rafmacalaba/datause-encoder-data huggingface dataset:
source revenues on fiscal responses and among others, Devarajan et al. (2015) sheds light on this issue by simulating the impact of resources windfalls on long-term economic growth and welfare under resource price uncertainty. Ag ́enor (2014) provides a characterization of the optimal fiscal response in the presence of oil price shocks using a small open low-income country DSGE model. While these papers focus on oil price shocks, we analyze the optimal fiscal response from a different perspective by focusing on oil production shocks only, because our paper’s objective is to provide policy advice to countries that, like Uganda, have not yet started producing. Tilak et al. (2015) uses the IMF DSGE model to assess some fiscal policy options: i) Household transfers, ii) Front-load public investment and iii) Gradual public investment. Their fiscal policies are similar to Berg et al. (2013) as well as Giovanni et al. (2014); but slightly different from ours, because we do not consider the front-loading option.

Although we focus our impulse response analysis on oil production shocks as we aim to assess how a sudden increase in public revenues due to oil production affects macroeconomic dynamics, in our investigations regarding the optimal share of oil windfalls to use for public investment, we reckon that most of the volatility in oil revenue for a new oil producer comes from oil price volatility instead of production volatility. For this reason, in these investigations, we use oil price volatilities as the main source of uncertainty.

We calibrate this model to reproduce key features of the Uganda economy, a Sub-Saharan Africa low-income country that recently discovered significant oil resources, with an estimated 2.5 billion barrels of reserves. This is a significant development milestone, as it represents great opportunities for the financing of Uganda’s National Development Plan. According to the World Bank’s economic projections, oil revenues in Uganda are expected to be relatively important at an estimate around of 3 percent of the national Gross Domestic Product (GDP) over the next five years. These estimates, associated with large movements in commodity prices in the world natural resources market over the past decade, have sparked renewed interest in a better understanding of the impact of this natural windfall on the Ugandan economy.

Our results show that a better fiscal management is to save the resource income in a sovereign wealth fund for future generation when public capital is almost unproductive. We also find that the gradual scaling-up of public investment ( _The Sustainable-Investing Approach_ ) yields the 4
size of the migrant network, and household unobservables, ui.

# 4 Empirical Methodology

## 4.1 Estimating the Effect of Migration on Consumption

The theoretical framework above suggests the following empirical specification for household consumption, cit: The logarithm of per capita household consumption in period t will be a function of measured household physical and financial wealth per capita at the end of period t − 1, wit−1, and the relative size of the migrant labor force from village j, Mjt. Household characteristics, Xit, influence consumption through endowments, such as human capital, which affect household permanent income, and through demographic characteristics which influence consumption preferences. Since ability to participate in or benefit from the migrant labor market may affect households differently depending on their wealth level, we are also explicitly interested in the interaction, wit−1 · Mjt. We include time-varying village variables to pick up heterogeneity across villages in policies and economic conditions, Zjt, that may influence consumption through effects on productivity. We use village dummy variables, vj, to control for other observable and unobservable fixed characteristics of villages that may affect consumption, such as location, connections to off-farm markets and proximity to employers. Additionally, village specific trends, tj, related to underlying endowments and initial conditions in the village, may further affect consumption. At the household level, we also expect that fixed unobservables, ui, will be related to consumption preferences and to the ease with which the household participates in the migrant labor market.

Household wealth is typically difficult to measure accurately because the valuation of productive asset stocks depends upon assumptions about depreciation and the useful life of assets, and the value of financial assets is frequently under-reported in household surveys. Moreover, access to transfers and informal loans from non-resident family members and friends will have an impact on expected lifetime wealth and current consumption, but the ability to receive transfers and loans will be unobservable to the econometrician. To proxy for lagged household wealth in equation (7), we use lagged household consumption, implicitly assuming that lagged consumption is strongly 10
correction towards the long run equilibrium level in the current period<sup>6</sup> . The positive sign on the land/labour price coefficient is of exactly the same value as in the levels regression reported in Table 2 and is in agreement with Frisvold's (1991) prediction. Lagged public R&D expenditures are positive and significant and the positive coefficient for farm size agrees with de Janvry et al.'s (1989) argument that larger farms will have a greater machinery-using bias, due to the costs of labour management.

**Table** 4: Unrestricted ECM Results: Significant Variables Only

|[<br>Variable|**Coefficient**|**M/L**Ratio|**COeffiCient**|**F/A**Ratio|
|---|---|---|---|---|
|[SHORT<br>RUN|||||
|CONSTANT|0,|3.26 (3.5)|60|-3.33 (-2.0)|
|**A(PM/PL)I**|42|-0.37 (-3.4)|I<br>_I||
|I A(PF/PA),|||12<br>^1-0.36|(-1.9)|
|A(PA/PL),|**$3**|NS|**63**|-0.26 (-1.5)|
|TAXD|**$6**|0.15 (2.5)|||
|INTD|**4',**|0.09 (2.5)|||
|ILONG<br>RUN|_|I|||
|(M/L),-<br>1|A|-0.71 (-5.7)||I|
|**(F/A),1**|||AX|-0.26 (-3.1)|
|(PM/PI)I-I|Ao,|-0.34 (-2.1)||I|
|(PF1PA),|_||**_AR,_**|-0.58 (-2.2)|
|**(PA/PL),.,**|**_As2_**|0.12 (2.9)|_AX2_|-0.45 (-2.2)|
|RD,,||0.07 (1.4)||I|
|**CHP.,**I_||_|**AR**|0.12 (1.4)|
|**EXT,,**|||**_AB,_**|0.15 (1.7)|
|SIZE**,**|**_Aa,_**|0.21 (1.5)|**_AR4_**|-0.78 (-1.6)|
|RI R||0.66<br>||0.42<br>|
|DW|_|j<br>2.3||J<br>2.0|

The critical t values are 1.3 **for** 90 percent confidence, 1.68 for 95 percent and 2.02 for 97.5%.

**6** Although the sign is correct and significant, the current period adjustment is too great.

20
# **The Political Economy of the middle class in the Dominican Republic:**

## **Individualization of public goods, lack of institutional trust and weak collective action.**

### **Miguel Eduardo Sánchez (JPO, The World Bank LCSPE) Roby Senderowitsch (Country Manager, The World Bank LCCDO)**

_Acknowledgments: Rolando Guzmán (Pareto Consultants), Rodolfo Fermín (DR National Energy Commission), Luis Madera (DR National Office for Statistics), Jamele Rigolini (WB-LCRCE), Pedro Antmann (WB-SEGEN), Chico Ferreira (WB-LCRCE), Davide Zucchini (WB-LCCDO), Andrea Gallina (WB-LCCDO), Gisela Redondo (WB-LCCDO)._
<!-- Start of picture text --> Intervention Design<br>One-line Gold Standard Individual recall Household informant<br>TO T1 T2 T3 T4<br>Current VHLSS Current Individual Individual- Household-level<br>(1-line) VHLSS (1-line) 7-day level 7-day 7-day recall<br>30-day recall 7-day recall diary recall (with bounding)<br>\ 1 visit 1 visit | | Up to 4 visits 1+ visit 2 visits }<br>y<br>FAFH<br>Take-away meals (new)<br><!-- End of picture text -->
Taxing the Good? Distortions, Misallocation, and Productivity in Sub-Saharan Africa<sup>_∗_</sup> Xavier Cirera<sup>_†_</sup> Roberto N. Fattal Jaef<sup>_‡_</sup> Hibret B. Maemir<sup>_§_</sup>

**JEL Classification Code** : O1, O4.

**Keywords** : Total factor productivity; distortions; misallocation; business environment; Africa.

> _∗_ We would like to thank Luis Serven for valuable comments. We wish also like to thank Neil Rankin for generously providing us the Ghanaian data. The authors acknowledge support from the World Bank Strategic Research Fund (SRP).

> _†_ The World Bank, Trade and Competitiveness Global Practice, Washington, D.C. E-mail: `xcirera@worldbank.org` .

> _‡_ Corresponding author: The World Bank, Development Research Group, Washington, D.C. E-mail: `rfattaljaef@ worldbank.org` .

> _§_ The World Bank, Development Research Group, Washington, D.C. E-mail: `hmaemir@worldbank.org` .
# **APPENDIX: DATA SOURCES**

It is only relatively recent that panel data for product-level trade policy instruments for many countries have become publicly and freely available and widespread enough for a wide group of political-economic researchers to access.

Important sources for product-level applied and bound tariff data across countries include the WTO’s Integrated Database and Consolidated Tariff Schedule; these are available from both the WTO website and through the World Bank’s free, on-line, World Integrated Trade Solutions (WITS) software platform. Preferential tariff data compiled by UNCTAD (TRAINS) has also been made available through WITS.

Antidumping, countervailing duty, and safeguards policy use data across countries has been freely and publicly available in electronic format since 2005 through the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2012) and _Global Antidumping Database_ .

With respect to other, more difficult to measure, nontariff instruments of import protection, other useful sources for data construction include the information collected by the Global Trade Alert, WTO’s Trade Policy Reviews, and to the WTO Committees on Technical Barriers to Trade (TBT), as well as Sanitary and Phytosanitary (SPS) measures.

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**Figure 2: Predicted change in MFN tariffs**

<!-- Start of picture text --> USA<br>CAN<br>AUS<br>CHLARG NZL<br>EUN<br>GTMSLV BRAMEX ISLNOR<br>MOZ PNGBOLHNDNICJAMECUMARPRY PERBLZCOLPANNAMTUNTURURYCRIBWAHRVTTO ISR<br>SWZ<br>LSO MKD<br>GUY<br>SAU JPN<br>JOR<br>GEO CHN<br>IND KOR<br>IDN THAZAF BHR<br>NGA BGDPAK PHL MYS SGP<br>TZA UGA LKA<br>CIV<br>MDGRWA SENZWEGHAKHM GAB MUS<br>MWINER BENTCD<br>TGOKGZ<br>6 7 8 9 10 11<br>Log of GDP per capita (2005)<br>10<br>5<br>Predicted Change in MFN<br>0<br><!-- End of picture text -->

# **5. Concluding Remarks**

The amount of policy space left by WTO legal tariff bindings allows for an increase in MFN tariffs similar to what we observed during the great depression and this after controlling for the smoke in the tariff water, i.e., bindings above prohibitive levels and widespread regional trade agreements.

However, when looking at recent economic crisis, it seems that this large policy space has been rarely used by countries facing economic crisis. Large countries tend to increase their tariffs more than small countries when facing a crisis, and even more when the crisis also affects neighboring countries. Increases in intermediate goods’ tariffs tend to be smaller than increases in final goods tariffs as one would expect in a world with a growing share of trade in intermediate goods. Also tariff increases do not seem to be constrained by the available policy space. Nevertheless, the overall increase in MFN tariffs remains modest.

16
structure of the broader economy and its location in relation to other national and regional economies or trade groupings. These other differences seem to explain, for example, why some non-resource rich countries have stronger linkages to GVCs than others. Additional factors include countries’ physical, cultural, political proximity to larger economies, their trade policies, and membership in regional trading blocs. An estimated gravity model of sources of the import content of manufactured exports and destinations of the indirect export component highlights and quantifies the roles of these other factors.

# **References**

- AfDB, OECD and UNDP. (2014). _African Economic Outlook 2014: Global Value Chains and Africa’s Industrialization._

- Allard C., Kriljenko, J.I.C., Chen, W., Gonzalez-Garcia, J., Kitsios, E., & Trevino, J. (2016). Trade Integration and Global Value Chains in Sub-Saharan Africa: In Pursuit of the Missing Link. IMF Africa Department, Washington, DC.

- Antras, P., & de Gortran, A. (2016). On the Geography of Global Value Chains. <u>https://scholar.harvard.edu/files/antras/files/gvc_ag_latest_draft.pdf</u>

- Baldwin, R. (2011). Trade and Industrialization After Globalization’s 2nd Unbundling: How Building and Joining a Supply Chain are Different and Why It Matters,” NBER Working Paper 17716, NBER, December.

- Baldwin, R., & Robert-Nicoud, F. (2014). Trade-in-Goods and Trade-in-Tasks: An Integrating Framework, _Journal of International Economics_ , 92:51-62.

Baldwin, R. (2016), _The Great Convergence_ , Cambridge MA: Harvard University Press.

- Bernard, A., Redding, S., & Schott, P. (2007). Comparative Advantage and Heterogeneous Firms. _Review of Economic Studies_ , 74(1): 31-66.

IMF. 2012. Regional Economic Outlook: Sub-Saharan Africa. Washington, October.

- ___. 2013a. Trade Interconnectedness: The World with Global Value Chains. August

___.2013b. Regional Economic Outlook: Sub-Saharan Africa. Washington, October ___. 2014. Regional Economic Outlook: Sub-Saharan Africa. Washington, October.

Johnson, R., & Noguera, G. (2012). Accounting for Intermediates: Production Sharing and Trade in Value Added,” _Journal of International Economics_ , 86(2): 224-236.

23
7 thdt EGS attendances in a given month do not adjusW instantaneously to the current values of these var.ables. Serial correlation in attendances can arise in a number of ways. For example, changes in asicultural output may6 have lagged effects on income of the poor and, hence, their demand for relief work. Under certain conditions (notably that the lags can be smoothed exponentially), simply adding lagged attendances to the model will adequately capture such lagged effects of the explanatory variables. The fortnightly payment period used by the EGS may also generate some "stickiness" in the adjustment of employment to changing conditions. It can also be conjectured that the attendance count will tend to be more serially correlated than person-days of employment.

Labor attendance data are available on a monthly basis for the 14-year period spanning July 1975 through June 1989.9 However, only annual data on the EGS time wage are available for this period; the time wage is measured as the average wage per person day for the EGS financial year April through March. Thus we have had to use the same value of the wage variable for the months April through March. lhe nominal wcnge rates were deflated by the Consumer Price Irlex for Agricultural Laborers ' r Maharashtra to obtain real EGS wages at 1975/76 prices.

Data on output of foodgrains pertain to the agriculturai year July to June,10 and (as for the EGS real wage) the foodgrain output variable takes on the same value over these months. Foodgrain output is of course observed only at the end of the agricultural year, and it may appear odd to use it to explain EGS attendances for the earlier months (of the agricultural year). However, it seems plausible that, insofar as EGS attendances in the earlier months depend on the level of agricultural activity in those months, the
**Table 3:** Weighted summary statistics of covariates by sample type for 2017 campaign.

|||Mean|Sd|Min|Median|Max|N|
|---|---|---|---|---|---|---|---|
|**Self-reported Yi**|**elds (kg/ha)**|||||||
||_X−cc_|864.85|1072.22|0.00|552.19|7111.11|3592|
|**Millet**|_Xcc_|830.30|1001.05|0.00|550.00|7142.86|1607|
||_X−cc −Xcc_|34_._549||||||
||_X−cc_|654.49|960.22|0.00|394.76|8235.29|2436|
|**Sorghum**|_Xcc_|719.22|1050.64|0.00|430.15|8125.00|1155|
||_X−cc −Xcc_|_−_64_._738||||||
||_X−cc_|2208.12|2926.16|0.00|1291.97|20851.06|2291|
|**Rice**|_Xcc_|1434.14|1941.22|0.00|791.65|17500.00|699|
||_X−cc −Xcc_|773_._972<sup>_∗∗∗_</sup>||||||
||_X−cc_|1175.39|1594.14|0.00|760.43|20930.23|2506|
|**Maize**|_Xcc_|1214.71|1686.57|0.00|769.23|20242.42|1162|
||_X−cc −Xcc_|_−_39_._322||||||
||_X−cc_|428.54|555.76|0.00|238.10|2994.01|576|
|**Cowpea**|_Xcc_<br>_X−cc −Xcc_|472.67<br>_−_44_._128|552.33|0.00|312.39|2727.27|342|
||_X−cc_|967.94|962.66|0.00|666.67|5386.00|3230|
|**Groundnut**|_Xcc_|975.78|970.35|0.00|686.46|5312.50|1300|
||_X−cc −Xcc_|_−_7_._843||||||
|**Plot area (ha)**||||||||
||_X−cc_|2.43|2.51|0.02|1.59|28.86|3592|
|**Millet**|_Xcc_|2.35|2.64|0.01|1.55|30.02|1607|
||_X−cc −Xcc_|0_._079||||||
||_X−cc_|2.62|2.48|0.02|1.94|17.83|2436|
|**Sorghum**|_Xcc_|2.40|2.31|0.03|1.82|17.51|1155|
||_X−cc −Xcc_|0_._216<sup>_∗_</sup>||||||
||_X−cc_|1.00|1.35|0.01|0.58|35.00|2291|
|**Rice**|_Xcc_|0.95|1.05|0.00|0.59|9.86|699|
||_X−cc −Xcc_|0_._044||||||
||_X−cc_|2.08|2.12|0.01|1.43|16.16|2506|
|**Maize**|_Xcc_|1.88|1.89|0.01|1.29|14.04|1162|
||_X−cc −Xcc_|0_._205<sup>_∗∗∗_</sup>||||||
||_X−cc_|1.35|1.82|0.01|0.60|13.90|576|
|**Cowpea**|_Xcc_|1.30|2.05|0.01|0.63|14.28|342|
||_X−cc −Xcc_|0_._049||||||
||_X−cc_|0.97|1.59|0.01|0.46|15.99|3230|
|**Groundnut**|_Xcc_|0.90|1.36|0.02|0.46|13.35|1300|
||_X−cc −Xcc_|0_._071||||||

_Notes:_ Number of observations may differ by variable (missingness) depending on the level (plot or household).<sup>_†_</sup> denotes a dummy variable. Difference is assessed with t-statistics based on design-adjusted standard errors corrected for clustering at the enumeration area. Statistical significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01.

20
that are difficult to reach from the national capital are also hotbeds of rebellion and political conflict. Using a map of African road networks to calculate access to ethnic homelands from national capitals and ethnic group interconnectivity, they estimate the effects of these two variables on political conflicts, showing that political conflicts occurred in areas where the state had a weak physical presence. To demonstrate the robustness of this finding, they created a theoretical network comprised of hypothetical road segments that ensure the shortest travel time between the busiest ethnic groups; using these hypothetical road networks, they build instrumental variables estimating the accessibility of ethnic homelands from political centers, and the degree of interconnectivity of ethnic groups that would occur if these networks were to exist. Their findings are robust to the use of these instruments, confirming the role of road networks in both extending political control and reducing political instability; they show that political authority and stability are strengthened in ethnic groups that are accessible from political capital.

Thus, to construct our instrumental variables to examine the interaction of the proximity to main roads and high-speed internet, we rely on this historical literature, which highlights political leaders’ motivation to build roads to expand their capacity to govern. In doing so, we follow a large body of literature that shows that non-economic factors influence African governments’ willingness to invest in public infrastructure (Herbst, 2014; Burgess et al., 2015; Bonfatti et al., 2019). We assume that African leaders build roads to facilitate territorial administration by easing the movement of administration officials and military troops from the capital city to the rest of the country (Herbst, 2014; Muller-Crepon et al., 2020).

Following these assumptions, we use a map of African ethnolinguistic divisions created by an American anthropologist in 1959 (Murdock, 1959) to identify the ethnic homelands within all countries of our sample. For all purposes, these ethnic homelands are generally regarded as being exogenous. Using the least-cost-path algorithm, we create hypothetical roads that connect the centroids of different ethnic homelands to the capital city of each country. We exploit the assumption 20
results show that well-being has an effect on political actions taken by individuals and hence underscore the importance of assessing the effect of policies on subjective well-being. Second, using subjective well-being data we find evidence for a grievance-based model to nonviolent uprisings. This contradicts previous conclusions that there are no systematic models of civil resistance; our model can help not only explain, but also predict uprisings in the future. Third, whereas many analyses of the causes of conflict focus on factors that change little over time (e.g. resources, ethnic fractionalization, institutions, geography), the level of subjective wellbeing can change relatively quickly, which allows us to exploit short-term dynamics in conflict. Hence, we exploit the within-country variation in uprisings rather than using a cross-sectional design. Accordingly, our results shed light not only on _whether_ conflicts occur, but also _when._ This study is in part limited by data availability. Our dependent variable represents the number of peaceful protests and does not capture the intensity of these events. As a robustness check, we redo our analysis with an alternative data source only capturing major uprisings, which confirms our results. However, future research could consider the relationship between subjective well-being and the intensity of non-violent uprisings. The non-violent and violent campaigns and outcomes (NAVCO) data set provides information on the number of protestors in these events, but it currently covers fewer than 30 countries and good estimates of the number of participants are often lacking.

Future research could analyze how the effect of subjective well-being on both nonviolent and violent rebellion depends on contextual factors not covered in this paper. A decrease in subjective well-being might be a necessary condition for conflict to occur; however, it is unlikely to be a sufficient one, as the decision to engage in rebellion depends on the benefits and the costs of doing so (Klandermans, 1996). Although this study finds no evidence that political opportunities or the ease of mobilization affect the relationship between subjective well-being and nonviolent uprisings, there might be other factors that influence the costs or 37
represents a growth shock. The second difference is between the growth shocks experienced by more and less informal districts.

_Figure 9: Change in GDP growth in 2016/17Q3 vs 2015/16Q3._ <!-- Start of picture text --> 2 2 2<br>0 0 0<br>-2 -2 -2<br>-4 -4 -4<br>-6 -6 -6<br>-8 -8 -8<br>Urban Rural High Low Banking Many Few Regular<br>Banking Access Regular Wage<br>Access Wage Earners<br>Earners<br><!-- End of picture text --> _Note_ : Quarterly GDP is distributed across districts according to the spatial approach outlined above. The rates reported are the median growth rates among districts. The socio-economic variables are from the South Asia Spatial Database (Li et al. 2015).

The results suggest that more informal districts performed worse. The difference in local growth relative to a normal year was very small in urban districts, as well as in those with greater access to finance and with more prevalent regular wage employment. On the other hand, more informal districts experienced drops in local GDP in the range of 4.7 to 7.3 percentage points. These shocks were temporary, so that their impact on the annual GDP of the affected localities was probably modest. But in the short-term the local impacts were sizeable.

22