is 16 percent higher than the share among the latter. But those whose main market is the Andean Community pay wages (per worker) that are, on average, 38 percent higher than wages paid by exporters whose main market is the U.S. This fact can be related to the categories of product mostly exported for these two distinct markets: to the U.S., it is mostly agricultural products; to the Andean Community, while agricultural products are important, the share of manufactured products in the total exports to this economic bloc has been increasing overtime and their production demands more skilled-labor, which is paid higher wages.

**Table 5: Means of selected variables for Ecuador’s exporters by destiny**

|**Characteristic**|**Ex**|**porters**<br>|**Non-Exporters**|**Total**|
|---|---|---|---|---|
||**Main**<br>**U.S.**|**Main Andean**<br>**Community**|||
|Number of employees|260.0|153.6|55.8|90.3|
|Wage per worker (US$ per month)***|317.8|440.1|277.4|314.5|
|Sales per worker (US$ 1,000)|48.3|58.2|59.0|61.1|
|Age (years)|27.0|26.9|19.9|21.6|
|Quality certificate (percent)|36.6|29.7|10.7|16.8|
|Web-use (percent)*|80.5|69.2|47.9|55.1|
|Training offered (percent)|75.6|85.7|63.2|69.2|
|Loan (percent)|78.1|84.6|68.7|72.8|
|Plant abroad (percent)|31.7|37.4|8.8|17.2|
|Publiclylisted companyS.A.(percent)|58.5|63.7|49.8|54.0|

Notes: *Mean difference test for U.S. and Andean Community statistically significant at the 10 percent level. ***Mean difference test for U.S. and Andean Community statistically significant at the 1 percent level.

Source: ICS-Ecuador.

The figures in this section indicate that variables considered to be important appear to be determinants of the export decision and the export intensity for firms in the Ecuadorian manufacturing industry. However, other factors may also affect these two decisions made by firms. Thus, in the next section, we continue with an econometric analysis to control for these factors.

> 16 On May 16, 2006, the U.S. announced that it had frozen negotiations on the FTA with Ecuador after the latter country decided to annul an operating contract with Occidental Petroleum Corporation (OPC). The U.S. considered this action as a violation of the bilateral investment treaty between the nations. However, Ecuadorian authorities have been working on the re-establishment of FTA negotiations with the U.S.

12
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- **Tankard, Margaret E. and Elizabeth Levy Paluck** , “Norm Perception as a Vehicle for Social Change,” _Social Issues and Policy Review_ , 2016, _10_ (1), 181–211.

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- **Tur-Prats, Ana** , “Family Types and Intimate Partner Violence: A Historical Perspective,” _Review of Economics and Statistics_ , 2019, _101_ (5), 878–891.

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- **WHO** , “Ethical and Safety Recommendations for Intervention Research on Violence against Women’,” 2016.

34
shocks ( _Aijt_ ), since this is a perfect competition model and all rms face the same price. We treat the sector-level prot shock _bjt_ as a single random variable, and we assume that _Aijt_ and _bjt_ follow two independent time-invariant rst-order Markov processes. As in Cooper and Haltiwanger (2006), we assume that the prot shock _bjt_ has two states, _high_ and _low_ .

The two assumptions on materialsthat materials transform into output in xed proportions and that rms source materials from their own sector onlyare key to keep the estimation tractable. Sourcing only from own-sector implies that rms consider only own-sector state variables in the dynamic optimization problem; whereas the combination of the two assumptions on materials results on sector-level shocks entering multiplicatively into prots and therefore being treated as a single state variable. This is not the case in a Cobb-Douglas or CES production function case. We further assume that technology parameters are the same across sectors, so that all sectors can be treated symmetrically in the estimation. For suciently aggregate sectors such as ours, evidence from input-output tables shows that most input-output sourcing does occur within each sector. Calculations from IO tables from Argentina for the year 1997 show that materials sourced from own-sector account for 75 percent of all materials for food and beverages, 59 percent for textiles, 80 percent for metals, 26 percent for minerals, and 70 percent for other manufacturing industries. To assess the xed proportions technology assumption, we present an alternative specication with a Cobb-Douglas technology in on-line Appendix B. The qualitative conclusions of the paper remain unchanged.

Given the rm-level predetermined capital and the idiosyncratic productivity shock ( _K, A_ ), as well as the sector-level prot shock and wage ( _b, w_ ), rms choose labor to maximize instantaneous prots. From the static prot maximization problem we obtain rm-level labor demand, output supply, and indirect instantaneous prots. We denote the indirect instantaneous prot function with _π_ ( _Kijt, Aijt, bjt, wjt_ ) . Let _μjt_ denote the cross-section joint distribution of capital and productivity shocks ( _K, A_ ) in sector _j_ at time _t_ , and let the mass of rms be normalized to one. Integrating rm-level labor demand over the distribution of rms we obtain aggregate sector-level labor demand given by We now turn to the dynamic problem. Firms choose gross investment _Iijt_ to maximize intertemporal 8
Because there is no theoretical model on the estimation of the weights used in the construction of our index, before proceeding with the analysis we tested the reliability of the ICI as a predictor of a good investment climate in India by correlating it with a number of other performance indicators.

Being the composite ICI a summary statistics of the quality of the investment climate we expect states with a better investment climate to present a higher level of domestic private investments. Therefore we correlate our ICI with the share of domestic private investment in GDP for the 16 Indian states in our sample. Figures 1 presents this relationship and confirm our hypothesis by showing a clear and significant association between ICI and domestic private investments.<sup>7</sup> <!-- Start of picture text --> Fig. 1. Private domestic Investment (implemented) as<br>share of SGDP and ICI, 2005<br>50<br>40 Karnataka Gujarat Madhya<br>Andhra<br>Pradesh<br>30 Pradesh<br>Maharashtra Bihar<br>Jharkhand<br>20 Kerala HaryanaTamil Nadu<br>Punjab Rajasthan<br>West Bengal<br>10 Uttar<br>Pradesh<br>0<br>1 2 3 4<br>Investment Climate Index<br>Share of State GDP (%)<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2.GDP growth and ICI, 2005<br>12<br>10 Bihar<br>Karnataka<br>Tamil N<br>8<br>Kerala<br>Haryana Orissa<br>Maharashra<br>6<br>West<br>Andhra<br>Pradesh Bengal<br>4 Punjab<br>Gujarat<br>UP<br>2<br>Madhya Pr<br>0<br>1 2 3 4<br>Investment Climate Index<br>GDP growth%<br><!-- End of picture text --> A better investment climate should lead to higher growth and help reduce poverty. To establish this relationship, and the validity of our composite indicator, we also tested the relationship between ICI and GDP growth. Even in this case the data shows a significant association between ICI and state level growth (Figure 2).<sup>8</sup> These tests give us some degree of confidence that ICI is a reliable indicator of the investment climate in India.<sup>9</sup> 7 The relation is significant at the 12% level. Delhi is omitted because not available and Orissa because it is an outlier. The relation is significant at 6% if Kerala is excluded.

8 The relation is significant at the 10% level. Ideally we would like to test causality by correlating the ICI with subsequent growth. Unfortunately at the time this paper was finalized data on 2006 8