rafmacalaba/datause-encoder-data huggingface dataset:
confirm that local food price changes are uncorrelated with the initial livelihood strategies and child human capital, implying that at least in this restricted dimension, price changes are exogenous to the household. This finding is important since we can exclude the possibility of potential endogeneity of local food price changes. As shown later, a full specification that includes all explanatory variables (village‐average) also confirms the result.

# **5. Empirical Results**

This section summarizes the empirical results. As discussed, we use the Laspayres index of main food commodities as the measure of price changes, based on quantities and calories. Results using both constructions are presented in the following tables.

Table 4 shows the benchmark results on changes in the height‐for‐age z scores. Columns 1 and 2 presents the key results using quantity‐based and calorie‐based price indices, respectively. The specifications include the non‐producer indicator that has the value of one if the proportion of main food consumption own produced is smaller than 0.035 and zero otherwise, the Laspeyres price index, their interaction term, age in months, age squared, male dummy, log of birthweight (kg), and province dummies. The results show a significant positive effect of the non‐producer indicator and a negative effect of its interaction with the price index. The price index itself is not significant. Both results in quantity and calories are qualitatively similar. The non‐producer households experienced higher child growth (without food price spikes) but they are more vulnerable to food price spikes than the producers, resulting in a significant reduction in child growth. Age has a significantly positive but convex effect. Children in Lumpung, East Java, NTB and North Sulawesi grew faster relative to those in Central Java.

Columns 3 and 4 include, as controls, household and village characteristics: the number of household members, household head’s age (years), household income (labor earnings in 2007, not including transfer incomes and non‐labor incomes), male head dummy, total asset values (residential and durable goods in 2007), income loss due to negative shocks during 2008‐2010,<sup>11</sup> asphalt inter‐village road indicator, distance to provincial capital (kilometers) and province dummies. Both household income and total assets are interacted with male dummy. Asphalt inter‐ village road indicator and distance to provincial capital are also interacted.

Both Columns 3 and 4 confirm a negative effect of the interaction of the non‐producer indicator and the price index.<sup>12</sup> The price index itself is again not significant. Both results in quantity and

> 11 There were 19 villages that reported crop failure. The effects of such incidents were included in this variable.

> 12 In this sense, the substitution effect seems to overweigh the income effect among food non‐producers, whereas the balance is not clear among food producers.

13
- 45 -

and for personal income and corporate taxes) makes it difficult to crosscheck information, especially between income and sales taxes as well as between customs revenues and domestic transaction taxes. Seventh, existence of a large number of taxes with limited yields and complicated tax structures make it difficult to administer taxes and increase tax compliance costs for taxpayers.

185. One result of the weak administration of taxes has been the low number of contributors for taxes. Table IV shows the number of individual contributors and tax paying legal entities under major taxes in El Salvador as an example.

# Table IV: Number of Contributors Under Major Taxes in El Salvador, 1987

|Taxes|Number of Contributors|
|---|---|
|Income Tax|51,657|
|Individuals|(49,376)|
|Legal Entities|( 2,281)|
|Property Income|14,480|
|Individuals|(11,931)|
|Legal Entities|( 2,549)|
|Sales|8,086|
|Individuals|( 6,549)|
|Legal Entities|( 1,537)|

Source: IMF, Bank Staff Estimates.

186. Conclusions. To reduce evasion and increase collection from existing and proposed taxes, the following actions are suggested:

- (i) establishing reasonable penalty rates for tax delinquencies and forcefully implementing them (i.e. high penalty rates would likely prove to be politically difficult to apply);

- (ii) providing training and technical assistance to tax administration agencies, adjusting salaries of tax administrators in line with changes in the cost of living in order to reduce incentives for seeking other sources of income and firing staff in cases of demonstrated lack of integrity;

- (iii) concentrating tax collection efforts on large corporations and on wealthy people;

- (iv) establishing a unified tax registry;
|_φ_|_α_|_η_|_A_<sup>_∗_</sup>|_β_|_θ_| ̃_α_|_γ_<sup>_DE_</sup>|_MUγ_(_v_)|_MUA_(_v_)|_v_<sup>_∗_</sup>|
|---|---|---|---|---|---|---|---|---|---|---|
|Ben|chma|rk ec|onom|y|||||||
|0.4|0.8|0.3|0.4|0.96|1|0.50|7.5%|22.08|-16.34|0.28|
|0.4|0.8|0.6|0.4|0.96|1|0.68|14.4%|7.44|-22.77|.|
|0.4|0.4|0.3|0.4|0.96|1|0.34|1.4%|4.93|-13.06|.|
|0.4|0.9|0.5|0.4|0.96|1|0.66|13.7%|15.24|-21.82|.|
|0.4|0.5|0.2|0.4|0.96|1|0.32|0.6%|14.97|-12.74|0.22|
|Rela|tivel|y clos|ed ec|onomy|||||||
|0.2|0.8|0.3|0.4|0.96|1|0.40|3.7%|15.81|-7.06|0.68|
|0.2|0.8|0.6|0.4|0.96|1|0.64|12.9%|5.19|-10.47|.|
|0.2|0.4|0.3|0.4|0.96|1|0.32|0.6%|3.33|-6.37|.|
|0.2|0.9|0.5|0.4|0.96|1|0.58|10.6%|11.02|-9.34|0.27|
|0.2|0.5|0.2|0.4|0.96|1|0.26|-1.7%|10.35|-5.94|0.68|
|Pati|ent e|cono|my||||||||
|0.4|0.8|0.3|0.4|0.975|1|0.50|9.2%|60.36|-28.07|0.44|
|0.4|0.8|0.6|0.4|0.975|1|0.68|16.2%|21.03|-40.56|.|
|0.4|0.4|0.3|0.4|0.975|1|0.34|3.0%|13.26|-22.04|.|
|0.4|0.9|0.5|0.4|0.975|1|0.66|15.4%|42.85|-38.65|0.08|
|0.4|0.5|0.2|0.4|0.975|1|0.32|2.2%|40.20|-21.46|0.51|
|Low|er ela|sticit|y of s|ubstitu|tion|econo|my||||
|0.4|0.8|0.3|0.4|0.96|2|0.50|3.7%|9.00|-26.92|.|
|0.4|0.8|0.6|0.4|0.96|2|0.68|7.0%|1.40|-24.90|.|
|0.4|0.4|0.3|0.4|0.96|2|0.34|0.7%|5.39|-35.06|.|
|0.4|0.9|0.5|0.4|0.96|2|0.66|6.6%|3.11|-24.94|.|
|0.4|0.5|0.2|0.4|0.96|2|0.32|0.3%|19.34|-37.12|.|

Table 1: Optimal reserve accumulation for selected parameter values 21
were newly developed.

# **VII External Resources**

This section provides information on external resources available for the study, including the location of the described data. For each resource, contributors can provide a _type_ and free-form _description_ , _citation_ , _link (DOI/URL)_ , and information on the _access policy_ for this resource.

If available, contributors should not only point to the data itself, but also to resources describing the data and study such as academic publications, reports, codebooks, ethics protocols, etc. The controlled vocabulary for the resource _type_ is adapted from the World Bank Microdata Catalog but allows as additional types “database or data repository entry” (to describe the locations of the study’s datasets), “trial registration”, “pre-analysis plan”, “populated pre-analysis plan”, and “research ethics documentation”. The last four entries complement the information in section VI.

Data from a single RCT may be stored in different locations (e.g. replication data in a repository like Harvard Dataverse, access-restricted identifying data with the researcher, administrative data with a separate data provider); each resource should be linked. Conversely, different external resources, such as the data itself along with replication code, are sometimes stored in the same location or with the same citation information. The CV allows contributors to enter the information once and tick all options that apply under _type_ and provide additional detail in the _description_ .

# **5 Creating a Metadata Catalog Based on the Metadata Schema**

We close with a few considerations for implementing a catalog based on the proposed schema, with a focus on maximizing the quality, consistency, and usability of the metadata and catalog.

**Catalog:** An RCT metadata catalog consists principally of a back-end data entry interface; front-end search, filtering, and display functions; and an underlying database of metadata fields. Catalog owners may consider modifying the schema. For users interested in adopting only a subset of the metadata fields, we made an effort to minimize dependencies across modules (with the exception of core properties of the RCT such as the study arms, which appear in several modules). Catalog owners may also be interested in expanding the catalog beyond recording the properties of the data, for example by adding fields for analysis results, such as standardized treatment effect sizes, or contextual information, such as intervention costs.<sup>13</sup>

To fully support interoperability and bulk uses of the metadata, we recommend an _API interface_ to access and download the catalog contents. This functionality also allows the catalog to be read by data search

> 13These are not part of our schema because they often require a separate effort: verification (e.g. by replicating the analysis), modification (e.g. by standardizing effect sizes or harmonizing analysis approaches), and possibly additional research/estimation of data not in the public domain (such as the fixed and variable intervention costs).

19
augmented with capacity utilization to account for efficiency loss due to unused capacity and low market demand.<sup>7</sup> The schooling variable is a weighted average of education years of the workforce and captures elements of labor quality.<sup>8</sup> Nominal values are converted to U.S. dollars using nominal exchange rates.<sup>9</sup> The regression is estimated using ordinary least squares with White correction for heteroskedasticity and allows for clustering within countries. The specification is as follows: (3) ln ( _Outputi_ ) = _cons_ + _β m_ ln ( _Materiali_ ) + _β l_ ln ( _Labori_ ) + _β c_ ln ( _Capitali_ ) 4 + _β u_ ln ( _CapUtili_ ) + _β s_ ln ( _Schoolingi_ ) + _β tSectoralDu mmy_ + ∑ _β nDn_ + _ε i n_ = 1 The different production technologies used by the two sectors could mean a need to estimate separate equations for each sector. In an unreported exercise the equation is therefore estimated with sectoral dummy variables interacted with the input coefficients. The parameter estimates are not jointly significant, however. Consequently, the data are pooled across sectors to take advantage of a large sample size and the results of the production function estimation are reported in Table 5.

Moldova is chosen as the base country, and country-specific productivity gaps expressed in relative percentage terms are retrieved from the estimated coefficients on the country

> 7 Capacity utilization was estimated by simply asking managers what they considered their capacity utilization to be. No effort was made to define a standard number of shifts or correct for differences in the length of the workweek.

> 8 There was no reliable information on worker training or experience in the Business Environment and Enterprise Productivity Extension Survey.

> 9 Using nominal rather than real exchange rates  introduces bias because many countries intervene to support the value of their currency. Uzbekistan in particular has an overvalued exchange rate. But in comparisons of international competitiveness nominal exchange rates make more sense because these are what exporters face.

18
Gibson, J., T. Le, and B. Kim. 2017. “Prices, Engel Curves, and Time-Spatial Deflation: Impacts on Poverty and Inequality in Vietnam.” _World Bank Economic Review_ 31 (2): 504–530.

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26
Table D4: ANCOVA Regressions by Urban/Rural [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 1 HHI 2<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>55<br><!-- End of picture text --> _Note:_ The figure plots ANCOVA regression results with region and round controls and standard errors clustered at the household level (see Equation C.1). We display coefficients for the interaction of lagged income diversity indices (Indices 3-6) and a head-of-household gender indicator. Male-headed households serve as the comparison group. Horizontal lines represent 95 percent confidence intervals.
the time of issue of these bonds, many states have pre-paid, and only Rs.144.23 billion (US$ 3.23 billion<sup>42</sup> ) remained as at end-March 2011.

In addition to the above debt restructuring program to link with institutional reform and move toward market access, the role of the RBI is also important. First, as the regulator of the banking sector, the RBI sets the statutory requirements for banks to hold state debt. This increases the acceptability of state securities by the market. Second, the RBI tightened the regulation for use of the overdraft facility by states. Previously, states had resorted to the facitily as a way to roll over short-term borrowing to finance structural deficits. The terms and conditions for facilty use were formula based and specified. Moreover, the use of the facility by the states is disclosed to the market on an ex-post basis. For example, the market has information on the better performers compared to the chronic-deficit states (table 5 shows that Punjab, Uttarkhand, and West Bengal, depended heavily on this facility during 2010–11 to meet their temporary resource gap). Such information influences market sentiment and spreads, while lowering credit ratings.

|**Tab**|**le 5: States Ov**<br>|**erdrafts and A**<br>|**ccess to Cash-**<br>|**credit (numbe**<br>|**rof days)**<br>||
|---|---|---|---|---|---|---|
||**Special **|**WMA**|**Normal **|**WMA**|**Over**|**raft**|
||**2009-10**|**2010-11**|**2009-10**|**2010-11**|**2009-10**|**2010-11**|
|**1**|**2**|**3**|**4**|**5**|**6**|**7**|
|Andhra Pradesh|1|3|–|–|–|–|
|Haryana|7|10|5|10|–|8|
|Kerala|18|–|2|–|–|–|
|Madhya Pradesh|11|–|11|–|–|–|
|Maharashtra|–|–|–|–|–|–|
|Karnataka|–|–|–|–|–|–|
|Nagaland|69|–|45|–|13|–|
|Punjab|130|133|128|132|29|13|
|Rajasthan|–|–|–|–|–|–|
|Uttar Pradesh|8|4|8|4|–|–|
|West Bengal|95|195|15|113|8|62|
|Himachal Pradesh|–|–|–|–|–|–|
|Manipur|–|–|–|–|–|–|
|Mizoram|29|25|15|15|–|–|
|Goa|–|–|1|–|–|–|
|Uttarakhand|69|35|26|12|9|10|
|Meghalaya|–|1|–|–|–|–|
|Jharkhand|–|–|–|–|–|–|

Note: WMA= ways and means advances. Normal WMA is formula-based, special WMA is after access to normal WMA but is collateralized. ̳- ̳indicates no access to facility and strong cash management position. Source: Reserve Bank of India, Annual report 2010-11

> 42 Exchange rate as at end-March 2011 at Rs 44.65 =US$ 1.0 (source: IFS ).

26
# TABLE 2 **EMPLOYMENT** AND LABOUR PRODUCIIVITY **IN** KOREA BY **SECTOR, 1963-86**

||||Employment||Pr<br>(1975|oduction Per Wor<br>Constant Thousan|ker<br>d Won)|
|---|---|---|---|---|---|---|---|
||Employed<br>Population<br>(thousand|Agriculture,<br>Forestry,<br>and Fishery|Mining<br>and<br>Manufacturing|Social<br>Overhead<br>Capital and<br>Others|Agriculture,<br>Forestry,<br>and Fishery|Mining<br>and<br>Manufacturing|Social<br>Overhead<br>Capital and<br> <br>Others|
||person)|(%a)|(%a)|( a||||
|1963<br>|7662|63.1|8.7|28.2||||
|1964<br>|7799|61.9|8.8|29.3||||
|1965<br>|8206|58.6|10.3|31.0||||
|1966<br>|8423<br>|57.9<br>|10.8|31.3|432|1902|692|
|1967|8717|55.2|12.8|32.0||||
|1968|9155|52.4|14.0|33.6||||
|1969|9414|51.3|14.3|34.4||||
|1970<br>|9745<br>|50.4|14.3|35.2|541|3110|1041|
|1971|10066|48.4|14.2|37.4||||
|1972|10559|50.9|14.2|35.2||||
|1973|11139|.).0|16.3|33.7||||
|1974|11586|<br>48.2|17.8|34.0||||
|1975|11830|45.9|19.1|35.0|658|4589|1851|
|1976<br>|12556|44.6|21.8|33.5||||
|1977<br>|12929<br>|41.8|22.4|35.8||||
|1978<br>|13490<br>|38.4|23.2|38.4||||
|1979<br>|13664<br>|35.8|23.7|40.5||||
|1980<br>|13706|34.0|22.6|43.4|731|9190|4667|
|1981|14048|34.2|21.3|44.5||||
|1982|14424|32.1|21.9|46.1||||
|1983|14515|29.7|23.3|47.0||||
|1984<br>|14417<br>|27.1|24.2|48.7||||
|1985<br>|14935<br>|24.9<br>|24.5|50.6||||
|1986|15505|23.6|25.9|50.0||||

Note: a Percent of total employed population Sources: Economic Planning Board (1982, 1986, 1988) and Kim (1988).
nevertheless, that since ε!"!! is negatively correlated with ε!"<sup>,Crossing</sup> !,!!!<sup>is</sup> also probably correlated with the demeaned error term and, hence, the 2SLS estimator of the demeaned equation will likely under-estimate, _ceteris paribus_ , the true effect of aid on economic growth.

We propose two approaches to circumvent this potential statistical problem. The first approach relies on a smoothing method of the latent process that determines our instrumental variable. Specifically, we do the following. We take a panel of 130 developing countries that appear in the official DAC aid recipient country list at any time between 1987 and 2010, other than the 35 countries in our original sample.<sup>19</sup> We demean all the series in our extended panel (165 countries in total) by projecting the annual log of nominal per capita GNI onto a set of country fixed effects, denoted y!<sup>.</sup> We then take the residuals, e!"<sup>. For each of the 35 countries in our working sample,</sup> we construct a set of weights w! ∈ w!, w!, . . . , w! bounded between 0 and 1 for the 130 donor countries added to the dataset such that the following distance function is minimized: where || ⋅|| is the Euclidian distance operator. The vector e includes the residuals e!"<sup>. For each country</sup><sup>_i_, we use all the years in our sample because that</sup> minimizes the influence of a given observation around the period of crossing the IDA ∗ threshold. Denote the optimal weight assigned to country _j_ as w!". We then define

∗ e!"<sup>=</sup> ! w!" ⋅ e!" , and construct the predicted per capita nominal GNI as y!"<sup>=</sup>

> 19 Since the both our sample and the extended dataset are unbalanced panels, for each of the 35 countries in our sample we only use a panel of balanced available donors.

15
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20