rafmacalaba/data-use-mentions-extended huggingface dataset:
sample. Any sampling for the second source leads to additional error and should be avoided if possible. For the area-based estimates described in this report, publicly available satellite, station, and geographic data aggregated to the district-level are used, none of which suffers from additional constraints due to sampling.

To ensure that the definitions of key indicators defined in the microdata are as similar across countries as possible, the harmonization standard applied by the World Bank in its ECAPOV initiative in generating official World Bank estimates of poverty is used here.

# **II.I – Districts by Country**

Presenting the results and matching the survey data to satellite imagery require identifying the cartography of each country at the district level. This was accomplished using standardized map shapefiles and matching each to the disaggregated location data available for each survey.

## **Table 1: Survey Data Used**

|Country|Year|Hhlds|Svy<br>Dist.|Map<br>Dist.|Impute|
|---|---|---|---|---|---|
|KAZ|2017|<sup>47445</sup>|178|202|24|
|KGZ|2016|<sup>19575</sup>|40|42|2|
|TJK|2015|2999|149|159|10|
|UZB|2018|4013|126|190|64|
|TKM|None|.|.|.|.|

The analyses discussed in this report primarily use the data from the diary/list of food consumed, non-food expenditure, and household composition.

# **II.II – Kazakhstan HBS data for 2017**

Poverty estimates and other welfare indicators for Kazakhstan rely on the 2017 round of the HBS, conducted by the Statistics Agency of the Republic of Kazakhstan. For household consumption and expenditure, the survey is usually considered nationally representative, representative at the oblast (region) level,
Policy Research Working Paper 8864

# **Abstract**

What sorts of conditions make some countries more prone to corruption than the others? This is an important question for understanding how corruption arises and how to combat it. The present paper attempts to answer this question by exploring the link between the size of the country and corruption. Economic theory suggests advantages and disadvantages of being a large country. Fixed costs in monitoring and punishing corrupt politicians and bureaucrats implies lower corruption in larger countries. However, congestion or administrative costs may escalate with country size. Further, greater diversity in the larger countries implies that such countries may find it harder to reach a consensus on growth-enhancing anti-corruption reforms. Thus, the corruption and country size relationship is an empirical issue. Using firm-level survey data for 135 countries, this paper finds that the level corruption experienced by the firms is positively correlated with country size. This holds for a measure of overall corruption and petty corruption that arises in availing specific government services. According to a conservative estimate, moving from a country the size of Namibia (25th percentile level in size) to a country the size of Morocco (75th percentile level) is associated with an increase in the level of overall corruption by 0.28 percentage point or about 23 percent of its mean value. The results are robust to several controls, alternative corruption measures, sample alternations, and different country size measures.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mamin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if
Policy Research Working Paper 10211

# **Abstract**

This paper quantifies the trade creation effects of South Asia’s trade agreements within the region and with the rest of the world. The paper uses an extensive database of bilateral trade covering the manufacturing, agriculture, and service sectors in 190 countries over 1990–2015. The estimates of various specifications of a structural gravity model, including domestic trade flows, capture the potential heterogeneous effects. The main finding is that these effects are in general stronger for trade agreements signed by South Asian countries and even stronger in the case of intraregional agreements. The effects of free trade agreements vary substantially among countries and sectors and between final and intermediate goods. The paper shows that the trade policy implemented in South Asia in the previous decades has been successful, but at the same time the results point toward the existence of clear missing opportunities for the region. The opportunities lay in (i) better backward integration with the rest of the world to improve efficiency and help strengthen exports, and (ii) further deepening of intraregional agreements to continue making progress in regional integration.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at sfranco2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction
The estimation of these structural parameters requires survey data with sufficient price variation at the household level. This is rarely the case. One possibility is to combine household surveys with official price information. Some studies exploit time and regional variation in official prices (Deaton, 1997; Porto, 2003; Ravallion, 1990; Wolak, 1996); others use community price questionnaires (Edmonds and Pavcnik, 2004a; 2004b). In this paper, I use unit values, the ratio of reported expenditures and quantities, as measures of prices.

The main advantage of using unit values in household models is the substantial cross-sectional variability. However, unit values are not the same as prices when consumers jointly choose quantity and quality (Deaton, 1987). Since unit values combine measures of price and quality, unit values can only be used as proxies for prices. This may lead to biases in the estimation of the relevant structural parameters. Further, endogenous income responses generate additional income effects that can bias the estimates of Marshallian demand elasticities.<sup>2</sup> Building on the seminal work of Deaton (1987, 1988, 1990), I develop methods to correct the estimates of consumption and income responses.

The procedure works as follows. By modeling consumer choices of quantity and quality simultaneously, I am able to extract the right price signals from unit values, expenditures, and quality choices. Then, I use this price information to estimate the response of consumption and wage agricultural income. To do this, I develop a general equilibrium econometric model of agricultural prices, quantities consumed, unit values, and household income.

I estimate the own- and cross-price elasticities as well as the wage-price elasticities using survey data for rural Mexico. It is shown that using unit values instead of prices may indeed lead to inconsistent results, and that the corrections suggested in this paper are empirically important. On the income side, the endogeneity of unit values and measurement error lead to attenuation bias in the estimates of the wage elasticities. On
"Do you believe this regulation is an obstacle or not?"). If the answer is yes, then ask how severe the obstacle is.

* Be aware of the effects of context. The act of asking questions can affect the answers given on subsequent, related questions.

- Think carefully about how to ask sensitive questions. Consider using a self-administered module for sensitive questions. Alternatively, a randomized response mechanism may be a useful, truth-revealing mechanism.

- - This paper a product of Regulation and Competition Policy, Development Research Group is part of a larger effort in the group to develop consistent cross-country firm level surveys. Copies of the paper are available free from rhe World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Paulina Sintim-Aboagye, room MC3-422, telephone 202473-7644, fax 202-522-1155, email address psintimaboagye@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The authors may be contacted at frecanatini@worldbank.org, wallsten@leland.stanford.edu, or Ixul@worldbank.org. March 2000. (44 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
contacts and the balance of interactions per contact), their spatial patterns based on cell tower locations (for example, the number of unique antennas visited and the radius of gyration), and their recharge patterns (including the average amount recharged and the time between recharges). The distributions of a sample of these indicators are shown in Figure S4.

# **2.4 Machine Learning Predictions**

**CDR-Based Method** Extending the approach described in Blumenstock et al. (2015), we test the extent to which ultra-poor status can be predicted from CDR. This analysis uses the 535 households who match to CDR to train a supervised machine learning algorithm to predict ultra-poverty status from the mobile phone features. The intuition — also highlighted

8
fraction of positive and negative words in each article to capture the tone of news published, we first construct a daily news-based sentiment index for 25 advanced and emerging countries between 1991 and 2015. We then estimate the response of equity prices to sentiment shocks using Jordà’s (2005) local projection method. We first estimate the effect of US news sentiment on US equity returns and compare our results to existing studies on the US such as Tetlock (2007). We then test whether these results extend to all countries, after controlling for known sources of predictability in international equity returns, both locally and globally (e.g. commodity prices, VIX etc.). Overall, we find panel estimates to be close to the US benchmark. Positive sentiment shocks also imply a significant impact on equity returns of 10 basis points that partially reverses after a few days, confirming that the effect of news sentiment on asset prices is a pervasive phenomenon that is not limited to the US.

Since a large fraction of news articles in our corpus refer to multiple countries, we then explore whether sentiment shocks are mainly driven by local (country-specific) news, or by global news having an impact on a country. To isolate the effect of local news from that of global news, we start by recomputing the daily sentiment index for each country after having excluded articles that mention any other country than the country of interest, allowing us to
; mental<br>disability.|“Have a disability?”. If yes: “Type<br>of disability” [with pre-coded<br>answers]; “Cause of disability”<br>[with pre-coded answers].|
|Jamaica 1998|LSMS|Physical or mental disability.|“Is X physically or mentally<br>disabled?”|
|Jamaica 2000|LSMS|Sight; hearing; speech; physical (legs and<br>arms); multiple disability; mental retardation.|“Is X physically or mentally<br>disabled?” [with pre-coded answers<br>that include types of disabilities];<br>“If yes, when did this disability<br>occur?” [with pre-coded answers<br>such as “since birth,” “In child<br>under 5 years,” ...].|
|Mongolia 2000|MICS2|Difficulty seeing; difficulty hearing.|Specific wording not available.|
|Myanmar 2000|MICS2|Visual problem; hearing problem.|Specific wording not available.|
|Romania 1995|LSMS|Amputation of limb(s); Paralysis of limb(s);<br>ankylosis of limb(s) or column; physical<br>deformation(s); unilateral or bilateral cecity;<br>deaf; mute; epilepsy; mental retardation; menta<br>disorder.|l<br>“Do you suffer from a handicap?”;<br>If yes: “Type of handicap” [with<br>pre-coded answers].|
|Sierra Leone 2000|MICS2|Blindness; crippled; lost limbs; deafness; mute.|Specific wording not available.|

17
log of the number of battles during the rebellion, and with the log of duration of the Taiping occupation (in months). Here, we allow the effects to differ by Taiping “dose.” The qualitative results remain similar: the Taiping effects remain stable, and no recovery in population had taken place relative to the comparison group.

_Instrument Variable Estimates_ . The estimate of the rebellion’s effect would suffer from the omitted variable bias if the Taiping areas differ systematically from the other areas conditional on our controls. To tackle this, we pursue an IV approach: historical documents show that the rebellion’s top leaders pursued a military strategy that resulted in a spatial pattern of Taiping Army occupation that is related to the longitude, and we thus use the longitude of the prefectural seat as the IV for the Taiping analysis, holding key geographical features constant.

Firstly, the longitude satisfies the relevance condition as the instrument of the Taiping Rebellion. The rebellion officially started in the remote Guangxi Province in early 1851. A key

> 39 See <u>https://en.wikipedia.org/wiki/Black_Death.</u>

> 40 Indeed, when we repeat the analysis in column (1) but include the TP-Occupied prefectures in the control group in column (2), the results are almost identical.

> 41 The data set of Palace Scholar covers the period 1793-1911. For the base year 1820, Jinshi measures the newly selected Palace Scholar in the period between 1793 and 1820. This variable is automatically zero after year 1911 (since this exam was no longer held after the fall of Qing).

27
)|(0.044)|(0.058)|(0.060)|
|N|4,596|157|4,753|4,753|

This table shows average attitudes and behaviors of Albanian immigrants and Greek natives. In the fourth column, the table reports regression estimates of the refugee-native gap after controlling for age and gender dummies. Standard errors in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the European Social Survey (ESS).

Table 4: Refugee resettlement and sport clubs

|Dep. var:||Prese|nce of at l|east one sp|ort club||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Share of refugees in 1928|0.031|0.035|0.049|-0.048|-0.053|-0.042|
||(0.033)|(0.036)|(0.036)|(0.034)|(0.039)|(0.040)|
|Share of refugees in 1928 * small community||||0.151**<br>(0.060)|0.161***<br>(0.060)|0.157***<br>(0.058)|
|_R_<sup>2</sup>|0.288|0.294|0.327|0.289|0.295|0.328|
|Observations|4,393|4,393|4,393|
the wages of employed migrants were rising again by the end of 2009. Trends in monthly wage income for all three data sources, shown in Figure 8, suggest that increases in the demand for labor were again driving up migrant wages by the end of 2009.

Evidence from the latest round of the China Urban Labor Survey, shown in Table 4, also indicates renewed upward pressure on wages in the labor market. For migrants employed in late 2009, neither working hours, nor monthly earnings, nor hourly earnings declined. Steady increases in both monthly and hourly earnings through February 2010 suggest that the labor market was tightening once again and that the slowdown and decline in earnings evident in the CNRS data were temporary.

# **6. Conclusions**

This paper has examined evidence from firm and household surveys on the effects of the global financial crisis on employment in China. After highlighting descriptive statistics from firm surveys suggesting that most of the adjustment was borne by migrant workers, the paper reviewed rural household survey data to examine the net effect of the crisis on employment of rural registered workers. Job losses—ranging from 20 million to 36 million—were concentrated among migrant workers, who have typically lacked employment protection, have tended to be concentrated in export-oriented sectors, and were among the easiest to lay off when the crisis hit.

In response to the crisis and fears of widespread unemployment, China’s government responded with a massive stimulus program, equivalent to over 13 percent of annual GDP, complemented by a range of active labor market programs, training programs, and credit support for small and medium enterprises. Neither information on the implementation of these programs (which was highly decentralized, nonrandom, and left to local governments) nor important data (such as expenditures) are publicly available, frustrating any effort to determine the relative role of programs, stimulus, and general economic growth in moderating employment shock. Nonetheless, available evidence does suggest that the stimulus helped expand employment 16
Table 12: Civil Society Leader’s Information and Information Exchange

||Mean Control|Effect of VH<br>Training|Effect of VH<br>+ CL Training|CL Effect||
|---|---|---|---|---|---|
|CL’s knowledge test score (0-1)|0.693|-0.018|0.064|0.082*|196|
|(CL survey)|(0.214)|(0.038)|(0.039)|(0.044)||
|No. of times exchange information|1.758|-0.268|0.433|0.701*|189|
|btwn CL and VH per week (CL survey)|(1.991)|(0.352)|(0.356)|(0.407)||

Table displays standard errors in parentheses. *, ** and *** indicate significance at the 90, 95 and 99 percent confidence levels respectively.

Table 13: Civil Society Leader Effects by Human Resource Levels

||Know|ledge of VH|(0-1)|Procedural|Compliance o|f VH(st. index)|
|---|---|---|---|---|---|---|
|Measure of human resource|CL Effect|CL Effect|Difference|CL Effect|CL Effect|Difference|
|level|High HR|Low HR||High HR|Low HR||
|Whether VH completed|0.072|0.076|0.004|0.793***|-0.137|-0.930**|
|primary education|(0.050)|(0.070)|(0.088)|(0
tions to resume operations. While calibrated to an annual unemployment rate of 4% in the steady state, which implies not so severe frictions, the mass of temporarily laid off workers congests the labor market when the shutdown is lifted, leading to a higher wage in the hiring market and a lengthy rehiring process. The capital stock dynamics in the bottom right panel accompanies the persistently high unemployment. Consumption, in turn, overshoots the quarter after the lockdown in reflection of the rise in wages and the fall in investment. In short, incorporating rest unemployment is important for the model to match the quick dissipation of recall unemployment in the US data in Hall and Kudlyak (2020).

Lastly, we discuss the role of debt-financed unemployment insurance and costless firing of workers. As a reminder, non-essential entrepreneurs in our baseline can lay off their workers during the lockdown without any severance payment or firing cost. The government issues new debt to provide full wage replacement to the unemployed. The “no wage subsidy” case in Figure 14 is the case in which non-essential businesses have to pay their wage bills while in lockdown. This keeps the unemployment rate low during the lockdown, since idled workers are still employed, the unemployment rate goes up by 1 percentage point when the lockdown ends and remains persistently higher than in the benchmark. The GDP also recovers more slowly. The reason for the excess unemployment lies in the interaction between the balance sheet effect exacerbated by the wage bill payment during the lockdown and the capital rental constraint. Unlike in the benchmark, the additional financial wealth that must be devoted to wage payments during the lockdown (and no-revenue) period deteriorates the non-essential firms’ balance sheet more significantly, leading to a smaller labor demand after the lockdown and to a rise in unemployment.

# **5 Complementary Impulse: Reallocation Shock**

Previous sections considered three plausible channels through which cross-country differences in the recovery from a lockdown may emerge: difference in lockdown size, duration, and policies supporting firms and workers. Here, we
is often associated with broader welfare losses (Ulyssea, 2018). Taken together, this evidence suggests that the conflict-induced increase in informal competition contributes to explaining the reduction in firms’ sales documented in Table 1.

# **4.2.4 Demand**

An alternative explanation for the observed reduction in sales and total costs is that conflict reduces consumer demand. In this case, firms would respond to falling consumer expenditure by scaling back production and, consequently, reducing input purchases—leading to lower sales. While we cannot directly test this channel due to the lack of consumer demand data in the WBES, Table A24 provides suggestive evidence that this mechanism alone is unlikely to fully account for our results.

Column 1 examines whether the effect of conflict exposure on sales varies by the firm’s main destination market—local, national, or international. If the decline in sales were driven solely by a conflict-induced drop in domestic demand, we would expect firms selling primarily to international markets to be less affected. However, this is not what we find: the effect of conflict exposure on sales does not differ significantly across the three market categories. This suggests that the negative impact on sales is not confined 18
-49-

The average PPPs for the sample as a whole can also be taken from table 3.11 and 3.12. In fact these averages can be calculated by weighting the PPPs for the individual industries by their value added (at the US census concept). Thus the average PPP at US quantity weights is calculated according to the following formula: The average PPP with quantity weights of country X is calculated as follows: with VAU and VAX = value added (US census concept) in country U and country X U X PPP and PPP = purchasing power parity with quantity weights of country U and country X The PPPs for the individual industries show that 31 of the 51 PPPs were below the exchange rate for the Brazil/USA comparison and 27 of the 51 PPPs for the Mexico/USA comparison. On average the PPPs are below or above the exchange rate depending on the quantity weights used for the comparisons. In the Brazil/USA comparison the average PPP at US weights is just above the exchange rate, but clearly below the exchange rate in case of Brazilian weights, as is the geometric average PPP. The average PPP at US weights in the Mexico/USA comparison is clearly above the exchange rate, but slightly below the exchange rate in case of Mexican weights. In contrast to the Brazil/USA comparison, the geometric average PPP for Mexico compared to the USA is slightly above the exchange rate.