rafmacalaba/datause-encoder-data huggingface dataset:
transfers (as above) as instruments. Multipliers are similar to the parsimonious specication and are signicant at the 5% level.<sup>24</sup> 3.4. Placebo Tests. Even if I cannot control for potential confounding variables, I can test for omitted variable bias by running a placebo regression using counterfactual growth rates when the confounding variable might be inuential, but no transfers were actually paid. A non-zero multiplier during these periods might indicate a spurious relationship. In the rst set of placebo regressions, shown in Panel A of Figure 2, I test whether states receiving large one-o transfers always grew faster during recessions by regressing growth rates during the three quarters of the 19901991 recession on the 2001 and 2008 transfers.<sup>25</sup> The estimated multipliers are always insignicant and are usually close to zero.

Alternatively, the potential confounding variable might be inuential in the years around the time the transfer is actually paid. To test for this, I counterfactually move transfers backward or forward by up to six quarters (using the Benchmark with controls specication from Table 1). In addition to detecting spurious results, it can also pick up anticipation eects (for short leads, _t <_ 0 ) or delayed eects (for short lags, _t >_ 0 ). For permanent transfers, the results are shown in Figure 2, Panel B. One can see that the largest t-statistic is at t = 0 (when the actual transfers occurred), and all other leads and lags are insignicant at the 5% level (t-statistic less than the red cuto of 2), which is what one would expect to see.<sup>26</sup> 24This endogenous BEA category includes all transfers that are not Social Security Benets, Medicare or Medicaid payments or state unemployment compensation. The BEA classies tax rebates as transfers if they are at least partially refundable, which includes all 2008 transfers but excludes the 2001 transfers and so the latter are not included in the IV estimation. Multipliers are similar in the IV and reduced form specication because growth in All other personal current transfer receipts in 2008Q2 is extremely well explained by growth in ESP transfers as constructed above (see Appendix Figure 3.1, Panel B), with rst-stage coecients close to one and rst-stage F-stats over 800. This provides an external validation of my construction of the cross-state allocation of the 2008 ESPs.

25 According to the NBER's denition, the 19901991 recession lasted through 1990Q3, 1990Q4, or 1991Q1, so I report placebo payouts during all of those dates. I thank an anonymous referee for suggesting this placebo test. The sample period for these regressions is 19901997 and they use the benchmark specication.

> 26The estimated multiplier (green line) appears to be negative in the couple of quarters in anticipation of the Social Security rate increase, positive in the few quarters following the

23
tacts from their social networks that we use for detecting potential spillovers. In all locations, before taking the baseline survey, study participants were asked to list two friends to whom they regularly talked and who lived in the community (‘network members’).

In each location, we administered the baseline and the follow-up surveys to a random sample of 15 network members among those indicated by main respondents who were _not_ in the ‘Friends’ treatment. No more than one friend per main respondent was interviewed. By construction, contacts of those main respondents who had been given extra tickets for friends (and who could have therefore attended the screenings) are not part of the spillover sample.

# **B. Timeline of activities**

In each location, all baseline survey and screening activities were concluded in four weeks. Implementation was rolled out so that activities in a given location were completed before moving to the next, in order to minimize attrition due to subjects forgetting about the screenings, travelling or relocating.

The overall timeline of the project across all locations was as follows: listing, Screening 0 and the baseline survey occurred between mid-September and the first week of December 2014; Screenings 1 and 2 between October and end of December; the endline survey and health camps between end of May and August 2015. In each location, baseline and endline were 8 weeks apart.

The typical sequence of activities in a given location was as follows: MONTH 1

- Week 1: listing

- Weekend 1: Screening 0

- Weeks 2-3: baseline survey

- Weekend 3: Screening 1

- Weekend 4: Screening 2

## MONTH 8

- Week 1: Follow-up survey

- Weekend 1: Health camp 1 - Chlamydia testing and distribution of referral letters for HIV testing

- Weekend 3: Health camp 2 - test results for Chlamydia test and post-test counselling

iii
(column 1), NMB profitability is significantly lower than other private banks upon its sale to Rabobank, which could reflect some initial dislocation associated with new management.<sup>20</sup> Although similar coefficients and significance levels are found for the clustered errors model when we drop the top and bottom 5 percent of our ROA observations (not shown in Table 5), those coefficients are not significant in the median regressions (column 2). At the least, however, it is safe to draw the conclusion that NMB has been about as profitable as other private domestic banks since its inception, which is impressive given its mandate.

Bank size (total assets) is the only control variable that is significant in both models, a reflection of scale economies in Tanzanian banking. Foreign ownership is significant and negative in the median regression which has also been found in some other developing countries (Berger et al., 2005), though this is more common in developed countries (DeYoung and Nolle, 1996; Berger et al., 2000). The coefficient is, however, small in absolute value (-0.003). Also, foreign banks tended to be relatively large, especially at the tail end of the period (Table 3), so the negative foreign ownership coefficient is likely reflective of a handful of small unprofitable banks. Finally, the share of assets devoted to lending is positive and significant in the median regression indicating that firms that focused on lending tended to earn high returns during this period.

It should be noted that the R-squared is relatively low for the ROA regressions. Such a result is not surprising when a quarterly income statement variable (net income) that can fluctuate wildly is regressed on balance sheet variables (loans, assets, etc.) that tend to be more stable. When we run the regressions using yearly ROA data, the R- squared increases slightly though the results remain qualitatively the same as in the reported regressions.<sup>21</sup>

# **B. Portfolio Quality**

In columns 3 and 4 of Table 5, the pre-privatization coefficient for NBC is positive and significant indicating that its share of non-performing loans was 20-30

> 20 See Berger et al. (2005) for similar results for Argentina.

> 21 For the median regressions, we report the pseudo-R2 statistic, which measures the proportion of the loglikelihood value explained by the model's non-intercept independent variables, i.e., 1 - (log LΩ)/(log Lω), where LΩ denotes the likelihood value of estimation with all the exogenous variables and Lω denotes the likelihood value of estimation with only the intercept. Pseudo R<sup>2</sup> -squared statistics are therefore not directly comparable to ordinary R<sup>2</sup> statistics, and in our experience, can be relatively low.

15
Figures 5, 6, and 7 present analogous results for matooke, cassava, and sweet potato, respectively. Turning first to matooke (Figure 5), we see that most households in the Central, Eastern, and Western regions are net buyers. The share of total consumption is relatively small, except in the Western region, where net purchases of matooke average 5 to 13% of total consumption for poor urban households (solid line in the middle graph of Figure 5). In rural areas of the Central, Eastern, and Northern regions the poor tend to be net sellers of matooke. In the Northern region average net sales and purchases of matooke are nearly zero across all income levels, in both urban and rural areas.

Most of the poor in both urban and rural areas are net buyers of cassava, with the exception of the poorest urban households in the Northern region (Figure 6). Net purchases of cassava as a proportion of total consumption are large in the urban parts of the Central region, and to a lesser extent in the rural areas of the Northern region. Figure 7 shows the net buyer/seller results for sweet potatoes. On average most households in the Central, Eastern, and Western regions are net buyers of sweet potatoes, while average net purchases and sales in the Northern region are negligible for all income groups. Net purchases of sweet potato are more important as a share of total consumption for poor households than for nonpoor households, especially in rural areas. Only in the rural parts of the Eastern region do households tend to be net sellers of sweet potatoes.

# **5. Poverty Impact of Food Price Increases**

The NBR analysis above has shown that poor households in Uganda tend to be net purchasers of staple food commodities, even in rural areas. Because of the multiplicity of staple foods, the net buyer or seller status of individual households is more complex than is the case in countries with only one or two major staples. Analysis of the 2005–06 UNHS reveals that in rural areas, 63% of households are net sellers of at least one staple food, but 87% are also net buyers of at least one staple food. Net buyers of staples typically do not sell any of that staple, as opposed to selling at harvest time and buying in the market later in the year. Net purchases are financed by household income from other sources, including sale of other agricultural products (including cash crops such as coffee), nonfarm enterprises, wage income, and remittances. The finding that most rural farm households are net food buyers is consistent with other studies in Uganda (Benson et al. 2008) and elsewhere in sub-Saharan Africa (Minten and Barrett 2008; Poulton et al. 2006; Jayne and Chisvo 1991). Therefore 11
# FIGURE 8. SUMMARY OF MATLAB FINDINGS ON EFFECTS OF NUMBER OF TETANUS TOXOID DOSES UPON 4-14 DAY MORTALITY

<!-- Start of picture text --> L=0 TT =Z1 TT _ 2 TT<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>D<br>40-<br>h<br>p<br>0 30<br>0<br>0i 20 -<br>V<br>r 10<br>t<br>h<br>0 9-20 MO12OS<br>POST-IMMUNIZATION POST-IMMUNIZATION<br><!-- End of picture text --> <!-- Start of picture text --> M DE<br>DURING 1974 DURING 1978-79<br>VACCINE TRIAL PROGRAM<br><!-- End of picture text --> SOURCE: BLACK, ET AL. (1980) SOURCE: RAIIMAN, ET AL. (1982)
Table 10: Occupation type and historic modes of production

||(1)<br>manager|(2)<br>services|(3)<br>sales|(4)<br>agri|(5)<br>domestic|
|---|---|---|---|---|---|
|agriculture|_−_0.0039***|_−_0.00189***|0.0519***|_−_0.01676***|_−_0.00074|
||(0.0009)|(0.00014)|(0.0016)|(0.00147)|(0.00099)|
|fsh/hunt/gath|_−_0.0035***|0.0024**|0.0277***|_−_0.0267***|0.00502***|
||(0.0007)|(0.0001)|(0.0012)|(0.0010)|(0.00072)|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Full set of FEs|Yes|Yes|Yes|Yes|Yes|
|Observations|473,586|473,586|473,586|473,586|473,586|
|R<sup>2</sup>|0.1081|0.0206|0.055|0.1053|0.0131|

**_Note:_** _∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01. This table reports a set of estimates of the relationship between occupation and historic modes of production. It is now considered whether an individual works in a specific position or sector, including manager, services, sales, agriculture or domestic. All specifications control for DHSCLUST features including, population density, and whether it is in rural or urban areas and for interaction between administration subdivision and year fixed effect. Standard errors clustered at the administrative subdivision level are reported in parenthesis.

37
- 61 -

Early discussions suggested that this difference was critical and that a system based on opportunities was likely to be perceived as fundamentally unfair.<sup>42</sup> More recently, this difference has been shown to be more apparent than real in many important cases; in particular, as long as investments can be replicated, tax rates are constant across time periods, and the government can invest tax proceeds (which are received at an earlier point in time under the tax prepayment approach) at the average rate of return in the economy, both investor returns and government receipts are the same under the two approaches.

A simple example will illustrate this point. Consider an investor in a 30 percent tax bracket who earns and invests $1,000. The investment has a 0.2 probability of a five-fold return and a 0.8 probability of becoming worthless after one "period"; the average or expected gross rate of return in the economy, which is assumed to equal the government's discount rate, is thus 0.2 (0.2 x 5 + 0.8 x (-4) - 0.2). Under the ITP approach (the "opportunities" approach where tax is prepaid), the investor pays tax of $300 in the first period, iuivests the remaining $700, and has period two consumption of either $4,200 (6 x 700) or zero, depending on the outcome of the investment. Under the ICF (the "outcomes" approach), the investor invests the full $1,000, but must pay tax at a rate of 70 percent on any returns. Depending on the outcome, the investor again has period two consumption of either $4,200 (6 x 1000 x 0.7) or zero, but pays tax in period two of either $1,800 (6 x 1000 x 0.3) or zero. Consumption possibilities are clearly the same under either approach for both "winners" and "losers." Moreover, as long as the government can invest at the average rate of return in the economy, the expected value of tax revenues 42/ See Graetz (1979) and U.S. Treasury (1977).
governments). To rule out this hypothesis, we adjust our estimates for the ideology of the government (Table A12). Our main results are about the same (both in magnitude and in statistical significance).

These augmented models cannot entirely rule out endogeneity concerns. To mitigate concerns about reverse causality and third variables bias, we follow the CBI literature to address these concerns (J ́acome and V ́azquez, 2008; Bodea and Hicks, 2015 _a_ ; Garriga and Rodriguez, 2019).

First, following Bodea and Hicks (2015 _b_ ), we verify that removing observations for the two years prior to CBI reform or the following two years does not a↵ect our estimates (Table A4 and A5). Likewise, our results remain qualitatively similar when including a dummy variable that captures whether a government has implemented CBI reform in the past 5 years (Table A6).<sup>10</sup> Second, we implement two separate instrumental variables approaches. Following J ́acome and V ́azquez (2008) and Bodea and Hicks (2015 _a_ ), we use the lagged values of the CBI Index. The first stage F-value is well above the conventional level of 10. Our results remain virtually identical when compared to baseline (Table A7). Acknowledging the limitation of this approach, we construct another instrument from the interaction of the lagged value of CBI and the average CBI level in neighboring countries. The rational is that CBI displays a regional di↵usion pattern (McNamara, 2002; Polillo and Guill ́en, 2005; Garriga and Rodriguez, 2019), which should not necessarily impact domestic inequality. However, policy change often follows a regional pattern which is important in our context (e.g., Simmons and Elkins, 2004).<sup>11</sup> To account for potentially competing e↵ects, we form a compound instrument and interact this variable with the lagged value of CBI (e.g., Nunn and Qian, 2014). We report the results in Table A8. Our results remain very similar in comparison to our baseline estimations.

Finally, we verify that our empirical results hold when using a dynamic general methods of moments (GMM) estimator. Besides helping us to mitigate concerns about endogeneity, using a GMM model allows to eliminate the e↵ect of short-term economic fluctuations and thus to account for long-run or general equilibrium e↵ects of our proposed relationship (Garriga and Rodriguez,

> 10For a similar approach, see Bodea and Hicks (2018).

> 11To verify that our findings do not follow a regional di↵usion pattern, we re-estimate our baseline model using Discroll-Kraay standard errors (Driscoll and Kraay, 1998). This does not impact our results, which we report in Table A9.

15
|Variable|Definition|Source|
|---|---|---|
|% workforce with >12 yrs years|||
|education|Percentage of workforce that has more than 12 years of education defined as university or higher.<br>Highest education level of the manager takes values 1 to 6 according to the following categories-did not|Investment Climate Survey|
|Highest level of education of<br>manager|complete secondary school (1), secondary school (2), vocational training (3), some university training (4),<br>graduate degree(5)andpostgraduate degree(6)|Investment Climate Survey|
|**_Ownership_**||Investment Climate Survey|
|State Ownership|State Ownership is a dummy variable that takes the value 1if the state owns 50% or more of the company<br>and 0 otherwise.|Investment Climate Survey|
||Domestic Company is a dummy variable that takes the value 1 if the domestic private sector owns 50% or||
|Domestic|<br>more and takes the value 0 if the foreign private sector owns 50% or more.|Investment Climate Survey|
|Foreign|Foreign Company is a dummy variable that takes the value 1 if the foreign private sector owns 50% or<br>more and takes the value 0 if the domestic private sector owns 50% or more.<br>Individual, Family, Financial Institution, Managers, Employees and Domestic Corporation are dummy|Investment Climate Survey|
|Identity of the Controlling<br>Shareholder|<br>variables which take the value 1 if the largest shareholder or owner in the firm is an individual, family,<br>bank or investment fund,manager of the firm,employees of the firm,or domestic corporation|Investment Climate Survey|
|**_External Financing_**||Investment Climate Survey|
|External Financing|100-Percentage of new investment over the last year coming from internal funds or retained earnings|Investment Climate Survey|
|Equity|Percentage of new investment over the last year coming from equity, sale of stock|Investment Climate Survey|
|Local Bank|Percentage of new investment over the last year coming from local commercial banks (loan, overdraft)|Investment Climate Survey|
|Foreign Bank|Percentage of new investment over the last year coming from foreign owned commercial banks|Investment Climate Survey|
|Leasing|Percentage of new investment over the last year coming from leasing arrangement|Investment Climate Survey|
|Investment Fund|Percentage of new investment over the last year coming from investment funds/special development<br>financing or other state services|Investment Climate Survey|
|Trade Credit|Percentage of new investment over the last year coming from supplier or customer credit|Investment Climate Survey|
|Credit Card|Percentage of new investment over the last year coming from credit cards|Investment Climate Survey|
|Family|Percentage of new investment over the last year coming from family and friends|Investment Climate Survey|
|Informal|Percentage of new investment over the last year coming from informal sources (eg: moneylender)|Investment Climate Survey|

55
approximation in finite samples when all moment conditions are linear. Thus, in our estimations we use Windmeijer’s correction, as implemented in STATA by Roodman (2006).<sup>13</sup>

# **3.2 Growth, infrastructure stocks and the quality of infrastructure services**

As a preliminary step, the top half of Table 2 shows that the aggregate indices of infrastructure quantity and quality are strongly and positively associated with long-run growth. Specifically, we find a positive correlation (0.34) between average annual growth in real GDP per capita over 1960-2005 and the average of each of the two synthetic indices of infrastructure quantity over the same period. We find an even stronger positive correlation between average growth per capita and the synthetic indices of aggregate quality of infrastructure services (0.42 for **IQ** 1 and 0.35 for **IQ** 2). Across infrastructure sectors (not shown in the table), growth is positively correlated with telecommunication stocks (0.24 for total phone lines, and 0.21 for main phone lines), electricity generating capacity (0.15) and the length of the road network (0.22). In addition, growth is positively associated with the quality of telecommunications (0.23), quality of electricity supply (0.14) and road quality (0.23).

Table 3 reports the GMM estimates of the parameters of the growth regression augmented by the synthetic indices of infrastructure performance. As already noted, the two alternative indices of infrastructure quantity, as well as those of quality, are very highly correlated, and hence to save space we only report results using **IK1** and **IQ1** . The set of standard control variables included in the regressions comprises measures of human capital (secondary enrollment, from Barro and Lee 2001), financial depth (from

> 13 The number of instruments is also a potential concern. The GMM estimators described in the text are designed for panels with a small time dimension T. The number of available internal instruments grows with the square of T (Windmeijer 2005; Roodman 2007). Using too many instruments in finite samples causes several problems in GMM-system estimators. First, a large set of instruments tends to over-fit the endogenous variables, thus failing to remove their endogenous component and yielding biased coefficient estimates. Second, the estimated variance matrix of the moments may not be accurately estimated, especially in finite samples. Usually, the number of elements in the estimated matrix is quadratic in the number of instruments —which, as noted, is itself quadratic in T. Even for modest sizes of T this tends to reduce the power of the Hansen/Sargan tests for joint validity of the instruments (as well as the differenceSargan tests for subsets of instruments), thus leading to Type I errors —that is, accepting the null of validity of instruments when it is not true.  Following Roodman (2007), we tested the robustness of the estimates and the specification tests to reductions in the instrument count. Overall, the results were supportive of the empirical model. These experiments are not reported here to save space, but are available form the authors.

18
FIGURE 3 BRAZIL: BASIC INDICATORS (1986-1987) <!-- Start of picture text -->
1.  pi  year<br>I  e'C'1-/<br>2<br>0~<br>A2....  Industrial  Production<br>12-  g  s/  \  /  (t/t-4)<br>Trade Surplus<br>0  A~~~~~<br>b/-<br>2-  /<br>1985  1986  V  1987  1988<br><!-- End of picture text -->

<!-- Start of picture text -->
-36  -<br><!-- End of picture text -->
**Figure 11: Determinants of FDI/GDP Growth (Regional Groupings), 2000-10**

# **Inward FDI/GDP stock, 2000-10**

Source data: UNCTAD statistics and World Bank staff calculations

# **Energy Loss Reduction, 2000-10**

Source Data: WDI and World Bank staff calculations

# **Financial Sector Development Growth, 2000-10**

# **Corporate Tax Reduction, 2000-10**

Source Data: WDI and World Bank staff calculations

**Natural Resources per capita, 2000**

Source Data: World Bank, Wealth of Nations Catalog

# **Macroeconomic Stability, 2000-10**

28