rafmacalaba/datause-encoder-data huggingface dataset:
- **Younger firms tend to be more productive** : over time, a firm’s old age may make its knowledge, capacities, and skills obsolete and could result organizational decay in (for related evidence, see Agarwal and Gort, 1996 and 2002). But after a certain threshold of age (fourth quintiles in the firm sample), firm productivity rises sharply, suggesting that the oldest and most established firms tend to maintain a productive edge despite the potential downsides of firm age.

- **State and municipal ownership show strong, negative, and statistically significant relationships with productivity** , perhaps reflecting the weak commercial incentives in state and municipal firms compared with private firms. At the same time, state firms tend to be more productive than municipal firms.

- **A firm’s dominance in an industry is key to productivity in Russia.** A firm’s market share of an industry’s sales exerts the most significant influence on firm productivity.

Third, based on the analysis of BEEPs data, firms that complain about access to and/or the **_quality of infrastructure_** – particularly water, electricity, transport, and the internet– tend to show lower productivity; this corroborates other recent analyses showing rising infrastructure gaps in Russia and their impact on firm investment and economic activity.

Fourth, **_water supply_** deficiencies exert the largest negative impact on firm productivity.

21
large proportion of entrants will fail to survive, entrepreneurs in Ethiopia make little impact on the labor market and do not last very long anyway.

**Which cities are creating and destroying jobs?** Figure 13 depicts job creation and destruction across cities in Ethiopia. Some towns create lot of jobs and destroy fewer jobs – such as, Sebeta and Mekele. It also summarizes the net job creation across cities in Ethiopia over the last decade. It is interesting that there is much disparity and volatility across NJC by cities in Ethiopia – this could be a factor of natural advantages of the city (for instance its proximity to Addis), or it could be a function of the citylevel policies (such as investments in infrastructure, fiscal policies, etc.). Indeed, net job destruction within a city is worrying – firms and employment could be moving away from certain areas owing to natural decline, or anti-market policies pursued by some regions.

**Figure 13: Job creation and destruction (by city)**

<!-- Start of picture text --> 6,000<br>4,000<br>2,000<br>0<br>‐2,000<br>‐4,000<br>‐6,000<br>‐8,000<br>‐10,000<br>ADWA NAZARETHAWASSA SEBETADEBREZIETMEKELE WONJI DIRE DAWABURAYUMETEHARA AGARO MERTI BAHIR DAR AKAKI HARAR<br>Job creation (by Incumbents) Job Destruction (by Firm Exits)<br>2001 2004 2008 2011 2003 2007 2010 2002 2006 2009 2001 2004 2008 2011 2003 2007 2010 2002 2006 2009 2001 2004 2008 2011 2003 2007 2010 2002 2006 2009 2001 2004 2008 2011 2003 2007 2010 2002 2006 2009 2001 2004 2008 2011 2003 2007 2010 2002 2006 2009<br><!-- End of picture text --> 14
generates a sgnificant share of rural income, plays an important role during the low season for agriculture when alternative job opportunities are limited, and is particularly important for women.

Ouside of Africa, Deininger, Jin and Sur (2007) find for Sri Lanka that infrastructure contraints negatively affect firm productivity and investment, particularly for small firms. They also show that new startups are negatively affected by infrastructure constraints. However, the authors do not analyze growth per se but focus on a number of firm performance indicators.<sup>2</sup> Fajnzylber, Maloney and Montes (2006) analyze the role of age, education, and many other covariates on firm growth for Mexico and find similar effects as for the US. The authors thus conclude that microentreprises in Mexico are comparable to those of developed countries since they offer job opportunities to low-productivity workers. Fisman and Svensson (2007) show that institutionnal problems measure by bribe payments of Ugandan firms is negatively correlated to firm growth. Johnson McMillan and Woodruff (2002) find the same results for Eastern European transition economies, using property rights protection as the institutional quality indicator.

This analysis uses a unique Rural Investment Climate Survey (RICS) collected by the National Bureau of Statistics (NBS) during January and March 2005.<sup>3</sup> Data was collected using face-to-face interviews with members of selected rural households, community leaders and owners or managers of nonfarm enterprises. Three separate, but interrelated survey questionnaires for households, enterprises and communities were used to collect data. The survey covers a total of 150 communities, 1,239 enterprises and 1,610 households in selected rural areas and small market towns. Agricultural households that operate a non farm enterprise make about 40% of the sample, households that do not operate an enterprise make up another 40%, and enterprises that are not household-based

> 2 Dollar, Hallward-Driemeier and Mengistae (2005) also analyze the impact of investment climate on urban firm performance based on panel data.

> 3 For an overview describing the methodology of the Rural Investment Climate Survey and some comparative findings for Sri Lanka, Nicaragua and Tanzania see World Bank (2007b).

3
9 conducted in the United States. Specifically, Iestimate the following relationships in both countries over time: (3) In (wage)t = Ol + a21n (min)t + a3 In (GNP)t + c±41n (price)t + a5 trend + E (4) In (emp/pop)t = 1 In (min/avewage)t + 321n (GNP) + 331n (price)t + I 4trend + s where min is the legally imposed minimum wage, min/avewage is the minimum wage deflated by an average (manufacturing) wage which may be weighted by coverage and is equal to the weighted national average wage in Mexico and the large cities minimum wage in Colombia,<sup>13</sup> and real GNP (or the unemployment rate) is used to control for business cycle conditions and its affect on both wages and employment.

Table 6 presents the estimates from equations (3) and (4) using annual manufacturing data for Mexico and Colombia. The aggregate data show significant minimum wage effects in Colombia but not in Mexico over overlapping periods of data. The implied elasticities suggest that the increase in the relative value of the minimum wage in Colombia from 1977 to 1987 (roughly 15 percent) had the effect of reducing manufacturing employment by 5 percent over this period. This effect is similar in magnitude to that found in Puerto Rico (Castillo-Freeman and Freeman (1992)) and in the U.S. for young workers (Hamermesh (1993)).

# _Panel Data from Firms_

The difficulties associated with evaluating the effects of minimum wages from a cross-section of firms are well known (Hamermesh (1993), Brown, Gilroy, Kohen (1982); Freeman (1979)). The biggest problem comes from the fact that most of the variation in the minimum wage variable-- typically 13Data limitations prevent weighting by coverage in either case, although this omission presents larger problems in the case of Columbia. Specifically, the fixed provision that subjects Columbian firms with assets greater than pesos $200,000 to minimum wage laws has had the effect of extending minimum wage coverage, since inflation and devaluation have eroded the real value of this nominal target over time. The fact that smaller firms have increasingly become subject to minimum wage laws in Columbia suggests that the above equations may underpredic **t** he true negative impact of minimum wages on employment/population rados in Columbia over this period.
employed in rural areas. Urban incomes are also much more stable over time in Uzbekistan. Only 17.5 percent of working-age people living in rural areas are employed continuously throughout the year, while more than 29 percent in urban areas are, rising to 44 percent in the city of Tashkent.

# III.I – “Propiska” Registration

Uzbekistan inherited a residency permit and domestic passport system after gaining independence from the Soviet Union in 1991.<sup>10</sup> The Soviet state used this system to direct economic development and social resources (Bukley, 1995).<sup>11</sup> Likewise, in its present form the system is primarily used to manage internal migration, to administer public services, and to distribute resources. Registration in a person’s place of residence is referred to as a _propiska_ , which often also refers to a person’s domestic passport.<sup>12</sup> The system is implemented by the Ministry of Internal Affairs in Uzbekistan, and an additional motivation often cited by policy makers is to ensure security and public safety.

Unlike other Soviet Republics – such as Georgia, Moldova, and others – where the practice was largely eliminated following independence, Uzbekistan replaced the Soviet propiska with a stricter one.<sup>13</sup> Tightening of propiska restrictions culminated in a presidential decree in 1999 when it was announced that the capital, Tashkent, would became a firmly “closed city” to which only previously registered families and certain government officials were permitted to move.<sup>14</sup> Sales or purchases of housing in Tashkent were banned if the buyer did not already hold a permanent Tashkent propiska. Similar limits on registration were expanded in 2011 to include the surrounding region (oblast) outside of the city or Tashkent (Tukmadiyeva, 2016). Although Tashkent is the focus of several propiska regulations, there are also significant concentrations of people living without registration in other urban areas outside of the capital, most notably in the city of Samarkand (Table 2). It is important to note that though these estimates are derived from official records, they are likely affected by non-response and

> 10 The propiska system traces its origin to the administration of serfdom in the Russian Empire, and thus substantially pre-dates the Soviet Union. 11 When the revitalized Soviet propiska system was introduced in 1932, the documents required to obtain permission to migrate were made available to citizens living in a city or workers’ settlement. Collective farmers, who represented most of the rural population were excluded (Bukley, 1995).

> 12 Details regarding rules and administration of the propiska system are summarized in appendix A.

> 13 Ukraine is the latest example of a country from the former Soviet Union eliminating the practice. These reforms were completed in 2019.

> 14 This designation echoed the Soviet system’s list of closed cities, which included Tashkent starting in 1956.

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-4-

kinds of empirical studies of international merchandise trade. The deficiencies of these indexes affect studies of international competitiveness, of real exchange rates, of income, price, and substitution elasticities, of. export supply, and of the terms of trade.

What are these deficiencies? Most countries do not survey export and import prices at all, although the number doing so has increased over the last twenty years. Most trade studies rely on indexes of export and import unit values, despite a long history of adverse appraisals of their accuracy as price measures and despite the use of unit values in some studies to measure quality differences or quality changes, uses that imply their inaccuracy for measuring price changes- Not only are the unit values inaccurate as measures of the prices of individual products, but in different countries they are combined with different weights and using different index-number formulas. They are relied on because they are almost universally available from customs records and have been for a long time.

An alternative to the unit-value indexes is the use of domestic wholesale or producer-price indexes. These are collected with a much higher degree of quality control than is applied to the unit-value indexes, but the prices themselves, and their weighting in the indexes, do not purport to apply to external trade. The producer-price indexes are computed with different weighting, coverage, and index-number formulas in different countries, as are the unit-value indexes.

A more general problem with both sources of data, and also with existing export and import price indexes, is that new products are underrepresented for some period after their introduction, and complex products are permanently underrepresented. A related problem is that price indexes for some manufactured
**Table 2. Market Share Changes for Competing Countries: Who Displaced Africa in OECD Markets?**

|||Cha|nge in Country G|roup's Share|of OECD Ma|rkets: 1962-6|4 to 1991-93|||
|---|---|---|---|---|---|---|---|---|---|
|||HighIncom|eCountries||Low|and Middle|IncomeCoun|tries||
|Export Product (SITC)|Sub-<br>Saharan<br>Africa|OECD<br>Countries|non-OECD<br>Countries|Low<br>Income<br>Asia|Middle<br>Income<br>Asia|North<br>Africa|Latin<br>America|Middle<br>East|Other<br>Europe<br>& Asia|
|Unwrought copper alloys (682.1)|-22.5|-1.4|0.1|0.1|1.1|0.0|4.5|0.4|16.1|
|<br>Green or roasted coffee (071.1)|-7.2|10.7|0.0|0.6|4.9|0.0|-9.4|-0.1|-0.0|
|Cocoa beans raw or roasted (072.1)|-9.9|1.4|0.0|-0.2|12.7|-0.0|-3.6|-0.0|0.0|
|Groundnuts green (221.1)|-79.9|41.1|0.4|29.6|-0.1|-0.4|11.7|-0.2|0.4|
|<br>Non-conifer saw logs (242.3)|-16.1|10.0|-0.2|-2.7|17.0|0.0|-0.3|-0.1|4.9|
|Raw cotton (263.1)|1.8|7.6|0.0|3.2|0.1|4.2|-28.7|-0.3|14.3|
|Unmanufactured tobacco (121.0)|-1.6|-11.6|0.0|0.2|0.5|-0.1|14.7|-0.1|-1.6|
|<br>Ironore (281.3)|-6.3|0.2|-0.2|1.9|0.6|-2.4|12.0|0.0|0.4|
|Raw beet and cane sugar (061.1)|5.8|3.3|-6.5|-2.0|6.9|0.0|2.5|0.0|-2.9|
|Palm nuts and kernels (221.3)|-69.2|20.9|-0.0|0.2|38.7|-0.1|11.1|0.2|0.0|
|Natural rubber and gums (231.1)|-2.7|1.5|-1.4|-3.9|50.3|0.1|-0.2|-0.0|0.0|
|Fresh bananas (051.3)|-9.8|2.0|-2.5|-0.0|7.2|0.0|14.3|0.0|0.0|
|Palm oil (422.2)|-54.1|13.7|-0.0|-0.0|56.2|-0.0|0.3|0.0|0.0|
|Vegetable oil residues (081.3)|-8.8|-12.5|-0.0|4.5|2.1|-1.1|29.6|-2.0|-1.9|
|Agave fibers (265.4)|18.5|4.5|-0.1|0.5|-0.1|-0.0|10.7|-0.0|0.6|
|<br>Manganese ore (283.7)|4.2|18.9|0.0|-11.0|-1.0|-7.8|-15.5|-0.2|-7.2|
|Groundnut oil (421.4)|-19.1|23.4|0.5|-6.4|0.1|-0.1|2.6|0.0|-0.5|
|Shaped lumber (243.3)|-6.7|-1.1|0.2|-3.0|20.7|0.0|1.8|0.0|-6.9|
|Tea (074.1)|13.7|19.1|2.8|-41.8|3.4|0.0|3.6|0.0|0.0|
|Base metals nes (689.5)|-16.0|13.8|0.4|2.9|-0.5|0.0|-3.4|0.0|0.1|
|Posts and poles (242.9)|-56.2|20.5|3.2|2.2|0.1|0.1|30.2|0.0|0.0|
|Fixed vegetable oils (422.9)|-46.8|51.1|2.3|-2.7|2.5|0.0|-7.8|0.2|1.2|
|Nonindustrial diamonds (667.2)|4.3|-24.6|1.2|11.9|1.8|0.0|0.1|0.4|3.2|
|Unwrought tin alloys (687.1)|-8.5|-6.5|1.8|8.8|23.3|0.2|24.0|0.0|0.7|
|Inorganic bases (513.6)|-11.4|4.0|0.1|2.3|0.1|0.8|5.9|0.2|4.4|
|<br>Industrial diamonds (275.1)|-16.3|-28.0|0.7|0.8|0.3|-0.1|0.3|-0.0|0.2|
|Unwrought aluminum alloys (684.1)|-1.6|-23.0|0.5|0.2|1.5|0.8|10.9|1.0|9.0|
|<br>Tin ores (283.6)|26.2|35.0|0.5|2.5|-29.3|0.0|-32.1|0.0|2.2|
|Crude asbestos (276.4)|0.3|-5.3|0.0|-0.0|0.1|0.0|2.5|0.0|4.1|
|Natural gums and resins (292.2)|11.8|17.0|-0.4|-14.2|-3.7|0.6|-6.1|-4.9|-0.0|
|<br>All Above Items|-11.1|9.9|-0.1|-0.1|4.2|-0.7|-4.0|-0.2|3.6|

Source: Computed from United Nations COMTRADE statistics
# **Annex 2: Government Debt Dynamics**

Debt dynamics are driven by primary deficits and interest payments, which add up to the gross fiscal deficit. As debt is usually expressed as a ratio of GDP, growth also comes into play. Equation (1) is the standard difference equation for government debt: where _d_ is the government debt-to-GDP ratio, _pd_ is the ratio of the primary deficit to GDP, _DR_ is divestment (privatization) receipts, _F_ stands for the new flow of debt issued in connection with financial sector bailouts (both as ratios of GDP), _r_ is the real interest rate, _g_ is the real growth rate and the subscript refers to fiscal years.<sup>70</sup> The LHS of (1) is the increase in the debt-GDP ratio relative to the previous year. It is explained by the primary deficit (pd) less divestment revenues (DR - as these lower borrowing requirements) plus financial sector bailout costs (F – as these necessitate more debt issue). The last term captures the interaction between interest rates and growth: the interest rate determines the interest payment on public debt while growth acts to lower the ratio of GGD to GDP.

The implied difference between the real interest rate and real growth rate for a given year, ( _rt_ − _g t_ ), can be obtained through straightforward rearrangement of the terms in equation (1). The key points to note about the real interest rate thus obtained are (i) it reflects all capital gains and losses through real exchange rate changes and inflation on the outstanding stocks of foreign currency-denominated and domestic currency debt respectively; and (ii) the implicit GDP deflator is effectively being used to convert nominal to real.

> 70 The last term on the RHS can be equivalently written <u>(</u> _it_ − _Gt_ <u>)</u> _d t_ −1 , where _i_ is the nominal interest rate on GGD 1(+ _Gt_ )

> and _G_ is the nominal rate of growth of GDP.

28
the new poverty line and an improved consumption aggregate derived from a 7-day recall of food expenditure. As such, it is not comparable to any of the earlier official estimates and doing so would be technically incorrect.

Figure A1: Poverty Trends in Nepal since 1995 <!-- Start of picture text --> 70%<br>63.8%<br>60%<br>49.4%<br>50%<br>40%<br>41.8%<br>30.8%<br>30%<br>30.8%<br>25.2%<br>20%<br>10%<br>12.5%<br>0%<br>1995-96 2003-04 2010-11 2014/15<br>Official Poverty Estimate in 2010 Trend on 1995 Poverty Line<br>Trend on 2010 Poverty Line<br><!-- End of picture text -->

# Source: World Bank (2013)

In light of constructing comparable poverty estimates over time, what is of interest is the poverty rates in the earlier years had we used the “new” poverty line and considered total consumption comparable to the 30-day recall versions. The appropriate trend for this version would require a recalculation of poverty for all three rounds of the NLSS using the new poverty line and new consumption aggregate based on the 30-day recall. Under this approach, the poverty rate in 2010 would be 30.8 percent. In the earlier rounds, poverty rates were 63.8 percent in 1995 and 49.4 percent in 2003. Regardless of the choice of methodology and poverty lines, poverty declined significantly between 1995 and 2010.

Some may argue that even the new poverty line may be too parsimonious and the resulting poverty estimates too low. One in four Nepalis were unable to afford basic minimum needs for food and nonfood items that cost only Rs. 1,605 a month. At the same time, the government deserves much praise in its decision to raise the poverty line and its renewed commitment to poverty reduction as this change significantly increased the poverty rate as well as the number of poor.

27
the infrequently visited household diary, it will lie between US$280 and US$334. Clearly the diary approach, especially the benchmark of a personal diary, is a much more resource intensive form of consumption measurement, roughly six to ten times the cost of administering a recall survey to the same household. The cost-savings from a frequently supervised household diary is a relatively modest 25 percent of total personal diary costs. Given the performance of the household diary, especially in urban areas, these resource savings may not be worth the trade-off in terms of downward biases in consumption measurement.

# **6. Conclusions**

In most developing countries, consumption expenditures measured by household survey is the empirical basis for studying poverty and inequality. Yet there is substantial variation both within and across countries in the design of consumption modules. These differences result in challenges to the cross-population comparisons of poverty and living standards as well as efforts to study changes in poverty over time. This study explores the implications of alternative consumption modules design in Tanzania. We designed and fielded eight alternative surveys, selected to capture the most common designs in practice. We benchmark our comparisons to frequently-supervised personal diaries, which we assume come closest to measuring actual consumption by accounting for personal outside-the-house consumption as well as minimizing recall and telescoping error due to the frequent supervision. This method is, however, impractical for large-scale survey work due to high costs – we estimate that it will cost roughly 6 to 10 times as much as a recall format, and roughly twice as much as infrequently supervised household diaries.

With regard to diaries, the quality of reporting in household diaries did not vary a great deal with the frequency of field staff visits designed to minimize recall and telescoping error. This is true for all consumption categories (although food consumption was marginally higher (6 percent higher) in the infrequent diary). One notable exception is the literacy of the household. The diary method will dramatically underestimate consumption if the household is illiterate and receives infrequent supervision. For other households, the relative similarities in measured consumption between the frequently and infrequently supervised diary modules suggests that resources can be saved through the infrequent supervision schedule. The gap between both household diary types and the personal diary likely reflects the omission of personal and out-of-household consumption in the household diary, and this gap, while not noticeable in rural areas, is substantial among urban households. The design decision is then whether to invest in the full personal diary approach, which costs roughly three times as much as the infrequent household diary, or cover many more
**Figure 1:** Timeline of the experiment and of the 2017 and 2018 cotton seasons

<!-- Start of picture text --> Timing of oxen distribution<br>By treatment status<br>Harvest<br>Begin End<br>Planting<br>Land<br>Prep<br>Planting<br>Land<br>Prep<br>1/2016 7/2016 1/2017 7/2017 1/2018 7/2018<br>Date<br>Control Treatment<br>.6<br>.4<br>.2<br>Fraction received oxen<br>0<br><!-- End of picture text --> Note: No date reported for 13% of the sample that received oxen. These households are assumed to receive the oxen at the mean delivery date within their given strata.

seasons in Cˆote d’Ivoire. Delays compiling the lists of eligible farmers and procuring the oxen pushed the delivery of the oxen to April-November 2017, meaning that most treatment households received the oxen after the 2017 land-preparation and planting activities but before harvest. With the delivery timeline and the timing of the survey in mind, we focus on agricultural outcomes that span two seasons: agricultural production and harvest for the 2017 season, and agricultural inputs for the 2018 season.

# **3.2 Sample characteristics**

At endline, we tracked and administered a survey to 91% of farmers in the sampling frame, resulting in a sample size of 2,314 producers: 1,146 producers in the control group and 1,168 producers in the treatment group.<sup>9</sup> We focus on the 2,113 producers who reported comprehensive agricultural production, sales, and time use data.<sup>10</sup>

> 9The main survey respondent was the household head, with specific questions on the receipt and use of oxen addressed to the producer in instances where the two diverged. Moreover, we randomly selected and surveyed one girl and one boy in the household and administered age-appropriate numeracy and literacy assessments using the Uwezo instrument (accessible at `http: //www.uwezo.net/wp-content/uploads/2012/08/KE_2012_Tests.pdf` ).

> 10Of the 201 cotton producers surveyed at endline but dropped from the study, 136 reported partial agricultural output or sales data, 41 were secondary producers in multi-producer households, and 24 reported incomplete time use data.

8