rafmacalaba/datause-encoder-data huggingface dataset:
# **Policy Research Working Paper Series**

||**Title**|**Author**|**Date**|**Contact for paper**|
|---|---|---|---|---|
|WPS4263|HIV/AIDS and Social Capital in a<br>Cross-Section of Countries|Antonio C. David|June 2007|A. David<br>82842|
|WPS4264|Financing of the Private Sector in<br>Mexico, 2000–05: Evolution,<br>Composition, and Determinants|Constantinos Stephanou<br>Emanuel Salinas Muñoz|June 2007|S. Coca<br>37474|
|WPS4265|The Structure of Import Tariffs in the<br>Russian Federation: 2001–05|Oleksandr Shepotylo|June 2007|P. Flewitt<br>32724|
|WPS4266|The Economic Community of West<br>African States: Fiscal Revenue<br>Implications of the Prospective<br>Economic Partnership Agreement<br>with the European Union|Simplice G. Zouhon-Bi<br>Lynge Nielsen|June 2007|S. Zouhon-Bi<br>82929|
|WPS4267|Financial Intermediation in the<br>Pre-Consolicated Banking Sector in<br>Nigeria|Heiko Hesse|June 2007|G. Johnson<br>34436|
|WPS4268|Power to the People: Evidence from<br>a Randomized Field Experiment of a<br>Community-Based Monitoring Project<br>in Uganda|Martina Björkman<br>Jakob Svensson|June 2007|I. Hafz<br>37851|
|WPS4269|Shadow Sovereign Ratings for<br>Unrated Developing Countries|Dilip Ratha<br>Prabal De<br>Sanket Mohapatra|June 2007|N. Aliyeva<br>80524|
|WPS4270|Jump-Starting Self-Employment?<br>Evidence among Welfare Participants<br>in Argentina|Rita Almeida<br>Emanuela Galasso|June 2007|A. Bonfeld<br>31248|
|WPS4271|Construction, Corruption, and<br>Developing Countries|Charles Kenny|June 2007|C. Kenny<br>33540|
|WPS4272|Migration, Remittances, Poverty,<br>and Human Capital: Conceptual and<br>Empirical Challenges|David McKenzie<br>Marcin J. Sasin|July 2007|M. Sasin<br>36877|
|WPS4273|Rules of Origin and the Web of East<br>Asian Free Trade Agreements|Miriam Manchin<br>Annette O. Pelkmans-Bala|July 2007<br>oing|L. Yeargin<br>81553|
|WPS4274|Are Labor Regulations Driving<br>Computer Usage in India’s Retail<br>Stores?|Mohammad Amin|July 2007|S. Narsiah<br>88768|
|WPS4275|Can Foreign Lobbying Enhance<br>Development? The Case of Tourism<br>in the Caribbean|Kishore Gawande<br>William Maloney<br>Gabriel V. Montes Rojas|July 2007|V. Cornago<br>84039|
|WPS4276|Human Capital, Trade Liberalization,<br>and Income Risk|Tom Krebs<br>Pravin Krishna<br>William Maloney|July 2007|V. Cornago<br>84039|
|WPS4277|Climate Change Adaptation in Africa:<br>A Microeconomic Analysis of<br>Livestock Choice|Sungno Niggol Seo<br>Robert Mendelsohn|July 2007|P. Kokila<br>33716|
|WPS4278|Endogenous Irrigation: The Impact of<br>Climate Change on Farmers in<br>Africa|Pradeep Kurukulasuriya<br>Robert Mendelsohn|July 2007|P. Kokila<br>33716|
|WPS4279|The Impact of Climate Change on<br>Livestock Management in Africa:<br>A Structural Ricardian Analysis|Sungno Niggol Seo<br>Robert Mendelsohn|July 2007|P. Kokila<br>33716|
- Earle, Timothy K. "Chiefdoms in Archeological<sup>and Ethnohistorical Perspective," Annual</sup> Review of Anthropology. 16: 279-308.

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Heer, Friedrich. The Medieval World. Canada:<sup>Mentor, 1962.</sup>

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- Huntington, Samuel. Political Order in Changing Societies.<sup>New Haven, Conn.: Yale</sup> University Press, 1968.

Ingrao, Pietro. Theories of States. New York:<sup>New York University Press, 1983.</sup>

- Jones, Grant D. and Kautz , Robert R. (eds).<sup>The Transition to Statehood in the New</sup> World. New York: Cambridge University<sup>Press, 1981.</sup>

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33
Both the pooled OLS and the FE estimations of equation (9) (which models seasonality trigonometrically) show a statistically significant price elasticity, but the FE estimates are larger (more negative) than those from pooled OL S (Table 4). The estimated seasonal gap of 3.2 percent is correspondingly higher, though still modest in absolute terms. The FE estimates also appear superior from the statistical standpoint. In the remainder, the focus is on the FE estimates.<sup>22</sup> The seasonal and deseasonalized components of staple prices do not show a difference in their effects, removing concerns about potential over or underestimation of price induced seasonality due to differences in the caloric response to regular and irregular price changes. Also, linearity in the price elasticity is not rejected—the coefficient on squared prices is insignificant.

The results in Table 4 are based on a representative national sample. They show a modest price‐induced seasonal gap in caloric intake, averaging across the entire Tanzanian mainland. However, households are heterogeneous both in terms of their locations (urban versus rural), wealth levels and, for rural households, also in their net marketing position (net food buyers or net food sellers). We consider a three by two disaggregation distinguishing households resident in Dar es Salaam, which LSMS‐ISA regards as entirely urban, other urban households and rural households. Then, within each group we distinguish between asset poor and asset non‐poor households. The staple price elasticity is estimated separately for each subgroup (conditional �����,�,��� �����,�,� ������̂�,�,��� ���1, ... , �� where hats upper scripts respectively stand for the estimated price seasonal factors and price drift. We further improve on this formulation by taking into account the current period residuals from regions where the data is present: �����,�,��� �����,�,� ������̂�,�,��� � ∑���� �<sup>�</sup> �,��,����,�,��� ���1, ... , �� where _si_ is the region in which the residuals from equation (4) are most highly correlated with those for region _r_ over the subset of observations of which observations are available for both regions and _b_<sup>ˆ</sup> _r,si ,i_ is the simple regression coefficient associated with the maximum correlation. The accuracy of this interpolation will diminish as the gap length _r_ increases. Omission of all observations involving a missing price implies a fall in the number of observations available for estimation of 6.8 percent for the other urban sub‐sample, 9.8 percent for the rural net purchasers sub‐sample, and 8.6 percent for the rural net sellers sub‐sample. There is no loss for the Dar es Salaam sub‐sample. The interpolated prices are used only in estimation of the caloric intake equation (10).

> 22 The Hausman test rejects equality of the coefficients of the pooled and FE estimators ( <sup>2</sup> 39<sup>= 95.41 (tail</sup>

probability 0.0000) when applied to the complete set of coefficients, and 1<sup>2= 16.13 (tail probability 0.0000) when</sup> only the price elasticities are compared).

20
framework.<sup>18</sup> This, together with the fact that the transition has occurred while maintaining open capital accounts, reflects an improvement in the quality of their institutions; this strength is further reflected in the waning of the fear of exchange rate volatility.<sup>19</sup> We notice a related phenomenon below. We observe that a “disconnect” has occurred between the procyclicality of currency and monetary policy since 2000. In Figure 11, Panel A, while the CCI and monetary policy procyclicality are positively correlated when calculated for the entire period or for the pre-2000 period separately, the correlation is insignificant in the post-2000 period. The same phenomenon is observed when we use rolling observations. Specifically, we calculate the CCI over overlapping five year periods, each time dropping the data for the first four quarters and adding the data for four quarters at the end. We similarly calculate the rolling averages of the monetary policy procyclicality.<sup>20</sup> The same disconnect that we observed earlier between the procyclicality of monetary policy and the CCI is seen in the rolling observations as well. In Figure 11, Panel B, below, the linear fit between monetary policy procyclicality and the CCI is positive and significant when estimated for the entire period, but insignificant when estimated for the data since 2000.

**Figure 11: Monetary Policy Cyclicality and Currency Cyclicality Index over Time**

<!-- Start of picture text --> B. Monetary Policy Cyclicality and CCI –<br>A. Monetary Policy Cyclicality and CCI<br>Rolling data<br>URY<br>URY<br>CRI<br>BGRMAR CRICHNISR INDPERGRCPRTCHL AUSINDMEXISR TURTUR<br>THA<br>JPNIRLBGRCYP JPNCYPFRAPERAUTMYSCHNNORMARDNKZAFGBRNORESAUSPTHACOLBRACZEFINPHSWE L PHLBRANZLMEXMYS KOR KOR<br>CHE CHENLD ZAFDEUUSABEL CANITA COL CAN NZLSWE<br>USA CZE GBR<br>CHL<br>DNK<br>-.5 0 .5 1 -1 -.5 0 .5 1<br>CCI CCI<br>Full Period Full: Monetary Pol. Cycl. = -0.28(6.25)+0.35(2.25)*CCI Pre 2000 Post 2000<br>Sub Period:2000-2013 Sub: Monetary Pol. Cycl. = -0.36(5.12)+0.13(0.73)*CCI All: MPC= -0.38(23.11)+0.18(5.38)*CCI Post 2000: MPC = -0.36(10.50)+0.04(0.60)*CCI<br>t-stats in parenthesis. CCI = Currency Cyclicality Index t-stats in parenthesis<br>.5 .5<br>0 0<br>-.5 -.5<br>Monetary Policy Cyclicality Monetary Policy Cyclicality<br>-1 -1<br><!-- End of picture text --> Similar trends are observed in Table 1 below where, using the rolling observations, we regress monetary policy cyclicality on the CCI interacted with a dummy for the pre 2000 period, and

> 18 Vegh and Vuletin (2002) measure the fear of floating as the correlation between the cyclical component of the shortterm interest rate and the rate of depreciation of the exchange rate.

> 19 A similar point is made in Adler and Magud (2013) who show that better policies, particularly the sizable increase in aggregate saving rates, may explain why Latin America did relatively well during the recent commodity boom and may also be able to face better the downturn.

> 20 Rolling regressions provide more robust estimates of how the CCI has evolved and its relationship with other correlates changed over time.

16
expectations among jobseekers, causing them to raise their reservation wages and only search for jobs in the area in which they were trained (Acevedo et al., 2020).

In part due to the evidence from the first waves of rigorous training evaluations, policy efforts have aimed to improve the effectiveness of vocational training programs. The two approaches usually mentioned are to make training more demand-driven, and to link payments for the training program more clearly to results. Demand-driven programs aim to have private sector firms and providers, rather than the government, determine what courses are offered and how they are delivered, and to link on-the-job training explicitly to employer demand. An often-mentioned example is the _Jóvenes_ programs in Latin America, including the Colombian program studied by Attanasio et al. (2011), which had the largest impacts among the government programs summarized in Figure 1. Attanasio et al. (2017) link participants in this program to social security records and find, up to a decade later, a lasting effect of 3.8 percentage points on being employed in the formal sector, with trained individuals earning US$13 more per month in formal earnings. But simply having private sector providers offering the training may be insufficient: Hirschleifer et al. (2016) find privately run courses in Türkiye have larger short-term impacts than governmentrun courses, but that this difference disappears in the three years after training.

Results-based contracting aims to increase the incentives for providers to deliver employment impacts by linking some of the payments to targets such as the percentage of trainees in jobs. World Bank (2020) discusses some of the practical issues and experiences with such an approach. It sounds promising in theory, but in practice many governments lack the administrative capacity to measure results and manage such a process. In addition, the share of the total payment linked to performance may in practice end up being relatively small and incentivize only shortterm, and not long-term, employment outcomes.

A more optimistic view of the potential impact of job training programs has emerged from impact evaluations of several programs implemented by nongovernment organizations. Particularly influential here has been the work by Alfonsi et al. (2020), who evaluate the impact of vocational training and firm-provided training in the form of apprenticeships in programs operated in Uganda by the nongovernment organization BRAC. The program is much smaller in scale than government programs (697 youth get vocational training and 283 get apprenticeships), relatively intensive (six months duration), with training restricted to a narrow set of sectors 15
ban areas play an important role in providing service delivery, which in turn influences poverty. Different measures of urbanity have been proposed in the literature (e.g Dijkstra and Poelman, 2014; Aubrecht et al., 2016; Roberts et al., 2017). We take advantage of geospatial subnational gridded global data to measure travel time to major cities, the agglomeration index, population count, and built-up area.

We construct population density and totals from two main sources of global model population distribution data: Landscan (e.g. Bright et al., 2013) and GHS-Pop (Freire et al., 2016).<sup>21</sup> Landscan is a model that uses dasymetric and spatial modeling methods to estimate population. The model takes advantage of multiple sources of spatial microdata including land cover, road, slopes and urban areas data, and population census data. Figure A2.6 illustrates the spatial distribution of the total population across the enumeration area.

GHSpop data are derived from a raster-based dasymetric model estimating global population. Using newly processed LandSat data to provide multiple periods in time (1975, 1990, 2000 and 2014), this model leverages newly available built-up data (GHSBUILT) to define the locations where the GHSpop allocates population. The estimates of population are from the highest number of spatial units available to account for the total population known across subnational areas. We construct the following variables of total population within the administrative unit: _GHS_ _~~p~~ optot_ _~~1~~ 975_ , _GHS_ _~~p~~ optot_ _~~1~~ 990_ , _GHS_ _~~p~~ optot_ _~~2~~ 000_ , and _GHS_ _~~p~~ optot_ _~~2~~ 014_ . GHS-BUILT at 300m spatial resolution is the source of built-up area within each administrative unit from which we derive the variables: _GHSL_ _~~b~~ uilt_ _~~k~~ m2_ _~~1~~ 975_ , _GHSL_ _~~b~~ uilt_ _~~k~~ m2_ _~~1~~ 990_ , _GHSL_ _~~b~~ uilt_ _~~k~~ m2_ _~~2~~ 000_ , and _GHSL_ _~~b~~ uilt_ _~~k~~ m2_ _~~2~~ 014_ .

The share of urban population within an administrative unit is constructed as follows. We first define urban population as the number of people within a given threshold of density from the 2014 GHS population grid ( _ghspop14_ ) and the Landscan 2012 population grid ( _lpop12_ ). Next we use the World Urban Prospects estimates of the urban share at the country level provided by the World Bank World Development Indicators to inform a population density threshold that sums to the estimated urban share at the country level<sup>22</sup> . Finally, we construct the share of urban population that meet these density criteria within the administrative unit as the urban share of total population ( _urban_ _~~l~~ pop12_ ).

# **Socio-economic: Travel time and accessibility**

Transport infrastructure enables improved economic growth by lowering costs of moving goods and people. Nelson (2008) derives the travel time from all land area to

> 21For a recent review of gridded population datasets see Leyk et al. (2019).

> 22Another method of country consistent urban rates from World Urbanization Prospects is the iUrban method by Aubrecht et al. (2016).

48
# **IV.  Data from China and Brazil**

The analysis in this paper focuses on China and Brazil. These countries have two of the largest economies in the developing world and present interesting contrasts in terms of economic history and development strategy. Brazil has a long history of trade orientations, starting as a commodity-based export economy, followed by a long experiment in import substitution that has recently given way to a more liberal trade policy with export-led growth strategies in some sectors. However, Brazil has higher barriers to investment than most Latin American countries surveyed. In particular, dealing with three levels of government (state, province, and municipality) is a big burden to firms (World Bank 2002).

On the other hand, China was until recently relatively closed to market economies, but has recently experienced meteoric growth in manufacturing sectors that produce both for export and for the domestic market.

# <u>China</u>

As Table 1 shows, the data for China includes 975 usable observations that are about evenly distributed across five cities: the rapidly growing coastal cities, Shanghai and Guangzhou; Beijing and the nearby port of Tianjin; and the interior provincial capital, Chengdu. They are roughly equally distributed across five industry groups: Apparel and Leather Goods; Consumer Products; Electronic Components; Electronic Equipment and Vehicles and Parts (Table 2). About two-fifths of all firms in the survey sample are state-owned – a proportion that is quite consistent across industry groups.

As an indicator of the importance of international trade, Tables 3 and 4 break the firms down into those that export at least part of their output and those that do not. At least 30% of firms in all industry groups export, but the share of exports is substantially higher in Apparel and Leather Goods and Electronic Components. Not surprisingly, a very high proportion of firms in Shanghai and Guangzhou export, while a high proportion of firms in Chengdu do not.

Ownership status is also related to export behavior. About 50% of privately owned firms export, compared with only 30% of state-owned firms that export.

7
Table S.11: Omitted Variables: Corruption

||TI|WGI|WGImed|
|---|---|---|---|
||(1)|(2)|(3)|
|TI Corruption|0.073<sup>_∗∗∗_</sup><br>(0.025)|||
|WGI Corruption||0.081<sup>_∗∗_</sup><br>(0.041)||
|WGI Corruption>Median|||0.142<sup>_∗∗∗_</sup><br>(0.049)|
|Public Investment Management Index|-.012|0.004|0.033|
||(0.067)|(0.056)|(0.057)|
|Foreign frm or JV|0.251<sup>_∗∗_</sup>|0.258<sup>_∗∗_</sup>|0.262<sup>_∗∗_</sup>|
||(0.125)|(0.111)|(0.112)|
|World Bank|-.006|0.003|-.020|
||(0.089)|(0.097)|(0.096)|
|Bilateral Donor|0.162|0.189|0.18|
||(0.137)|(0.139)|(0.136)|
|Obs.|3322|3322|3322|
|_R_<sup>2</sup>|0.898|0.899|0.9|
|TI Corruption|0.067<sup>_∗∗∗_</sup><br>(0.02)|||
|WGI Corruption||0.117<sup>_∗∗∗_</sup><br>(0.042)||
|WGI Corruption>Median|||0.161<sup>_∗∗∗_</sup><br>(0.049)|
|Log of DB Enforce Contract|0.384<sup>_∗∗∗_</sup><br>(0.093)|0.407<sup>_∗∗∗_</sup><br>(0.098)|0.406<sup>_∗∗∗_</sup><br>(0.101)|
|Foreign frm or JV|0.225<sup>_∗_</sup>|0.257<sup>_∗∗_</sup>|0.254<sup>_∗∗_</sup>|
||(0.134)|(0.119)|(0.121)|
|World Bank|-.029|-.020|-.044|
||(0.085)|(0.086)|(0.087)|
|Bilateral Donor|0.133|0.152|0.142|
||(0.128)|(0.128)|(0.126)|
|Obs.|3322|3322|3322|
|_R_<sup>2</sup>|0.899|0.899|0.899|

_Notes_ : Dependent variable is the log of cost per km; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects and region fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

14
procedure, covering 2,153 unique individual respondents. Columns 1-3 provide simple pooled OLS regressions, adding more control variables moving from left to right. The coefficient reported in column 1 suggests those relying on SMS on average report about 1.5 more reported behaviors, significantly higher reporting than those who do not list SMS messages as a primary source. Adding more control variables to this basic estimation (columns 2 and 3) does not materially affect the coefficient. The sign and significance of the control variables suggest that people living in urban areas report more risk reducing behaviors on average, as do higher income people (columns 2 and 3).

But as these simple relationships reported in the first three columns of table 5 are merely descriptive, they could be driven by many potential causes. A more rigorous approach to assess the effect of messages is to focus on changes in reporting risk reducing behaviors within the same individuals over time, as described in equation 1. Column 4 of table 5 reports the results for this specification including the same (time-varying) independent variable of reliance on SMS messages as a primary source of COVID-19 information. The value of about .15 for the coefficient of interest suggests that reporting SMS messages significantly increases the total number of reported behaviors by .15 units (with p=0.000). The case that messages themselves are the driver of reported changes in behavior is more compelling using such a fixed effects approach because the fixed effects estimator fully accounts for any influential omitted time-invariant (or nearly invariant) characteristics such as location, education, gender, etc. As an additional control, including log (real) per capita income (column 5) does not materially affect the coefficient of interest.

**Table 5: Relying on SMS vs. Total Number of Behaviors Reported**

||y|=number of|behaviors rep|orted (out of 9|)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|SMS primary source|1.530***|1.428***|1.375***|0.154***|0.154***|
||(0.057)|(0.056)|(0.055)|(0.038)|(0.038)|
|Urban||0.203***|0.194***|||
|||(0.068)|(0.068)|||
|Ln(income pc)||0.289***|0.290***||0.008|
|||(0.012)|(0.012)||(0.008)|
|Constant|5.588***|4.674***|3.328***|5.900***|5.876***|
||(0.034)|(0.051)|(0.175)|(0.008)|(0.025)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.052|0.108|0.153|0.002|0.002|
|Unique individuals|2,153|2,153|2,153|2,153|2,153|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.

A second measure of exposure to messages is the respondent reporting having received an official message in the week before the interview. Results for specifications using this measure are reported in table 6. This measure is more stringent than the variable of interest in table 5, as it limits the concept of “treatment” to only the period immediately preceding the interview. Although survey results suggest that more than 90 percent of households received messages at some point in the period 10