rafmacalaba/data-use-mentions-extended huggingface dataset:
|Household Owns Irrigation Equipment -<br>SECC 2013|0.062|0.062|-0.2|0.933|
|---|---|---|---|---|
|Percentage of Households having Source of<br>Light: Electricity-Census 2011|42.517|42.777|-0.7|0.808|
|Log of Total Geographical Area (Hectares) in<br>Village-Census 2011|6.006|6.04|-4|0.19|
|Status of Pucca Road in the Village – Census<br>2011|0.788|0.796|-1.9|0.504|
|Log District Head Quarter Distance (kms)<br>from Village–Census 2011|3.888|3.883|0.6|0.82|
|Population Density of the Village – Census<br>2011|2.652|2.536|4|0.214|
|Status of Village Agricultural Marketing<br>Society–Census 2011|0.428|0.428|-0.1|0.986|
|Percentage of Households having Bicycle -<br>Census 2011|39.491|38.618|3.1|0.316|
|Primary Health Centre (Numbers) - Census<br>2011|0.03|0.033|-1.3|0.679|
|Worker force participation rate of females<br>in the Village-Census 2011|0.489|0.494|-3.2|0.285|

**Table 3: Household Selection Model: Generating Propensity Scores**

|**NRLP Intensive vs Non-intensive**||
|---|---|
|Age of Head of Household|-0.01<sup>***</sup>
assume that the barrier will only be cut by 15 percentage points, since pressure from the Working Party in these sectors is not strong.

# **Share of expatriate labor employed by multinational service providers.** In section 2

we explained that imported foreign primary inputs in services production by multinationals are partial equilibrium substitutes, but could be general equilibrium complements. In our applied model, the impact of liberalization of barriers to foreign direct investment in business services sectors on the demand for labor in these sectors will depend importantly on the share of expatriate labor used by multinational firms. We obtained estimates of the share of expatriate labor or specialized technology that is used by multinational service providers in Russia from Russian research institutes that specialize in these sectors. In general, we found that multinational service providers use mostly Russian primary factor inputs and only small amounts of expatriate labor or specialized technology. In particular, the estimated share of foreign inputs used by multinationals in Russia is: telecommunications, 10% plus or minus 2%; financial services, 3%, plus or minus 2%; maritime transportation, 3%, plus or minus 2%; and air transportation, 12.5%, plus or minus 2.5%.

**Tariff data** .  For about 1700 tariff lines, Russia employs a ―mixed system‖ in which the maximum of the ad valorem or specific tariff applies. Due to acquisition of trade flow data at the tariff line level, the actual tariffs were first calculated precisely by Shepotylo and Tarr (2008),

> Russia of multinationals, 10% is the amount by which telecommunications prices are elevated due to the barriers and if we assume Russian domestic service providers prices are unaffected, then we may solve for x from:  sx + (1s)*0 = .10. That is, x= .10/s  Our data indicate that s = .15, then x = .67 or 67%.

> Barriers to foreign direct investment, however, have an indirect
location characteristics such as the market size, possibly related to long-run TFP growth and the local economic structure. Our evidence does not support the idea that the estimated dynamic knowledge externalities are driven by either suppliers or clients. Rather, it suggests that those externalities are likely to occur through other types of interactions driven by the local proximity of sectors.

Our findings have important policy implications for the design of urban development policies. By showing that locations with a more diverse set of industrial activities exhibit faster TFP growth, our evidence does not support the formation of homogeneous but rather of heterogeneous industrial clusters. The rest of the paper is organized as follows. In Section 2, we discuss the data and the TFP measures. Section 3 describes the empirical methodology and the indices measuring the local economic structure. Section 4 presents the main findings and Section 5 discusses the sensitivity analysis. Section 6 concludes.

# **2. Data and TFP Measures**

# **2.1. Data**

We explore the Encuesta Nacional Industrial Annual (ENIA) - an annual census covering all formal Chilean manufacturing plants with more than 10 employees - between 1992 and 2004.<sup>8</sup> It is an unbalanced panel that includes an average of about 4,900 plants per year and provides comprehensive accounting information covering sales, intermediate

> 8 Alvarez and Claro (2011) state that ENIA is a representative survey of Chilean manufacturing plants with 10 or more workers (their study focuses on the period 1996–2005) and the National Statistical Institute updates the survey annually by incorporating plants that started operating and by excluding plants that stopped operating in each year. There is some concern though that for more recent years the ENIA data has become less representative due to the attrition bias.

7
) are the same ( _p_ =0.427); in other words, the effects across these three samples are statistically indistinguishable. In addition, we verify that our treatment effects are robust to alternate empirical specifications, including using only intervention period data to examine treatment effects (Appendix Tables A3 and A4).

These results indicate that reducing the likelihood of transfer requests on savings has a marked impact on workers’ willingness to supply labor. This, in turn, has a large impact on their total earned income.

# **7.3 Heterogeneity in treatment effects**

Consistent with our hypothesized mechanism, the Private accounts have larger labor supply impacts among workers who report more redistributive pressure at baseline. In Table 4, Col. (1), we examine heterogeneity by whether workers state that they cannot accumulate savings due to redistributive pressure—i.e. those that agree or strongly agree with the statement, “I have difficulty saving over time for large goals, because if I put money aside, someone else will need it for something urgent”. Among such workers, being offered the option to shield savings from transfer requests increases earnings by 249 FCFA or 15.0% ( _p_ =0.018).

> 29The average treatment effect on daily earnings is 184.4 FCFA, corresponding to a 2,213 FCFA increase per paycycle (comprised of 12 workdays). Mean daily earnings conditional on working among the Non-private group is 2,626. This gives 2626/2213 = 1.19 workdays per paycycle.

27
<mark>Figure 1 graphically illustrates the nature of our data by comparing damaged area at the field level (in red) to public data from ACLED (in green)</mark> for one period, <mark>showing that the former are likely to contain more information on the intensity and location of damages that may be relevant for agriculture than the ACLED data. The spatial nature and time variation in the data, aggregated at the VC level, is illustrated for conflict incidence (figure 2) using official data (panel A) and ACLED data (panel B) for battles and explosions as well as intensity of damages (figure 3), based on satellite imagery (panel A) and ACLED data using the number of fatalities in battles and explosions (panel B). In all cases, VCs that were affected by the conflict before and after September 2022 are in red, and those affected by the conflict only before or after this date are in ochre or orange, respectively. W</mark> hile all the data sources point toward a large segment in the country’s northeast that was affected by fighting before September 2022 but not thereafter, comparing panel B in figures 2 and 3 to panel A in figure 3 suggests that ACLED includes violent events far from the front in the country’s interior, especially in cities, while providing less detail on it the incidence and severity of damages to agricultural fields close to the frontline.

<mark>Table 1 displays the resulting conflict measures for all two-week periods since the start of the war. Column 1 presents data on the share of VCs where some agricultural area was damaged; column 2, the mean area damaged per VC; column 3, the total extent of such crop damage; and column 4, an indicator of the share of VCs where publicly available data pointed to any type of conflict. This measure decreased from a high of 16.1% in period 2 to about 10% in the summer of 2022 and fluctuated
1 in the appendix.

The first specification in table 2 is the baseline specification: It controls for State dummies in addition to the controls cited above. The mean level of education of members from the same caste in the same village is positive and significant: an additional year in the mean level of education of members from the same caste increases agricultural productivity by 4.4 to 6.1 percent. On the contrary, the education level of members from other castes in the village has no impact. Whatever the set of controls, the coefficient is close to zero and not significant. The education of the head of household does not seem to have any impact on agricultural production.

However, there may be measurement error in the education of the head of household. It could be that the education of members from the same caste is a better measure of the education of the head of household than her/his own response. Specification 2 instruments the education level of the head of household, taken from the _survey_ , by the measure of the education level of the head of household in the _listing_ . Instrumenting by a measure of one variable by another measure of the same variable corrects for measurement error if the two measures have uncorrelated measurement errors (Wooldridge 2010). The household survey and the listing are two sets of data on the same households that are independently collected. The two measures of the education of the head of household are strongly but not perfectly correlated: the correlation between the two measures is 0.78. The first stages are reported in table S1.2 in the supplementary appendix S1. In the second stage the coefficients change very little compared to specification 1. There is still a strong effect of the education level of members from the same caste but no cross-caste effects. Moreover, instrumenting the education level of the head of household only marginally changes its coefficient estimate.

Specification 3 further checks if the correlation between the education level of members from the same caste and the agricultural productivity of the household is
33</sup> industrialization by the shares of manufacturing and of non-agricultural employment in 2000, and human capital by the average years of schooling and the mortality rate.

_The Taiping Rebellion_ **.** Based on Guo (1989) and Hua (1991) (see Appendix B), we construct two Taiping treatments. The first concerns all the areas in the Taiping jurisdiction (“Taiping”). The second concerns all those that were occupied by the Taiping Army but not officially under Taiping control (“Occupied”).<sup>34</sup> We use the Qing boundary in 1820 to define Early and Late and require Early and Late prefectures to be under Taiping control during that Stauffer (1922). Ullman (1961) offers a more comprehensive population estimation of cities in China in 1938, 1953, and 1957. Perkins (1969) tallies the population of cities in China in 1910 and 1920. Historical measures of urbanization have serious measurement issues. For example, in the Yangzi River area, where clustered market towns were close to each other, the distinction between rural and urban areas is blurred (Brandt et al. 2014). 29 See Acemoglu (2002), Davis and Weinstein (2002), Brakman et al. (2004), Jia (2014), Chen and Kung (2016). 30 However, no similar cross-sectional relationship can be found using recent data, likely because the demographic relationship between income and the number of children has changed (Livi Bacci, 1992).

> 31 Using the administrative boundary in 1820 as the benchmark for the prefectures, we calculated the population in the same prefecture for different periods from 1953 to 2000 using the area as the weight, assuming that the population distributes evenly inside the prefecture.

> 32 Because the data come from various sources, the modern variables center around years 2000 to 2010, but are not of the same year. See Appendix B for data sources.

> 33 For most variables, the jurisdiction is
conflict (Federle _et al._ 2022).

The farm survey data analyzed here shows that any direct war effect on area cultivated was eclipsed by large increases in prices for imported inputs that affect all farmers together with changes in labor cost that vary across regions in line with labor availability. Together with marked drops in output prices and reduced market participation, high input prices reduced farmers’ profitability and liquidity, an outcome most pronounced but not limited to areas affected directly by conflict.

Finally, a distinguishing feature of Ukraine’s agriculture sector is that many producers remain informal (Nivievskyi _et al._ 2021). While there has been considerable progress on characterizing and quantifying informality ( Ohnsorge & Yu 2021) and its correlates in terms of financial development (Capasso _et al._ 2022) and other development outcomes (Ulyssea 2020), the debate on the direction of causality remains largely open. If informal firms are less productive than formal ones (Nguimkeu 2022), they will be unable to compete (La Porta & Shleifer 2014) or afford fixed formalization fees. If this is the case, informal firms

> 2 Short-term health outcomes may also be affected (Akresh _et al._ 2011), especially close to the location of violent events (Akresh _et al._ 2022). 3 Due to egalitarian distribution of land when large holdings were privatized and a moratorium on land sales that was only lifted recently, most Ukrainian households own and rent out land. To the extent that rental contracts can adjust, better agriculture sector performance could thus provide broad-based benefit via higher land values.

> 4 USDA’s April 2023 USDA/WASDE report projects wheat production in Ukraine at 21.0 million tons, a drop of 12.1 million tons compared to previous years (see <u>https://www.usda.gov/oce/commodity/wasde/wasde0423.pdf), much higher than what is estimated by the EU (Ben Aoun</u> _et al._ 2022
# **II. Data**

The main source of firm-level data is the EBRD-EIB-World Bank’s Enterprise Surveys (WBES) conducted around 2018-2019. The choice of this particular set of WBES is because they contain questions on family management that are not available for the regular Enterprise Surveys carried out by the World Bank. This set of surveys were a joint venture between the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) and the World Bank (see EIB-EBRD-WB 2022). Data is available for 41 economies encompassing developing and advanced economies in Europe, the Middle East and North Africa (MENA) region, and Mongolia. The surveys have a section on management practices that was only implemented for medium and large firms (those with at least 20 permanent, full-time employees), where management practices were more likely to matter. The surveys also contain the standard World Bank Enterprise Surveys modules, collecting information on a representative sample of formal (registered) private firms operating in manufacturing or service sectors. The WBES data are fully comparable across countries and are collected via face-to-face interviews with business owners or top managers by using a global methodology. The data have been widely used by several studies to explore the private sector in developing economies (Paunov, 2016; Besley and Mueller, 2018; Chauvet and Ehrhar, 2018; Hjort and Poulsen, 2019). One advantage of this round of surveys is that they consist of a set of economies surveyed around a similar timeframe, employing a consistent survey instrument and methodology. These surveys were largely completed before the COVID-19 pandemic outbreak.

The key outcome variable is the quality of management practices, following the methodology implemented by Bloom et al. (2013b). This consists of eight components: (i) Problem resolution, (ii) Number of performance indicators measured, (iii) Level of ease or difficulty to achieve production or service provision targets, (iv) Knowledge of production or service provision targets, (v) Basis of manager bonuses,
**Figure 2.  Food Access and Food Assistance by 2017 IPC Classification, January 2017 – June 2017**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>IPC 2 IPC 3 IPC IPC 4 IPC 2 IPC 3 IPC IPC 4 IPC 2 IPC 3 IPC IPC 4<br>3+ 3+ 3+<br>Food Consumption Score Reduced Coping Strategy Index Share Receiving Food<br>Assistance<br>Upper Bound 95% CI Lower Bound 95% CI Average<br><!-- End of picture text --> Notes: Authors’ calculations using the WFP mobile Vulnerability Analysis and Mapping Survey. IPC groupings are as reported in the 2017 IPC announcement, and IPC 3 + governorates refers to regions that were classified as IPC 3 but would have been IPC 4 in absence of humanitarian assistance.

26
Increasing computation capacity allows us to have a large number of households in an MH model. To take an extreme case, the total number of households in a household survey could be used. This approach then allows the model to take into account the full detail in household data, and avoids pre-judgment about aggregating households into categories. The main disadvantages of this type of approach are that data reconciliation can be difficult, and that the size of the model can become a constraint.

The third approach, which we call MS, draws on micro-simulation techniques. Here, a CGE model generates aggregate changes that are later communicated to a micro-simulation model based on a large unit record database. Savard (2003) points out that the drawbacks to the approach are coherence between models, since the causality usually runs from the CGE model to the micro-simulation model, with no feedback between them.

The approach pursued in this paper takes advantage of the same general idea raised by Savard (2003) to overcome the difficulties posed by the three first options above-mentioned: the use of a CGE model linked to a micro-simulation model, but with a bi-directional linkage between them that would guarantee a convergence of solution for both models. Savard (2003) links the models by running them in a repeated sequence of CGE-MS model runs, first computing the CGE simulation, then the MS model simulation, in a looping way, until convergence occurs. The main advantages of this approach are that: we avoid scaling the microeconomic data to match the aggregated macro data; we can accommodate more households in the MS model; and the MS model may incorporate discrete-choice or integer behavior that might be difficult to incorporate in the CGE model.

The CGE model used here is a static inter-regional model of Brazil based on the wellknown ORANI-G model of Australia (Horridge, 2000). The model’s structure is quite standard: 6
# ALLOCATION AND MANAGEMENT OF PUBLIC SPENDING

16. Weakenl:g of Ministries of Planning has undoubtedly occurred, but as an effect rasther than a cause of economic crisis. They have suffered, as have othez government agencies, from general debilitation associated with prolonged budgetary crises. In many cases too their status has been devalued by the sharp diminution In the development budget resources in their gift, at the same time as their morale has been undermined by the palpable Irrelevance of many of the planning documents to which they devote so much of their efforts. See Box 4 below for an example.

# DilaRidation of Planning/Budget Systems

17. Assessment of institutional performance and capacity is inevitably somewhat subjective, but it can be based on indice **s** uch as:

- whether, and with what lags, government accounts appear;

- - the credibility of the expenditure records that are kept;

- whether a recognised budgeting routine and timetable is adhered to;

- - whether Estimates appear on time (i.e. at or near the beginning of the FY to which they refer);

- whether EstImates are published and/or adequately circulated within government;[31

- the degree of credibility of the Estimates as a means of controlling the volume and compositien of public eypenditure;[41

- the information available about and the control exerted over public service employment.

- [31 It is not uncommon to find spending ministries or departments that have never seen the Estimates that are supposed to govern expenditure. In some cases standards have deteriorated to the point where this is no longer considered abnormal by those affected.

- (41 Aid agencies are very sensitive to the quality of financial administration by governments. In most cases they choose to bypass the government institutions inwhich they have no confidence. This is understandable, but may exacerbate the problem of rehabilitating government capacities. (For example, the separate routing of aid funds often makes It harder for governments to manage and monltor their development programmes.)

Page 5
We also provide a test of our methodology to calculate AVEs of NTBs.<sup>18</sup> Using the observed tariff information we created a dummy which takes the value 1 if the tariff is positive and zero otherwise. We then use this new dummy variable and calculated its impact on imports and proceeded to transform the quantity impact into a price-equivalent as we did with the NTB information.<sup>19</sup> We then calculated the correlation between the ad-valorem equivalents for tariffs obtained using this methodology and the actual tariffs. The correlation is 0.31 and signi fi cant at the 1 percent level The average actual tariff in the sample is 10.7 percent with a standard deviation of 23.0 percent, whereas the ad-valorem equivalent of the tariff dummy has an average of 11.3 with a standard deviation of 23.7 percent. Overall this suggests that the methodology we used to estimate ad-valorem equivalent of border barriers is doing a relatively good job.

# 5.2 Trade restrictiveness indices

Table 3 provides our estimates of TRIs, OTRIs and MA-OTRIs for 91 countries (counting European Union members as 1 country).<sup>20</sup> The fi rst three columns provide estimates of trade restrictiveness using tariff data only. The following three columns show estimates that include both tariff and NTBs. The following two columns provide estimates of OTRI and MA-OTRI for agriculture products (HS 01 to 24) that include both tariffs and NTBs. The last two columns provide similar indices but for manufacturing products (HS 25 to 97).

One can make several important observations. First, NTBs have a signi fi cant contribution to the level of trade restrictiveness. Indeed, NTBs add on average an additional 70 percent to the level of trade restrictiveness imposed by tariffs. In 21 countries (out of 91) the contribution of NTBs to the overall level of restrictiveness is higher than the contribution of tariffs.<sup>21</sup> Thus neglecting the restrictiveness of NTBs can be