rafmacalaba/datause-encoder-data huggingface dataset:
|2007|Bloom<br>and<br>Van Reenen|United States, France,<br>Germany,<br>United<br>Kingdom|summer 2004|total factor productiv-<br>ity,<br>sales,<br>return on<br>capital employed|double-blind<br>survey<br>technique for manage-<br>ment scoring on 18 key<br>practices|Management practices are<br>always positively and sig-<br>nifcantly associated with<br>the longer-run component<br>of<br>TFP.<br>Management<br>also has a signifcant and<br>positive association with<br>ROCE.|
|---|---|---|---|---|---|---|
|2013|Bloom,<br>Eifert,<br>Mahajan,<br>McKenzie<br>and Roberts|India|August 2008 to<br>August<br>2010;<br>August 2011 to<br>November 2011|quality, inventory, out-<br>put, and total factor<br>productivity (TFP)|exogenous<br>consulting<br>treatment intervention|The<br>treatment<br>interven-<br>tion led to signifcant im-<br>provements in quality, in-<br>ventory, and output.|
|2017|Bloom,<br>Sadun<br>and<br>Van Reenen|34 countries|2004 to 2014|performance:<br>value<br>added|original survey data on<br>management<br>practices<br>over<br>11,000<br>frms<br>in<br>four survey waves|Diferences<br>in<br>manage-<br>ment practices account for<br>about 30% of total factor<br>productivity<br>diferences<br>both<br>between<br>countries<br>and<br>within<br>countries<br>across frms.|
|2018|Schweiger<br>and Stepanov|38 countries in Cen-<br>tral and Eastern Eu-<br>rope, Central Asia and<br>Middle East and North<br>Africa|2011 to 2016|energy intensity value<br>measure|frm-level data on man-<br>agement practices and<br>energy expenditures in<br>about 2,000 manufac-<br>turing frms|An improvement in man-<br>agement from the 25th to<br>the 75th percentile is as-<br>sociated with a 23% fuel<br>intensity reduction when<br>fuel subsidies are low (neg-<br>ative) and with a 2% fuel<br>intensity reduction when<br>fuel subsidies are high.|

**46**
regional divergence or convergence over time—that is, whether the processes of cumulative causation that produce “polarization” are outweighed by “spread effects.” The former are likely to be significant in the initial stages of economic development but eventually give way to the latter as competitive advantages are eroded by rising costs. There is a good deal of research on this question in relation to China. The evidence tends to favor absolute divergence, but in line with economic theory, conditional convergence exists. This pattern was found by Lau (2010) in an examination of the GDP per capita among provinces over the 1978–2005 period.

Unfortunately, the use of province-level GDP per capita as the dependent variable is liable to produce biased results (Tsui 2007; Li and Gibson 2012). Whereas GDP data relate to production in the province, population data generally refer to the population registered in the province and exclude rural-urban migrants from other provinces who retain their rural _hukou_ registration. This approach overstates the GDP per capita in the richer provinces that attract migrants. Because migration has grown rapidly, the GDP per capita growth rates of these provinces are exaggerated. Thus, evidence of absolute divergence might be an artifact.

Income or consumption based on representative household surveys is therefore probably a more reliable guide to changes in regional income inequality. Kanbur et al. have studied regional income inequality in China using consumption per capita figures derived from the NBS annual household surveys (for instance, Kanbur and Zhang 2005; Fan et al. 2008, 2011). These authors generate a series for China’s 11
Table 14. Semester fixed effect results

|(1)|(2)<br>|
|---|---|
|Dispersion|Volatility|
|**Semester** **FE** **(First** **available** **semester** **excluded)**||
|2010 ‐ 2nd semester<br>0.007|‐‐|
|(0.100)|‐‐|
|2011 ‐ 1st semester<br>0.492***|0.393***|
|(0.113)|(0.076)|
|2011 ‐ 2nd semester<br>0.487***|0.174|
|(0.159)|(0.146)|
|2012 ‐ 1st semester<br>0.235|‐0.297**|
|(0.147)|(0.127)|
|2012 ‐ 2nd semester<br>0.449***|‐0.694***|
|(0.152)|(0.135)|
|2013 ‐ 1st semester<br>0.702***|‐0.142|
|(0.166)|(0.160)|
|2013 ‐ 2nd semester<br>0.606***|0.231|
|(0.184)|(0.141)|
|2014 ‐ 1st semester<br>0.787***|0.716***|
|(0.206)|(0.155)|
|2014 ‐ 2nd semester<br>0.574***|‐0.413**|
|(0.208)|(0.166)|
|2015 ‐ 1st semester<br>0.841***|‐0.095|
|(0.229)|(0.186)|
|2015 ‐ 2nd semester<br>0.685***|‐0.861***|
|(0.214)|(0.197)|
|Constant<br>15.042***|5.394***|
|(0.823)|(0.322)|
|Observations<br>412,017|663,508|
|R‐squared<br>0.148|0.645|
|Clustered standard errors by city in parentheses.<br>*** p<0.01, ** p<0.05, * p<0.1||
|Regressions include additional controls: City FE, Good|FE and Outlet type FE|

# Market structure and competition regressions

The tables that follow contain the results of the effects of the large retailer’s entries and COFECE sanctions on market efficiency. We include the results of split regressions by goods groups and geographical regions to have a clearer idea of how markets might be responding to such variables.

Specifications related to the large retailer’s store entries. We use the cumulative entries of stores since the beginning of the period of study to measure the effect of the large retailer in Mexican markets. Equation (8) shows the specification, which also includes FE dummies. This means that the value of variable large retailer indicates the total of openings in city c until time period t. We have the possibility of running regressions using the large retailer’s third brand store entries only, the first brand stores entry only, or the effect of any of the large retailer’s brand stores entering in markets. In regressions where dependent variables are pass-through indicators, the variable large retailers refers to the total openings of stores by city during the period of study. This is because pass-through indicators are calculated for the complete period 2010-2015 and thus do not allow for time variation.

31
**TABLE 3**

**KEY BIOFUEL STATISTICS**

|**_2000‐01_**|**_2002‐03_**|**_2004‐05_**|**_2006‐07_**|**_2008‐09_**|
|---|---|---|---|---|
|**_Biofuels as a share of global grain and oilseed area_**|**_(percent)_**||||
|EU oilseeds<br>0.00|0.06|0.15|0.24|0.34|
|US maize<br>0.13|0.27|0.37|0.76|1.11|
|**_Land used for US ethanol from maize as a share of_**|**_(percent)_**||||
|US Maize area<br>3.63|7.32|9.45|18.03|27.54|
|US Grain area<br>0.99|2.00|2.79|5.68|8.44|
|Worldgrain area<br>0.16|0.32|0.43|0.85|1.26|

**Notes:** The shares have been calculated based on average world yields.

**TABLE 4**

**STATIONARITY STATISTICS FOR KEY FOOD COMMODITY PRICES, 1960‐2008**

||**_Without_**|**_Trend_**|**_With T_**|**_rend_**|
|---|---|---|---|---|
||**_ADF_**|**_PP_**|**_ADF_**|**_PP_**|
|**_REAL (MUV‐Deflate_**|**_d) SERIES_**||||
|Wheat|‐2.32|‐1.70|‐1.83|‐1.47|
|Maize|‐1.59|‐1.49|‐1.84|‐1.83|
|Rice|‐1.59|‐1.71|‐3.08|‐1.92|
|Soybeans|‐1.72|‐1.59|‐2.09|‐1.96|
|Soybean oil|‐1.34|‐2.06|‐0.96|‐2.28|
|Palm oil|‐1.41|‐2.14|‐1.31|‐2.47|
|**_NOMINAL SERIES_**|||||
|Wheat|‐1.63|‐0.84|‐3.10|‐2.02|
|Maize|‐1.69|‐1.22|‐2.76|‐2.21|
|Rice|‐2.53|‐1.62|‐3.40<sup>*</sup>|‐2.27|
|Soybeans|‐1.32|‐1.23|‐2.11|‐2.14|
|Soybean oil|‐1.08|‐1.17|‐1.99|‐2.56|
|Palm oil|‐1.41|‐2.14|‐1.31|‐2.47|

**Notes:** ADF and PP denote the Augmented Dickey‐Fuller (Dickey and Fuller 1979) and Phillips‐ Perron (Phillips and Perron, 1988) statistics for unit roots. Asterisks in this and the following tables denote significance at 10% (*), 5% (**), and 1% (***) levels. The corresponding _t‐statistics_ are ‐2.60, ‐2.93, and ‐3.58 for the tests without trend and ‐3.18, ‐3.50, and ‐4.16 for the tests with trend. The ADF statistic corresponds to the MacKinnon one‐sided _p‐value_ . The lag length of the ADF equations was determined by minimizing the Schwarz‐loss function while the bandwidth of the PP statistic was based on the Newey‐West method.

**_Page 23_**
an employer of unskilled workers. This sector spends nearly 60% of total costs on nonqualified workers’ salaries (Annex Table 1). Fourth, this assessment relies on the estimates of investment needs for infrastructure in the MENA region and its three subregions, presented in Table 5. Thus, we link the demand for infrastructure with the supply side effects of infrastructure provision on employment and bring realism into the projections.

Table 8 illustrates our methodology for the oil importing countries. The detailed results for the two other subregions are provided in Appendix Tables 2 and 3. The direct job per billion $ in the sector presented in column (1) is obtained by combining the shares of qualified and nonqualified labor inputs in Appendix Table 1 and the wages in Table 7.<sup>12</sup> The share of each sector in the infrastructure needs of the region in column (2) is taken from Table 5. From (1) and (2) we derive the number of direct jobs created in each sector by US$ 1 billion of infrastructure investment (3). We use the type 2 multipliers of the Egyptian IO table (4) to estimate the total job creation in each subsector (5).

**Table 8 Estimated, potential job creation in OIC**

||Direct job /<br>billion $ in<br>the sector (1)|Share of investment<br>needs (2)|Direct job /<br>billion $ in<br>infrastructure<br>(3)|Type II<br>multiplier (4)|Total<br>jobs (5)|
|---|---|---|---|---|---|
|Paved Roads|95000|0.24|22353|1.09|24359|
|Roads|45000|0.10|4538|1.09|4945|
|Rails|68333|0.02|1148|1.82|2090|
|Ports|83333|0.02|1401|1.61|2255|
|Telephone<br>mainlines|125000|0.03|3782|1.34|5067|
|Mobile lines|125000|0.10|12395|1.34|16609|
|Electricity<br>generation|50000|0.35|17647|1.35|23826|
|Electricity Access|93333|0.07|6745|1.49|10050|
|Water|208333|0.03|6653|1.21|8050|
|Sanitation|226667|0.04|9905|1.21|11985|
|**Total**||**1.00**|**86566**||**109236**|

Source: Schwartz et al. (2009), Tong (2010), ILO (2011), Foreman and Beauvais (1999), Gardiner and Theobald (2007), Note 3, Bacon and Kojima (2011) and own computations.

> 12 Following Schwartz (2009), we consider a yearly average of 2000 hours per worker.

12
In the first round, more than 14,000 individual respondents were selected to provide detailed accounts of their employment, current and historical—going back five years to 1988. Target respondents expanded further in subsequent rounds.<sup>5</sup> By the fifth round in 2014, more than 34,000 individuals were interviewed in detail. Combining the current and recall employment survey modules enables us to construct an annual employment history from 1988 to 2015 for individuals who were successfully tracked in all five waves.

IFLS also includes a module for ever married women ages 15-49, which includes detailed questions about all their pregnancy incidences—still in womb, resulting in livebirth, stillbirth, and miscarriage. In the first round, close to 5,000 women were interviewed. Tracking the same women over time allowed us to add in subsequent pregnancies that occurred after the first wave. For each live birth, respondents were interviewed at about the year of (or age at) childbirth. We can, therefore, complement our annual employment data with information on children’s age at each year, and thus their preschool eligibility. To implement the fixed effect model discussed in Section 4.3, we further restricted our sample to women who were found to be between ages 19 and 45 in at least two waves.<sup>6</sup> In the remaining text, we refer to this as our “constructed panel.” We define preschool access as the number of preschools per 1,000 preschool-aged children, aged 3-6, in each district (henceforth, referred to as preschool density). The number of preschools is obtained from pooling Village Census (PODES) cross-sections from 1990 to 2014. PODES is fielded, roughly, once every three years.<sup>7</sup> The population of preschool-aged children comes from the annual<sup>8</sup> National Socioeconomic Survey (SUSENAS) cross-sections of respective PODES years.

Following decentralization reform in 1999, regional governments have been entrusted with more policy-making decisions. Province, district, and village-level governments have legislative power<sup>9</sup> to make policies and to allocate public goods, in this case, pertaining to preschool access. We aggregate preschool access at the district-level because, apart from PODES, the other two

> 5 For more details, see: Frankenberg and Thomas (2000), Strauss et al. (2004), and Strauss et al. (2009). 6 For each individual mother, we need at least two observations. Some of our analyses, such as on main activity, are not available in the historical employment module. Hence, we have to ensure that individuals are observed in at least two rounds.

> 7 Specifically, between 1990 and 2014 Podes was administered in 1990, 1993, 1996, 2000, 2003, 2005, 2008, 2011, and 2014.

> 8 SUSENAS are sometimes fielded more than once per year. In such cases, we use the round with the biggest sample. 9 Sub-district is an administrative level between district and village. It serves to demarcate geographic boundaries, but it does not have any legislative power.

7
8 Governments sometimes also intervene with an export subsidy<sup>_s_</sup> _x_ (or an export tax in which case<sup>_s_</sup> _x_ would be negative). If that were the only intervention: (3) _NRABS_ = _CTE_ = _sx_ If any of these trade taxes or subsidies were specific rather than ad valorem (e.g., $y/kg rather than z percent), its ad valorem equivalent can be calculated using slight modifications of equations (1), (2) and (3).

# _Domestic producer and consumer price-distorting measures_

Governments sometimes intervene with a direct production subsidy for farmers,<sup>_s_</sup> _f_ (or production tax, in which case<sup>_s_</sup> _f_ is negative, including via informal taxes in kind by local and provincial governments). In that case, if only this distortion is present, the effect on producer incentives can be measured as the **_nominal rate of assistance_** to farm output conferred by domestic price support **_(NRADS_** ), which is as above except<sup>_s_</sup> _f_ replaces<sup>_t_</sup> _m_ or<sup>_s_</sup> _x_ , but the _CTE_ in that case is zero. Similarly, if the government just imposes a consumption tax<sup>_c_</sup> _c_ on this product (or consumption subsidy, in which case<sup>_c_</sup> _c_ is negative), the _CTE_ is as above except<sup>_c_</sup> _c_ replaces<sup>_t_</sup> _m_ or<sup>_s_</sup> _x_ , but the _NRADS_ in that case is zero.

The combination of domestic and border price support provides the total rate of assistance to output, _NRAo_ .

# _What if the exchange rate system also is distorting prices?_

Should a multi-tier foreign exchange rate regime be in place, then another policy-induced price wedge exists. A simple two-tier exchange rate system creates a gap between the price received by all exporters and the price paid by all importers for foreign currency, changing both the exchange rate received by exporters and that paid by importers from
<!-- Start of picture text --> 2000<br>1500<br>1000<br>500<br>0<br>2.5 MALAWI_2010-11 UGANDA_2010-11<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>Agriculture Industry Services Unknown<br>Mean hours per person per year<br><!-- End of picture text --> Figure 4: Average hours worked per year by sector participants. This sample includes all individuals between the ages of 16 and 65 who actively participate in the labor force. 95% confidence intervals for the mean are also depicted.

24
2019; Wu et al., 2020), childhood stunting (Balietti et al., 2022), decreases labor supply (Arag ́on et al., 2017), and reduces labor productivity (Chang et al., 2016; Fu et al., 2021). Air pollution also negatively impacts crop yields (Burney and Ramanathan, 2014a; Burney, 2020).<sup>1</sup> These negative impacts closely mirror the well-documented negative impacts of exposure to high temperatures at the individual level. Exposure to high temperature increases mortality (Barreca et al., 2016), reduces labor supply (Graff Zivin et al., 2017), labor productivity (Somanathan et al., 2015), human capital formation (Garg et al., 2020; Park, 2020; Park et al., 2020), and crop yields (Schlenker and Roberts, 2009). The negative consequences of heat measured at the micro level have been shown to aggregate to substantial macro-effects. Burke et al. (2015) show that across both rich and poor countries higher temperatures reduce GDP levels. Even at current levels of heat exposure these effects are sizable, especially in lower income countries.

Despite the similarity in the micro level impacts of air pollution and exposure to extreme heat there has been limited analysis of the macro level impacts of air pollution. The consistency in the micro level impacts of heat and air pollution combined with the existence of substantial macro level effects of heat suggests that air pollution may also have macro level effects. Increased worker absenteeism, reduced job performance (reduced productivity), and reduced levels of human capital accumulation as a result of air pollution are likely to transmit to reductions in overall economic activity. These consequences of pollution exposure might be expected to reduce year-over-year changes in GDP following years with higher levels of pollution. This hypothesis finds support in recent work examining air pollution in Europe at the NUTS-3 level that finds a 1 _μg/m_<sup>3</sup> increase in PM2 _._ 5 levels reduces annual changes in GDP by 0.8% (Dechezleprˆetre et al., 2019) and evidence from the United States that pollution reduces GDP by 0.4% in rural counties (Avila Uribe, 2023).<sup>2</sup> This paper investigates whether increasing pollution in a given year has negative consequences for economic activity in that year using a panel of district level GDP data from India covering the years 1998-2020. Existing empirical work demonstrating that pollution exposure can impact aggregate GDP is based on evidence from Europe and the United States. However, exposure to pollution in both these locations is substantially lower than average exposure across low- and middle-income countries. On average the level of PM2 _._ 5 in the countries studied in Dechezleprˆetre et al. (2019) was 10 _μg/m_<sup>3</sup> . Average levels of exposure in India and China exceed 50 _μg/m_<sup>3</sup> . In some districts of India the annual average level of

> 1For a recent review of the literature on the variety of negative impacts of air pollution, especially those on human capital, see Aguilar-Gomez et al. (2022).

> 2NUTS-3 is the smallest administrative unit in Europe.

2
address them at the micro level. Such data can help inform, both retrospectively and for the future, the design and evaluation of interventions such as SWC activities in near real time and at a scale that is more aligned with how such interventions are designed. Treatment data quality, in particular deficiencies in geocoding and disaggregation by type of these data, thus emerges as the main constraint on the ability to use remotely sensed data for more in-depth analysis. For this reason, routine generation of geographically explicit, disaggregated, and clearly documented intervention data is likely to have very high payoffs and offer opportunities for fruitful follow-up research in at least two respects: First, it would allow to assess not only overall impacts but also their variation with initial conditions and over space in near real time and at a scale commensurate with how interventions are normally designed. Moreover, linking remotely sensed vegetation indices to geo-referenced data -based either on surveys at the household- and plot-level or on local readings of climatic variables by community members along the lines reported here or by sensors that may be controlled remotely- offers opportunities to better understand the biophysical correlates and economic meaning of such data, as well as the channels through which they may affect individuals’ behavior in response to climate change.

15
# **6. Conclusions**

Our paper is motivated by three main questions. First, is there a pattern of volatility across commodities over time? Second, is there a long-run trend in commodity price volatility? Third, are there identifiable breaks in commodity price volatility? For example, do we see changes in volatility when the world went from relatively open (1820 – 1913) to closed (1914 – 1949) and then to open (1950-2009)?

Drawing on a large unbalanced panel of monthly prices of 45 individual commodities and 5 commodity price indices we address these issues by implementing two tests for detecting structural change. Both tests have the advantage of not imposing a priori the dates of potential structural breaks. While both tests are based on CUSUM-type test statistics, they differ in how they establish the threshold above which a change in volatility is considered a structural break. The Kokoszka-Leipus test uses an estimate of the long-run variance of the entire time series to determine such a threshold level. As a result, it is a more stringent test resulting in fewer breaks and by and large only three periods in history are associated with breaks in volatility in most (but not all) commodities: the two world wars and the collapse of the Bretton-Woods system. The Inclan-Tiao (IT) test, in contrast, considers only the properties of the time series since the last identified break in volatility. As a result, threshold volatilities are re-defined every time a break is found. This results in less stringent thresholds and therefore more breaks in volatility – both upwards and downwards. Therefore, even though break-points may overlap, the IT test, constructed to be more sensitive, picks up more frequent shifts. However, the historical breaks coincide under both methodologies.

The main conclusions are as follows. First, the timing and number of breaks in volatility vary considerably across individual commodities. This result cautions against broad generalizations and the use of commodity price indices to analyze changes in volatility. Second, the three most significant breaks common to most (but not all) commodities, are the two world wars and the collapse of the Bretton-Woods system. In recent years, however, there has been an uptick in price volatility in a number of commodities. During the last food crisis though, it is clear that volatility spiked, starting to rise before the actual increase in price levels especially for the most tradable commodities. However, the 18