rafmacalaba/datause-encoder-data huggingface dataset:
The OECD (2011a) provides a mixed picture on progress toward greater donor use of country systems, apart from the issue of responsiveness to system quality. For the 32 countries included in both the 2005 and 2010 surveys, it reports that average use of country PFM systems increased from 40% to 48%, and increased from 40% to 44% for country procurement systems. These improvements may appear impressive for the short 5-year time span covered, but as the report notes, the 48% figure fell far short of the 55% target value. This “failure” should be interpreted in its broader context. The PD targets – as with the Millennium Development Goals – were designed to be ambitious, to motivate action more effectively. In these same 32 countries, for example, the number of parallel project implementation units (PIUs) declined by nearly a third (from 1,696 to 1,156) between 2005 and 2010, but the target was for an even more dramatic decline of two-thirds (to only 565).

This study follows the methodological approach of Knack (2013), namely by applying panel data methods to the three-round PDMS dataset, controlling for other factors that influence use of country systems, and correcting for possible selection and endogeneity bias. However, it differs from Knack (2013) in several important respects. First, it directly tests the importance of PFM systems quality for their use by donors, measuring quality with the CPIA indicator specified in the PD. The CPIA indicator is more appropriate for a second reason: it is designed specifically to assess public sector systems for managing public funds including aid. Other wellknown “governance” indicators are designed for commercial (e.g. assessing risks to foreign investors) or advocacy (democracy, human rights, transparency) purposes. Second, it addresses the related issue of quality and use of recipient government procurement systems. Finally, this study goes beyond Knack (2013) in reporting not only results for the full three-dimensional (donor/recipient/year) panel, but also donor-specific results. Findings support the hypothesis that 6
Table 4: Harmonized firm panel for 139 countries from 2006-16: Sector-specific identification of inflation effects on innovation

|||I|nnovation||
|---|---|---|---|---|
||sector volatility|sector growth|sector R&D intensity|sector fxed-assets|
|infation _∗_sector-volatility|-.001<sup>_∗∗_</sup><br>(-2.14)||||
|infation _∗_sector-growth||-.090<sup>_∗∗_</sup>|||
|||(-3.68)|||
|infation _∗_sector-R&D|||-.004<sup>_∗_</sup>||
||||(-1.72)||
|infation _∗_sector-fx-asset||||-.002<sup>_∗∗_</sup>|
|||||(-2.06)|
|loan|.133<sup>_∗∗_</sup>|.132<sup>_∗∗_</sup>|.132<sup>_∗∗_</sup>|.133<sup>_∗∗_</sup>|
||(11.3)|(11.4)|(11.7)|(11.8)|
|ln(empl)|.046<sup>_∗∗_</sup>|.046<sup>_∗∗_</sup>|.062<sup>_∗∗_</sup>|.046<sup>_∗∗_</sup>|
||(10.9)|(11.0)|(11.2)|(11.5)|
|ln(age)|-.024|-.024|-.024|-.024|
||(-1.06)|(-1.08)|(-1.09)|(-1.12)|
|subsidiary|.090<sup>_∗∗_</sup>|.088<sup>_∗∗_</sup>|.089<sup>_∗∗_</sup>|.089<sup>_∗∗_</sup>|
||(3.11)|(3.10)|(3.10)|(3.11)|
|Sector FE|Yes|Yes|Yes|Yes|
|Country_∗_Year FE|Yes|Yes|Yes|Yes|
|Firms|44,842|44,842|44,721|44,721|

World Bank Enterprise Surveys establishment-level data for 139 developing countries from 2006-16; marginal effects reported. Heteroscedasticity robust s.e. clustered at country _∗_ year level; t-statistics in parenthesis. **,* significant at 5%, 10%.

namely that firms that innovate hold more liquid assets to insure against the associated risk (see equation (10)).

To the best of our knowledge, reliable balance sheet information on corporate liquidity holdings is not available for developing countries. We thus complement our analysis of the WBES with balance sheet data for U.S. firms from Compustat. The data provide information about cash holdings, the direct counterpart to the concept of corporate liquidity in our model. The Compustat data are also well suited for our analysis since they have the longest available time period for a panel of firms for a single country, providing sufficient variation in inflation 24
exits (8.1%), leading to a net contribution of the destination country margin equal to 1.7%. The new set of rows then depicts decisions regarding entry, exit and net contribution to the growth rate by sectors – i.e. at the HS2-digit level. Between 2002 and 2003, existing exporters contributed to growth (5.4%) by entering new sectors, but this contribution was more than offset (19.9%) by exits so that the net contribution to growth was negative by 14.6 percentage points. The same logic is applied when studying the net contributions of individual products, i.e. at the HS8-digit level, wherein the net contribution equaled 0.2% between 2002 and 2003.

The sum of the net contribution of firm, destination country, sector and product extensive margin gives the overall extensive margin, which posted a contraction of 11.2% in exports from Pakistan over the period under analysis. In the same vein, the intensive margin illustrates the contribution of expanding the value of exports versus that of reducing it. The difference between these two dimensions equals the intensive margin, which was of 28.8% between 2002 and 2003.

**Table 10**

**The Margins of Export, Annual Export Growth**

||**2002-2003**|**2003-2004**|**2004-2005**|**2005-2006**|**2006-2007**<br>|**2007-2008**|**2008-2009**|**2009-2010**|
|---|---|---|---|---|---|---|---|---|
|Firm Entry|9.3|5.1|4.6|6|5|6|4.9|4.9|
|Firm Exit|-7.8|-7.3|-2.8|-4.4|-3|-3.2|-3.4|-3.5|
|**Net Firm**|**1.5**|**-2.2**|**1.8**|**1.6**|**1.9**|**2.8**|**1.5**|**1.4**|
|CountryEntry|9.8|9.4|10.4|7.4|7.9|9.4|7.6|8.4|
|Country Exit|-8.1|-7.3|-7.9|-7.2|-6.1|-5.7|-7|-5.7|
|**Net Country**|**1.7**|**2.1**|**2.5**|**0.2**|**1.8**|**3.7**|**0.6**|**2.6**|
|Sector Entry|5.4|10|4.2|1.9|2.4|1.5|1.5|1.3|
|Sector Exit|-19.9|-2.4|-5.9|-5|-2.8|-2.1|-1.1|-0.9|
|**Net Sector**|**-14.6**|**7.6**|**-1.7**|**-3.1**|**-0.5**|**-0.5**|**0.4**|**0.4**|
|Product Entry|18.5|29.6|31|17.4|20.3|12.4|10.3|9.5|
|Product Exit|-18.3|-14.6|-23|-19.8|-17.4|-12.9|-9.1|-7.4|
|**Net Product**|**0.2**|**15**|**8**|**-2.4**|**2.9**|**-0.5**|**1.2**|**2**|
|**Net Extensive**|**-11.2**|**22.5**|**10.5**|**-3.7**|**6.1**|**5.5**|**3.7**|**6.5**|
|Intensive Positive|42.9|20.2|30.8|35.9|32.1|48|27.2|39.4|
|Intensive Negative|-14|-29.8|-25.8|-20|-27.2|-19.6|-26.1|-21.4|
|**Net Intensive**|**28.8**|**-9.6**|**5**|**16**|**4.9**|**28.4**|**1.1**|**17.9**|
|**Total**|**17.6**|**12.9**|**15.4**|**12.3**|**11**|**33.9**|**4.8**|**24.4**|

Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

38
# **Agriculture as a Sector of Opportunity for Young People in Africa**

Karen Brooks, Sergiy Zorya, Amy Gautam, and Aparajita Goyal<sup>1</sup> _Keywords:_ Jobs, youth employment, agriculture in Africa _JEL Classification_ : Q1 Agriculture _Sector Board_ : Agriculture and Rural Development (ARD) 1 Karen Brooks is from the International Food and Policy Research Institute. Sergiy Zorya and Aparajita Goyal are from the Agriculture and Environmental Services Department of the World Bank. Amy Gautam is the World Bank’s Consultant. Contact author: Sergiy Zorya, szorya@worldbank.org. We thank Louise Fox, Deon Filmer, Shantayanan Devarajan, Christopher Delgado, Mark Cackler, and the team of the Agriculture, Rural Development, and Irrigation Unit, Africa Region, World Bank for their thoughtful comments, suggestions, and guidance.
# **1. Introduction**

Traditional rational‐agent economic models rely on the assumption that people make decisions based on a rational and deliberate consideration of all costs and benefits associated with the action, conditional on available knowledge. However, many individuals (especially low‐income) regularly make seemingly sub‐ optimal financial decisions and there are strong correlations between financial knowledge, sound financial decisions and use of financial products (e.g. Hilger _et al.,_ 2003). This has led to a growing body of literature exploring the importance of providing financial education and training to individuals and entrepreneurs to effectively improve knowledge, leading to improved financial capabilities and decisions. Despite strong correlations (e.g. Lusardi 2007), rigorous causal impact evaluations of financial literacy training programs have shown mixed results, often with little to no effect on actual behavior (e.g. Cole _et al_ ., 2011) or showing positive impacts only through resource‐intensive interventions (e.g. Bruhn _et al._ , 2013). These limited effects could be explained by (i) only small increases in actual knowledge; or (ii) the fact that people do not fully apply this knowledge when making financial decisions such as when and how much to save. Evidence from psychology and behavioral economics highlights the fact that people act within “bounded rationality”, often relying on heuristics to simplify their choices. Kahneman (2003) presents a framework that differentiates between two states that drive human decision making: intuition and reasoning. Decisions based on intuition are “fast, automatic, effortless, and often emotionally charged”, whereas reasoning is “slower, effortful, and deliberately controlled”. He argues that most decisions are based on intuition, where reasoning acts as a safeguard, rather than motivator, of many behaviors. This insight has important potential implications on how best to influence financial behavior. Even when people are fully aware of the most appropriate action to take, cognitive biases and heuristics may prevent this knowledge from translating into action. Thus, the traditional causal framework linking improved financial knowledge to changes in awareness, perceptions, attitudes and behavior, may underestimate important psychological barriers to financial inclusion that weaken the suggested causal chain. Acknowledgement that we base many decisions on heuristics rather than full information helps to explain why, for instance, “rule‐of‐thumb” approaches to financial education can be more effective at changing behavior than teaching more detailed accounting principles (Drexler _et al._ , 2012).

Although improvements have been registered in the last 3 years, 46% of the Nigerian population remains financially excluded, with no access to formal or informal financial services.<sup>1</sup> This compares unfavorably

> 1 Results presented here are based on a recent nationally representative survey of 20,000 consumers conducted by EFinA in 2010: <u>http://www.efina.org.ng/our‐work/research/access‐to‐financial‐services‐in‐nigeria‐survey/.</u>

2
coefficient on the cash amount is 0.76. Thus the 70 percent of the cash treatment that owners say they invest in the business or save is approximately the amount we find capital stock to increase by, and certainly does not appear an overstatement. Therefore it appears that firm owners are responding honestly to the question of how they invest the cash, making it likely they are also not deliberately overreporting profits.

The second potential concern is that the receipt of a prize makes firms trust our survey team more, and alleviate concerns about the possibility of the survey somehow being affiliated with tax collection.<sup>11</sup> Our survey provides two checks on whether reporting changed with treatment. As a first check, we can use the fact that firms were asked for March 2005 sales twice: once in the baseline sample before any treatments were administered, and again in the second round, after the first group of firms had received treatment. We take the ratio of the round 2 report of sales to the round 1 report, and test whether this differs according to whether firms received treatment between the two rounds. The mean ratio is 1.296 for treated firms and 1.306 for untreated firms, with a t-test of equality having a p-value of 0.963. Thus in both cases firms increased their report of sales compared to the first round, but this had no relationship to treatment status.

As a second check on whether reporting changed with treatment, we can use the fourth round questionnaire, taken in January 2006, which asked about sales for October, November, and December 2005. The second round of treatments occurred in November 2005. We can then test whether the January 2006 reporting of October sales differs for firms treated in November compared to never-treated firms. Mean sales for October are 17,818 rupees for the treated group, compared to 17,764 for the untreated group. A t-test of equality has a p-value of 0.980. Thus again there is no evidence that reporting changed with treatment.<sup>12</sup>

> 11 The main taxes affecting the firms in our sample are registration and assessment fees, if they register with the municipal government. Income taxes only apply for annual income above 300,000 rupees, which only applies to approximately 5 percent of our firms.

> 12 Alternatively, we can look at the growth of sales between September, reported in the October survey before the round 3 treatment, and November, reported in January after the treatment. Again, we find no significant difference between the treated and untreated enterprises.

19
Comparing weather data from 1986 to 2002 with their historic means (from 1951 to 1985), there appears to be an increase in the number of _temperature_ shocks (both negative and positive), but no similar increase in _rainfall_ shocks (Table 3) in Mexico.<sup>19</sup> The survey date is used to match each household to the weather information. Each household is assigned the wet season and dry season prior to the survey. That is, if a household was surveyed in dry season of year _t_ , the weather shocks would based on the weather in the dry season _t-1_ and the wet season _t-1_ . However, if the household was surveyed in the wet season of year _t_ , the weather shocks would be based on weather in dry season _t_ and wet season _t-1_ . As an illustration, for the households in the 2002 wave of the MxFLS, the weather variables of interest are rainfall and GDD from the 2001 wet season and the 2002 dry season (Figure 2). The harvest from the 2002 wet season would not have been harvested prior to the surveys and thus the households‘ income and production would be based on the 2001 wet season and the 2002 dry season harvests.

Tables 4a and 4b show the distribution of rainfall and GDD shocks for the rural municipalities in the final samples from MxFLS and ENN, respectively. Although the number of municipalities from which the household surveys are drawn is relatively small, we do still have some variability in the weather variables. There are municipalities that experienced positive and negative rainfall as well as GDD events. As Table 4 shows, there are more GDD shocks than rainfall shocks in the sample.

The original MxFLS localities, those chosen for the 2002 survey, come from 16 different Mexican states and from all the different regions of the country. Although these states vary in the percentage of land cultivated under rainfed technologies, in most at least 75% of the land is rainfed (Table 5). Also, in most at least 50% of the land cultivated in the wet season is in corn and in all the area cultivated in the wet season is greater than the area cultivated in the dry season. These figures suggest that we can expect for an average rural household in our sample the income, as well as production for self consumption, to be relatively highly dependent on the weather and especially on the weather during the wet season. Also, given the relative importance of corn, the pre- _canícula_ period is of interest.

> 19 The correlation of the 6 different weather shock variables for the MxFLS sample is given in Appendix 1.  The rainfall deviations from mean for the various periods (annual, wet season and pre- _canícula_ period) are positively correlated with annual rainfall and wet season rainfall being very highly correlated.  The GDD deviations from mean are all very highly correlated.  Given the high correlations among the different time periods, we only include weather variables from one time period in each regression.

16
WPS 3234

# **Product Market Integration and Household Labor Supply in a Poor Economy: Evidence from Vietnam**<sup>*****</sup>

Eric V. Edmonds Department of Economics Dartmouth College and NBER and Nina Pavcnik Department of Economics Dartmouth College, CEPR, and NBER

**Abstract:** This paper considers how product market integration in a country's primary agricultural export alters the economic activities of men and women in a poor economy. Between 1993 and 1997, Vietnam relaxed its rice export quota and freed internal restrictions on the trade of rice across regions.  These reforms contributed to an almost 30 percent increase in the real price of rice. Using a panel of rural Vietnamese communities that spans the period of policy change, we relate the regional and intertemporal variation in the price of rice to changes in the economic activities of children, young adults, and adults by gender.  We find that higher rice prices are associated with lower participation in wage work by boys, girls and young adults, and lower participation in household production by adults.  Moreover, higher rice prices are associated with less time devoted to household production for all age groups and adults devote more hours to wage work.  Finally, with the exception of children, labor market responses to changes in rice prices mostly do not differ statistically for males and females.

JEL Codes: F15, F14 Keywords: Market Integration, Trade Liberalization, Labor Supply, Gender

> *We grateful to Susan Razzaz, Alexandra Van Selm, Zongmin Li, Gurushri Swamy, and Nayantara Mukerji for comments and to Savina Rizova for outstanding research assistance.  This research was funded in part by the World Bank-Netherlands Partnership Program's Economic Policy and Gender Initiative, and we appreciate their support. Correspondence to Edmonds or Pavcnik at 6106 Rockefeller Hall.  Dartmouth College.  Hanover, NH 03755 USA. Email: eric.edmonds@dartmouth.edu, nina.pavcnik@dartmouth.edu.
appears that is more productive than the old technology (assuming the same amount of time has been spent learning in each case). Then the Leader may not adopt it because its experience with the old technology means that the old technology is still more attractive to it. But a newly industrializing country (the Follower) will adopt the new technology because it has no experience with either and so the new technology is more attractive. Over time as the Follower learns, it eventually eclipses the Leader and dominates the industry. This model is of course highly stylized, and the result is in part driven by the implicit assumption that the Follower has the skilled workers, infrastructure and intermediate goods and service producers that are necessary to support the new technology.

If this is an accurate representation of reality, then it raises the possibility that a developing country might, for example, eschew fossil fuel-based technologies and move directly to an economy based on green energy. The evidence for leapfrogging of this magnitude, however, is weak. There is evidence such as that from Lovely and Popp (2011) noted above that the transition from dirty to clean technologies can be faster for developing countries than for developed countries. As another example, Hilton (2001) found that the phasing out of lead in gasoline happened faster in the countries that began the process after other countries had already done so (he compared countries that began the process before and after 1979). However, there is little evidence of the more dramatic form of leapfrogging at a national level in which a developing country leaps ahead of incumbents by adopting a more advanced technology that the incumbents had chosen to avoid. Cell phones are a popular example, as many communities in developing countries have skipped the landline telephone technology and gone right to cell phones. However, this process built on a base of experience with cell phone adoption in developed countries.

Comin and Hobijn (2004) in their study of the diffusion of 20 technologies across 23 countries over the period 1788-2001 found that most new technologies were adopted first 24
|RM1|LMPI<br>DLCPI|RP|LEXCH|TREND|
|---|---|---|---|---|
|�1<br>�0:007<br>[�2:58]|0:01<br>[2:97]<br>0|1|0|0|
|�2<br>�0:16<br>[�18:98]|0<br>1|0|�0:05<br>[�8:09]|0:0013<br>[18:03]|
|�3<br>1|�0:11<br>[�2:46]<br>0|0|0:21<br>[5:67]|�0:0077<br>[�25:89]|
||�1||�2|�3|
|DRM1|�0:13<br>[�0:13]||0:91<br>[0:97]|�0:71<br>[�4:96]|
|DLMPI|0||0|0|
|DDLCPI|0:39<br>||�1:00|�0:15|
||[2:35]||[�6:45]|[�6:26]|
|DRP|�0:13<br>[�13:70]||0:003<br>[0:38]|0:0002<br>[0:17]|
|DLEXCH|0:47<br>[0:37]||3:84<br>[3:26]|0:41<br>[2:25]|
|Table 6:|An overidentifying|long|-run struc|ture<br>[t-values]|

Thus, the ...rst long-run relationship quali...es as a type of interest rate policy function in which interest rates react positively to real money and negatively to output: From the adjustment coe¢ cients, the interest rate signi...cantly responds to this long-run relationship. An overidentifying test that inverse velocity holds is accepted with a p-value of 0.16. In such a scenario, interest rates would be raised, if real money growth exceeds output growth. Secondly, we obtain the following in‡ation adjustment equation, in which there is in‡ationary pressure in the economy arising from higher real money balances and the trend-adjusted depreciation of the exchange rate: We observe the usual exchange rate channel, in which depreciation increases domestic exports and aggregate output that subsequently feeds back into in‡ationary pressure. One possible reason for including the trend on the exchange rate is that the nominal exchange rate was out of line with fundamental factors. Therefore, we use a time trend that potentially picks up the exceptional adjustment of the misaligned exchange rate after the crisis. In addition, besides the sensitivity of the in‡ation rate to this long-run relationship, the exchange rate reacts positively to an out-ofequilibrium of the in‡ation adjustment equation.

The third cointegrating relationship corresponds to a money demand function in which real money responds positively to the output gap and negatively to the exchange rate: The e¤ect of the output gap on real money supply relates to the transaction motive for money holdings whereas the exchange rate e¤ect could be characterized as the opportunity cost of holding 15