rafmacalaba/datause-encoder-data huggingface dataset:
|**Control:**|**Sales**<br>**change **|**Foreign**<br>**share**|**Exporter**<br>**share**|**Innovative**<br>**firm share**|**Average labor**<br>**productivity**|
|---|---|---|---|---|---|
|HGF Downstream share|0.035***|0.013**|0.014**|0.017***|0.015***|
||(8.639)|(2.138)|(2.107)|(2.989)|(3.097)|
|HGF Upstream share|0.016***|0.020***|0.019***|0.016***|0.009**|
||(4.379)|(5.347)|(5.056)|(4.434)|(2.430)|
|HGF intra‐industry share|0.009***|0.006*|0.005|0.009***|‐0.000|
||(2.621)|(1.762)|(1.480)|(2.658)|(‐0.049)|
|Control Downstream|0.007|0.030***|0.023***|0.039***|0.081***|
||(1.474)|(3.338)|(2.653)|(5.499)|(16.340)|
|Control Upstream|‐0.012***|‐0.006|‐0.007|0.004|0.027***|
||(‐2.793)|(‐0.784)|(‐1.017)|(0.718)|(3.943)|
|Control intra‐industry|0.005|0.019***|0.029***|0.034***|‐0.007|
||(1.352)|(3.684)|(4.705)|(9.586)|(‐1.241)|
|Region‐Year FE|yes|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|yes|
|Observations|139,615|137,051|137,051|140,594|140,594|
|R‐squared|0.216|0.216|0.216|0.217|0.219|

|**Dependent:**<br>|||**TFP (3‐year cha**|**nge)**<br>||
|---|---|---|---|---|---|
|**Control:**|**Sales change**|**Foreign**<br>**share**|**Exporter**<br>**share**|**Innovative**<br>**firm share**|**Average labor**<br>**productivity**|
|HGF Downstream share|0.042***|0.021***|0.020**|0.016**|0.020***|
||(8.123)|(2.609)|(2.490)|(2.283)|(3.634)|
|HGF Upstream share|0.012**|0.016***|0.016***|0.011**|0.001|
||(2.309)|(3.103)|(3.338)|(2.229)|(0.221)|
|HGF intra‐industry share|0.022***|0.017***|0.011***|0.019***|0.003|
||(5.404)|(4.266)|(2.911)|(4.820)|(0.779)|
|Control Downstream|0.010|0.033***|0.023**|0.053***|0.132***|
||(1.436)|(2.798)|(2.303)|(6.416)|(26.849)|
|Control Upstream|‐0.010*|‐0.020**|‐0.025***|‐0.007|0.025***|
||(‐1.743)|(‐2.097)|(‐2.947)|(‐1.079)|(3.261)|
|Control intra‐industry|‐0.004|0.021***|0.058***|0.034***|‐0.009|
||(‐0.860)|(3.415)|(8.225)|(8.982)|(‐1.404)|
|Region‐Year FE|yes|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|yes|
|Observations|139,615|137,051|137,051|140,594|140,594|
|R‐squared|0.171|0.171|0.173|0.171|0.178|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. The explanatory variables show the share of HGFs in different industries at the same microregion and county, by using the 2‐digit input‐out matrix from OECD STAN. Firm controls are log employment, labor productivity and log capital per labor ratio. We always include a similarly calculated upstream, downstream and same industry measure of the different control variables mentioned in the column headings. Standard errors are clustered at the county‐ industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year Birch employment definition. Small firms are firms with less than 5 employees.

20
Table 11: Impact <u>of hosting refugees on societal cooperation</u>

|||Worked for|other HHs|for free (=1|)<br>Indiv|# of ot<br>iduals|her HHs an|ID has supp|lied free la|bor for|
|---|---|---|---|---|---|---|---|---|---|---|
||All|All|All|Rural|Urban|All|All|All|Rural|Urban|
||(1)|(2)|(3)|(4)|(5)|(1)|(2)|(3)|(4)|(5)|
|Refugee Intensity|-0.023|-0.011|0.023|0.050|-0.109|0.206|0.291|0.556|0.699|-0.193|
||(0.060)|(0.064)|(0.062)|(0.071)|(0.105)|(0.347)|(0.392)|(0.419)|(0.484)|(0.397)|
|Constant|0.633*|0.367|0.249|0.154|0.657|0.938|-0.143|0.141|-0.212|1.696|
||(0.375)|(0.362)|(0.464)|(0.542)|(0.580)|(2.124)|(2.180)|(2.804)|(3.302)|(2.452)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|EA|ID|ID|ID|ID|EA|ID|ID|ID|ID|
|Controls|NO|NO|YES|YES|YES|NO|NO|YES|YES|YES|
|R-squared|0.163|0.001|0.006|0.009|0.026|0.155|0.002|0.007|0.010|0.019|
|# of EAs|433|433|433|290|143|433|433|433|290|143|
|N|33741|28869|28609|20944|7665|33740|28868|28608|20943|7665|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on alternative measures of societal cooperation: a binary variable indicating an individual’s participation (the first five columns) and number of households an individual has worked for (the last five columns) within the labor sharing arrangements. Individual sample weights from ESS2 applied. Columns labeled (1) to (3) report results for all individuals while columns labeled (4) and (5) report results for rural and urban individuals, respectively. All columns control for round FEs. Further, columns labeled (1) control for EA FEs while all other columns control for individual (ID) FEs. Columns labeled (3) to (5) additionally control for 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. I report the overall and the within R-squared in columns labeled (1) and other columns, respectively. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

49
- Armstrong, J., C. Block, and K.P. Srinath (1993) ‘Two-phase sampling of tax records for business surveys.’ _Journal of Business & Economic Statistics_ 11(4), 407–419

- Beegle, K., J. De Weerdt, J. Friedman, and J. Gibson (2012) ‘Methods of household consumption measurement through surveys: Experimental results from tanzania.’ _Journal of Development Economics_ 98(1), 3–18

- Bose, C. (1943) ‘Note on the sampling error in the method of double sampling.’ _Sankhy ̄a_ 6, 329– 330

- Christiaensen, L., P. Lanjouw, J. Luoto, and D. Stifel (2012) ‘Small area estimation-based prediction methods to track poverty: validation and applications.’ _Journal of Economic Inequality_ 10(2), 267–297

- Cochran, W.G. (1977) _Sampling Techniques,_ 3rd edition ed. (John Wiley & Sons)

- Davidov, O., and Y. Haitovsky (2000) ‘Optimal design for double sampling with continuous outcomes.’ _Journal of Statistical Planning and Inference_ 86, 253–263

- Deaton, A. (2003) ‘Adjusted Indian poverty estimates for 1999-2000.’ _Economic and Political Weekly_ 38(4), 322–326

- (2005) ‘Data and dogma: The great indian poverty debate.’ _World Bank Research Observer_ 20(2), 177–199

- Deaton, A., and J.P. Dr`eze (2002) ‘Poverty and inequality in India: A reexamination.’ _Economic and Political Weekly_ 37(36), 3729–3748

- Diamond, A., M. Gill, M. Dellepiane, E. Skoufias, K. Vinha, and Y. Xu (2015) ‘Estimating poverty rates in target populations: An assessment of the simple poverty scorecard and alternative approaches.’ _mimeo_

- Douidich, M., A. Ezzrari, R. van der Weide, and P. Verme (2015) ‘Estimating quarterly poverty rates using labor force surveys: A primer.’ _World Bank Economic Review._ Advance Access pulished 2015

32
# **_Are there winners and losers?_**

Next, we try and disaggregate this result using data on firm ownership. Firms are classified as state-owned enterprises (SOEs) and other (which includes collectives, private firms, and firms with mixed ownership). We are interested in understanding if SOEs seem to outperform their non-SOE counterparts after upgrading to city status. We find that although the aggregate results across all firms seem to suggest that cities are not using their powers to help firms, it turns out that SOEs in newly-upgraded cities do significantly better than non-SOEs, compared to those in matched counties. SOEs sell more and employ more (skilled) labor, although they are no more profitable.

**Table 9:  Firm-level outcome of city upgrading by ownership type using PSM for incumbents**

|**_Incumbent_**<br>**_Firms_**|Log main<br>operating<br>revenue|Log main<br>operating<br>cost|Log main<br>operating<br>profit|Log # of<br>employees|Log labor<br>vocational|Log wage<br>per<br>employee|Log<br>export|
|---|---|---|---|---|---|---|---|
|**City**|-0.0828|-0.0827|-0.118|-0.268***|-0.241**|-0.0776|-0.160|
||(0.106)|(0.106)|(0.125)|(0.0812)|(0.0978)|(0.0519)|(0.233)|
|**SOE**|-0.911***|-0.908***|-0.573***|-0.0435|-0.00604|-0.293***|-0.630***|
||(0.0720)|(0.0730)|(0.0740)|(0.0559)|(0.0713)|(0.0326)|(0.157)|
|**City × SOE**|**0.305****|**0.304****|**0.249**|**0.390*****|**0.404*****|**0.0584**|**0.332**|
||(0.150)|(0.151)|(0.163)|(0.102)|(0.143)|(0.0552)|(0.368)|
|**Constant**|11.10***|10.64***|9.303***|6.038***|2.724***|8.623***|9.259***|
||(0.600)|(0.576)|(0.690)|(0.256)|(0.149)|(0.563)|(0.469)|
|Observations|32,517|32,428|21,283|18,193|3,892|17,496|4,759|
|R-squared|0.296|0.304|0.186|0.203|0.169|0.197|0.152|
|Block FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry**×**Year||||||||
|FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

1. Significance levels of 10%, 5%, 1% are represented by *, **, ***, respectively.

2. Standard errors are in parentheses and clustered at county-level.

3. Analysis is restricted to firms opened before 1997.

4. Vocational labor data is only available in 2004.

# **_Why do SOEs outperform their private counterparts?_**

In this section, we try to solve the puzzle of why SOEs gain disproportionately compared with other types of firms. Going back to Table 3, one of the main benefits of city upgrading is that a city can now establish branches of state-owned banks (SOBs). According to Wei and Wang (1997), bank loans made from state-owned banks clearly favored SOEs. In the 1990s, many state-owned banks imposed softer budget constraints on SOEs than in the 1980s, such that bank finance and firm productivity were no longer linked (Cull and Xu, 2000). In other words, the literature provides evidence of favorable lending from SOBs to SOEs.

We go back to firm-level data and examine whether SOEs receive more credit from SOBs than other firms. Ideally, we would like to identify the source of the increase in credit to SOEs – however, the data do not provide a breakdown of the sources of debt financing. Instead, we use current debt and total debt as proxies to measure credit from SOBs and assume that most of the 13
processors and distributors. Thus the price liberalization of January and March 1992 took place in an environment dominated by unrestructured state enterprises.

Food prices moved up sharp:y after the January liberalization and subsequently continued to climb, although somewhat lagging the general price level, throughout 1992. The fact that food prices did not stabilize after the initial jump led many people to question whether liberalization accomplished anything positive. This paper uses data on movements in food prices and volumes between December 1991 and August 1992 to examine how retail food markets responded to liberalization. Is there any evidence that after liberalization food returned to retail outlets that were essentially bare in December 1991? Is there evidence that transactions took place in response to price differentials, i.e. did markets begin to emnerge despite lack of privatization and demonopolization? Did city to city price differentials evolve to reflect a price surface explainable by transportation costs and other economic variables? If not, why not? This paper attempts to answer these and related questions using data for January through August 1992.

# le Data

Goskomstat of the Russian Federation began an extensive effort to collect retail food prices in January 1992. These data are organized by the Center for Economic Analysis and Forecasting and evaluated to track price movements. A weekly survey covers 69 food and related commodities in 132 cities throughout Russia. Both state-owned stores and private central markets (collective farm markets) are covered, but not individual private sellers who generaliy buy at the former outlets for resale on the street in small quantities.

The survey is taken on Wednesday of each week, typically with one enumerator for each 3 or 4 state stores, and one enumerator for each private central market. Not all outlets are sampled, but the sample size appears adequate-about 70 enumerators in Moscow, for example, covering about 100 state 2
Table 4: Effect of Self-interviewing (SI) on IPV (Individual Questions)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||=1 if responded y|es to _individual_ q|uestion in the fo|llowing category:|All<br>|
||Controlling<br>Behavior|Emotional<br>IPV|Physical<br>IPV|Sexual<br>IPV|questions<br>pooled|
|**Panel A. Malawi**||||||
|SI|0.09***|0.05***|0.01*|0.03***|0.05***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|FTFI mean|0.11|0.07|0.03|0.04|0.07|
|Number of individuals|1,715|1,711|1,712|1,709|1,716|
|Observations|11,887|6,802|10,181|5,095|33,965|
|**Panel B. Liberia**||||||
|SI|0.05***|0.01|0.01|0.03***|0.03**|
||(0.01)|(0.02)|(0.01)|(0.01)|(0.01)|
|FTFI mean|0.20|0.19|0.09|0.04|0.14|
|Number of individuals|1,259|1,259|1,259|1,259|1,259|
|Observations|8,752|5,006|7,508|3,758|25,024|

Note: Regressions are at the respondent- _question_ level (violence is not aggregated into indexes). See Table 5 for results in which IPV questions are aggregated into indices. Regressions include question-level fixed effects and individual controls (including all variables in Table 1). Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

Table 5: Effect of Self-interviewing (SI) on IPV Indices

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||=1 if responded y|es to _at least one_|question in the f|ollowing category:|Any|
||Controlling<br>Behavior|Emotional<br>IPV|Physical<br>IPV|Sexual<br>IPV|IPV|
|**Panel A. Ma**|**lawi**|||||
|SI|0.18***|0.10***|0.05***|0.06***|0.13***|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.02)|
|FTFI mean|0.38|0.16|0.08|0.07|0.20|
|Observations|1,737|1,737|1,737|1,737|1,737|
|**Panel B. Lib**|**eria**|||||
|SI|0.07***|0.04|0.01|0.08***|0.04|
||(0.03)|(0.03)|(0.02)|(0.02)|(0.03)|
|FTFI mean|0.57|0.34|0.23|0.07|0.38|
|Observations|1,261|1,261|1,261|1,261|1,261|

Note: IPV measures are indexed by category; index is set equal to 1 if the respondent answered “yes” to any question in the category. Regressions include individual controls (including all variables in Table 1). Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

22
households in order to identify households with migrants. Fawcett and Arnold (1987) note also that non-response can be a major problem in immigration surveys, particularly in urban areas. They point out that while individuals usually have a legal obligation to answer questions in the census, surveys generally carry no legal sanctions for refusal to respond. In addition, in urban areas, immigrants who work often work long hours, making it difficult to find them at home, while undocumented immigrants may be reluctant to take part in a survey for fear of being found by government authorities.

A second method commonly used to sample rare populations is the chain-referral method, in which an initial sample of individuals is taken, and each of these is asked to provide referrals to other individuals in the population of interest. Snowball sampling (Goodman, 1961) and respondent-driven sampling (Heckathorn, 1997) are the most common examples. In snowball sampling, each individual in the sample is asked to name _k_ different individuals, and each of these is then asked to name _k_ different individuals, and so on. Snowball sampling has been used by the Mexican Migration Project to sample permanent Mexican migrants in the United States (Massey and Singer, 1987), and was used in part by the NIDI/Eurostat survey to survey immigrants in Spain (Groenewold and Bilsborrow, 2004).

A necessary condition for successful application of snowballing is that members of a rare population know each other (Kalton and Anderson, 1986). Such an approach is likely to hold for ethnic minorities, making it appropriate for sampling migrants at destination, and in our case, sampling a rare ethnic group in Brazil. Moreover, recent work by Heckathorn (1997, 2002) has shown that it is possible to obtain a representative sample through chain referral methods, based on the idea of “six degrees of separation”, in which each person in a population is linked to each other person through six intermediaries on average. However, applying this in practice requires that the chain referrals be long, and that adjustments are made for the fact that subjects with larger personal networks are more likely to be oversampled. Other problems which can arise in practice is that the subjects may not refer friends in order to protect their privacy, and that contact information is frequently inadequate, so attrition rates can be high. For example, Bilsborrow (2006) 6

-  -
Policy Research Working Paper 8128

# **Abstract**

This paper investigates the dynamics between the exchange rate and consumer price inflation in Zambia. The analysis uses a structural vector autoregression, with quarterly data for 1995–2014 and a combination of short-run sign- and zero-restrictions to identify relevant global and domestic shocks. The findings suggest that the passthrough of exchange rates to consumer prices depends greatly on the shock that originally caused the exchange rate to fluctuate. Although the price of copper is the most important driver of the exchange rate, the fluctuations it caused are associated with a low pass-through of only about 7 percent. Exchange rate fluctuations caused by monetary shocks come with a pass-through of up to 25 percent. Food inflation is equally affected by genuine exchange rate shocks, but appears more reactive to changes in copper prices or the money supply. Historical variance decomposition shows that, across periods, the main drivers of exchange rate fluctuations varied substantially.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at gsmith3@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 and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
national kput-output table for 1979/80. Both approaches provide eotknbtes of the agrIcultural income multipiler, defined as the increase In value added In the nonfarm sector attrlbutable to a one rupee Increase In agricultural value added.

v. The sconometrli analysis leads to an estbiated Income multipiler of Rs 0.64, distrlbuted as Rs 0.39 _In_ the rural towns and Rs 0.25 In rural areas. The multiplier Is largest In high-Income agricultural regions; Rs 0.93 In Punjab and Haryana versus Rs 0.46 In low productivity agricultural states such as Madhya Pradesh and Blhar.

vI. In contrast, the semi-input-output model leads to an agricultural Income multiplier of Rs 1.35. This Is twice as large as the econometric esthiate because the semi-Input-output multipller pertains to the national, not just the rural, economy and **assumes** highly elastic supplies of nonagricultural outputs. The seml-Input-Gutput model also provides disaggregated multipliers for different types of agricultural growth. The income multiplier for Irrigated agriculture is Rs 1.56 but It is only Rs 1.23 for rainfed agriculture.

vil. The models are used to project, through the year 2040, the volume of nonfarm Income, employment and rural demand for manufactured goods that will result from agricultural growth. They project nonfarm Increases under a range of agricultural growtt. rates and separately for Irrigated versus rainfed expansion. The following conclusions emerge:

- 0 Because of strong agrc'iltural growth linkages, rural nonfarm income and employment will both grow faster than their agricultural counterparts. A sustalned agricultural growth rate of 2.4 percent (equal to past trend) will lead to 3.0 percent growth in nonfarm income In rural areas plus rural towns, and 2.8 percent growth in nonfarm emipioyment. These growth rates Increase to 5.8 percent band 4.0 percent, resrectively, If agriculture grows at the Planning Commisslon's target of 4 percent.

- 11 -
By the nature of the problem, the solution procedure is sequential. Given the core’s stock, (6a) is solved for the core’s rent, and then given this rent in the core, the supply of housing in the periphery is calculated directly from (6b).

# **2.3 Congestion externalities, fuel use and CO2 emissions**

The aggregate daily commutes by mode are next calculated as follows, given the number of commuters _N f_ in quintile _f_ : Then, the sum of motorized vehicle traffic ( _m_  2,3) in car-equivalent units, is obtained as: where  2,  3 are the inverse ratios of vehicle occupancies (car-equivalent buses per bus person-trip, and fractional cars per person-trip by car respectively). (8) converts person trips to car equivalent vehicular trips. Given aggregate road capacities, _Zij_ , the congested round trip travel time per trip is then calculated from the Bureau of Public Roads type of congestion function,

> where _dij_ 3 are the given round-trip distances by car in kilometers that remain fixed in the model (Table 3). ˆ

> Car speed then is calculated as _sij_ 3 :

11
CES composite of the outputs of the oil fired combined cycle/gas turbine electricity industry (XDCGO) and the gas fired combined cycle/gas turbine electricity industry (XDCGG).

The demand for electricity generated from various types of technologies as well as the demand for primary factors, energy, and material inputs are derived as discussed in other industries above. For example, demand for and price of thermal electricity are given as follows: where p κ is the aggregate of unit costs of electricity generation from steam turbine (p υ ), combined cycle/gas turbine (p Ω ) and internal combustion technologies (xdpIC). σ<sup>HYTH</sup> is the elasticity of substitution between electricity generated from hydro and thermal power plants, and σ<sup>TH</sup> is the elasticity of substitution between electricity generated from steam turbine, combined cycle/gas turbine and internal combustion technologies.

# **2.2. The household sector**

# _2.2.1. Household demand_

This study considers a representative household that follows a five-step hierarchical optimization process to maximize its utility as shown in Figure 3. At the left hand side of the bottom of the nested structure (i.e., Tier 5 in Figure 3), household consumption of electricity (CHEL) and electrical appliances (CHDG) are combined through a Cobb-Douglas function to get electrical services for the households ( η )<sup>6</sup> . At the right hand side of the same tier, a fuel aggregate ( φ ) is obtained through a CES aggregation of different fuels, such as coal, oil, gas and fuelwood. The aggregate energy service ( ν ) in the household sector is derived through a CES combination of the electrical services and the aggregate fuel consumption ( φ ) (please

> 6  A sensitivity analysis is also presented later (Section 5), considering an alternative functional form (i.e., CES) to combine consumption of electricity and electrical appliances in the household sector.

8