rafmacalaba/datause-encoder-data huggingface dataset:
31VU RAL IQUATIONS AND ESTATEIAD COU1ICIZUTS IN MULTDSM

||||C|oetficients||||**Page**1 of 2|
|---|---|---|---|---|---|---|---|---|
|squations<br>LES|US|JA|R|LI|BI|DC|H0<br>RW|R8S|
|I<br>Alog(C)|.094<br>(21)|.094<br>(21|.094<br>(21)|.095<br>(21)|.094<br>(21)|||lo&(W-.1 C-P)|
||.<br>-.415|.<br>-.415|.<br>-.415|.<br>-.415|.<br>-.415|||MLR|
||(1.7)<br>.506<br>(5.6)|(1.7)<br>.506<br>(5.5)|(1.7)<br>.506<br>(5.6)|(1.7)<br>.506<br>(5.6)|(1.7)<br>.506<br>(5.6)|.366<br>(2.5)|<br>|AloS(YD)|
|||||||.373||log(YD.)|
||||||-|<br>(4.2)<br>.318<br>(3.9)||1<br>loa(C.<br>1 <sup>)</sup>|
|Oil Consuaotion<br>Zauation|s||||||||
|2<br>Alog(COIL)|<br>.723|1.499|1.54|1.749|.868|||Alo(GDP)|
||(2.6)<br>|(7.2)|(5.2)|(4.2)|(2.2)||||
||-. 054|-. 026|-. 100|-. 043|-.065|||Alos(POIL./IGUP)|
||(25)|(14)|(51)|(21)|(42)||||
||.<br>-.049|.<br>-. 049|.<br>-. 049|.<br>-.049|.<br>-.049|||log(POIL-I/PMP-l)|
||(9.3)|(9.3)|(9.3)|(9.3)|(9.3)||||
||075|075|075|075|075|||log(GDPM/COIL)|
||.<br>(2.9)|.<br>(2.9)|.<br>(2.9)|.<br>(2.9)|.<br>(2.9)|||1.1|
|Chcnro in Canital Stock|laustions|(Investm|ent)||||||
|3<br>Alog(K)|.156|.156|.156|.156|.156|||loa(GDP-<br>1<sup>LK-</sup>1 <sup>)</sup>|
||(8.5)<br>|(8.5)<br>|(8.5)<br>|(8.5)<br>|(8.5)<br>||||
||.086|.086|.086|.086|.086|||Alog(GDP)|
||(1.5)|(1.5)|(1.5)|(1.5)|(1.5)||||
||-. 064|-. 064|-.084|-. 084|-. 084|||UCSTCAP|
||(1.8)|(1.8)|(1.8)|(1.8)|(1.8)||||
||-. 011|-. 011|-. 011|-. 011|-. 011|||DUM74|
||(3.2)|(3.2)|(3.2)|(3.2)|(3.2)||||
|Manufactured<br>KInorta|||||||||
|4<br>logS(M)|1|1|1|1|1||1.|log(VM)|
|<br>|.<br>-. 496|.<br>-. 496|.<br>-. 496|.<br>-. 496|.<br>-. 496||<br>-. 496|REER|
||(9.3)|(9.3)|(9.3)|(9.3)|(9.3)||(9.3)||
||-. 015|.026|-. 012|-. 011|-. 015||.010|T|
||(6.6)|(12)<br>'|(5.5)|(5.2)|(6.6)||(4.2)||
|Lmorts of Manufactures|||||||||
|5<br>logt(M)|1.&/|_1.1I_|1.&/|.294|_1.AI_||1.&/|log(A)|
||0|-.581 /|<br>0|0|0|||log(A.1)|
||.800<br>(8.3)|-. 592<br>(3.3)|-. 740<br>(5.2)|-. 483<br>(2.0)|-.221<br>(1.6)|||log(PIMA/PGN<br>?O)|
||.035|<br>0|<br>.031|<br>0|0|||T|
||(17.)||(16.)||||||
||<br>0|.581<br>51|0|.705_s_|_I_<br>0|||log(IMl1)|
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|The numbers in**parenthesis**|**are**t'|ratioc.|||||(0.4)<br>0.745)oa(IM|)-log(A)|
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_Figure 1: Comparison of the official equivalence scale with the square root equivalence scale and the per capita equivalence scale_ <!-- Start of picture text --> Comparison of three equivalence scales<br>1.80<br>1.60<br>1.40<br>1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00<br>Household size<br>Official ES SQRT ES Per-capita ES<br>Equivalence scale<br><!-- End of picture text -->

**Source** : Authors’ calculations based on HIECS 2015.

**Note** : The calorie requirement considered are 3,400 calories for a male, 2,750 for a female, and 2,000 for a child. The imputed share of food is computed from the coefficients regressed in 2015 in Upper Rural Egypt.

# **4.2 Comparison with alternative poverty lines**

In the final subsection, we contrast the official poverty lines with two benchmark alternative poverty lines. The objective is to further investigate the joint implications of the two non-standard aspects of the official poverty lines, namely their hybrid definition and their endogenous implicit equivalence scale. We assess: (a) how different the poverty picture would be if these aspects were taken out; and (b) whether the official poverty lines perform well in terms of identifying disadvantaged households. We also explore whether the official poverty lines behave more similarly to purely absolute or purely relative poverty lines.

# **Definition of the purely absolute and purely relative lines**

As shown in the previous subsection, the most natural alternative to the current endogenous equivalence scales would be to use the per capita equivalence scale. That is, the poverty line of any household is obtained by multiplying the poverty line for a single individual by household size. The most natural alternatives to the current hybrid poverty line would be to define either a purely absolute line, whose real value is fixed across time and space, or a purely relative line, established as a fixed fraction of median per capita total expenditure.

To allow a direct comparison of the characteristics of individuals who are identified as poor under the three different poverty lines, we define the purely absolute line (as well as the purely relative line) in such a way that the national poverty rate in 2015 is the same as that obtained by the official poverty lines. That is, we select through iterations the value of the absolute line (as well as the value of the constant fraction of median per capita total expenditure) in such a way as to match the national poverty rate in 2015.

14
In 2007, Moldova’s momentum (or speed) of rigid and discretionary items were both positive, which means they were growing (Figure 16). In 2008 though, we see that force was negative for both rigid and discretionary items, with the magnitude greater for discretionary items. In this case both expenditure types had the brakes applied which meant that momentum must have slowed down. In the case of rigid items, momentum remained positive, meaning that rigid expenditures continued to grow despite the negative force. The negative force applied to discretionary items was -4.2 percentage points and this reduced momentum down from 4.9 percentage points to 0.6 percentage points.

**Figure 16: Moldova Momentum and Force by discretionary and Rigidity**

<u>(percentage points)</u> <!-- Start of picture text --> Moldova Momentum and Force<br>Force Rigid expenditures<br>Force Discretionary Expenditures<br>20% Momentum Rigid Expenditures<br>Momentum Discretionary Expenditures<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: BOOST dataset and Force Toolkit

**Figure 17: Bulgaria Momentum and Force by discretionary and Rigidity**

<u>(percentage points)</u> <!-- Start of picture text --> Bulgaria Momentum and Force<br>Force Rigid expenditure<br>Force Discretionary Expenditure<br>15% Momentum Rigid Expenditures<br>Momentum Discretionary Expenditures<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: BOOST dataset and Force Toolkit

**Figure 18: Armenia Momentum and Force by discretionary and Rigidity**

<u>(percentage points)</u>

**Figure 19: Paraguay Momentum and Force by discretionary and Rigidity**

<u>(percentage points)</u> <!-- Start of picture text --> Armenia Momentum and Force Paraguay Momentum and Force<br>Force Rigid Expenditures 35% Force Rigid Expenditures<br>Force Discretionary Expenditures<br>25% Momentum Rigid Expenditures 30% Force Discretionary Expenditures<br>Momentum Rigid Expenditures<br>20% Momentum Discretionary Expenditures 25% Momentum Discretionary Expenditures<br>15%<br>20%<br>10%<br>5% 15%<br>0% 10%<br>-5%<br>5%<br>-10%<br>0%<br>-15%<br>-20% -5%<br>-25% -10%<br>2007 2008 2009 2010 2011 2012 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Source: BOOST dataset and Force Toolkit Source: BOOST dataset and Force Toolkit<br><!-- End of picture text --> In 2009, an even larger negative force was applied to rigid items than discretionary but this was still not enough to stop the positive momentum of rigid expenditures which slowed from 15.8 percentage points to
|**Code**|**Description**|
|---|---|
|PIP|"Pipelines transportation (*)"|
|AIR|"Air transportation (*)"|
|TRO|"Other transportation (*)"|
|TRD|"Wholesale and retail trade"|
|CON|"Construction"|
|TMS|"Telecommunications (*)"|
|PST|"Post (*)"|
|OTH|"Other goods and services"|
|HOU|"Housing and other communal services"|
|HEA|"Public health, sports, social security, education, culture and arts"|
|SCI|"Science, geology, mineral exploration, geodesy and hydrometeorology "|
|FIN|"Financial intermediaries, insurance, management and public organizations"|

_Source:_ Authors’ mapping based on the Russian input-output table of 2001. See the main text for details.

117
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## **2.5 Auxiliary Data**

To construct control variables and measures of grocery industry concentration in addition to other measures of interest, we use a variety of auxiliary data sources, which we document in detail in Appendix Section F.

# **3 Empirical Strategy**

## **3.1 Empirical Specifications**

The discussion in Section 2 makes it clear that, despite the maximum benefit levels being set at the federal level, state-level idiosyncrasies can be introduced to the benefit series. To estimate the impact of SNAP benefits on prices, we first lay out typical panel fixed-effects approaches before combining it with our IV. Our research design exploits variation in magnitudes of benefit increases across states and events, because benefit increases mostly occur at the same time across states due to federal implementation of the Farm Bill and the ARRA. While both price indices at the state and store level can be constructed, we report store-level regressions because more information is available on store type and geographic location, although results are robust to using indices aggregated up to the state level. In addition, we report results using monthly price indices to take advantage of the sharp timing of the events. In our preferred specification, the log of outcome _Yit_ for store _i_ in state _s_ is regressed on state-level log benefits per population _Bst_ for the store-year-month panel with store and period fixed effects to control for unobserved store characteristics and common time trends that affect prices, as shown in equation (3): _Yit_ are outcomes such as the store price index or store real sales. Because the level of the price index is not interpretable, only relative changes are relevant. The log-log specification gives the interpretation of _β_ as the elasticity of prices (or real sales) with respect to SNAP benefits per population.<sup>27</sup> We also include control variables _Xit_ matched to the store’s location, such as log state housing price, log county unemployment rate, log county average

> 27Note that since we define real sales as nominal sales divided by the price index, the sum of the coefficients on log prices and log real sales gives the coefficient on log nominal sales, which we do not focus on for brevity. Regarding the use of SNAP benefits per population as the independent variable of interest, we show in Section 5 that the price response, i.e., the benefit pass-through elasticity, can be written as a function of SNAP benefits per population. In the theory, we remain agnostic to whether the increase in the SNAP benefits per population is stemming from an (exogenous) increase SNAP benefits per recipient or an (exogenous) increase in SNAP participation.

15
it gives non-members an incentive to increase trade barriers towards the region. The incentive for members to lower trade barriers echoes the literature on multilateralism (Estevadeordal et al. (2008), Bagwell and Staiger (1999)).

The rest of the paper is organized as follows: the second section presents the data sources and the construction of key variables. The third section analyses the effects of Regional Trade Agreements abstracting from Global Value Chains. Section four extends the analysis to tease out the role played by Global Value Chains in our findings from Section 3. Section 5 discusses the implications of our findings for the debate on regionalism and multilateralism. Section 6 concludes.

# **2 Data**

## **2.1 Data on Trade Flows**

We collect data on bilateral trade flows from the Observatory of Economic Complexity (MIT). This database covers 215 countries over the period 1962-2014. The data are classified according the 4-digit Standard International Trade Classification (SITC), Revision 2. Only products and commodities are considered. Then, we aggregate these bilateral trade flows at the country-level.

To classify trade flows into final and intermediate goods, we use a concordance table<sup>3</sup> from SITC Rev. 2 to Broad Economic Categories (BEC). We then classify goods into five categories: primary, semi-finished goods, parts and components, capital goods, consumption goods, and a residual category called goods non-specified. We group these categories into intermediate and final goods as follows: intermediate goods are primary goods, semi-finished goods, parts and components; and final goods are consumption goods and capital goods.

## **2.2 Data on Trade Agreements**

The data on trade agreements comes from a World Bank database<sup>4</sup> . We use the “bilateral observations” file where each observation is a country-pair agreement for one year. There are 189 countries. The period covered is 1958-2015, during which 279

> 3The concordance table from SITC Rev2 to BEC can be found on the UN Trade Statistics webpage: https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp .

> 4See Hofmann Claudia and Ruta (2017)

5
||Product bundling|continuous|34.83|38.54|287,041|
|---|---|---|---|---|---|
||Quantity of purchased<br>goods (number of units)|continuous|23198.31|105542.8|287,041|
|**Indirectl**<br>**y policy**<br>**ifl**<br>**Bidder/**<br>**supplier**<br>**ht**|Buyer-supplier from the<br>same state (Y/N)|binary|No||282,028|
|**nuenc**<br>**eable**<br>**carac**<br>**eristics**|Supplier size (micro, small,<br>large company)|categorical|large||258,341|
||Supplier specialisation:<br>number of markets the<br>company supplies|continuous|83.87|39.65|284,718|
|**Bidding**<br>**outcom**|Number of bidders|continuous|4.32|7.21|48,703|
|**es**|Annual winner market<br>share (%)|continuous|57.04|38.47|284,614|
||Annual winner share in<br>buyer spending (%)|continuous|7.01|14.74|284,614|

_Notes: * most frequent values for categorical variables._

# 4.3 Methods

Given the unique scope of the data set we compiled, i.e. item-level health care purchases for 7 countries over many years, we set out to conduct both descriptive and explanatory analyses. The goal of the descriptive analysis is simply to demonstrate the variation in unit prices across as well as within countries. To this end we offer distribution graphs as well as variance decomposition by territory. Given the focus on average prices of most of the previous research, demonstrating the large within country and market variation in prices is both interesting on its own and serves as a motivation for our further analysis.

In order to identify effective procurement strategies improving value for money, we also conduct explanatory analyses which incorporate the wide array of indicators listed above. We compare traditional regression methods (Ordinary Least Squares) with random forests in order to identify the model with the best fit. Once the best model is identified, we can derive feasible changes in the predictor parameters, i.e. policy scenarios, which decrease unit prices in the models.

17
Table 2.5 Estimated Parameters of the Underlying Lorenz Function

|Parameter|National|Rural|Urban|
|---|---|---|---|
|β1|1.52|2.16|1.46|
|β2|-0.89|-1.42|-1.83|
|β3|0.02|0.08|-0.15|
|e|-1.65|-1.82|-0.48|
|m|-5.29|-6.60|-2.51|
|n|2.86|4.84|2.37|
|r|8.11|10.50|2.82|

Source: Author’s calculations

# **_3.  The Poverty Impact of Macroeconomic Shocks and Policies_**

We use the above simulation framework to analyze the poverty implications of some macroeconomic events. We consider first two types of exogenous shocks, an increase in foreign transfers and a deterioration of the terms of trade. Finally we analyze the case of budgetary policy.

# **_3.1 Exogenous Shocks_**

Table 3.1 Structural and Poverty Implications of Exogenous Shocks<sup>5</sup>

||Base|Dutch|Deterioration TOT|
|---|---|---|---|
|Exports|30.0|78.63|94.50|
|Domestic Good|75.0|106.98|101.83|
|Final Imports|27.0|115.59|85.89|
|Intermediate Imports|3.0|79.19|94.67|
|Total Consumption|100.0|109.87|97.54|
|Rural Consumption|40.0|101.50|96.63|
|Urban consumption|60.0|115.48|98.15|
|Total Poverty Incidence|59.2|96.61|103.39|
|Rural Poverty Incidence|78.3|98.72|102.56|
|Urban Poverty Incidence|30.5|83.33|103.33|
|Overall Poverty Gap|29.7|96.67|103.33|
|Rural Poverty Gap|38.7|97.44|102.56|
|Urban PovertyGap|16.3|87.50|106.25|

Source: Author’s calculations

> 5 Except for the base case, results are reported in percentage of base case values

14
<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text --> Notes: Based on long‐differenced specification between 2000 and 2012

# **4.2 Rwanda**

For Rwanda, nightlights-based GDP also tracks measured GDP, though the level of correspondence is lower than for Kenya (Figure 7). Night lights tend to substantially overestimate GDP up until the early 2000s, driven in particular by the rapid recovery of lights (compared to GDP) in the wake of the genocide. Growth in lights stagnated between 2000 and 2005, leading to an underestimation of GDP growth during this period. Based on night lights, we estimate an annual growth rate of 5.4 percent between 2000 and 2012, substantially lower than the national accountsbased true growth rate of 8 percent. This difference may potentially be explained by the relatively large importance of agriculture for the Rwandan economy: Between 2000 and 2012 agriculture accounted for about 2 percentage points of aggregate growth on a yearly basis. As agriculture is largely unlit, the part of growth accounted for by agriculture would not be picked up by nightlights.

Figure 7: Estimated and measured GDP in Rwanda 14
|Table 4b continued||||||||
|---|---|---|---|---|---|---|---|
||Year|Share of<br>wage<br>employment<br>in<br>agriculture|Share of<br>wage<br>employmern<br>in<br>industry|Share of<br>wage<br>employment<br>in<br>services|Working age<br>population<br>in 1995<br>(thousands)|Sourox|Income<br>group|
|||_ _ _ _ _ _ _<br>**(°/)**|_ _ _ _ _<br>(% )|**(%o)**|_|||
|Greece|1990|4.2|70.7|65.3|6,893|1|middle|
|Belarus||27.0|88.9|86.3|6,965|e|niiddle|
|Czech Rep.|1980|44.1|98.8|99.0|6,967|c|middle|
|Yugoslavia, Fed. Rep.|1981|11.7|94.2|93.4|7,083|c|middle|
|Hungary|1991|44.4|75.1|81.3|7,088|o|middle|
|Kazakstan||21.6|83.5|81.2|11,135|e|middle|
|Uzbekistan||15.7|75.5|74.0|12,860|e|mniddle|
|Romania|1990|18.3|95.4|93.8|15,611|o|middle|
|Poland|1988|21.4|94.9|96.7|25,420|c|middle|
|Ukraine||20.7|80.9|79.3|34,820|e|middle|
|Turkey|1991|2.9|76.0|62.8|38,123|1|middle|
|Russian Fed.||22.7|79.5|79.9|101,575|e|middle|
|**Latin America and Ca**|**ribbean**|||||||
|FaLkland Islands|1986|71.9|93.3|95.5|75|c|middle|
|Belize||55.8|83.8|83.0|124|e|middle|
|Barbados|1982|85.5|83.3|89.9|169|I|middle|
|Bahamas|1980|58.2|80.1|91.5|190|c|high|
|Martinique||56.4|86.7|82.6|246|e|middle|
|Guadeloupe||56.0|86.7|82.1|274|e|middle|
|Surname|1980|62.4|87.6|84.1|284|c|middle|
|Guyana||33.7|59.6|55.2|532|e|low|
|Trinidad and Tobago|1991|52.8|92.1|76.7|790|I|middle|
|Jamaica||44.7|74.5|71.2|1,595|e|middle|
|Panama|1989|29.2|70.4|78.5|1,643|I|middle|
|Uruguay|1985|56.8|78.1|73.0|2,018|c|middle|
|<br>Costa Rica|1991|58.0|74.2|75.6|2,066|h|middle|
|Nicaragua||31.8|54.7|52.8|2,259|e|low|
|Puerto Rico|1992|59.0|93.7|83.2|2,396|1|middle|
|Paraguay||43.1|73.9|69.2|2,777|e|middle|
|El Salvador|1991|65.5|67.2|63.2|3,184|h|middle|
|Honduras|1991|32.1|64.7|49.6|3,188|1|low|
|Haiti|1990|6.2|53.1|39.7|4,010|o|low|
|Bolivia|1991|42.7|61.3|46.1|4,551|h|middle|
|Dominican Rep.|1981|19.4|77.9|62.4|4,741|c|middle|
|<br>Guatemala|1989|34.5|60.7|63.8|<br>5,544|b|middle|
|Ecuador|1990|26.2|46.6|52.0|6,965|c|middle|
|Cuba|1981|78.2|99.1|97.0|7,524|c|middle|
|Chile|1991|54.9|78.0|69.6|8,991|1|middle|
|Venezuela|1991|40.9|67.6|62.9|13,135|h|middle|
|Peru||36.5|66.3|61.0|14,414|e|middle|
|Argentina|1980|52.9|74.9|73.6|21,271|c|middle|
|Colombia||38.7|68.9|63.5|21,975|e|middle|
|Mexico|1990|41.2|83.4|71.8|56,158|c|rniddle|
|Brazil|1988|38.8|83.0|71.1|<br>_101,150_|h|middle|

32
Table (D10) Impacts of the Program on Household Composition

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Number of adults in household|-0.02|(0.17)|[1.00]|2.69|(1.38)|1,610|
|Number of girls under 18 years in household|0.05|(0.15)|[1.00]|1.60|(1.40)|1,610|
|Number of boys under 18 years in household|0.25*|(0.13)|[0.33]|1.68|(1.34)|1,610|
|Number of children under 12 years in household|0.08|(0.16)|[1.00]|2.49|(1.71)|1,610|
|Number of girls under 12 years in household|-0.00|(0.12)|[1.00]|1.21|(1.23)|1,610|
|Number of boys under 12 years in household|0.08|(0.12)|[1.00]|1.28|(1.15)|1,610|
|Number of children aged 13-17 years in household|0.22*|(0.13)|[0.33]|0.79|(0.98)|1,610|
|Number of girls aged 13-17 years in household|0.06|(0.10)|[1.00]|0.39|(0.65)|1,610|
|Number of boys aged 13-17 years in household|0.16**|(0.07)|[0.33]|0.40|(0.66)|1,610|

**_Notes_** :

- The table shows the regression results on not pre-specified household composition outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The outcomes that require definitions are: Child hunger last week is defined as an indicator variable that equals 1 if any child in household had to go to bed hungry in the past week and 0 otherwise. Learning activities are done in the last 24 hours and include homework, e- learning, educational programs and videos, and reading. Child alertness is the respondent’s answer to “On a scale of 1-7, with 1 being tired and 7 being alert, how does (child name) feel right now? ” and is normalized by deducting the control group’s mean and then dividing the result by the standard deviation of the control group.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 8 in the pre-analysis plan.

12
# Short-Term Impacts of Targeted Cash Grants and Business Development Services: Experimental Evidence from Entrepreneurs in Burkina Faso

Michael Grimm University of Passau, RWI Research Network, and IZA Bonn Sidiki Soubeiga University of Passau and World Bank Michael Weber World Bank and IZA Bonn

**JEL codes:** D22; O12; Q13.

**Keywords:** Firm support programs; cash grants; finance; matching grants; business development services; agribusiness; randomized controlled trial.