rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 3: Correlation between Aid and Missions</u>

||||World B|ank aid 19|95–2014|||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Catholic mission (Roome)|0.20<sup>_∗∗∗_</sup>||0.18<sup>_∗∗∗_</sup>|||0.19<sup>_∗∗∗_</sup>||
||(0.03)||(0.03)|||(0.03)||
|Protestant mission (Roome)||0.15<sup>_∗∗∗_</sup><br>(0.02)|0.14<sup>_∗∗∗_</sup><br>(0.02)|||||
|Any mission (Roome)||||0.17<sup>_∗∗∗_</sup><br>(0.02)||||
|Protestant mission (Beach)|||||0.13<sup>_∗∗∗_</sup><br>(0.03)|0.11<sup>_∗∗∗_</sup><br>(0.03)||
|Any mission|||||||0.15<sup>_∗∗∗_</sup><br>(0.02)|
|Ethnic dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dependent variable|0.25|0.25|0.25|0.25|0.25|0.25|0.25|
|Oster bound|0.11|0.07||0.08|0.09||0.10|
|_R_-squared|0.40|0.40|0.40|0.40|0.39|0.40|0.40|
|Observations|6,876|6,876|6,876|6,876|6,876|6,876|6,876|

_Source_ : Authors’ analysis based on mission locations from Beach (1903) and Roome (1924); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A. _Note_ : Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude). Cells that are more than 90 percent covered by barren land or more than 90 percent covered by forest are excluded from the sample. The lower bound on the coefficient of interest is calculated as in Oster (2019): the _R_ -squared from the hypothetical regression on Mission _ik_ and both observed and unobserved controls is set to 1.3 times the _R_ -squared from the actual regression on Mission _ik_ and the observed control.<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 dummy. In column 5 the regression includes only Protestant missions from Beach (1903), and in column 6 it includes Protestant missions from Beach (1903) and Catholic missions from Roome (1924). Finally, in column 7, the regression includes a dummy for Protestant and Catholic missions collapsed into one using data from both sources together.

The correlation between historical mission presence and Word Bank aid location is positive and significant across the columns of table 3. The estimated coefficients imply that cells with missions are approximately 45–80 percent more likely to host a World Bank project, relative to the sample mean. In order to assess the bias from unobservables, the analysis draws on the procedure developed by Oster (2019) to obtain a lower bound for _β_ . This test formalizes the common practice of inspecting the stability of the coefficient of interest when controls are added. It is a refinement of the approach 16
**Table 6: Relationship between temperature, humidity and observed demand**

|Dependent var: Log (consumptionper dayover billingcycle)||
|---|---|
|Log (average temperature over billing cycle)|1.219***<br>(0.134)|
|Log (average rainfall over billing cycle)|-0.0268**<br>(0.0119)|
|Constant|-3.002***<br>(0.462)|
|Observations|7,613|
|R-squared|0.041|
|Robust standard errors in parentheses||
|*** p<0.01, ** p<0.05, * p<0.1||

Notes: Averages of temperature and rainfall are calculated at the household level for each billing cycle in the data set. The regression is weighted by sampling probabilities and clustered at the town and village level.

# **Table 7: Relationship between temperature, humidity and demand for energy services based on appliance ownership and usage data**

|Dependent var: log(predicted dailyconsumption)<br>||
|---|---|
|Log (average temperature over billing cycle)|0.262***<br>(0.0624)|
|Log (average rainfall over billing cycle)|-0.0554***<br>(0.0134)|
|Constant|-0.0154<br>(0.233)|
|Observations<br>R-squared|3,828<br>0.010|
|Robust standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1||

Notes: Averages of temperature and rainfall are calculated at the household level for each billing cycle in the data set. The regression is weighted by sampling probabilities and clustered at the town and village level. The sample is trimmed to include approximately 365 days of temperature and rainfall data for each household.

**Table 8: Nonlinear Least Squares Estimates Electricity Production Function**

|δ<br>**Lamba: Heating and Coo**|2.971<br>(0.23)<br>**ling energy services**|**Lamba: Lighting ener**|**gy services**|
|---|---|---|---|
|Constant|-3.287<br>(0.30)|Constant|-3.481<br>(0.34)|
|Temperature|0.0642<br>(0.04)|Temperature|0.081<br>(0.02)|
|Rain|0.0376<br>(0.04)|Rain|-0.061<br>(0.05)|
|**Lambdas: Domestic end-**|**use energy services**|**Lambdas: Business en**|**d-use energy services**|
|Constant|-1.938<br>(0.27)|Constant|-2.977<br>(0.63)|

48
# **Section 1: Introduction**

Condoms are one of the best methods for protection against HIV (Davis and Weller, 1999) and they have great potential to keep the epidemic from expanding further if used broadly. However, across Sub-Saharan Africa, where HIV prevalence is among the highest in the world, condom use is generally insufficient to lower infection rates (Lagarde et al. 2001). Condom use varies widely among different sub-sectors of the population and in different contexts. While condom use needs to increase overall, it is important to consider this variation to identify areas of particular need and direct specific prevention efforts.

An additional reason for this inquiry into condom use stems from recent data from five African countries (de Walque, 2007) showing a surprisingly high number of discordant couples (cohabitating or married), in which one partner is HIV positive and the other is not. Among couples in which at least one of two partners is HIV infected, approximately one-third are concordant positive (male and female positive), one-third are discordant male (male positive, female negative) and one-third are discordant female (female positive, male negative). After exploring different possible reasons, de Walque concludes that extramarital sex is an important source of discordant HIV infection. Though men self-reported extramarital sex levels that are consistent with these results, women report much less frequent extramarital sex. Therefore, it is likely that women are reporting much less extramarital sex than they are having or that they are having extramarital sex in riskier conditions than men. Having extramarital sex without using a condom could be one of those risky situations. This paper investigates whether this is the 2
**33**

_Data source:_ G-Cubed model simulations .

# **_Trade protectionism_**

The unfortunate tendency to trade protection was noted earlier. So far there has not been an all-out trade war, possibly due to the hard lessons learned during the Great Depression when such a trade war did break out with disastrous consequences. While industrial economies are in theory able to applied tariff rates up to bound tariffs, as they are legally entitled to do under WTO rules, it is possible to go further by invoking special circumstances and by creating no tariff impediments to trade.

To try and capture a plausible change in protection, the actual shock assumed is a rise in all tariff rates by 10 percentage points (i.e. if a tariff was 5% it becomes 15%). The impacts on real GDP from countries increasing tariff rates are shown in Chart set 5.3. The overall impacts on global trade are shown in Chart 5.5.

The rise in tariffs by 10 percentage points has a significant negative impact on GDP. The decline in real GDP relative to baseline varies in 2009 between 1.4 percent for the United States and United Kingdom to 4.0 percent for Germany. The outcomes reflect the relative openness of the economies and the trade linkages between economies. Overall the effects of a rise in tariffs by 10 percentage points, is to reduce trade by nearly 17% by 2012 (see Chart 5.5).

As tariffs rise, the input costs of industries increase which tends to raise costs and reduce demand in the economy. The rise in relative prices of imports also causes import demand to fall which reduces incomes of the exporting countries. This contraction in global trade and contraction in global incomes is self reinforcing and hence the world economy contracts. There is a reallocation of global capital away from sectors in which tariffs have risen because the return to capital in those sectors is expected to fall because demand for those goods that have become more expensive is expected to fall. There is also a rise in the imported price of capital goods which are traded and therefore the physical amount of capital created from a constant expenditure on investment is less in all economies. This further contracts potential output.

Table 5.4 decomposes the effects of a change in global tariffs into the effects from the change in tariffs from each country or region listed across the columns on each country. Thus in 2011 the tariff scenario reduces US GDP by 1.28 percent below baseline. The impact of the US tariff increase alone on the United States is 0.28 percent in 2011. The US tariff reduces Canadian GDP by 1.76 percent in 2011 which is the major part of the total loss to Canada of 2.2 percent of GDP. Most countries are too small to gain from a rise in tariffs although several regions do initially experience a small rise in GDP from
## Figure E.10.2

# COSTA RICA LABOR INTENSIVE EXPORTS

<!-- Start of picture text --> LABOR INTENSIVE TOTAL EXPORTS<br><!-- End of picture text --> <!-- Start of picture text --> 300000<br>250000<br>;a 250000 _<br>D 200000<br>6 150000<br>C100000<br>I 50000 - I<br>1970 1980 1990<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1980<br>ROW (6.14%)<br>NA (27.64%)<br>MEX (4.41%)<br>CA (61.80%)<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1970<br>ROW (2.1 1%0 NA (7.33%)<br>O 2S X(0.02%)<br>LABOR INTENSIVE EXPORTS BY PARTNER<br>1990<br>ROW (13.46%)<br>CA (13 70%)<br>MEX (1.00%)<br>NA (71.84%)<br><!-- End of picture text -->
TABLE 1: First stage estimation - Impact of changes in unanticipated mining revenues on changes in public expenditure by area

|_Dependent variable:_|Pu|blic Spending||
|---|---|---|---|
||Whole sample IV|Rural IV|Urban IV|
|Unanticipated mining revenues|8.119|10.952|6.352|
||(0.206)***|(0.444)***|(0.201)***|
|Other controls|YES|YES|YES|
|Number of observations|1,902|688|1,213|
|Partial R<sup>2</sup>of excluded instrument|0.454|0.481|0.457|
|F-test for weak identification|1554|608|995|

_Source:_ Young Lives Study Round 1 and 2, ENAHO 2002, 2006, and Ministry of Economic and Finance Budgetary Expenditure for 2000–6.

_Note_ : The independent variable is the level of unanticipated regional mining revenues. The estimations include regional fixed effects, time fixed effects, and all controls included in the second-stage estimation. Spending measured in 100 million soles. Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. These include regional fixed effects and time fixed effects.

28
One advantage of the quadratic form (8) is that statistical tests of global quasi-concavity (in the ai) can be readily implemented (Lau, 1976), because the relevant Hessian matrix of second partials consists only of the estimated 6is. If (8) is quasi-concave in the ai, it is then possible to solve for the expected profit-maximizing investment portfolio for each weather environment and thus to measure farmer-specific deviations in investment portfolios from their expected-profit optimum. Moreover, the riskiness r of each farmer's portfolio of investments based on (3) and (8) is of a simple form: (9) r = sqrt((Eyiai + y')2) i The appropriate procedure to obtain estimates of the pi, 6i, and y. depends on what is assumed about the error terms. If the time-invariant error vk is correlated with the investments, then a farmer fixed effects estimator (Mundlak, 1978) provides consistent parameter estimates. As the portfolio of ails is measured at the beginning of the crop season, we assume that neither ot, the realization of weather in period t, nor ekt, the unobserved shock to production, is known to the farmer prior to the implementation of his asset plan for period t. The orthogonality between actual weather in period t and the ex ante investment portfolio, net of the permanent distributional characteristics of weather, is testable and we test it below.

The remaining issue in obtaining estimates of the technology parameters is the characterization of weather. We take an empirical approach. We used the daily rainfall information to construct six measures of rainfall: the beginning and end dates of the rainy season (monsoon), where the monsoon onset is determined as the date after which there has been at least 20 mm of rain within several consecutive days after June 1; the fraction of days within the season with rain; the average rain per day during the season, and the length of up to 11
Figure 3.2: Average Size Dynamics during Acceleration Episodes and Post-Communist Transitions <!-- Start of picture text --> Hungary (1992) Singapore (1967)<br>Romania (1992) Japan (1949)<br>China (1992) S. Korea (1961)<br>Chile (1980)<br>Average<br>0 5 10 15 0 5 10 15 20<br>Periods After Liberalization Periods After Liberalization<br>1.0 5<br>0.8 4<br>0.6 3<br>0.4 2<br>Relative to Time Zero (T0) Relative to Reform Date<br>0.2 1<br><!-- End of picture text --> Left panel illustrates average size dynamics for post-communist countries. Acceleration episodes are plotted on the right. Horizontal axes measure years after period 0, which corresponds to the year of reforms in the case of accelerations, and the rst available year with rm level data in the case of post-communist transitions. Given the substantial dierences in average size dynamics across growth accelerations, we also plot the behavior of the simple average of average size dynamics across these episodes. In all cases, the vertical axes measure the ratio of the average size relative to period 0.

to extend this analysis to a more recent period and to revisit the previous ndings through the lens of newer datasets.

Similarly, the fact that average rm size tends to increase with development has also been noted before in the literature. In fact, our data for average size dynamics during accelerations draw exactly from that in Buera and Shin (2013). What has not been equally emphasized before is that divergences from this average behavior can be driven by the nature of the underlying transformations taking place in the economy and that one such transformation that diers from the average is a post-communist liberalization.

# 3.2 From the Data to a Theory of Transitions

Growth accelerations tend to be highly unpredictable. However, large-scale economic reforms constitute one of the few successful predictors of growth acceleration, as shown by Hausmann et al. (2005) in the context of reduced-form regressions and by 6.2 million. The bulk of these are small-scale operations, most of them in rural areas, and run by individuals, townships, and village level governments. A major reason for the success of these new rms is that their labor costs are much lower than in state-owned enterprises, their capitalization is much more modest, and they are freer to respond to market demand. Many benet from special tax privileges granted by local authorities. 10
The companies also produce cotton meal, obtaining a recovery rate from seed of about 55 percent—a little higher than Uganda’s average of 50 percent. While some limited quantities of meal are sold locally, most is transported else‐ where, including to Dar es Salaam and Arusha, with some exported to Uganda, Kenya, and South Africa. Only one company reported an export cake price: US$340 per ton in 2008 and US$140 per ton in 2009.

Table B2 reports the weight and value composition of cottonseed, based on information received from three seed processing companies. On average, one ton of cottonseed generate 140 kgs of cotton oil, 550 kgs of husks, 260 kgs of meal, and 50 kgs of waste. In 2008 US$ value terms, the total revenue from one ton of cottonseed was US$195: of this US$129 came from cotton oil (66 percent), US$55 from cake (28 percent), and US$11 (6 percent) from husks.

**Table B2**

**Revenue Breakdown of One Ton of Cottonseed for 2008 (Four Companies)**

||**_Quantity (kgs)_**|**_Unit Price (TZ shs/kg) _**|**_Revenue (TZ shs)_**|**_Revenue (US$)_**|
|---|---|---|---|---|
|**Oil**|140|1,100|154,000|128.72|
|**Meal**|260|120|66,000|55.17|
|**Husk**|550|150|13,000|10.87|
|**Waste**|50|0|0|0.00|
|**Total**|1,000|NA|233,000|194.76|

**Notes:** The US$ value was calculated at an exchange rate of US$ = 1,196 TZ shs. Some husks are used for energy generation. ‘NA’ means non‐applicable. **Source:** Vasilaky (2009).

Following a calculation similar to Uganda for the three years presented here, prices paid by cotton seed processor as a ratio to the prices received by farmers (properly adjusted by the ginning outturn ratio of 0.35) gives a ratio 0.30, indicating that almost one third of the revenues realized by the ginning compa‐ nies comes from cotton by‐products.<sup>**12**</sup> Asked what were the key problems facing the industry, the managers and operators of all the companies cited the erratic supply of electricity and asso‐ ciated disruptions. When asked about the prospects of using cotton oil as a feeds‐ tock for biodiesel production, they all dismissed the idea as either unprofitable or unnecessary for their firms, given the high demand in Tanzania for cotton oil for human consumption (Tanzania, like Uganda, is a net edible oil importer.) Only

> **12** Several respondents in Tanzania noted that cotton oil is the most profitable part of their opera‐ tion (including ginning).

**_Page 37_**
Table 6: Dependent variable: Forward GVC <u>participation</u> shares <u>(EORA)</u>

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Regressions|LS - BE|IV - BE|IV|LS|LS|
|Avg. tari¤ rate (%)|0.001|0.004**|0.004*|0.000|-0.001|
||(0.001)|(0.002)|(0.002)|(0.001)|(0.001)|
|FDI in‡ows (log)|-0.008|-0.006|-0.002|-0.006|-0.008|
||(0.006)|(0.012)|(0.012)|(0.005)|(0.005)|
|Distance to GVC hubs (log)|-0.042*|-0.052**|-0.043**|-0.036*|-0.044*|
||(0.021)|(0.023)|(0.019)|(0.019)|(0.023)|
|Political stability index|-0.018*|-0.011|-0.017*|-0.022*|-0.015|
||(0.010)|(0.011)|(0.010)|(0.011)|(0.011)|
|Domestic industrial capacity|0.010*|0.011|0.006|0.007**|-0.007|
|(log)|(0.005)|(0.009)|(0.008)|(0.003)|(0.012)|
|Rents from resources / GDP|0.002***|0.002***|0.002***|0.002**|0.002**|
|(%)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Capital / GDP (log)|-0.012|-0.014|-0.010|-0.008|-0.004|
||(0.015)|(0.015)|(0.017)|(0.017)|(0.017)|
|Land / GDP (log)|0.009***|0.011***|0.010***|0.009**|0.009**|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Med/High-skilled labor / GDP (log)|0.017*|0.016|0.019*|0.019*|0.011|
||(0.009)|(0.010)|(0.011)|(0.011)|(0.016)|
|Low-skilled labor / GDP (log)|-0.030***|-0.032***|-0.034***|-0.032***|-0.035***|
||(0.009)|(0.009)|(0.010)|(0.010)|(0.010)|
|Exch. rate appreciation|-0.000|0.000|0.000|-0.000|0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Import elasticity|||||-0.117|
||||||(0.113)|
|Import elasticity X Population (log)|||||0.009|
||||||(0.007)|
|Population (log)|||||0.003|
||||||(0.018)|
|Statutory Corporate Tax Rate|||||-0.000|
||||||(0.001)|
|Transitional Economy Status|||||-0.009|
||||||(0.019)|
|Observations|290|290|121|121|121|
|R-squared|0.409|0.341|0.344|0.394|0.436|
|Number of countries|121|121|121|121|121|
|Decade ...xed e¤ects|YES|YES|NO|NO|NO|

Note: LS standard errors in parentheses for Columns (1) and (2); Robust standard errors for Columns (3)-(5); ***, **, and * indicate signi...cance levels of 1%, 5%, and 10%, respectively.

38
|**size_**|Cumulative|
|---|---|
|**stage_2**|depth/size of all<br>debates during the<br>given stage; number<br>of characters of<br>speeches held or the<br>length of the report|
|**size_s**<br>**tage_n**|Cumulative<br>depth/size of all<br>debates during the<br>given stage; number<br>of characters of<br>speeches held or the<br>length of the report|
|**stages_**<br>**count**|Total number of<br>legislative stages|
|**committee_**<br>**count**|Count of committees<br>from the committees<br>data table|
|**committee_**<br>**name**|Involved<br>Committees; list the<br>names of all of them|
|**committee_**<br>**date**|Date of start of<br>committee stage|
|**committee_**<br>**role**|Committee role:<br>national term (e.g. in<br>Hungary kiijelölt<br>bizottság)|
|**committee_**<br>**hearing_**<br>**count**|Total number of<br>hearings (sessions)<br>for the committees<br>stage. Number can<br>be larger than<br>committee_count<br>(row 32)|
|**ia_dummy**|Coded 1 if an impact<br>assessment was<br>conducted as part of<br>the legislative<br>process,otherwise 0|
|**ia_title**|Short title of the<br>impact assessment<br>document|
|**ia_text**|Full text in machine<br>processible format of<br>the impact<br>assessment|
|**ia_date**|Date of publication<br>of the impact<br>assessment|

31
Figure 1). Finally, the home economy is 1% of the world economy, representing a small SSA country ( .<sup>21</sup> **Persistence and volatility of commodity prices.** The most important nn= 0.01) parameter is the persistence of commodity price shocks, . To calibrate this parameter, we first construct a commodity price index for SSA. The index is a ρρ weighted average of prices of commodities that countries in SSA export. Each commodity is assigned a weight based on its average share of export value out of total goods exports in 2008–2012.<sup>22</sup> We consider 61 commodities for which price data is available either at the World Bank Commodity Price Database (The Pink Sheet) or from Jacks (2013). Export trade data is taken from the UN Comtrade Database (four-digit HS 2002 code). Column (1) of Appendix Table 2 provides the list of 61 commodities we consider.

For each commodity price, we estimate an AR(1) process with OLS using the longest time series available at an annual frequency.<sup>23</sup> Appendix Table 2 reports the implied half-life, which is our baseline measure of persistence, and the price standard deviation. The table shows that commodity prices are highly volatile and shocks are persistent: the median half-life is 5.4 years, and the price annual standard deviation is 42%.<sup>24</sup> Next, we simulate 1,000 observations from the estimated AR(1) process for each commodity price and compute the implied trajectory of the SSA commodity price index. We then use this time series to estimate the autocorrelation and standard deviation of the SSA commodity price index––by OLS with an AR(1) model. Panel B of Table 1 reports the results. The quarterly autocorrelation of the commodity price in the NK model is set to 0.975 (which implies a half-life of 6.7

> 21 There is a larger steady state transfer to the hand-to-mouth households to equalize steady state consumption across households, given that the Ricardian households also profit from firms.

> 22 Appendix Table 1 lists the two most important commodities for each country.

> 23 As is well known, estimates of persistence are biased on finite samples. However, usually a long time series of commodity price data––typically more than a century––minimizes the size of the bias and hence the severity of the problem.

> 24 Appendix Table 2 also reports the data source and the length of each time series in columns (2) and (3), respectively. Columns (4) and (5) report the OLS point estimate of the autocorrelation and error standard deviation ( ), respectively. pp σσ

14